Targeted Patent: Patent: US11238344B1 Filed: 2016-11-02 Issued: 2022-02-01 Patent Holder: (Original Assignee) Individual     (Current Assignee) AUTONOMOUS DEVICES LLC Inventor(s): Jasmin Cosic Title: Artificially intelligent systems, devices, and methods for learning and/or using a device's circumstances for autonomous device operation	Cross Reference / Shared Meaning between the Lines	Charted Against: Patent: US9465857B1 Filed: 2013-09-26 Issued: 2016-10-11 Patent Holder: (Original Assignee) Groupon Inc     (Current Assignee) Groupon Inc Inventor(s): Matthew DeLand, Chander J. Iyer Title: Dynamic clustering for streaming data
[FEATURE ID: 1] system, first device, server, fourth device	machine, controller, network, sensor, device, user, platform	[FEATURE ID: 1] computer, processor
[TRANSITIVE ID: 2] comprising	for, with, of, by, containing, having, indicating	[TRANSITIVE ID: 2] comprising, including, representing
[FEATURE ID: 3] processors, object representations	entities, nodes, sensors, attributes, subjects, characters, agents	[FEATURE ID: 3] objects
[TRANSITIVE ID: 4] accessing, operating, learning	providing, processing, updating, determining, selecting, identifying, maintaining	[TRANSITIVE ID: 4] receiving, generating
[FEATURE ID: 5] memory	network, library, source, database	[FEATURE ID: 5] core group
[FEATURE ID: 6] knowledgebase, third circumstance representation, third device, fourth circumstance representation, fourth circumstance	first, second, collection, subset, third, list, number	[FEATURE ID: 6] stream, group, first cluster, second cluster, fourth cluster
[TRANSITIVE ID: 7] correlated, learned	associated, identified, linked, defined, determined, modeled, configured	[TRANSITIVE ID: 7] represented, closest
[FEATURE ID: 8] instruction sets, information	inputs, signals, parameters, indications, data, statements, observations	[FEATURE ID: 8] data points
[FEATURE ID: 9] first circumstance	condition, content, state, status	[FEATURE ID: 9] feature
[TRANSITIVE ID: 10] detected	identified, defined, generated, determined	[TRANSITIVE ID: 10] clustered
[FEATURE ID: 11] sensors, operations	ones, features, behaviors, conditions, aspects, components, elements	[FEATURE ID: 11] properties
[FEATURE ID: 12] portion, copy	set, subset, feature, sample, characteristic, vector, value	[FEATURE ID: 12] object, instance, multi-dimensional feature vector, dimension k, tuning parameter, first standard deviation, minimum number, core cluster
[FEATURE ID: 13] second circumstance representation	message, communication, feedback, command, response, recommendation, prompt	[FEATURE ID: 13] request
[FEATURE ID: 14] second device	second, reference, target, first	[FEATURE ID: 14] minimum standard deviation
[FEATURE ID: 15] response	parallel, addition, close proximity, direct response, reply, turn, accordance	[FEATURE ID: 15] part, response
[FEATURE ID: 16] claim	figure, clair, paragraph, embodiment, claim of, clause, item	[FEATURE ID: 16] claim
[FEATURE ID: 17] states	features, objects, attributes, properties	[FEATURE ID: 17] clustering dimension k
[FEATURE ID: 18] second	reference, respective, corresponding, first	[FEATURE ID: 18] data
1 . A system [FEATURE ID: 1] comprising [TRANSITIVE ID: 2] : one or more processors [FEATURE ID: 3] configured to perform at least : accessing [TRANSITIVE ID: 4] a memory [FEATURE ID: 5] that stores at least a knowledgebase [FEATURE ID: 6] that includes a first circumstance representation correlated [TRANSITIVE ID: 7] with a first one or more instruction sets [FEATURE ID: 8] for operating [TRANSITIVE ID: 4] a first device [FEATURE ID: 1] , wherein the first circumstance representation represents a first circumstance [FEATURE ID: 9] detected [TRANSITIVE ID: 10] at least in part by one or more sensors [FEATURE ID: 11] of the first device , and wherein at least a portion [FEATURE ID: 12] of the first circumstance representation or at least a portion of the first one or more instruction sets for operating the first device is learned [TRANSITIVE ID: 7] in a learning process that includes operating the first device at least partially by a user ; generating or receiving a second circumstance representation [FEATURE ID: 13] , wherein the second circumstance representation represents a second circumstance detected at least in part by : the one or more sensors of the first device , or one or more sensors of a second device [FEATURE ID: 14] ; anticipating the first one or more instruction sets for operating the first device based on at least partial match between the second circumstance representation and the first circumstance representation ; and at least in response [FEATURE ID: 15] to the anticipating , executing the first one or more instruction sets for operating the first device , wherein the first device or the second device autonomously performs one or more operations [FEATURE ID: 11] defined by the first one or more instruction sets for operating the first device . 2 . The system of claim [FEATURE ID: 16] 1 , wherein the first one or more instruction sets for operating the first device include one or more information [FEATURE ID: 8] about one or more states [FEATURE ID: 17] of : the first device , or a portion of the first device . 3 . The system of claim 1 , wherein the first circumstance representation includes a first one or more object representations [FEATURE ID: 3] , and wherein the second circumstance representation includes a second [FEATURE ID: 18] one or more object representations . 4 . The system of claim 3 , wherein the second circumstance representation represents the second circumstance detected at least in part by the one or more sensors of the first device , and wherein the first device autonomously performs the one or more operations defined by the first one or more instruction sets for operating the first device . 5 . The system of claim 3 , wherein the second circumstance representation represents the second circumstance detected at least in part by the one or more sensors of the second device , and wherein the second device autonomously performs the one or more operations defined by the first one or more instruction sets for operating the first device . 6 . The system of claim 3 , wherein the system further comprising : a server [FEATURE ID: 1] that receives from the first device at least one of : the first circumstance representation , or the first one or more instruction sets for operating the first device , and wherein the second device receives from the server at least one of : the first circumstance representation , or the first one or more instruction sets for operating the first device , and wherein the second device autonomously performs the one or more operations defined by the first one or more instruction sets for operating the first device . 7 . The system of claim 3 , wherein the one or more processors are further configured to perform at least : modifying : the first one or more instruction sets for operating the first device , or a copy [FEATURE ID: 12] of the first one or more instruction sets for operating the first device , and wherein the anticipating the first one or more instruction sets for operating the first device based on the at least partial match between the second circumstance representation and the first circumstance representation includes : anticipating the modified the first one or more instruction sets for operating the first device based on the at least partial match between the second circumstance representation and the first circumstance representation , or anticipating the modified the copy of the first one or more instruction sets for operating the first device based on the at least partial match between the second circumstance representation and the first circumstance representation , and wherein the executing the first one or more instruction sets for operating the first device includes : executing the modified the first one or more instruction sets for operating the first device , or executing the modified the copy of the first one or more instruction sets for operating the first device , and wherein the autonomously performing , by the first device or by the second device , the one or more operations defined by the first one or more instruction sets for operating the first device includes : autonomously performing , by the first device or by the second device , one or more operations defined by the modified the first one or more instruction sets for operating the first device , or autonomously performing , by the first device or by the second device , one or more operations defined by the modified the copy of the first one or more instruction sets for operating the first device . 8 . The system of claim 3 , wherein the one or more processors are further configured to perform at least : modifying at least one of : the first circumstance representation , a copy of the first circumstance representation , the second circumstance representation , or a copy of the second circumstance representation , and wherein the at least partial match between the second circumstance representation and the first circumstance representation includes : ( i ) at least partial match between the modified the second circumstance representation and the first circumstance representation , ( ii ) at least partial match between the modified the copy of the second circumstance representation and the first circumstance representation , ( iii ) at least partial match between the second circumstance representation and the modified the first circumstance representation , ( iv ) at least partial match between the second circumstance representation and the modified the copy of the first circumstance representation , ( v ) at least partial match between the modified the second circumstance representation and the modified the first circumstance representation , ( vi ) at least partial match between the modified the copy of the second circumstance representation and the modified the copy of the first circumstance representation , ( vii ) at least partial match between the modified the second circumstance representation and the modified the copy of the first circumstance representation , or ( viii ) at least partial match between the modified the copy of the second circumstance representation and the modified the first circumstance representation . 9 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation [FEATURE ID: 6] correlated with a second one or more instruction sets for operating the first device , and wherein the third circumstance representation represents a third circumstance detected at least in part by the one or more sensors of the first device , and wherein at least a portion of the third circumstance representation or at least a portion of the second one or more instruction sets for operating the first device is learned in another learning process that includes operating the first device at least partially by the user . 10 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating the first device , and wherein the third circumstance representation represents a third circumstance detected at least in part by the one or more sensors of the first device , and wherein at least a portion of the third circumstance representation or at least a portion of the second one or more instruction sets for operating the first device is learned in another learning process that includes operating the first device at least partially by another user . 11 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating a third device [FEATURE ID: 6] , and wherein the third circumstance representation represents a third circumstance detected at least in part by one or more sensors of the third device , and wherein at least a portion of the third circumstance representation or at least a portion of the second one or more instruction sets for operating the third device is learned in another learning process that includes operating the third device at least partially by : the user , or another user . 12 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating a third device , and wherein the third circumstance representation represents a third circumstance detected at least in part by one or more sensors of the third device , and wherein the one or more processors are further configured to perform at least : generating or receiving a fourth circumstance representation [FEATURE ID: 6] , wherein the fourth circumstance representation represents a fourth circumstance [FEATURE ID: 6] detected at least in part by one or more sensors of a fourth device [FEATURE ID: 1] ; anticipating the second one or more instruction sets for operating the third device based on at least partial match between the fourth circumstance representation and the third circumstance representation ; and at least in response to the anticipating the second one or more instruction sets for operating the third device , executing the second one or more instruction sets for operating the third device , wherein the fourth device autonomously performs one or more operations defined by the second one or more instruction sets for operating the third device . 13 . The system of claim 3 , wherein the system further comprising : an artificial intelligence system , and wherein the learning the at least the portion of the first circumstance representation or the at least the portion of the first one or more instruction sets for operating the first device includes learning [FEATURE ID: 4]		1 . A computer [FEATURE ID: 1] - implemented method , comprising [TRANSITIVE ID: 2] : receiving [TRANSITIVE ID: 4] a core group [FEATURE ID: 5] of clusters of objects [FEATURE ID: 3] , wherein each object [FEATURE ID: 12] is represented [TRANSITIVE ID: 7] by a corresponding instance [FEATURE ID: 12] of a multi-dimensional feature vector [FEATURE ID: 12] including [TRANSITIVE ID: 2] a dimension k [FEATURE ID: 12] , wherein k is a number , wherein the core group of clusters is clustered [TRANSITIVE ID: 10] based on the dimension k ; and wherein generating [TRANSITIVE ID: 4] the core group of clusters is based in part [FEATURE ID: 15] on at least one tuning parameter [FEATURE ID: 12] representing [TRANSITIVE ID: 2] clustering density ; receiving a stream [FEATURE ID: 6] of data points [FEATURE ID: 8] representing a group [FEATURE ID: 6] of objects , each data point respectively representing an instance of the dimension k describing a feature [FEATURE ID: 9] of an object within the group of objects ; and for said each data point , adding an object described by the data [FEATURE ID: 18] point to a first cluster [FEATURE ID: 6] of objects within the core group of clusters in response [FEATURE ID: 15] to classifying the object as belonging to the first cluster of objects ; updating properties [FEATURE ID: 11] of the first cluster of objects in response to adding the object , wherein updating the properties includes calculating a first standard deviation [FEATURE ID: 12] of clustering dimension k [FEATURE ID: 17] for the first cluster of objects ; and determining , by a processor [FEATURE ID: 1] , whether to update the core group of clusters using the updated properties of the first cluster of objects , wherein determining whether to update the core group of clusters comprises comparing the first standard deviation of the clustering dimension k to a minimum standard deviation [FEATURE ID: 14] of the clustering dimension k ; in an instance in which the first standard deviation of the clustering dimension k is greater than the minimum standard deviation of the clustering dimension k , splitting the first cluster of objects by dividing the first cluster of objects into a second cluster [FEATURE ID: 6] of objects and a third cluster of objects ; in an instance in which the first standard deviation of the clustering dimension k is less than or equal to the minimum standard deviation of the clustering dimension k , selecting a fourth cluster [FEATURE ID: 6] of objects that is closest [FEATURE ID: 7] to the first cluster of objects within the core group of clusters of objects ; calculating a combined standard deviation of the clustering dimension k for combined first cluster of objects and fourth cluster of objects ; and in an instance in which the combined standard deviation of the clustering dimension k is less than or equal to the minimum standard deviation of the clustering dimension k , generating a fifth cluster of objects within the core group of clusters by merging the first cluster of objects and the fourth cluster of objects . 2 . The method of claim [FEATURE ID: 16] 1 , further comprising : in response to receiving a request [FEATURE ID: 13] for core cluster information , updating the core group of clusters based on the tuning parameter representing clustering density . 3 . The method of claim 1 , wherein the tuning parameter is a minimum number [FEATURE ID: 12] of data points to form a core cluster [FEATURE ID: 12]

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Targeted Patent: Patent: US11238344B1 Filed: 2016-11-02 Issued: 2022-02-01 Patent Holder: (Original Assignee) Individual     (Current Assignee) AUTONOMOUS DEVICES LLC Inventor(s): Jasmin Cosic Title: Artificially intelligent systems, devices, and methods for learning and/or using a device's circumstances for autonomous device operation	Cross Reference / Shared Meaning between the Lines	Charted Against: Patent: US9462013B1 Filed: 2015-04-29 Issued: 2016-10-04 Patent Holder: (Original Assignee) International Business Machines Corp     (Current Assignee) Kyndryl Inc Inventor(s): Gregory J. Boss, II Rick A. Hamilton, Jeffrey R. Hoy, Agueda M. H. Magro Title: Managing security breaches in a networked computing environment
[FEATURE ID: 1] system, first device, portion, user, third device	computer, server, machine, device, network, processor, component	[FEATURE ID: 1] computer device, production system, cloud environment, system, decoy application server
[TRANSITIVE ID: 2] comprising	including, involving, performing, implementing, of, by, with	[TRANSITIVE ID: 2] comprising
[FEATURE ID: 3] processors, states, inputs	elements, operations, services, functions, components, processes, resources	[FEATURE ID: 3] security breaches, layers, valid users, steps, software
[TRANSITIVE ID: 4] accessing, learning	processing, determining, identifying, generating, recognizing, controlling, recording	[TRANSITIVE ID: 4] managing, detecting, receiving, routing
[FEATURE ID: 5] memory, learning process, second device, server, fourth device	system, platform, computer, network, facility, host, controller	[FEATURE ID: 5] method, networked computing environment, decoy system, service provider, service
[FEATURE ID: 6] stores, includes	provides, defines, include, maintains, contains, has, establishes	[FEATURE ID: 6] comprises
[TRANSITIVE ID: 7] correlated	along, paired, combined, connected, linked, corresponding, coupled	[TRANSITIVE ID: 7] interweaved, associated
[TRANSITIVE ID: 8] detected, learned	defined, received, identified, generated, recorded, selected, obtained	[TRANSITIVE ID: 8] determined
[FEATURE ID: 9] sensors	nodes, components, first, devices, users	[FEATURE ID: 9] first layers, elements
[FEATURE ID: 10] least	the, most, last, lease, lest	[FEATURE ID: 10] least
[FEATURE ID: 11] second circumstance representation, fourth circumstance representation	request, response, notification, command, message, second, third	[FEATURE ID: 11] communication
[FEATURE ID: 12] operations	services, events, processes, acts	[FEATURE ID: 12] traffic
[FEATURE ID: 13] claim	any claim, preceding claim, feature, item, clause, clam, embodiment	[FEATURE ID: 13] claim
[FEATURE ID: 14] second	subsequent, next, further, third, first	[FEATURE ID: 14] second layers
[FEATURE ID: 15] artificial intelligence system	environment, agent, application, appliance, object, apparatus, orchestrator	[FEATURE ID: 15] element, external security device
1 . A system [FEATURE ID: 1] comprising [TRANSITIVE ID: 2] : one or more processors [FEATURE ID: 3] configured to perform at least : accessing [TRANSITIVE ID: 4] a memory [FEATURE ID: 5] that stores [FEATURE ID: 6] at least a knowledgebase that includes [TRANSITIVE ID: 6] a first circumstance representation correlated [TRANSITIVE ID: 7] with a first one or more instruction sets for operating a first device [FEATURE ID: 1] , wherein the first circumstance representation represents a first circumstance detected [TRANSITIVE ID: 8] at least in part by one or more sensors [FEATURE ID: 9] of the first device , and wherein at least a portion [FEATURE ID: 1] of the first circumstance representation or at least a portion of the first one or more instruction sets for operating the first device is learned [TRANSITIVE ID: 8] in a learning process [FEATURE ID: 5] that includes operating the first device at least [FEATURE ID: 10] partially by a user [FEATURE ID: 1] ; generating or receiving a second circumstance representation [FEATURE ID: 11] , wherein the second circumstance representation represents a second circumstance detected at least in part by : the one or more sensors of the first device , or one or more sensors of a second device [FEATURE ID: 5] ; anticipating the first one or more instruction sets for operating the first device based on at least partial match between the second circumstance representation and the first circumstance representation ; and at least in response to the anticipating , executing the first one or more instruction sets for operating the first device , wherein the first device or the second device autonomously performs one or more operations [FEATURE ID: 12] defined by the first one or more instruction sets for operating the first device . 2 . The system of claim [FEATURE ID: 13] 1 , wherein the first one or more instruction sets for operating the first device include one or more information about one or more states [FEATURE ID: 3] of : the first device , or a portion of the first device . 3 . The system of claim 1 , wherein the first circumstance representation includes a first one or more object representations , and wherein the second circumstance representation includes a second [FEATURE ID: 14] one or more object representations . 4 . The system of claim 3 , wherein the second circumstance representation represents the second circumstance detected at least in part by the one or more sensors of the first device , and wherein the first device autonomously performs the one or more operations defined by the first one or more instruction sets for operating the first device . 5 . The system of claim 3 , wherein the second circumstance representation represents the second circumstance detected at least in part by the one or more sensors of the second device , and wherein the second device autonomously performs the one or more operations defined by the first one or more instruction sets for operating the first device . 6 . The system of claim 3 , wherein the system further comprising : a server [FEATURE ID: 5] that receives from the first device at least one of : the first circumstance representation , or the first one or more instruction sets for operating the first device , and wherein the second device receives from the server at least one of : the first circumstance representation , or the first one or more instruction sets for operating the first device , and wherein the second device autonomously performs the one or more operations defined by the first one or more instruction sets for operating the first device . 7 . The system of claim 3 , wherein the one or more processors are further configured to perform at least : modifying : the first one or more instruction sets for operating the first device , or a copy of the first one or more instruction sets for operating the first device , and wherein the anticipating the first one or more instruction sets for operating the first device based on the at least partial match between the second circumstance representation and the first circumstance representation includes : anticipating the modified the first one or more instruction sets for operating the first device based on the at least partial match between the second circumstance representation and the first circumstance representation , or anticipating the modified the copy of the first one or more instruction sets for operating the first device based on the at least partial match between the second circumstance representation and the first circumstance representation , and wherein the executing the first one or more instruction sets for operating the first device includes : executing the modified the first one or more instruction sets for operating the first device , or executing the modified the copy of the first one or more instruction sets for operating the first device , and wherein the autonomously performing , by the first device or by the second device , the one or more operations defined by the first one or more instruction sets for operating the first device includes : autonomously performing , by the first device or by the second device , one or more operations defined by the modified the first one or more instruction sets for operating the first device , or autonomously performing , by the first device or by the second device , one or more operations defined by the modified the copy of the first one or more instruction sets for operating the first device . 8 . The system of claim 3 , wherein the one or more processors are further configured to perform at least : modifying at least one of : the first circumstance representation , a copy of the first circumstance representation , the second circumstance representation , or a copy of the second circumstance representation , and wherein the at least partial match between the second circumstance representation and the first circumstance representation includes : ( i ) at least partial match between the modified the second circumstance representation and the first circumstance representation , ( ii ) at least partial match between the modified the copy of the second circumstance representation and the first circumstance representation , ( iii ) at least partial match between the second circumstance representation and the modified the first circumstance representation , ( iv ) at least partial match between the second circumstance representation and the modified the copy of the first circumstance representation , ( v ) at least partial match between the modified the second circumstance representation and the modified the first circumstance representation , ( vi ) at least partial match between the modified the copy of the second circumstance representation and the modified the copy of the first circumstance representation , ( vii ) at least partial match between the modified the second circumstance representation and the modified the copy of the first circumstance representation , or ( viii ) at least partial match between the modified the copy of the second circumstance representation and the modified the first circumstance representation . 9 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating the first device , and wherein the third circumstance representation represents a third circumstance detected at least in part by the one or more sensors of the first device , and wherein at least a portion of the third circumstance representation or at least a portion of the second one or more instruction sets for operating the first device is learned in another learning process that includes operating the first device at least partially by the user . 10 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating the first device , and wherein the third circumstance representation represents a third circumstance detected at least in part by the one or more sensors of the first device , and wherein at least a portion of the third circumstance representation or at least a portion of the second one or more instruction sets for operating the first device is learned in another learning process that includes operating the first device at least partially by another user . 11 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating a third device [FEATURE ID: 1] , and wherein the third circumstance representation represents a third circumstance detected at least in part by one or more sensors of the third device , and wherein at least a portion of the third circumstance representation or at least a portion of the second one or more instruction sets for operating the third device is learned in another learning process that includes operating the third device at least partially by : the user , or another user . 12 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating a third device , and wherein the third circumstance representation represents a third circumstance detected at least in part by one or more sensors of the third device , and wherein the one or more processors are further configured to perform at least : generating or receiving a fourth circumstance representation [FEATURE ID: 11] , wherein the fourth circumstance representation represents a fourth circumstance detected at least in part by one or more sensors of a fourth device [FEATURE ID: 5] ; anticipating the second one or more instruction sets for operating the third device based on at least partial match between the fourth circumstance representation and the third circumstance representation ; and at least in response to the anticipating the second one or more instruction sets for operating the third device , executing the second one or more instruction sets for operating the third device , wherein the fourth device autonomously performs one or more operations defined by the second one or more instruction sets for operating the third device . 13 . The system of claim 3 , wherein the system further comprising : an artificial intelligence system [FEATURE ID: 15] , and wherein the learning the at least the portion of the first circumstance representation or the at least the portion of the first one or more instruction sets for operating the first device includes learning [FEATURE ID: 4] , at least in part by the artificial intelligence system , the at least the portion of the first circumstance representation or the at least the portion of the first one or more instruction sets for operating the first device , and wherein the anticipating the first one or more instruction sets for operating the first device includes anticipating , at least in part by the artificial intelligence system , the first one or more instruction sets for operating the first device . 14 . The system of claim 13 , wherein the artificial intelligence system includes : one or more inputs [FEATURE ID: 3]		1 . A method [FEATURE ID: 5] of managing [TRANSITIVE ID: 4] security breaches [FEATURE ID: 3] in a networked computing environment [FEATURE ID: 5] , comprising [TRANSITIVE ID: 2] : detecting [TRANSITIVE ID: 4] , by at least one computer device [FEATURE ID: 1] , a breach of a production system [FEATURE ID: 1] in the networked computing environment , wherein the networked computing environment comprises [TRANSITIVE ID: 6] a decoy system [FEATURE ID: 5] interweaved [TRANSITIVE ID: 7] with the production system ; receiving [TRANSITIVE ID: 4] , by the at least one computer device , a communication [FEATURE ID: 11] after the detecting the breach ; determining , by the at least one computer device , the communication is associated [TRANSITIVE ID: 7] with one of a valid user and a malicious user ; and based on the determining , routing the valid user to an element [FEATURE ID: 15] of the production system when the communication is associated with the valid user and routing the malicious user to a corresponding element of the decoy system when the communication is associated with the malicious user ; wherein the networked computing environment comprises layers [FEATURE ID: 3] , and further comprising determining one of the layers at which the breach occurred ; and wherein : the communication is determined to be associated with the malicious user ; the routing [FEATURE ID: 4] is based on the determined [FEATURE ID: 8] one of the layers ; wherein the routing comprises : permitting the malicious user to access at least one element of the production system in one or more first layers [FEATURE ID: 9] up to and including the determined one of the layers ; and routing the malicious user to at least one element of the decoy system in one or more second layers [FEATURE ID: 14] downstream of the determined one of the layers . 2 . The method of claim [FEATURE ID: 13] 1 , further comprising maintaining the production system intact for servicing valid users [FEATURE ID: 3] after the detecting the breach . 3 . The method of claim 1 , further comprising generating automated traffic [FEATURE ID: 12] on elements [FEATURE ID: 9] of the decoy system . 4 . The method of claim 1 , wherein a service provider [FEATURE ID: 5] at least [FEATURE ID: 10] one of creates , maintains , deploys and supports the at least one computer device . 5 . The method of claim 1 , wherein steps [FEATURE ID: 3] of claim 1 are provided by a service provider on a subscription , advertising , and / or fee basis . 6 . The method of claim 1 , further comprising providing software [FEATURE ID: 3] as a service [FEATURE ID: 5] in a cloud environment [FEATURE ID: 1] to perform the steps of claim 1 . 7 . A system [FEATURE ID: 1] for managing security breaches , comprising : at least one computer device in a networked computing environment , wherein the at least one computer device is configured to : determine an identification of a malicious user and a detected layer of a breach of a production system of the networked computing environment ; route a valid user to an element of the production system ; and route the malicious user to a corresponding element of a decoy system of the networked computing environment based on the determined identification of the malicious user and the detected layer of the breach ; wherein the networked computing environment comprises : an external security device [FEATURE ID: 15] in a first layer ; a production application server and a decoy application server [FEATURE ID: 1]

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Targeted Patent: Patent: US11238344B1 Filed: 2016-11-02 Issued: 2022-02-01 Patent Holder: (Original Assignee) Individual     (Current Assignee) AUTONOMOUS DEVICES LLC Inventor(s): Jasmin Cosic Title: Artificially intelligent systems, devices, and methods for learning and/or using a device's circumstances for autonomous device operation	Cross Reference / Shared Meaning between the Lines	Charted Against: Patent: US9460288B2 Filed: 2014-12-08 Issued: 2016-10-04 Patent Holder: (Original Assignee) Shape Security Inc     (Current Assignee) Shape Security Inc Inventor(s): Justin D. Call, Marc Hansen Title: Secure app update server and secure application programming interface (“API”) server
[FEATURE ID: 1] system, first device, user, server, third device, fourth device	computer, device, machine, platform, controller, terminal, client	[FEATURE ID: 1] server system, secure app update server, processor, endpoint device, secure update app server, mobile device, smartphone
[TRANSITIVE ID: 2] comprising, accessing	implementing, having, with, containing, incorporating, featuring, involving	[TRANSITIVE ID: 2] comprising, including
[FEATURE ID: 3] processors, sensors, inputs	components, nodes, controllers, modules, elements, instructions, sources	[FEATURE ID: 3] memory, additional secure API servers, enterprise app servers, program modifications
[FEATURE ID: 4] memory, knowledgebase	repository, database, record, component, manifest, list, system	[FEATURE ID: 4] secure API server, range
[FEATURE ID: 5] stores, includes, represents	provides, identifies, contains, has, comprises, defines, holds	[FEATURE ID: 5] includes, executes
[FEATURE ID: 6] instruction sets, operations, information	commands, parameters, functions, inputs, services, changes, statements	[FEATURE ID: 6] calls, API calls, API requests, security measures
[TRANSITIVE ID: 7] learned	acquired, available, provided, obtained	[TRANSITIVE ID: 7] received
[FEATURE ID: 8] second circumstance representation, third circumstance representation, fourth circumstance representation, fourth circumstance	second, third, first, fourth, communication, fifth, representation	[FEATURE ID: 8] third channel, fourth channel
[FEATURE ID: 9] second device	second, first, network, third	[FEATURE ID: 9] second channel
[FEATURE ID: 10] claim	the claim, embodiment, preceding claim, of claim, item, clause, claim of	[FEATURE ID: 10] claim
[FEATURE ID: 11] states	versions, characteristics, features, aspects, attributes, properties, functions	[FEATURE ID: 11] valid versions, version numbers
[FEATURE ID: 12] second	further, secondary, different, third	[FEATURE ID: 12] additional secure app update servers
[FEATURE ID: 13] copy	version, portion, component, part	[FEATURE ID: 13] app code objects
[FEATURE ID: 14] artificial intelligence system	application, agent, environment, executable, object, endpoint, internet	[FEATURE ID: 14] app code object, enterprise app server, application programming interface, enterprise API server
1 . A system [FEATURE ID: 1] comprising [TRANSITIVE ID: 2] : one or more processors [FEATURE ID: 3] configured to perform at least : accessing [TRANSITIVE ID: 2] a memory [FEATURE ID: 4] that stores [FEATURE ID: 5] at least a knowledgebase [FEATURE ID: 4] that includes [TRANSITIVE ID: 5] a first circumstance representation correlated with a first one or more instruction sets [FEATURE ID: 6] for operating a first device [FEATURE ID: 1] , wherein the first circumstance representation represents [TRANSITIVE ID: 5] a first circumstance detected at least in part by one or more sensors [FEATURE ID: 3] of the first device , and wherein at least a portion of the first circumstance representation or at least a portion of the first one or more instruction sets for operating the first device is learned [TRANSITIVE ID: 7] in a learning process that includes operating the first device at least partially by a user [FEATURE ID: 1] ; generating or receiving a second circumstance representation [FEATURE ID: 8] , wherein the second circumstance representation represents a second circumstance detected at least in part by : the one or more sensors of the first device , or one or more sensors of a second device [FEATURE ID: 9] ; anticipating the first one or more instruction sets for operating the first device based on at least partial match between the second circumstance representation and the first circumstance representation ; and at least in response to the anticipating , executing the first one or more instruction sets for operating the first device , wherein the first device or the second device autonomously performs one or more operations [FEATURE ID: 6] defined by the first one or more instruction sets for operating the first device . 2 . The system of claim [FEATURE ID: 10] 1 , wherein the first one or more instruction sets for operating the first device include one or more information [FEATURE ID: 6] about one or more states [FEATURE ID: 11] of : the first device , or a portion of the first device . 3 . The system of claim 1 , wherein the first circumstance representation includes a first one or more object representations , and wherein the second circumstance representation includes a second [FEATURE ID: 12] one or more object representations . 4 . The system of claim 3 , wherein the second circumstance representation represents the second circumstance detected at least in part by the one or more sensors of the first device , and wherein the first device autonomously performs the one or more operations defined by the first one or more instruction sets for operating the first device . 5 . The system of claim 3 , wherein the second circumstance representation represents the second circumstance detected at least in part by the one or more sensors of the second device , and wherein the second device autonomously performs the one or more operations defined by the first one or more instruction sets for operating the first device . 6 . The system of claim 3 , wherein the system further comprising : a server [FEATURE ID: 1] that receives from the first device at least one of : the first circumstance representation , or the first one or more instruction sets for operating the first device , and wherein the second device receives from the server at least one of : the first circumstance representation , or the first one or more instruction sets for operating the first device , and wherein the second device autonomously performs the one or more operations defined by the first one or more instruction sets for operating the first device . 7 . The system of claim 3 , wherein the one or more processors are further configured to perform at least : modifying : the first one or more instruction sets for operating the first device , or a copy [FEATURE ID: 13] of the first one or more instruction sets for operating the first device , and wherein the anticipating the first one or more instruction sets for operating the first device based on the at least partial match between the second circumstance representation and the first circumstance representation includes : anticipating the modified the first one or more instruction sets for operating the first device based on the at least partial match between the second circumstance representation and the first circumstance representation , or anticipating the modified the copy of the first one or more instruction sets for operating the first device based on the at least partial match between the second circumstance representation and the first circumstance representation , and wherein the executing the first one or more instruction sets for operating the first device includes : executing the modified the first one or more instruction sets for operating the first device , or executing the modified the copy of the first one or more instruction sets for operating the first device , and wherein the autonomously performing , by the first device or by the second device , the one or more operations defined by the first one or more instruction sets for operating the first device includes : autonomously performing , by the first device or by the second device , one or more operations defined by the modified the first one or more instruction sets for operating the first device , or autonomously performing , by the first device or by the second device , one or more operations defined by the modified the copy of the first one or more instruction sets for operating the first device . 8 . The system of claim 3 , wherein the one or more processors are further configured to perform at least : modifying at least one of : the first circumstance representation , a copy of the first circumstance representation , the second circumstance representation , or a copy of the second circumstance representation , and wherein the at least partial match between the second circumstance representation and the first circumstance representation includes : ( i ) at least partial match between the modified the second circumstance representation and the first circumstance representation , ( ii ) at least partial match between the modified the copy of the second circumstance representation and the first circumstance representation , ( iii ) at least partial match between the second circumstance representation and the modified the first circumstance representation , ( iv ) at least partial match between the second circumstance representation and the modified the copy of the first circumstance representation , ( v ) at least partial match between the modified the second circumstance representation and the modified the first circumstance representation , ( vi ) at least partial match between the modified the copy of the second circumstance representation and the modified the copy of the first circumstance representation , ( vii ) at least partial match between the modified the second circumstance representation and the modified the copy of the first circumstance representation , or ( viii ) at least partial match between the modified the copy of the second circumstance representation and the modified the first circumstance representation . 9 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation [FEATURE ID: 8] correlated with a second one or more instruction sets for operating the first device , and wherein the third circumstance representation represents a third circumstance detected at least in part by the one or more sensors of the first device , and wherein at least a portion of the third circumstance representation or at least a portion of the second one or more instruction sets for operating the first device is learned in another learning process that includes operating the first device at least partially by the user . 10 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating the first device , and wherein the third circumstance representation represents a third circumstance detected at least in part by the one or more sensors of the first device , and wherein at least a portion of the third circumstance representation or at least a portion of the second one or more instruction sets for operating the first device is learned in another learning process that includes operating the first device at least partially by another user . 11 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating a third device [FEATURE ID: 1] , and wherein the third circumstance representation represents a third circumstance detected at least in part by one or more sensors of the third device , and wherein at least a portion of the third circumstance representation or at least a portion of the second one or more instruction sets for operating the third device is learned in another learning process that includes operating the third device at least partially by : the user , or another user . 12 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating a third device , and wherein the third circumstance representation represents a third circumstance detected at least in part by one or more sensors of the third device , and wherein the one or more processors are further configured to perform at least : generating or receiving a fourth circumstance representation [FEATURE ID: 8] , wherein the fourth circumstance representation represents a fourth circumstance [FEATURE ID: 8] detected at least in part by one or more sensors of a fourth device [FEATURE ID: 1] ; anticipating the second one or more instruction sets for operating the third device based on at least partial match between the fourth circumstance representation and the third circumstance representation ; and at least in response to the anticipating the second one or more instruction sets for operating the third device , executing the second one or more instruction sets for operating the third device , wherein the fourth device autonomously performs one or more operations defined by the second one or more instruction sets for operating the third device . 13 . The system of claim 3 , wherein the system further comprising : an artificial intelligence system [FEATURE ID: 14] , and wherein the learning the at least the portion of the first circumstance representation or the at least the portion of the first one or more instruction sets for operating the first device includes learning , at least in part by the artificial intelligence system , the at least the portion of the first circumstance representation or the at least the portion of the first one or more instruction sets for operating the first device , and wherein the anticipating the first one or more instruction sets for operating the first device includes anticipating , at least in part by the artificial intelligence system , the first one or more instruction sets for operating the first device . 14 . The system of claim 13 , wherein the artificial intelligence system includes : one or more inputs [FEATURE ID: 3]		1 . A server system [FEATURE ID: 1] comprising [TRANSITIVE ID: 2] : a secure app update server [FEATURE ID: 1] that transforms an app code object [FEATURE ID: 14] received [TRANSITIVE ID: 7] from an enterprise app server [FEATURE ID: 14] into a transformed app code object , wherein the app code object includes [TRANSITIVE ID: 5] application programming interface [FEATURE ID: 14] ( “ API ” ) calls [TRANSITIVE ID: 6] to an enterprise API server [FEATURE ID: 14] and wherein the transformed app code object includes API calls [FEATURE ID: 6] to a secure API server [FEATURE ID: 4] that operate differently than the API calls to the enterprise API server of the app code object , the secure app update server including [TRANSITIVE ID: 2] a processor [FEATURE ID: 1] and memory [FEATURE ID: 3] ; and the secure API server that interacts with an endpoint device [FEATURE ID: 1] that executes [TRANSITIVE ID: 5] the transformed app code object , wherein the secure API server is adapted to convert API requests [FEATURE ID: 6] made by the transformed app code object into renormalized API requests formatted for processing by the enterprise API server , wherein the transforming of the app code object results in API requests from the endpoint device that would constitute invalid API requests if presented to the enterprise API server without renormalization . 2 . The server system of claim [FEATURE ID: 10] 1 , comprising additional secure app update servers [FEATURE ID: 12] and additional secure API servers [FEATURE ID: 3] . 3 . The server system of claim 1 , wherein the secure app update server and the secure API server serve a plurality of endpoint devices . 4 . The server system of claim 1 , further comprising interfaces to a plurality of enterprise app servers [FEATURE ID: 3] and interfaces to a plurality of enterprise API servers . 5 . The server system of claim 1 , wherein a first channel between the enterprise app server and the secure update app server [FEATURE ID: 1] is more secure and / or less accessible than a second channel [FEATURE ID: 9] between the secure update app server and the endpoint device . 6 . The server system of claim 5 , wherein a third channel [FEATURE ID: 8] between the endpoint device and the secure API server is less secure and / or more accessible than a fourth channel [FEATURE ID: 8] between the secure API server and the enterprise API server . 7 . The server system of claim 1 , wherein the endpoint device is a mobile device [FEATURE ID: 1] . 8 . The server system of claim 7 , wherein the mobile device is a smartphone [FEATURE ID: 1] . 9 . The server system of claim 1 , wherein the secure API server maintains a range [FEATURE ID: 4] of valid versions [FEATURE ID: 11] for the transformed app code object and selectively responds to API calls based on version numbers [FEATURE ID: 11] of the transformed app code objects [FEATURE ID: 13] making the API calls . 10 . The server system of claim 1 , wherein the transformed app code object comprises program modifications [FEATURE ID: 3] that create security measures other than API call related security measures [FEATURE ID: 6]

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Targeted Patent: Patent: US11238344B1 Filed: 2016-11-02 Issued: 2022-02-01 Patent Holder: (Original Assignee) Individual     (Current Assignee) AUTONOMOUS DEVICES LLC Inventor(s): Jasmin Cosic Title: Artificially intelligent systems, devices, and methods for learning and/or using a device's circumstances for autonomous device operation	Cross Reference / Shared Meaning between the Lines	Charted Against: Patent: US20160267396A1 Filed: 2015-03-09 Issued: 2016-09-15 Patent Holder: (Original Assignee) Skytree Inc     (Current Assignee) Skytree Inc Inventor(s): Alexander Gray, Sergey Kirshner Title: System and Method for Using Machine Learning to Generate a Model from Audited Data
[FEATURE ID: 1] system, first device, user, second device, server, third device, fourth device	machine, controller, processor, device, network, platform, sensor	[FEATURE ID: 1] computer, system, memory
[TRANSITIVE ID: 2] comprising, operating	including, by, using, of, performing, for, implementing	[TRANSITIVE ID: 2] comprising, evaluating
[FEATURE ID: 3] processors, sensors, states, inputs	devices, elements, components, modules, controllers, nodes, users	[FEATURE ID: 3] processors
[TRANSITIVE ID: 4] accessing, learning	providing, generating, utilizing, employing, selecting, determining, monitoring	[TRANSITIVE ID: 4] receiving, applying, processing
[FEATURE ID: 5] memory, copy	record, component, part, source, change, data, sample	[FEATURE ID: 5] process
[FEATURE ID: 6] knowledgebase, portion	database, template, graph, scorecard, prediction, discriminator, heuristic	[FEATURE ID: 6] model, common identifier, classification model, regression model, semi-supervised model, density estimation model, clustering model, dimensionality reduction model, multidimensional querying model
[TRANSITIVE ID: 7] correlated	mapped, combined, integrated, tagged, coupled, linked, matched	[TRANSITIVE ID: 7] fused
[FEATURE ID: 8] instruction sets	rules, codes, command, statements, information, programs, indications	[FEATURE ID: 8] instructions
[TRANSITIVE ID: 9] detected, learned	defined, determined, provided, received, obtained, identified, generated	[TRANSITIVE ID: 9] created
[FEATURE ID: 10] learning process	process, transaction, task, workflow, procedure	[FEATURE ID: 10] complex processing workflow
[FEATURE ID: 11] second circumstance representation, fourth circumstance representation	report, message, query, command, feedback, prompt, response	[FEATURE ID: 11] preventive action, notification
[FEATURE ID: 12] claim	item, preceding claim, claimed, clair, statement, figure, paragraph	[FEATURE ID: 12] claim
[FEATURE ID: 13] information	data, metrics, feedback, assumptions, rules, statistics, metadata	[FEATURE ID: 13] ground truth data, causation, validity data, qualification data, quantification data, preference data
[FEATURE ID: 14] artificial intelligence system	application, operator, expert, actor, entity, object, engine	[FEATURE ID: 14] audit, action, auditor, ensemble model
1 . A system [FEATURE ID: 1] comprising [TRANSITIVE ID: 2] : one or more processors [FEATURE ID: 3] configured to perform at least : accessing [TRANSITIVE ID: 4] a memory [FEATURE ID: 5] that stores at least a knowledgebase [FEATURE ID: 6] that includes a first circumstance representation correlated [TRANSITIVE ID: 7] with a first one or more instruction sets [FEATURE ID: 8] for operating [TRANSITIVE ID: 2] a first device [FEATURE ID: 1] , wherein the first circumstance representation represents a first circumstance detected [TRANSITIVE ID: 9] at least in part by one or more sensors [FEATURE ID: 3] of the first device , and wherein at least a portion [FEATURE ID: 6] of the first circumstance representation or at least a portion of the first one or more instruction sets for operating the first device is learned [TRANSITIVE ID: 9] in a learning process [FEATURE ID: 10] that includes operating the first device at least partially by a user [FEATURE ID: 1] ; generating or receiving a second circumstance representation [FEATURE ID: 11] , wherein the second circumstance representation represents a second circumstance detected at least in part by : the one or more sensors of the first device , or one or more sensors of a second device [FEATURE ID: 1] ; anticipating the first one or more instruction sets for operating the first device based on at least partial match between the second circumstance representation and the first circumstance representation ; and at least in response to the anticipating , executing the first one or more instruction sets for operating the first device , wherein the first device or the second device autonomously performs one or more operations defined by the first one or more instruction sets for operating the first device . 2 . The system of claim [FEATURE ID: 12] 1 , wherein the first one or more instruction sets for operating the first device include one or more information [FEATURE ID: 13] about one or more states [FEATURE ID: 3] of : the first device , or a portion of the first device . 3 . The system of claim 1 , wherein the first circumstance representation includes a first one or more object representations , and wherein the second circumstance representation includes a second one or more object representations . 4 . The system of claim 3 , wherein the second circumstance representation represents the second circumstance detected at least in part by the one or more sensors of the first device , and wherein the first device autonomously performs the one or more operations defined by the first one or more instruction sets for operating the first device . 5 . The system of claim 3 , wherein the second circumstance representation represents the second circumstance detected at least in part by the one or more sensors of the second device , and wherein the second device autonomously performs the one or more operations defined by the first one or more instruction sets for operating the first device . 6 . The system of claim 3 , wherein the system further comprising : a server [FEATURE ID: 1] that receives from the first device at least one of : the first circumstance representation , or the first one or more instruction sets for operating the first device , and wherein the second device receives from the server at least one of : the first circumstance representation , or the first one or more instruction sets for operating the first device , and wherein the second device autonomously performs the one or more operations defined by the first one or more instruction sets for operating the first device . 7 . The system of claim 3 , wherein the one or more processors are further configured to perform at least : modifying : the first one or more instruction sets for operating the first device , or a copy [FEATURE ID: 5] of the first one or more instruction sets for operating the first device , and wherein the anticipating the first one or more instruction sets for operating the first device based on the at least partial match between the second circumstance representation and the first circumstance representation includes : anticipating the modified the first one or more instruction sets for operating the first device based on the at least partial match between the second circumstance representation and the first circumstance representation , or anticipating the modified the copy of the first one or more instruction sets for operating the first device based on the at least partial match between the second circumstance representation and the first circumstance representation , and wherein the executing the first one or more instruction sets for operating the first device includes : executing the modified the first one or more instruction sets for operating the first device , or executing the modified the copy of the first one or more instruction sets for operating the first device , and wherein the autonomously performing , by the first device or by the second device , the one or more operations defined by the first one or more instruction sets for operating the first device includes : autonomously performing , by the first device or by the second device , one or more operations defined by the modified the first one or more instruction sets for operating the first device , or autonomously performing , by the first device or by the second device , one or more operations defined by the modified the copy of the first one or more instruction sets for operating the first device . 8 . The system of claim 3 , wherein the one or more processors are further configured to perform at least : modifying at least one of : the first circumstance representation , a copy of the first circumstance representation , the second circumstance representation , or a copy of the second circumstance representation , and wherein the at least partial match between the second circumstance representation and the first circumstance representation includes : ( i ) at least partial match between the modified the second circumstance representation and the first circumstance representation , ( ii ) at least partial match between the modified the copy of the second circumstance representation and the first circumstance representation , ( iii ) at least partial match between the second circumstance representation and the modified the first circumstance representation , ( iv ) at least partial match between the second circumstance representation and the modified the copy of the first circumstance representation , ( v ) at least partial match between the modified the second circumstance representation and the modified the first circumstance representation , ( vi ) at least partial match between the modified the copy of the second circumstance representation and the modified the copy of the first circumstance representation , ( vii ) at least partial match between the modified the second circumstance representation and the modified the copy of the first circumstance representation , or ( viii ) at least partial match between the modified the copy of the second circumstance representation and the modified the first circumstance representation . 9 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating the first device , and wherein the third circumstance representation represents a third circumstance detected at least in part by the one or more sensors of the first device , and wherein at least a portion of the third circumstance representation or at least a portion of the second one or more instruction sets for operating the first device is learned in another learning process that includes operating the first device at least partially by the user . 10 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating the first device , and wherein the third circumstance representation represents a third circumstance detected at least in part by the one or more sensors of the first device , and wherein at least a portion of the third circumstance representation or at least a portion of the second one or more instruction sets for operating the first device is learned in another learning process that includes operating the first device at least partially by another user . 11 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating a third device [FEATURE ID: 1] , and wherein the third circumstance representation represents a third circumstance detected at least in part by one or more sensors of the third device , and wherein at least a portion of the third circumstance representation or at least a portion of the second one or more instruction sets for operating the third device is learned in another learning process that includes operating the third device at least partially by : the user , or another user . 12 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating a third device , and wherein the third circumstance representation represents a third circumstance detected at least in part by one or more sensors of the third device , and wherein the one or more processors are further configured to perform at least : generating or receiving a fourth circumstance representation [FEATURE ID: 11] , wherein the fourth circumstance representation represents a fourth circumstance detected at least in part by one or more sensors of a fourth device [FEATURE ID: 1] ; anticipating the second one or more instruction sets for operating the third device based on at least partial match between the fourth circumstance representation and the third circumstance representation ; and at least in response to the anticipating the second one or more instruction sets for operating the third device , executing the second one or more instruction sets for operating the third device , wherein the fourth device autonomously performs one or more operations defined by the second one or more instruction sets for operating the third device . 13 . The system of claim 3 , wherein the system further comprising : an artificial intelligence system [FEATURE ID: 14] , and wherein the learning the at least the portion of the first circumstance representation or the at least the portion of the first one or more instruction sets for operating the first device includes learning [FEATURE ID: 4] , at least in part by the artificial intelligence system , the at least the portion of the first circumstance representation or the at least the portion of the first one or more instruction sets for operating the first device , and wherein the anticipating the first one or more instruction sets for operating the first device includes anticipating , at least in part by the artificial intelligence system , the first one or more instruction sets for operating the first device . 14 . The system of claim 13 , wherein the artificial intelligence system includes : one or more inputs [FEATURE ID: 3]		1 . A computer [FEATURE ID: 1] - implemented method comprising [TRANSITIVE ID: 2] : receiving [TRANSITIVE ID: 4] input data ; receiving ground truth data [FEATURE ID: 13] from an audit [FEATURE ID: 14] evaluating [TRANSITIVE ID: 2] the input data ; fusing the input data and the ground truth data to create fused [TRANSITIVE ID: 7] data ; and applying [TRANSITIVE ID: 4] machine learning to create a model [FEATURE ID: 6] from the fused data . 2 . The computer - implemented method of claim [FEATURE ID: 12] 1 , further comprising : receiving unprocessed data ; processing [TRANSITIVE ID: 4] the unprocessed data with the model created [TRANSITIVE ID: 9] from the fused data to identify an action [FEATURE ID: 14] ; and one or more of providing the action and performing the action . 3 . The computer - implemented method of claim 1 , wherein fusing the input data and the ground truth data to create the fused data comprises : identifying a common identifier [FEATURE ID: 6] ; fusing the input data and the ground truth data using the common identifier ; and performing data preparation on the fused data . 4 . The computer - implemented method of claim 1 , wherein the input data is relating to a complex processing workflow [FEATURE ID: 10] . 5 . The computer - implemented method of claim 1 , wherein the ground truth data is received from an auditor [FEATURE ID: 14] . 6 . The computer - implemented method of claim 1 , wherein the model includes one or more of a classification model [FEATURE ID: 6] , a regression model [FEATURE ID: 6] , a ranking model , a semi-supervised model [FEATURE ID: 6] , a density estimation model [FEATURE ID: 6] , a clustering model [FEATURE ID: 6] , a dimensionality reduction model [FEATURE ID: 6] , a multidimensional querying model [FEATURE ID: 6] and an ensemble model [FEATURE ID: 14] . 7 . The computer - implemented method of claim 2 , wherein the action includes one or more of a preventive action [FEATURE ID: 11] , generating a notification [FEATURE ID: 11] , generating qualitative insights , identifying a process [FEATURE ID: 5] from the input data for additional review , requesting more data , delaying the action , determining causation [FEATURE ID: 13] , and updating the model . 8 . The computer - implemented method of claim 1 , wherein the ground truth data includes one or more of validity data [FEATURE ID: 13] , qualification data [FEATURE ID: 13] , quantification data [FEATURE ID: 13] , correction data , preference data [FEATURE ID: 13] , likelihood data or similarity data . 9 . A system [FEATURE ID: 1] comprising : one or more processors [FEATURE ID: 3] ; and a memory [FEATURE ID: 1] including instructions [FEATURE ID: 8]

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Targeted Patent: Patent: US11238344B1 Filed: 2016-11-02 Issued: 2022-02-01 Patent Holder: (Original Assignee) Individual     (Current Assignee) AUTONOMOUS DEVICES LLC Inventor(s): Jasmin Cosic Title: Artificially intelligent systems, devices, and methods for learning and/or using a device's circumstances for autonomous device operation	Cross Reference / Shared Meaning between the Lines	Charted Against: Patent: US20160259841A1 Filed: 2015-03-04 Issued: 2016-09-08 Patent Holder: (Original Assignee) Tegu LLC     (Current Assignee) Tegu LLC Inventor(s): Crispin C. Andrew, Hugh T. Winskill Title: Research Analysis System
[FEATURE ID: 1] system, first device, user, second device, server, fourth device	machine, computer, controller, device, platform, host, sensor	[FEATURE ID: 1] research analysis system, system, memory, processor
[TRANSITIVE ID: 2] comprising	including, involving, understanding, incorporating, featuring, containing, implementing	[TRANSITIVE ID: 2] comprising
[TRANSITIVE ID: 3] configured	set, implemented, effective, enabled, disposed, coupled, caused	[TRANSITIVE ID: 3] programmed, executed
[TRANSITIVE ID: 4] perform	provide, initiate, implement, control, cause, operate, effectuate	[TRANSITIVE ID: 4] execute, enable
[TRANSITIVE ID: 5] accessing, operating	processing, maintaining, implementing, providing, loading, executing, managing	[TRANSITIVE ID: 5] storing
[FEATURE ID: 6] memory	database, module, component, table, location, source, field	[FEATURE ID: 6] dimension, data source
[FEATURE ID: 7] stores, includes, represents	provides, identifies, defines, records, specifies, include, indicates	[FEATURE ID: 7] includes
[FEATURE ID: 8] knowledgebase, second circumstance representation, copy, fourth circumstance representation	representation, query, response, document, report, record, notification	[FEATURE ID: 8] related grouping, data value, naming convention
[TRANSITIVE ID: 9] correlated	related, coupled, configured, together, linked, corresponding, along	[TRANSITIVE ID: 9] associated
[FEATURE ID: 10] instruction sets	rules, parameters, records, signals, information	[FEATURE ID: 10] datum
[FEATURE ID: 11] portion	property, parameter, concept, variable, domain, feature, component	[FEATURE ID: 11] item, feature name, unique integer
[FEATURE ID: 12] learning process	process, test, task, step, method, procedure	[FEATURE ID: 12] processing
[FEATURE ID: 13] least	minus, lest, most, lease, last, only	[FEATURE ID: 13] least
[FEATURE ID: 14] claim	claim of, preceding claim, the claim, of claim, item, clause, embodiment	[FEATURE ID: 14] claim
[FEATURE ID: 15] states	characteristics, properties, dimensions, parameters, metrics, features, aspects	[FEATURE ID: 15] relationships, qualities
[FEATURE ID: 16] artificial intelligence system	expert, operator, identifier, ontology, index, application, algorithm	[FEATURE ID: 16] analysis tool, associated industry
[FEATURE ID: 17] learning	building, storing, generating, the	[FEATURE ID: 17] creation
1 . A system [FEATURE ID: 1] comprising [TRANSITIVE ID: 2] : one or more processors configured [TRANSITIVE ID: 3] to perform [TRANSITIVE ID: 4] at least : accessing [TRANSITIVE ID: 5] a memory [FEATURE ID: 6] that stores [FEATURE ID: 7] at least a knowledgebase [FEATURE ID: 8] that includes [TRANSITIVE ID: 7] a first circumstance representation correlated [TRANSITIVE ID: 9] with a first one or more instruction sets [FEATURE ID: 10] for operating [TRANSITIVE ID: 5] a first device [FEATURE ID: 1] , wherein the first circumstance representation represents [TRANSITIVE ID: 7] a first circumstance detected at least in part by one or more sensors of the first device , and wherein at least a portion [FEATURE ID: 11] of the first circumstance representation or at least a portion of the first one or more instruction sets for operating the first device is learned in a learning process [FEATURE ID: 12] that includes operating the first device at least [FEATURE ID: 13] partially by a user [FEATURE ID: 1] ; generating or receiving a second circumstance representation [FEATURE ID: 8] , wherein the second circumstance representation represents a second circumstance detected at least in part by : the one or more sensors of the first device , or one or more sensors of a second device [FEATURE ID: 1] ; anticipating the first one or more instruction sets for operating the first device based on at least partial match between the second circumstance representation and the first circumstance representation ; and at least in response to the anticipating , executing the first one or more instruction sets for operating the first device , wherein the first device or the second device autonomously performs one or more operations defined by the first one or more instruction sets for operating the first device . 2 . The system of claim [FEATURE ID: 14] 1 , wherein the first one or more instruction sets for operating the first device include one or more information about one or more states [FEATURE ID: 15] of : the first device , or a portion of the first device . 3 . The system of claim 1 , wherein the first circumstance representation includes a first one or more object representations , and wherein the second circumstance representation includes a second one or more object representations . 4 . The system of claim 3 , wherein the second circumstance representation represents the second circumstance detected at least in part by the one or more sensors of the first device , and wherein the first device autonomously performs the one or more operations defined by the first one or more instruction sets for operating the first device . 5 . The system of claim 3 , wherein the second circumstance representation represents the second circumstance detected at least in part by the one or more sensors of the second device , and wherein the second device autonomously performs the one or more operations defined by the first one or more instruction sets for operating the first device . 6 . The system of claim 3 , wherein the system further comprising : a server [FEATURE ID: 1] that receives from the first device at least one of : the first circumstance representation , or the first one or more instruction sets for operating the first device , and wherein the second device receives from the server at least one of : the first circumstance representation , or the first one or more instruction sets for operating the first device , and wherein the second device autonomously performs the one or more operations defined by the first one or more instruction sets for operating the first device . 7 . The system of claim 3 , wherein the one or more processors are further configured to perform at least : modifying : the first one or more instruction sets for operating the first device , or a copy [FEATURE ID: 8] of the first one or more instruction sets for operating the first device , and wherein the anticipating the first one or more instruction sets for operating the first device based on the at least partial match between the second circumstance representation and the first circumstance representation includes : anticipating the modified the first one or more instruction sets for operating the first device based on the at least partial match between the second circumstance representation and the first circumstance representation , or anticipating the modified the copy of the first one or more instruction sets for operating the first device based on the at least partial match between the second circumstance representation and the first circumstance representation , and wherein the executing the first one or more instruction sets for operating the first device includes : executing the modified the first one or more instruction sets for operating the first device , or executing the modified the copy of the first one or more instruction sets for operating the first device , and wherein the autonomously performing , by the first device or by the second device , the one or more operations defined by the first one or more instruction sets for operating the first device includes : autonomously performing , by the first device or by the second device , one or more operations defined by the modified the first one or more instruction sets for operating the first device , or autonomously performing , by the first device or by the second device , one or more operations defined by the modified the copy of the first one or more instruction sets for operating the first device . 8 . The system of claim 3 , wherein the one or more processors are further configured to perform at least : modifying at least one of : the first circumstance representation , a copy of the first circumstance representation , the second circumstance representation , or a copy of the second circumstance representation , and wherein the at least partial match between the second circumstance representation and the first circumstance representation includes : ( i ) at least partial match between the modified the second circumstance representation and the first circumstance representation , ( ii ) at least partial match between the modified the copy of the second circumstance representation and the first circumstance representation , ( iii ) at least partial match between the second circumstance representation and the modified the first circumstance representation , ( iv ) at least partial match between the second circumstance representation and the modified the copy of the first circumstance representation , ( v ) at least partial match between the modified the second circumstance representation and the modified the first circumstance representation , ( vi ) at least partial match between the modified the copy of the second circumstance representation and the modified the copy of the first circumstance representation , ( vii ) at least partial match between the modified the second circumstance representation and the modified the copy of the first circumstance representation , or ( viii ) at least partial match between the modified the copy of the second circumstance representation and the modified the first circumstance representation . 9 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating the first device , and wherein the third circumstance representation represents a third circumstance detected at least in part by the one or more sensors of the first device , and wherein at least a portion of the third circumstance representation or at least a portion of the second one or more instruction sets for operating the first device is learned in another learning process that includes operating the first device at least partially by the user . 10 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating the first device , and wherein the third circumstance representation represents a third circumstance detected at least in part by the one or more sensors of the first device , and wherein at least a portion of the third circumstance representation or at least a portion of the second one or more instruction sets for operating the first device is learned in another learning process that includes operating the first device at least partially by another user . 11 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating a third device , and wherein the third circumstance representation represents a third circumstance detected at least in part by one or more sensors of the third device , and wherein at least a portion of the third circumstance representation or at least a portion of the second one or more instruction sets for operating the third device is learned in another learning process that includes operating the third device at least partially by : the user , or another user . 12 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating a third device , and wherein the third circumstance representation represents a third circumstance detected at least in part by one or more sensors of the third device , and wherein the one or more processors are further configured to perform at least : generating or receiving a fourth circumstance representation [FEATURE ID: 8] , wherein the fourth circumstance representation represents a fourth circumstance detected at least in part by one or more sensors of a fourth device [FEATURE ID: 1] ; anticipating the second one or more instruction sets for operating the third device based on at least partial match between the fourth circumstance representation and the third circumstance representation ; and at least in response to the anticipating the second one or more instruction sets for operating the third device , executing the second one or more instruction sets for operating the third device , wherein the fourth device autonomously performs one or more operations defined by the second one or more instruction sets for operating the third device . 13 . The system of claim 3 , wherein the system further comprising : an artificial intelligence system [FEATURE ID: 16] , and wherein the learning the at least the portion of the first circumstance representation or the at least the portion of the first one or more instruction sets for operating the first device includes learning [FEATURE ID: 17]		1 . A research analysis system [FEATURE ID: 1] , the system [FEATURE ID: 1] comprising [TRANSITIVE ID: 2] : a memory [FEATURE ID: 1] for storing [TRANSITIVE ID: 5] computer - readable instructions associated [TRANSITIVE ID: 9] with an analysis tool [FEATURE ID: 16] ; and a processor [FEATURE ID: 1] programmed [TRANSITIVE ID: 3] to execute [TRANSITIVE ID: 4] the computer - readable instructions to enable [TRANSITIVE ID: 4] the operation of the analysis tool , wherein when the computer - readable instructions are executed [TRANSITIVE ID: 3] , the analysis tool is programmed to : input original data from a plurality of data sources , wherein the original data includes [TRANSITIVE ID: 7] at least [FEATURE ID: 13] one item [FEATURE ID: 11] that is of interest to an associated industry [FEATURE ID: 16] ; process the original data in manner to create a plurality of datum [FEATURE ID: 10] for input to a classification tool ; predict a plurality of relationships [FEATURE ID: 15] based on output from the classification tool ; predict a plurality of qualities [FEATURE ID: 15] based on output from the classification tool ; store the predicted plurality of relationships and the predicted plurality of qualities ; and generate at least one related grouping [FEATURE ID: 8] . 2 . The system of claim [FEATURE ID: 14] 1 , wherein creating a plurality of datum includes generating a feature and a corresponding value for each of the plurality of datum , wherein the feature includes a feature name [FEATURE ID: 11] that is mapped to a unique integer [FEATURE ID: 11] that corresponds to a dimension [FEATURE ID: 6] in the classification tool . 3 . The system of claim 2 , wherein the feature indicates a process to perform and the corresponding value represents a result from the processing [FEATURE ID: 12] . 4 . The system of claim 2 , wherein the feature indicates an element defined from the original data and the corresponding value represents a data value [FEATURE ID: 8] retrieved from the data source [FEATURE ID: 6] associated with the original data . 5 . The system of claim 2 , wherein the feature indicates an element defined polymorphically by the analysis tool and the corresponding value represents a data value computed by the analysis tool . 6 . The system of claim 2 , wherein the feature name remains consistent . 7 . The system of claim 2 , wherein the feature name is based on a naming convention [FEATURE ID: 8] derived from the original data . 8 . The system of claim 1 , wherein storing the predicted plurality of relationships is performed in a manner to enable creation [FEATURE ID: 17]

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Targeted Patent: Patent: US11238344B1 Filed: 2016-11-02 Issued: 2022-02-01 Patent Holder: (Original Assignee) Individual     (Current Assignee) AUTONOMOUS DEVICES LLC Inventor(s): Jasmin Cosic Title: Artificially intelligent systems, devices, and methods for learning and/or using a device's circumstances for autonomous device operation	Cross Reference / Shared Meaning between the Lines	Charted Against: Patent: US20160232575A1 Filed: 2015-02-06 Issued: 2016-08-11 Patent Holder: (Original Assignee) Facebook Inc     (Current Assignee) Meta Platforms Inc Inventor(s): Rituraj Kirti, Sue Ann Hong, Leon R. Cho Title: Determining a number of cluster groups associated with content identifying users eligible to receive the content
[FEATURE ID: 1] system, memory, learning process, user, second device, server, fourth device	computer, machine, controller, platform, network, processor, host	[FEATURE ID: 1] method, social networking system
[TRANSITIVE ID: 2] comprising	includes, of, having, for, involving, containing, implementing	[TRANSITIVE ID: 2] comprising, including
[FEATURE ID: 3] processors, inputs	nodes, elements, connections, entities, sensors, members, components	[FEATURE ID: 3] users
[TRANSITIVE ID: 4] accessing, operating, learning	providing, processing, identifying, storing, determining, obtaining, maintaining	[TRANSITIVE ID: 4] receiving, retrieving, generating
[FEATURE ID: 5] knowledgebase	first, collection, template, list, profile, group, rule	[FEATURE ID: 5] set, cluster model
[TRANSITIVE ID: 6] correlated	identified, coupled, labeled, embedded, stored, matched, combined	[TRANSITIVE ID: 6] associated, included
[FEATURE ID: 7] instruction sets	records, sets, information, indications	[FEATURE ID: 7] characteristics
[TRANSITIVE ID: 8] detected, learned	recorded, provided, received, identified, generated, captured, determined	[TRANSITIVE ID: 8] maintained
[FEATURE ID: 9] sensors, information	recommendations, users, processors, parameters, signals, nodes, actions	[FEATURE ID: 9] additional advertisement requests
[FEATURE ID: 10] portion	characteristic, classification, plurality, subset, parameter, group, combination	[FEATURE ID: 10] number, classifier
[FEATURE ID: 11] second circumstance representation, fourth circumstance representation	communication, message, response, notification, query, representation, signal	[FEATURE ID: 11] request, selection process
[FEATURE ID: 12] claim	any claim, preceding claim, claim of, of claim, item, clause, clam	[FEATURE ID: 12] claim
[FEATURE ID: 13] states, object representations	attributes, features, elements, parameters, characteristics, values, targets	[FEATURE ID: 13] criteria, additional targeting criteria, advertising parameters
[FEATURE ID: 14] artificial intelligence system	object, application, identifier, alert, offer, event, input	[FEATURE ID: 14] advertisement request, advertisement
1 . A system [FEATURE ID: 1] comprising [TRANSITIVE ID: 2] : one or more processors [FEATURE ID: 3] configured to perform at least : accessing [TRANSITIVE ID: 4] a memory [FEATURE ID: 1] that stores at least a knowledgebase [FEATURE ID: 5] that includes a first circumstance representation correlated [TRANSITIVE ID: 6] with a first one or more instruction sets [FEATURE ID: 7] for operating [TRANSITIVE ID: 4] a first device , wherein the first circumstance representation represents a first circumstance detected [TRANSITIVE ID: 8] at least in part by one or more sensors [FEATURE ID: 9] of the first device , and wherein at least a portion [FEATURE ID: 10] of the first circumstance representation or at least a portion of the first one or more instruction sets for operating the first device is learned [TRANSITIVE ID: 8] in a learning process [FEATURE ID: 1] that includes operating the first device at least partially by a user [FEATURE ID: 1] ; generating or receiving a second circumstance representation [FEATURE ID: 11] , wherein the second circumstance representation represents a second circumstance detected at least in part by : the one or more sensors of the first device , or one or more sensors of a second device [FEATURE ID: 1] ; anticipating the first one or more instruction sets for operating the first device based on at least partial match between the second circumstance representation and the first circumstance representation ; and at least in response to the anticipating , executing the first one or more instruction sets for operating the first device , wherein the first device or the second device autonomously performs one or more operations defined by the first one or more instruction sets for operating the first device . 2 . The system of claim [FEATURE ID: 12] 1 , wherein the first one or more instruction sets for operating the first device include one or more information [FEATURE ID: 9] about one or more states [FEATURE ID: 13] of : the first device , or a portion of the first device . 3 . The system of claim 1 , wherein the first circumstance representation includes a first one or more object representations [FEATURE ID: 13] , and wherein the second circumstance representation includes a second one or more object representations . 4 . The system of claim 3 , wherein the second circumstance representation represents the second circumstance detected at least in part by the one or more sensors of the first device , and wherein the first device autonomously performs the one or more operations defined by the first one or more instruction sets for operating the first device . 5 . The system of claim 3 , wherein the second circumstance representation represents the second circumstance detected at least in part by the one or more sensors of the second device , and wherein the second device autonomously performs the one or more operations defined by the first one or more instruction sets for operating the first device . 6 . The system of claim 3 , wherein the system further comprising : a server [FEATURE ID: 1] that receives from the first device at least one of : the first circumstance representation , or the first one or more instruction sets for operating the first device , and wherein the second device receives from the server at least one of : the first circumstance representation , or the first one or more instruction sets for operating the first device , and wherein the second device autonomously performs the one or more operations defined by the first one or more instruction sets for operating the first device . 7 . The system of claim 3 , wherein the one or more processors are further configured to perform at least : modifying : the first one or more instruction sets for operating the first device , or a copy of the first one or more instruction sets for operating the first device , and wherein the anticipating the first one or more instruction sets for operating the first device based on the at least partial match between the second circumstance representation and the first circumstance representation includes : anticipating the modified the first one or more instruction sets for operating the first device based on the at least partial match between the second circumstance representation and the first circumstance representation , or anticipating the modified the copy of the first one or more instruction sets for operating the first device based on the at least partial match between the second circumstance representation and the first circumstance representation , and wherein the executing the first one or more instruction sets for operating the first device includes : executing the modified the first one or more instruction sets for operating the first device , or executing the modified the copy of the first one or more instruction sets for operating the first device , and wherein the autonomously performing , by the first device or by the second device , the one or more operations defined by the first one or more instruction sets for operating the first device includes : autonomously performing , by the first device or by the second device , one or more operations defined by the modified the first one or more instruction sets for operating the first device , or autonomously performing , by the first device or by the second device , one or more operations defined by the modified the copy of the first one or more instruction sets for operating the first device . 8 . The system of claim 3 , wherein the one or more processors are further configured to perform at least : modifying at least one of : the first circumstance representation , a copy of the first circumstance representation , the second circumstance representation , or a copy of the second circumstance representation , and wherein the at least partial match between the second circumstance representation and the first circumstance representation includes : ( i ) at least partial match between the modified the second circumstance representation and the first circumstance representation , ( ii ) at least partial match between the modified the copy of the second circumstance representation and the first circumstance representation , ( iii ) at least partial match between the second circumstance representation and the modified the first circumstance representation , ( iv ) at least partial match between the second circumstance representation and the modified the copy of the first circumstance representation , ( v ) at least partial match between the modified the second circumstance representation and the modified the first circumstance representation , ( vi ) at least partial match between the modified the copy of the second circumstance representation and the modified the copy of the first circumstance representation , ( vii ) at least partial match between the modified the second circumstance representation and the modified the copy of the first circumstance representation , or ( viii ) at least partial match between the modified the copy of the second circumstance representation and the modified the first circumstance representation . 9 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating the first device , and wherein the third circumstance representation represents a third circumstance detected at least in part by the one or more sensors of the first device , and wherein at least a portion of the third circumstance representation or at least a portion of the second one or more instruction sets for operating the first device is learned in another learning process that includes operating the first device at least partially by the user . 10 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating the first device , and wherein the third circumstance representation represents a third circumstance detected at least in part by the one or more sensors of the first device , and wherein at least a portion of the third circumstance representation or at least a portion of the second one or more instruction sets for operating the first device is learned in another learning process that includes operating the first device at least partially by another user . 11 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating a third device , and wherein the third circumstance representation represents a third circumstance detected at least in part by one or more sensors of the third device , and wherein at least a portion of the third circumstance representation or at least a portion of the second one or more instruction sets for operating the third device is learned in another learning process that includes operating the third device at least partially by : the user , or another user . 12 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating a third device , and wherein the third circumstance representation represents a third circumstance detected at least in part by one or more sensors of the third device , and wherein the one or more processors are further configured to perform at least : generating or receiving a fourth circumstance representation [FEATURE ID: 11] , wherein the fourth circumstance representation represents a fourth circumstance detected at least in part by one or more sensors of a fourth device [FEATURE ID: 1] ; anticipating the second one or more instruction sets for operating the third device based on at least partial match between the fourth circumstance representation and the third circumstance representation ; and at least in response to the anticipating the second one or more instruction sets for operating the third device , executing the second one or more instruction sets for operating the third device , wherein the fourth device autonomously performs one or more operations defined by the second one or more instruction sets for operating the third device . 13 . The system of claim 3 , wherein the system further comprising : an artificial intelligence system [FEATURE ID: 14] , and wherein the learning the at least the portion of the first circumstance representation or the at least the portion of the first one or more instruction sets for operating the first device includes learning [FEATURE ID: 4] , at least in part by the artificial intelligence system , the at least the portion of the first circumstance representation or the at least the portion of the first one or more instruction sets for operating the first device , and wherein the anticipating the first one or more instruction sets for operating the first device includes anticipating , at least in part by the artificial intelligence system , the first one or more instruction sets for operating the first device . 14 . The system of claim 13 , wherein the artificial intelligence system includes : one or more inputs [FEATURE ID: 3]		1 . A method [FEATURE ID: 1] comprising [TRANSITIVE ID: 2] : receiving [TRANSITIVE ID: 4] an advertisement request [FEATURE ID: 14] including [TRANSITIVE ID: 2] a set [FEATURE ID: 5] of targeting criteria [FEATURE ID: 13] and an additional set of targeting criteria at a social networking system [FEATURE ID: 1] ; retrieving [TRANSITIVE ID: 4] characteristics [FEATURE ID: 7] of users [FEATURE ID: 3] of the social networking system maintained [TRANSITIVE ID: 8] by the social networking system ; generating [TRANSITIVE ID: 4] a cluster group associated [TRANSITIVE ID: 6] with the set of targeting criteria included [TRANSITIVE ID: 6] in the advertisement request including users having characteristics satisfying the set of targeting criteria and one or more users having one or more characteristics that do not satisfy the set of targeting criteria by applying a cluster model [FEATURE ID: 5] associated with the set of targeting criteria to characteristics of users having characteristics that do not satisfy the set of targeting criteria ; generating an additional cluster group associated with the additional set of targeting criteria included in the advertisement request including users having characteristics satisfying the additional set of targeting criteria and one or more users having characteristics that do not satisfy the set of additional targeting criteria [FEATURE ID: 13] by applying a cluster model associated with the additional set of targeting criteria to characteristics of users having characteristics that do not satisfy the additional set of targeting criteria ; determining an amount of overlap between the cluster group and the additional cluster group , the amount of overlap based at least in part on a number [FEATURE ID: 10] of users included in the cluster group and also included in the additional cluster group ; determining whether the amount of overlap equals or exceeds a threshold amount ; generating an overall group of users associated with the advertisement request by combining the cluster group and the additional cluster group subject to determining the amount of overlap equals or exceeds the threshold amount . 2 . The method of claim [FEATURE ID: 12] 1 , wherein generating the overall group of users associated with the advertisement request by combining the cluster group and the additional cluster group subject to determining the amount of overlap equals or exceeds the threshold amount comprises : associating a classifier [FEATURE ID: 10] with the overall group of users to indicate whether a user is included in the cluster group or is included in the additional cluster group . 3 . The method of claim 1 , wherein the advertisement request includes one or more advertising parameters [FEATURE ID: 13] associated with the set of targeting criteria and one or more alternative advertising parameters associated with the additional set of targeting criteria . 4 . The method of claim 3 , wherein the one or more advertising parameters include advertisement content and the one or more alternative advertising parameters include alternative advertisement content . 5 . The method of claim 3 , wherein the one or more advertising parameters include a bid amount and the one or more alternative advertising parameters include an alternative bid amount . 6 . The method of claim 3 , wherein the one or more advertising parameters include a duration and the one or more alternative advertising parameters include an alternative duration . 7 . The method of claim 3 , further comprising : receiving a request [FEATURE ID: 11] to present an advertisement [FEATURE ID: 14] to a user of the social networking system ; retrieving characteristics of the user maintained by the social networking system ; determining whether the user is included in the cluster group or in the additional cluster group based at least in part on the characteristics of the user ; responsive to determining the user is included in the cluster group or in the additional cluster group , including the advertisement in a selection process [FEATURE ID: 11] with one or more additional advertisement requests [FEATURE ID: 9]

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Targeted Patent: Patent: US11238344B1 Filed: 2016-11-02 Issued: 2022-02-01 Patent Holder: (Original Assignee) Individual     (Current Assignee) AUTONOMOUS DEVICES LLC Inventor(s): Jasmin Cosic Title: Artificially intelligent systems, devices, and methods for learning and/or using a device's circumstances for autonomous device operation	Cross Reference / Shared Meaning between the Lines	Charted Against: Patent: US9367814B1 Filed: 2011-12-27 Issued: 2016-06-14 Patent Holder: (Original Assignee) Google LLC     (Current Assignee) Google LLC Inventor(s): Glenn M. Lewis, Kirill Buryak, Aner Ben-Artzi, Jun Peng, Nadav Benbarak Title: Methods and systems for classifying data using a hierarchical taxonomy
[FEATURE ID: 1] system, first device, second device, server, third device, fourth device	controller, machine, device, network, second, user, platform	[FEATURE ID: 1] computer, readable medium
[TRANSITIVE ID: 2] comprising, stores, includes, operating, represents	of, having, provides, comprises, defines, identifies, implementing	[TRANSITIVE ID: 2] including, comprising, includes, representing
[FEATURE ID: 3] processors, operations, information	processes, commands, settings, hardware, steps, applications, procedures	[FEATURE ID: 3] instructions
[TRANSITIVE ID: 4] accessing, learning	providing, updating, determining, processing, utilizing, reading, establishing	[TRANSITIVE ID: 4] generating, applying
[FEATURE ID: 5] memory, second circumstance representation	database, repository, system, network, corpus, document, library	[FEATURE ID: 5] set, hierarchical taxonomy, corpus further
[FEATURE ID: 6] knowledgebase, copy	representation, subset, database, table, repository, vocabulary, taxonomy	[FEATURE ID: 6] corpus, hierarchical tree structure
[FEATURE ID: 7] instruction sets, object representations	information, records, statements, features, labels, expressions, attributes	[FEATURE ID: 7] training documents
[FEATURE ID: 8] sensors	modules, processors, filters, features, outputs, devices	[FEATURE ID: 8] document classifiers
[FEATURE ID: 9] portion	parameter, characteristic, property, value	[FEATURE ID: 9] confidence level
[FEATURE ID: 10] learning process	process, method, learning, task, training, procedure, technique	[FEATURE ID: 10] classification algorithm
[FEATURE ID: 11] user	technician, computer, subject, human, person, processor, consumer	[FEATURE ID: 11] user
[FEATURE ID: 12] claim	item, clam claim, claimed, clair, figure, paragraph, embodiment	[FEATURE ID: 12] claim
[FEATURE ID: 13] states	characteristics, parts, attributes, properties	[FEATURE ID: 13] sub issues
[FEATURE ID: 14] second	subsequent, next, respective, further, corresponding, third, first	[FEATURE ID: 14] different
1 . A system [FEATURE ID: 1] comprising [TRANSITIVE ID: 2] : one or more processors [FEATURE ID: 3] configured to perform at least : accessing [TRANSITIVE ID: 4] a memory [FEATURE ID: 5] that stores [FEATURE ID: 2] at least a knowledgebase [FEATURE ID: 6] that includes [TRANSITIVE ID: 2] a first circumstance representation correlated with a first one or more instruction sets [FEATURE ID: 7] for operating [TRANSITIVE ID: 2] a first device [FEATURE ID: 1] , wherein the first circumstance representation represents [TRANSITIVE ID: 2] a first circumstance detected at least in part by one or more sensors [FEATURE ID: 8] of the first device , and wherein at least a portion [FEATURE ID: 9] of the first circumstance representation or at least a portion of the first one or more instruction sets for operating the first device is learned in a learning process [FEATURE ID: 10] that includes operating the first device at least partially by a user [FEATURE ID: 11] ; generating or receiving a second circumstance representation [FEATURE ID: 5] , wherein the second circumstance representation represents a second circumstance detected at least in part by : the one or more sensors of the first device , or one or more sensors of a second device [FEATURE ID: 1] ; anticipating the first one or more instruction sets for operating the first device based on at least partial match between the second circumstance representation and the first circumstance representation ; and at least in response to the anticipating , executing the first one or more instruction sets for operating the first device , wherein the first device or the second device autonomously performs one or more operations [FEATURE ID: 3] defined by the first one or more instruction sets for operating the first device . 2 . The system of claim [FEATURE ID: 12] 1 , wherein the first one or more instruction sets for operating the first device include one or more information [FEATURE ID: 3] about one or more states [FEATURE ID: 13] of : the first device , or a portion of the first device . 3 . The system of claim 1 , wherein the first circumstance representation includes a first one or more object representations [FEATURE ID: 7] , and wherein the second circumstance representation includes a second [FEATURE ID: 14] one or more object representations . 4 . The system of claim 3 , wherein the second circumstance representation represents the second circumstance detected at least in part by the one or more sensors of the first device , and wherein the first device autonomously performs the one or more operations defined by the first one or more instruction sets for operating the first device . 5 . The system of claim 3 , wherein the second circumstance representation represents the second circumstance detected at least in part by the one or more sensors of the second device , and wherein the second device autonomously performs the one or more operations defined by the first one or more instruction sets for operating the first device . 6 . The system of claim 3 , wherein the system further comprising : a server [FEATURE ID: 1] that receives from the first device at least one of : the first circumstance representation , or the first one or more instruction sets for operating the first device , and wherein the second device receives from the server at least one of : the first circumstance representation , or the first one or more instruction sets for operating the first device , and wherein the second device autonomously performs the one or more operations defined by the first one or more instruction sets for operating the first device . 7 . The system of claim 3 , wherein the one or more processors are further configured to perform at least : modifying : the first one or more instruction sets for operating the first device , or a copy [FEATURE ID: 6] of the first one or more instruction sets for operating the first device , and wherein the anticipating the first one or more instruction sets for operating the first device based on the at least partial match between the second circumstance representation and the first circumstance representation includes : anticipating the modified the first one or more instruction sets for operating the first device based on the at least partial match between the second circumstance representation and the first circumstance representation , or anticipating the modified the copy of the first one or more instruction sets for operating the first device based on the at least partial match between the second circumstance representation and the first circumstance representation , and wherein the executing the first one or more instruction sets for operating the first device includes : executing the modified the first one or more instruction sets for operating the first device , or executing the modified the copy of the first one or more instruction sets for operating the first device , and wherein the autonomously performing , by the first device or by the second device , the one or more operations defined by the first one or more instruction sets for operating the first device includes : autonomously performing , by the first device or by the second device , one or more operations defined by the modified the first one or more instruction sets for operating the first device , or autonomously performing , by the first device or by the second device , one or more operations defined by the modified the copy of the first one or more instruction sets for operating the first device . 8 . The system of claim 3 , wherein the one or more processors are further configured to perform at least : modifying at least one of : the first circumstance representation , a copy of the first circumstance representation , the second circumstance representation , or a copy of the second circumstance representation , and wherein the at least partial match between the second circumstance representation and the first circumstance representation includes : ( i ) at least partial match between the modified the second circumstance representation and the first circumstance representation , ( ii ) at least partial match between the modified the copy of the second circumstance representation and the first circumstance representation , ( iii ) at least partial match between the second circumstance representation and the modified the first circumstance representation , ( iv ) at least partial match between the second circumstance representation and the modified the copy of the first circumstance representation , ( v ) at least partial match between the modified the second circumstance representation and the modified the first circumstance representation , ( vi ) at least partial match between the modified the copy of the second circumstance representation and the modified the copy of the first circumstance representation , ( vii ) at least partial match between the modified the second circumstance representation and the modified the copy of the first circumstance representation , or ( viii ) at least partial match between the modified the copy of the second circumstance representation and the modified the first circumstance representation . 9 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating the first device , and wherein the third circumstance representation represents a third circumstance detected at least in part by the one or more sensors of the first device , and wherein at least a portion of the third circumstance representation or at least a portion of the second one or more instruction sets for operating the first device is learned in another learning process that includes operating the first device at least partially by the user . 10 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating the first device , and wherein the third circumstance representation represents a third circumstance detected at least in part by the one or more sensors of the first device , and wherein at least a portion of the third circumstance representation or at least a portion of the second one or more instruction sets for operating the first device is learned in another learning process that includes operating the first device at least partially by another user . 11 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating a third device [FEATURE ID: 1] , and wherein the third circumstance representation represents a third circumstance detected at least in part by one or more sensors of the third device , and wherein at least a portion of the third circumstance representation or at least a portion of the second one or more instruction sets for operating the third device is learned in another learning process that includes operating the third device at least partially by : the user , or another user . 12 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating a third device , and wherein the third circumstance representation represents a third circumstance detected at least in part by one or more sensors of the third device , and wherein the one or more processors are further configured to perform at least : generating or receiving a fourth circumstance representation , wherein the fourth circumstance representation represents a fourth circumstance detected at least in part by one or more sensors of a fourth device [FEATURE ID: 1] ; anticipating the second one or more instruction sets for operating the third device based on at least partial match between the fourth circumstance representation and the third circumstance representation ; and at least in response to the anticipating the second one or more instruction sets for operating the third device , executing the second one or more instruction sets for operating the third device , wherein the fourth device autonomously performs one or more operations defined by the second one or more instruction sets for operating the third device . 13 . The system of claim 3 , wherein the system further comprising : an artificial intelligence system , and wherein the learning the at least the portion of the first circumstance representation or the at least the portion of the first one or more instruction sets for operating the first device includes learning [FEATURE ID: 4]		1 . A computer [FEATURE ID: 1] - implemented method including [TRANSITIVE ID: 2] executing instructions [FEATURE ID: 3] stored on a computer - readable medium [FEATURE ID: 1] , the method comprising [TRANSITIVE ID: 2] : generating [TRANSITIVE ID: 4] a set [FEATURE ID: 5] of document classifiers [FEATURE ID: 8] by applying [TRANSITIVE ID: 4] a classification algorithm [FEATURE ID: 10] to a trusted corpus [FEATURE ID: 6] , wherein the trusted corpus includes [TRANSITIVE ID: 2] a set of training documents [FEATURE ID: 7] representing [TRANSITIVE ID: 2] a hierarchical taxonomy [FEATURE ID: 5] , the hierarchical taxonomy including a hierarchical tree structure [FEATURE ID: 6] of domain specific issues that includes multiple levels of issue categories , subcategories , and sub issues [FEATURE ID: 13] of each issue , the trusted corpus further [FEATURE ID: 5] includes previously classified documents associated with a classification confidence level above a predetermined confidence level threshold ; executing one or more of the generated document classifiers against a first plurality of input documents to create a first plurality of classified documents , wherein each classified document is associated with a classification within the taxonomy and a classification confidence level ; selecting one or more classified documents that are associated with a classification confidence level below the predetermined confidence level threshold to create a set of low - confidence documents ; disassociating the low - confidence documents from each of the associated classifications ; prompting a user [FEATURE ID: 11] to enter a new classification within the hierarchical taxonomy for at least one low - confidence document , wherein the low - confidence document is associated with the entered classification and with a predetermined confidence level [FEATURE ID: 9] to create a newly classified document in at least one of the multiple levels of issue categories , subcategories , and sub issues of each issue of the hierarchical taxonomy ; applying a highest classification confidence level to the newly classified document ; including the newly classified document in the trusted corpus to create an updated trusted corpus ; and executing one or more of the generated document classifiers , by applying the classification algorithm to the updated trusted corpus against a second plurality of input documents to create a second plurality of classified documents , wherein each classified document is associated with a classification within the taxonomy and a classification confidence level . 2 . The method of claim [FEATURE ID: 12] 1 , wherein generating a set of document classifiers further comprises applying a classification algorithm to a trusted corpus by applying the classification algorithm to a trusted corpus including classified documents having a classification confidence level greater than a predetermined confidence level threshold . 3 . The method of claim 1 , wherein generating a set of document classifiers further comprises applying a classification algorithm to a trusted corpus by applying the classification algorithm to a trusted corpus including classified documents associated with a classification entered by a user . 4 . The method of claim 1 , further comprising selecting one or more of the classified documents from the second plurality of input documents that are associated with a classification confidence below a different [FEATURE ID: 14]

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Targeted Patent: Patent: US11238344B1 Filed: 2016-11-02 Issued: 2022-02-01 Patent Holder: (Original Assignee) Individual     (Current Assignee) AUTONOMOUS DEVICES LLC Inventor(s): Jasmin Cosic Title: Artificially intelligent systems, devices, and methods for learning and/or using a device's circumstances for autonomous device operation	Cross Reference / Shared Meaning between the Lines	Charted Against: Patent: US9348802B2 Filed: 2012-03-19 Issued: 2016-05-24 Patent Holder: (Original Assignee) Litera Corp     (Current Assignee) Litera Corp Inventor(s): Deepak Massand Title: System and method for synchronizing bi-directional document management
[FEATURE ID: 1] system, first device, user, second device, server, third device, fourth device	machine, controller, device, sensor, processor, network, first	[FEATURE ID: 1] computer, system, storage device
[TRANSITIVE ID: 2] comprising	including, having, containing, includes, comprises, with, involving	[TRANSITIVE ID: 2] comprising, being
[FEATURE ID: 3] processors, sensors, states	components, controllers, users, modules, operations, elements, engines	[FEATURE ID: 3] processors
[TRANSITIVE ID: 4] configured	oriented, implemented, enabled, directed, operated, disposed, coupled	[TRANSITIVE ID: 4] based, configured
[TRANSITIVE ID: 5] accessing, operating	providing, maintaining, managing, controlling, processing, access, implementing	[TRANSITIVE ID: 5] collaborating, publish
[FEATURE ID: 6] memory	repository, workspace, database, container, storage, computer, record	[FEATURE ID: 6] network, document, data storage separate, collaboration platform, virtual memory, collaboration document, second collaboration document
[FEATURE ID: 7] knowledgebase, portion, third circumstance representation, fourth circumstance representation, fourth circumstance	first, second, representation, content, template, document, fourth	[FEATURE ID: 7] workspace, second format, second version
[FEATURE ID: 8] instruction sets	instruction, settings, inputs, parameters, rules	[FEATURE ID: 8] input
[FEATURE ID: 9] first circumstance, second circumstance	fact, state, scenario, situation, particular circumstance, signal, status	[FEATURE ID: 9] workspace such
[TRANSITIVE ID: 10] detected, learned	provided, received, defined, generated, configured, recorded, identified	[TRANSITIVE ID: 10] created, stored
[FEATURE ID: 11] learning process	process, workflow, procedure, task, conversation, session, transaction	[FEATURE ID: 11] communication session
[FEATURE ID: 12] second circumstance representation	document, second, reference, file, first, third	[FEATURE ID: 12] first reviewer, first format
[FEATURE ID: 13] response	answer, addition, direct response, accordance, subject, parallel, proximity	[FEATURE ID: 13] response
[FEATURE ID: 14] operations	services, activities, tasks, applications	[FEATURE ID: 14] information
[FEATURE ID: 15] claim	formula, item, previous claim, preceding claim, ju claim, the claim, of claim	[FEATURE ID: 15] claim
[FEATURE ID: 16] information	settings, instructions, updates, inputs, data, suggestions, entries	[FEATURE ID: 16] access rights, adjustments
[FEATURE ID: 17] further	also, additionally, separately, furthermore, farther, not, optionally	[FEATURE ID: 17] further
[FEATURE ID: 18] copy	change, modification, snapshot, part, view, modified, portion	[FEATURE ID: 18] copy, content link
[FEATURE ID: 19] artificial intelligence system	object, individual, application, engine, asset, administrator, agent	[FEATURE ID: 19] original document, owner
1 . A system [FEATURE ID: 1] comprising [TRANSITIVE ID: 2] : one or more processors [FEATURE ID: 3] configured [TRANSITIVE ID: 4] to perform at least : accessing [TRANSITIVE ID: 5] a memory [FEATURE ID: 6] that stores at least a knowledgebase [FEATURE ID: 7] that includes a first circumstance representation correlated with a first one or more instruction sets [FEATURE ID: 8] for operating [TRANSITIVE ID: 5] a first device [FEATURE ID: 1] , wherein the first circumstance representation represents a first circumstance [FEATURE ID: 9] detected [TRANSITIVE ID: 10] at least in part by one or more sensors [FEATURE ID: 3] of the first device , and wherein at least a portion [FEATURE ID: 7] of the first circumstance representation or at least a portion of the first one or more instruction sets for operating the first device is learned [TRANSITIVE ID: 10] in a learning process [FEATURE ID: 11] that includes operating the first device at least partially by a user [FEATURE ID: 1] ; generating or receiving a second circumstance representation [FEATURE ID: 12] , wherein the second circumstance representation represents a second circumstance [FEATURE ID: 9] detected at least in part by : the one or more sensors of the first device , or one or more sensors of a second device [FEATURE ID: 1] ; anticipating the first one or more instruction sets for operating the first device based on at least partial match between the second circumstance representation and the first circumstance representation ; and at least in response [FEATURE ID: 13] to the anticipating , executing the first one or more instruction sets for operating the first device , wherein the first device or the second device autonomously performs one or more operations [FEATURE ID: 14] defined by the first one or more instruction sets for operating the first device . 2 . The system of claim [FEATURE ID: 15] 1 , wherein the first one or more instruction sets for operating the first device include one or more information [FEATURE ID: 16] about one or more states [FEATURE ID: 3] of : the first device , or a portion of the first device . 3 . The system of claim 1 , wherein the first circumstance representation includes a first one or more object representations , and wherein the second circumstance representation includes a second one or more object representations . 4 . The system of claim 3 , wherein the second circumstance representation represents the second circumstance detected at least in part by the one or more sensors of the first device , and wherein the first device autonomously performs the one or more operations defined by the first one or more instruction sets for operating the first device . 5 . The system of claim 3 , wherein the second circumstance representation represents the second circumstance detected at least in part by the one or more sensors of the second device , and wherein the second device autonomously performs the one or more operations defined by the first one or more instruction sets for operating the first device . 6 . The system of claim 3 , wherein the system further comprising : a server [FEATURE ID: 1] that receives from the first device at least one of : the first circumstance representation , or the first one or more instruction sets for operating the first device , and wherein the second device receives from the server at least one of : the first circumstance representation , or the first one or more instruction sets for operating the first device , and wherein the second device autonomously performs the one or more operations defined by the first one or more instruction sets for operating the first device . 7 . The system of claim 3 , wherein the one or more processors are further [FEATURE ID: 17] configured to perform at least : modifying : the first one or more instruction sets for operating the first device , or a copy [FEATURE ID: 18] of the first one or more instruction sets for operating the first device , and wherein the anticipating the first one or more instruction sets for operating the first device based on the at least partial match between the second circumstance representation and the first circumstance representation includes : anticipating the modified the first one or more instruction sets for operating the first device based on the at least partial match between the second circumstance representation and the first circumstance representation , or anticipating the modified the copy of the first one or more instruction sets for operating the first device based on the at least partial match between the second circumstance representation and the first circumstance representation , and wherein the executing the first one or more instruction sets for operating the first device includes : executing the modified the first one or more instruction sets for operating the first device , or executing the modified the copy of the first one or more instruction sets for operating the first device , and wherein the autonomously performing , by the first device or by the second device , the one or more operations defined by the first one or more instruction sets for operating the first device includes : autonomously performing , by the first device or by the second device , one or more operations defined by the modified the first one or more instruction sets for operating the first device , or autonomously performing , by the first device or by the second device , one or more operations defined by the modified the copy of the first one or more instruction sets for operating the first device . 8 . The system of claim 3 , wherein the one or more processors are further configured to perform at least : modifying at least one of : the first circumstance representation , a copy of the first circumstance representation , the second circumstance representation , or a copy of the second circumstance representation , and wherein the at least partial match between the second circumstance representation and the first circumstance representation includes : ( i ) at least partial match between the modified the second circumstance representation and the first circumstance representation , ( ii ) at least partial match between the modified the copy of the second circumstance representation and the first circumstance representation , ( iii ) at least partial match between the second circumstance representation and the modified the first circumstance representation , ( iv ) at least partial match between the second circumstance representation and the modified the copy of the first circumstance representation , ( v ) at least partial match between the modified the second circumstance representation and the modified the first circumstance representation , ( vi ) at least partial match between the modified the copy of the second circumstance representation and the modified the copy of the first circumstance representation , ( vii ) at least partial match between the modified the second circumstance representation and the modified the copy of the first circumstance representation , or ( viii ) at least partial match between the modified the copy of the second circumstance representation and the modified the first circumstance representation . 9 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation [FEATURE ID: 7] correlated with a second one or more instruction sets for operating the first device , and wherein the third circumstance representation represents a third circumstance detected at least in part by the one or more sensors of the first device , and wherein at least a portion of the third circumstance representation or at least a portion of the second one or more instruction sets for operating the first device is learned in another learning process that includes operating the first device at least partially by the user . 10 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating the first device , and wherein the third circumstance representation represents a third circumstance detected at least in part by the one or more sensors of the first device , and wherein at least a portion of the third circumstance representation or at least a portion of the second one or more instruction sets for operating the first device is learned in another learning process that includes operating the first device at least partially by another user . 11 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating a third device [FEATURE ID: 1] , and wherein the third circumstance representation represents a third circumstance detected at least in part by one or more sensors of the third device , and wherein at least a portion of the third circumstance representation or at least a portion of the second one or more instruction sets for operating the third device is learned in another learning process that includes operating the third device at least partially by : the user , or another user . 12 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating a third device , and wherein the third circumstance representation represents a third circumstance detected at least in part by one or more sensors of the third device , and wherein the one or more processors are further configured to perform at least : generating or receiving a fourth circumstance representation [FEATURE ID: 7] , wherein the fourth circumstance representation represents a fourth circumstance [FEATURE ID: 7] detected at least in part by one or more sensors of a fourth device [FEATURE ID: 1] ; anticipating the second one or more instruction sets for operating the third device based on at least partial match between the fourth circumstance representation and the third circumstance representation ; and at least in response to the anticipating the second one or more instruction sets for operating the third device , executing the second one or more instruction sets for operating the third device , wherein the fourth device autonomously performs one or more operations defined by the second one or more instruction sets for operating the third device . 13 . The system of claim 3 , wherein the system further comprising : an artificial intelligence system [FEATURE ID: 19]		1 . A computer [FEATURE ID: 1] - based [TRANSITIVE ID: 4] system [FEATURE ID: 1] for collaborating [TRANSITIVE ID: 5] information [FEATURE ID: 14] over a network [FEATURE ID: 6] , comprising [TRANSITIVE ID: 2] : a storage device [FEATURE ID: 1] ; and one or more processors [FEATURE ID: 3] configured [TRANSITIVE ID: 4] to : maintain a document [FEATURE ID: 6] in the storage device , the document being [TRANSITIVE ID: 2] a copy [FEATURE ID: 18] of an original document [FEATURE ID: 19] created [TRANSITIVE ID: 10] by an owner [FEATURE ID: 19] and stored [TRANSITIVE ID: 10] in a data storage separate [FEATURE ID: 6] from the storage device , publish [TRANSITIVE ID: 5] a content link [FEATURE ID: 18] to the document in a workspace [FEATURE ID: 7] that is configured by the owner with access rights [FEATURE ID: 16] for a first reviewer [FEATURE ID: 12] , provide content of the document to a collaboration platform [FEATURE ID: 6] in response [FEATURE ID: 13] to a selection , by the first reviewer , of the content link in the workspace that is rendered by the collaboration platform to the first reviewer ; receive from the collaboration platform , an adjustment to the document made by the first reviewer through the collaboration platform during a communication session [FEATURE ID: 11] , wherein the collaboration platform temporarily stores the document content in a virtual memory [FEATURE ID: 6] for rendering the document to the first reviewer during the communication session with the collaboration platform , and the collaboration platform deletes the document content from the virtual memory in response to the first reviewer ending the communication session with the collaboration platform , and synchronize the adjustment to the document with the data storage through a collaboration document [FEATURE ID: 6] created by the data storage that maintains the adjustment to the document made by the first reviewer such that the original document remains unadjusted . 2 . The computer - based system of claim [FEATURE ID: 15] 1 , wherein the one or more processors are further [FEATURE ID: 17] configured to create the collaboration document by converting the document from a first format [FEATURE ID: 12] to a second format [FEATURE ID: 7] based on the adjustment to the document . 3 . The computer - based system of claim 2 , wherein the one or more processors are further configured to create access rights for the first reviewer based on input [FEATURE ID: 8] from the owner , the access rights providing permissions for the first reviewer to access and make adjustments [FEATURE ID: 16] to the first document through the collaboration platform . 4 . The computer - based system of claim 1 , wherein the one or more processors are further configured to : create and maintain a second version [FEATURE ID: 7] of the document that includes the adjustments by the first reviewer , and re-publish the content link to the document in the workspace such [FEATURE ID: 9] that the content link links to the second version of the document . 5 . The computer - based system of claim 1 , wherein the one or more processors are further configured to configure the workspace with the access rights created by the owner for the first reviewer . 6 . The computer - based system of claim 1 , wherein the one or more processors are further configured to configure the workspace with the access rights created by the owner for a second reviewer . 7 . The computer - based system of claim 6 , wherein the one or more processors are further configured to provide content of the document to the collaboration platform in response to a selection by the second reviewer of the content link in the workspace that is rendered by the collaboration platform to the second reviewer . 8 . The computer - based system of claim 7 , wherein the one or more processors are further configured to receive from the collaboration platform , information reflecting adjustments to the document made by the second reviewer through the collaboration platform , wherein the collaboration platform temporarily stores the document content in the virtual memory for rendering to the second reviewer and the collaboration platform deletes the document content from the virtual memory in response to the second reviewer ending a communication session with the collaboration platform . 9 . The computer - based system of claim 8 , wherein the one or more processors are further configured to synchronize the adjustments to the document by the second reviewer with the data storage by creating a second collaboration document [FEATURE ID: 6]

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Targeted Patent: Patent: US11238344B1 Filed: 2016-11-02 Issued: 2022-02-01 Patent Holder: (Original Assignee) Individual     (Current Assignee) AUTONOMOUS DEVICES LLC Inventor(s): Jasmin Cosic Title: Artificially intelligent systems, devices, and methods for learning and/or using a device's circumstances for autonomous device operation	Cross Reference / Shared Meaning between the Lines	Charted Against: Patent: US20160132787A1 Filed: 2014-11-11 Issued: 2016-05-12 Patent Holder: (Original Assignee) Massachusetts Institute of Technology     (Current Assignee) Massachusetts Institute of Technology Inventor(s): Will D. Drevo, Kalyan K. Veeramachaneni, Una-May O'Reilly Title: Distributed, multi-model, self-learning platform for machine learning
[FEATURE ID: 1] system, user, second device, fourth device	controller, computer, server, machine, platform, processor, host	[FEATURE ID: 1] system, model methodology repository, processing cluster
[TRANSITIVE ID: 2] comprising, accessing, operating	implementing, including, performing, providing, having, identifying, processing	[TRANSITIVE ID: 2] comprising
[FEATURE ID: 3] processors, object representations	processes, sensors, machines, hardware, entities, features, models	[FEATURE ID: 3] machine learning models, multiple modeling methodologies
[TRANSITIVE ID: 4] configured	disposed, implemented, coupled, designed, adapted, arranged, structured	[TRANSITIVE ID: 4] configured
[TRANSITIVE ID: 5] perform	process, provide, promote, support, allow, conduct, facilitate	[TRANSITIVE ID: 5] automate, generate
[FEATURE ID: 6] memory, portion	repository, node, database, network, module, server, feature	[FEATURE ID: 6] modeling methodology, dataset repository, dataset UI
[FEATURE ID: 7] knowledgebase	list, record, map, representation	[FEATURE ID: 7] dataset location
[TRANSITIVE ID: 8] correlated, detected, learned	identified, provided, configured, obtained, corresponding, stored, determined	[TRANSITIVE ID: 8] associated, received, generated
[FEATURE ID: 9] instruction sets	parameters, records, sets, inputs	[FEATURE ID: 9] datasets
[FEATURE ID: 10] learning process	process, model, test, training, simulation, method	[FEATURE ID: 10] model parameterization
[FEATURE ID: 11] second circumstance representation	result, model, response, report	[FEATURE ID: 11] performance record
[FEATURE ID: 12] operations, inputs	elements, conditions, requests, triggers, signals, settings, aspects	[FEATURE ID: 12] parameters
[FEATURE ID: 13] claim	item, preceding claim, clair, statement, figure, paragraph, embodiment	[FEATURE ID: 13] claim
[FEATURE ID: 14] information	data, parameters, messages, requests, details, observations, hypotheses	[FEATURE ID: 14] records, models
[FEATURE ID: 15] states	parameters, configurations, properties, characteristics, applications, features, aspects	[FEATURE ID: 15] model methodology implementations, performance records, performance
[FEATURE ID: 16] server	client, browser, user, module, receiver, gateway, controller	[FEATURE ID: 16] dataset upload interface
[FEATURE ID: 17] further	additionally, optionally, also, configured, furthermore, farther, otherwise	[FEATURE ID: 17] further
1 . A system [FEATURE ID: 1] comprising [TRANSITIVE ID: 2] : one or more processors [FEATURE ID: 3] configured [TRANSITIVE ID: 4] to perform [TRANSITIVE ID: 5] at least : accessing [TRANSITIVE ID: 2] a memory [FEATURE ID: 6] that stores at least a knowledgebase [FEATURE ID: 7] that includes a first circumstance representation correlated [TRANSITIVE ID: 8] with a first one or more instruction sets [FEATURE ID: 9] for operating [TRANSITIVE ID: 2] a first device , wherein the first circumstance representation represents a first circumstance detected [TRANSITIVE ID: 8] at least in part by one or more sensors of the first device , and wherein at least a portion [FEATURE ID: 6] of the first circumstance representation or at least a portion of the first one or more instruction sets for operating the first device is learned [TRANSITIVE ID: 8] in a learning process [FEATURE ID: 10] that includes operating the first device at least partially by a user [FEATURE ID: 1] ; generating or receiving a second circumstance representation [FEATURE ID: 11] , wherein the second circumstance representation represents a second circumstance detected at least in part by : the one or more sensors of the first device , or one or more sensors of a second device [FEATURE ID: 1] ; anticipating the first one or more instruction sets for operating the first device based on at least partial match between the second circumstance representation and the first circumstance representation ; and at least in response to the anticipating , executing the first one or more instruction sets for operating the first device , wherein the first device or the second device autonomously performs one or more operations [FEATURE ID: 12] defined by the first one or more instruction sets for operating the first device . 2 . The system of claim [FEATURE ID: 13] 1 , wherein the first one or more instruction sets for operating the first device include one or more information [FEATURE ID: 14] about one or more states [FEATURE ID: 15] of : the first device , or a portion of the first device . 3 . The system of claim 1 , wherein the first circumstance representation includes a first one or more object representations [FEATURE ID: 3] , and wherein the second circumstance representation includes a second one or more object representations . 4 . The system of claim 3 , wherein the second circumstance representation represents the second circumstance detected at least in part by the one or more sensors of the first device , and wherein the first device autonomously performs the one or more operations defined by the first one or more instruction sets for operating the first device . 5 . The system of claim 3 , wherein the second circumstance representation represents the second circumstance detected at least in part by the one or more sensors of the second device , and wherein the second device autonomously performs the one or more operations defined by the first one or more instruction sets for operating the first device . 6 . The system of claim 3 , wherein the system further comprising : a server [FEATURE ID: 16] that receives from the first device at least one of : the first circumstance representation , or the first one or more instruction sets for operating the first device , and wherein the second device receives from the server at least one of : the first circumstance representation , or the first one or more instruction sets for operating the first device , and wherein the second device autonomously performs the one or more operations defined by the first one or more instruction sets for operating the first device . 7 . The system of claim 3 , wherein the one or more processors are further [FEATURE ID: 17] configured to perform at least : modifying : the first one or more instruction sets for operating the first device , or a copy of the first one or more instruction sets for operating the first device , and wherein the anticipating the first one or more instruction sets for operating the first device based on the at least partial match between the second circumstance representation and the first circumstance representation includes : anticipating the modified the first one or more instruction sets for operating the first device based on the at least partial match between the second circumstance representation and the first circumstance representation , or anticipating the modified the copy of the first one or more instruction sets for operating the first device based on the at least partial match between the second circumstance representation and the first circumstance representation , and wherein the executing the first one or more instruction sets for operating the first device includes : executing the modified the first one or more instruction sets for operating the first device , or executing the modified the copy of the first one or more instruction sets for operating the first device , and wherein the autonomously performing , by the first device or by the second device , the one or more operations defined by the first one or more instruction sets for operating the first device includes : autonomously performing , by the first device or by the second device , one or more operations defined by the modified the first one or more instruction sets for operating the first device , or autonomously performing , by the first device or by the second device , one or more operations defined by the modified the copy of the first one or more instruction sets for operating the first device . 8 . The system of claim 3 , wherein the one or more processors are further configured to perform at least : modifying at least one of : the first circumstance representation , a copy of the first circumstance representation , the second circumstance representation , or a copy of the second circumstance representation , and wherein the at least partial match between the second circumstance representation and the first circumstance representation includes : ( i ) at least partial match between the modified the second circumstance representation and the first circumstance representation , ( ii ) at least partial match between the modified the copy of the second circumstance representation and the first circumstance representation , ( iii ) at least partial match between the second circumstance representation and the modified the first circumstance representation , ( iv ) at least partial match between the second circumstance representation and the modified the copy of the first circumstance representation , ( v ) at least partial match between the modified the second circumstance representation and the modified the first circumstance representation , ( vi ) at least partial match between the modified the copy of the second circumstance representation and the modified the copy of the first circumstance representation , ( vii ) at least partial match between the modified the second circumstance representation and the modified the copy of the first circumstance representation , or ( viii ) at least partial match between the modified the copy of the second circumstance representation and the modified the first circumstance representation . 9 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating the first device , and wherein the third circumstance representation represents a third circumstance detected at least in part by the one or more sensors of the first device , and wherein at least a portion of the third circumstance representation or at least a portion of the second one or more instruction sets for operating the first device is learned in another learning process that includes operating the first device at least partially by the user . 10 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating the first device , and wherein the third circumstance representation represents a third circumstance detected at least in part by the one or more sensors of the first device , and wherein at least a portion of the third circumstance representation or at least a portion of the second one or more instruction sets for operating the first device is learned in another learning process that includes operating the first device at least partially by another user . 11 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating a third device , and wherein the third circumstance representation represents a third circumstance detected at least in part by one or more sensors of the third device , and wherein at least a portion of the third circumstance representation or at least a portion of the second one or more instruction sets for operating the third device is learned in another learning process that includes operating the third device at least partially by : the user , or another user . 12 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating a third device , and wherein the third circumstance representation represents a third circumstance detected at least in part by one or more sensors of the third device , and wherein the one or more processors are further configured to perform at least : generating or receiving a fourth circumstance representation , wherein the fourth circumstance representation represents a fourth circumstance detected at least in part by one or more sensors of a fourth device [FEATURE ID: 1] ; anticipating the second one or more instruction sets for operating the third device based on at least partial match between the fourth circumstance representation and the third circumstance representation ; and at least in response to the anticipating the second one or more instruction sets for operating the third device , executing the second one or more instruction sets for operating the third device , wherein the fourth device autonomously performs one or more operations defined by the second one or more instruction sets for operating the third device . 13 . The system of claim 3 , wherein the system further comprising : an artificial intelligence system , and wherein the learning the at least the portion of the first circumstance representation or the at least the portion of the first one or more instruction sets for operating the first device includes learning , at least in part by the artificial intelligence system , the at least the portion of the first circumstance representation or the at least the portion of the first one or more instruction sets for operating the first device , and wherein the anticipating the first one or more instruction sets for operating the first device includes anticipating , at least in part by the artificial intelligence system , the first one or more instruction sets for operating the first device . 14 . The system of claim 13 , wherein the artificial intelligence system includes : one or more inputs [FEATURE ID: 12]		1 . A system [FEATURE ID: 1] to automate [TRANSITIVE ID: 5] selection and training of machine learning models [FEATURE ID: 3] across multiple modeling methodologies [FEATURE ID: 3] , the system comprising [TRANSITIVE ID: 2] : a model methodology repository [FEATURE ID: 1] configured [TRANSITIVE ID: 4] to store one or more model methodology implementations [FEATURE ID: 15] , each of the model methodology implementations associated [TRANSITIVE ID: 8] with a modeling methodology [FEATURE ID: 6] ; a dataset repository [FEATURE ID: 6] configured to store datasets [FEATURE ID: 9] ; a data hub configured to store data run records [FEATURE ID: 14] and performance records [FEATURE ID: 15] ; a dataset upload interface [FEATURE ID: 16] ( UI ) configured to receive a dataset , store the received [TRANSITIVE ID: 8] dataset within the dataset repository , to generate [TRANSITIVE ID: 5] a data run record comprising the location of received dataset within the dataset repository , and to store the generated [TRANSITIVE ID: 8] data run record to the data hub ; and a processing cluster [FEATURE ID: 1] comprising a plurality of worker nodes , each of the worker nodes configured to select a data run record from the data hub , to select a dataset from the dataset repository , to select a modeling methodology from the model methodology repository ; to generate a parameterization within with the model methodology , to generate a model having the selected modeling methodology and generated parameterization , to train the generated model on the selected dataset , to evaluate the performance [FEATURE ID: 15] of the trained model on the selected dataset , to generate a performance record [FEATURE ID: 11] , and to store the generated performance record to the data hub . 2 . The system of claim [FEATURE ID: 13] 1 wherein each of the data run records comprising a dataset location [FEATURE ID: 7] identifying one of the stored datasets within the dataset repository , wherein the each of the worker nodes is configured to select a dataset from the dataset repository based upon the dataset location identified by the data run record . 3 . The system of claim 2 wherein each of the performance records is associated with a data run record and a modeling methodology , each of the performance records comprising a parameterization within the associated modeling methodology and performance data indicating the performance of the model parameterization [FEATURE ID: 10] on the associated dataset , wherein each of the worker nodes is configured to and to generate a performance record comprising the evaluated performance and associated with the selected data run , the selected modeling methodology , and the generated parameterization . 4 . The system of claim 2 wherein the dataset UI [FEATURE ID: 6] is further [FEATURE ID: 17] configured to receive one or more parameters [FEATURE ID: 12] and to store the one of more parameters with a data run record . 5 . The system of claim 4 wherein the parameters include a wall time budget , a performance threshold , number of models [FEATURE ID: 14]

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Targeted Patent: Patent: US11238344B1 Filed: 2016-11-02 Issued: 2022-02-01 Patent Holder: (Original Assignee) Individual     (Current Assignee) AUTONOMOUS DEVICES LLC Inventor(s): Jasmin Cosic Title: Artificially intelligent systems, devices, and methods for learning and/or using a device's circumstances for autonomous device operation	Cross Reference / Shared Meaning between the Lines	Charted Against: Patent: US20160110657A1 Filed: 2014-10-14 Issued: 2016-04-21 Patent Holder: (Original Assignee) Skytree Inc     (Current Assignee) Skytree Inc Inventor(s): Maxsim Gibiansky, Ryan Riegel, Yi Yang, Parikshit Ram, Alexander Gray Title: Configurable Machine Learning Method Selection and Parameter Optimization System and Method
[FEATURE ID: 1] system, second, server, third device, fourth device	controller, third, first, computer, device, platform, portion	[FEATURE ID: 1] first processor, second processor
[TRANSITIVE ID: 2] comprising, includes, represents	including, comprises, of, for, by, having, specifies	[TRANSITIVE ID: 2] comprising, using, includes
[FEATURE ID: 3] processors	modules, controllers, machines, components, programs, engines, hardware	[FEATURE ID: 3] processors
[TRANSITIVE ID: 4] accessing, operating, learning	providing, identifying, obtaining, performing, generating, processing, monitoring	[TRANSITIVE ID: 4] receiving, determining, tuning, outputting, determination
[FEATURE ID: 5] memory, learning process, second device	system, computer, machine, task, first, technique, process	[FEATURE ID: 5] first candidate machine learning method
[FEATURE ID: 6] knowledgebase, copy, fourth circumstance representation	subset, portion, record, configuration, data, report, representation	[FEATURE ID: 6] result, user, set
[TRANSITIVE ID: 7] correlated	configured, combined, paired, coupled, corresponding	[TRANSITIVE ID: 7] compared
[FEATURE ID: 8] first device	user, first, processor, second, first processor, system, user of	[FEATURE ID: 8] second processor ', first processor '
[FEATURE ID: 9] sensors, states, object representations	features, attributes, elements, components, characters, first, conditions	[FEATURE ID: 9] parameters
[FEATURE ID: 10] portion, second circumstance, third circumstance representation, third circumstance, fourth circumstance	first, parameter, second, value, condition, context, third	[FEATURE ID: 10] first parameter configuration, measure, second machine learning method, new parameter distribution
[TRANSITIVE ID: 11] learned	modified, generated, identified, selected, determined, derived	[TRANSITIVE ID: 11] differing
[FEATURE ID: 12] second circumstance representation	request, first, communication, query, trigger, notification, feedback	[FEATURE ID: 12] stop condition, information
[FEATURE ID: 13] partial match	information, consistency, differences, similarity	[FEATURE ID: 13] superior performance
[FEATURE ID: 14] response	correspondence, order, accordance, comparison, relation, close proximity	[FEATURE ID: 14] parallel
[FEATURE ID: 15] claim	formula, embodiment, the claim, of claim, item, clause, previous claim	[FEATURE ID: 15] claim
[FEATURE ID: 16] information	parameters, instructions, commands, state, values, output, settings	[FEATURE ID: 16] data, parameter distribution
1 . A system [FEATURE ID: 1] comprising [TRANSITIVE ID: 2] : one or more processors [FEATURE ID: 3] configured to perform at least : accessing [TRANSITIVE ID: 4] a memory [FEATURE ID: 5] that stores at least a knowledgebase [FEATURE ID: 6] that includes [TRANSITIVE ID: 2] a first circumstance representation correlated [TRANSITIVE ID: 7] with a first one or more instruction sets for operating [TRANSITIVE ID: 4] a first device [FEATURE ID: 8] , wherein the first circumstance representation represents [TRANSITIVE ID: 2] a first circumstance detected at least in part by one or more sensors [FEATURE ID: 9] of the first device , and wherein at least a portion [FEATURE ID: 10] of the first circumstance representation or at least a portion of the first one or more instruction sets for operating the first device is learned [TRANSITIVE ID: 11] in a learning process [FEATURE ID: 5] that includes operating the first device at least partially by a user ; generating or receiving a second circumstance representation [FEATURE ID: 12] , wherein the second circumstance representation represents a second circumstance [FEATURE ID: 10] detected at least in part by : the one or more sensors of the first device , or one or more sensors of a second device [FEATURE ID: 5] ; anticipating the first one or more instruction sets for operating the first device based on at least partial match [FEATURE ID: 13] between the second circumstance representation and the first circumstance representation ; and at least in response [FEATURE ID: 14] to the anticipating , executing the first one or more instruction sets for operating the first device , wherein the first device or the second device autonomously performs one or more operations defined by the first one or more instruction sets for operating the first device . 2 . The system of claim [FEATURE ID: 15] 1 , wherein the first one or more instruction sets for operating the first device include one or more information [FEATURE ID: 16] about one or more states [FEATURE ID: 9] of : the first device , or a portion of the first device . 3 . The system of claim 1 , wherein the first circumstance representation includes a first one or more object representations [FEATURE ID: 9] , and wherein the second circumstance representation includes a second [FEATURE ID: 1] one or more object representations . 4 . The system of claim 3 , wherein the second circumstance representation represents the second circumstance detected at least in part by the one or more sensors of the first device , and wherein the first device autonomously performs the one or more operations defined by the first one or more instruction sets for operating the first device . 5 . The system of claim 3 , wherein the second circumstance representation represents the second circumstance detected at least in part by the one or more sensors of the second device , and wherein the second device autonomously performs the one or more operations defined by the first one or more instruction sets for operating the first device . 6 . The system of claim 3 , wherein the system further comprising : a server [FEATURE ID: 1] that receives from the first device at least one of : the first circumstance representation , or the first one or more instruction sets for operating the first device , and wherein the second device receives from the server at least one of : the first circumstance representation , or the first one or more instruction sets for operating the first device , and wherein the second device autonomously performs the one or more operations defined by the first one or more instruction sets for operating the first device . 7 . The system of claim 3 , wherein the one or more processors are further configured to perform at least : modifying : the first one or more instruction sets for operating the first device , or a copy [FEATURE ID: 6] of the first one or more instruction sets for operating the first device , and wherein the anticipating the first one or more instruction sets for operating the first device based on the at least partial match between the second circumstance representation and the first circumstance representation includes : anticipating the modified the first one or more instruction sets for operating the first device based on the at least partial match between the second circumstance representation and the first circumstance representation , or anticipating the modified the copy of the first one or more instruction sets for operating the first device based on the at least partial match between the second circumstance representation and the first circumstance representation , and wherein the executing the first one or more instruction sets for operating the first device includes : executing the modified the first one or more instruction sets for operating the first device , or executing the modified the copy of the first one or more instruction sets for operating the first device , and wherein the autonomously performing , by the first device or by the second device , the one or more operations defined by the first one or more instruction sets for operating the first device includes : autonomously performing , by the first device or by the second device , one or more operations defined by the modified the first one or more instruction sets for operating the first device , or autonomously performing , by the first device or by the second device , one or more operations defined by the modified the copy of the first one or more instruction sets for operating the first device . 8 . The system of claim 3 , wherein the one or more processors are further configured to perform at least : modifying at least one of : the first circumstance representation , a copy of the first circumstance representation , the second circumstance representation , or a copy of the second circumstance representation , and wherein the at least partial match between the second circumstance representation and the first circumstance representation includes : ( i ) at least partial match between the modified the second circumstance representation and the first circumstance representation , ( ii ) at least partial match between the modified the copy of the second circumstance representation and the first circumstance representation , ( iii ) at least partial match between the second circumstance representation and the modified the first circumstance representation , ( iv ) at least partial match between the second circumstance representation and the modified the copy of the first circumstance representation , ( v ) at least partial match between the modified the second circumstance representation and the modified the first circumstance representation , ( vi ) at least partial match between the modified the copy of the second circumstance representation and the modified the copy of the first circumstance representation , ( vii ) at least partial match between the modified the second circumstance representation and the modified the copy of the first circumstance representation , or ( viii ) at least partial match between the modified the copy of the second circumstance representation and the modified the first circumstance representation . 9 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation [FEATURE ID: 10] correlated with a second one or more instruction sets for operating the first device , and wherein the third circumstance representation represents a third circumstance [FEATURE ID: 10] detected at least in part by the one or more sensors of the first device , and wherein at least a portion of the third circumstance representation or at least a portion of the second one or more instruction sets for operating the first device is learned in another learning process that includes operating the first device at least partially by the user . 10 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating the first device , and wherein the third circumstance representation represents a third circumstance detected at least in part by the one or more sensors of the first device , and wherein at least a portion of the third circumstance representation or at least a portion of the second one or more instruction sets for operating the first device is learned in another learning process that includes operating the first device at least partially by another user . 11 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating a third device [FEATURE ID: 1] , and wherein the third circumstance representation represents a third circumstance detected at least in part by one or more sensors of the third device , and wherein at least a portion of the third circumstance representation or at least a portion of the second one or more instruction sets for operating the third device is learned in another learning process that includes operating the third device at least partially by : the user , or another user . 12 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating a third device , and wherein the third circumstance representation represents a third circumstance detected at least in part by one or more sensors of the third device , and wherein the one or more processors are further configured to perform at least : generating or receiving a fourth circumstance representation [FEATURE ID: 6] , wherein the fourth circumstance representation represents a fourth circumstance [FEATURE ID: 10] detected at least in part by one or more sensors of a fourth device [FEATURE ID: 1] ; anticipating the second one or more instruction sets for operating the third device based on at least partial match between the fourth circumstance representation and the third circumstance representation ; and at least in response to the anticipating the second one or more instruction sets for operating the third device , executing the second one or more instruction sets for operating the third device , wherein the fourth device autonomously performs one or more operations defined by the second one or more instruction sets for operating the third device . 13 . The system of claim 3 , wherein the system further comprising : an artificial intelligence system , and wherein the learning the at least the portion of the first circumstance representation or the at least the portion of the first one or more instruction sets for operating the first device includes learning [FEATURE ID: 4]		1 . A method comprising [TRANSITIVE ID: 2] : receiving [TRANSITIVE ID: 4] data [FEATURE ID: 16] ; determining [TRANSITIVE ID: 4] , using [TRANSITIVE ID: 2] one or more processors [FEATURE ID: 3] , a first candidate machine learning method [FEATURE ID: 5] ; tuning [FEATURE ID: 4] , using one or more processors , one or more parameters [FEATURE ID: 9] of the first candidate machine learning method ; determining , using one or more processors , that the first candidate machine learning method and a first parameter configuration [FEATURE ID: 10] for the first candidate machine learning method are the best based on a measure [FEATURE ID: 10] of fitness subsequent to satisfaction of a stop condition [FEATURE ID: 12] ; and outputting [FEATURE ID: 4] , using one or more processors , the first candidate machine learning method and the first parameter configuration for the first candidate machine learning method . 2 . The method of claim [FEATURE ID: 15] 1 further comprising : determining a second machine learning method [FEATURE ID: 10] ; tuning , using one or more processors , one or more parameters of the second candidate machine learning method , the second candidate machine learning method differing [TRANSITIVE ID: 11] from the first candidate machine learning method ; and wherein the determination [FEATURE ID: 4] that the first candidate machine learning method and the first parameter configuration for the first candidate machine learning method are the best based on the measure of fitness includes [TRANSITIVE ID: 2] determining that the first candidate machine learning method and the first parameter configuration for the first candidate machine learning method provide superior performance [FEATURE ID: 13] with regard to the measure of fitness when compared [TRANSITIVE ID: 7] to the second candidate machine learning method with the second parameter configuration . 3 . The method of claim 2 , wherein the tuning of the one or more parameters of the first candidate machine learning method is performed using a first processor [FEATURE ID: 1] of the one or more processors and the tuning of the one or more parameters of the second candidate machine learning method is performed using a second processor [FEATURE ID: 1] of the one or more processors in parallel [FEATURE ID: 14] with the tuning of the first candidate machine learning method . 4 . The method of claim 2 , wherein a first processor of the one or more processors communicates with a second processor of the one or more processors in order to update the second processor ' [FEATURE ID: 8] s previously learned parameter distribution [FEATURE ID: 16] with a result [FEATURE ID: 6] of the first processor ' [FEATURE ID: 8] s tuning , wherein the result of the first processor ' s tuning is one of an intermediate and a complete tuning result . 5 . The method of claim 2 , wherein a greater portion of the resources of the one or more processors is dedicated to tuning the one or more parameters of the first candidate machine learning method than to tuning the one or more parameters of the second candidate machine learning method based on tuning already performed on the first candidate machine learning method and the second candidate machine learning method , the tuning already performed indicating that the first candidate machine learning method is performing better than the second machine learning method based on the measure of fitness . 6 . The method of claim 2 , wherein the user [FEATURE ID: 6] specifies the data , and wherein the first candidate machine learning method and the second machine learning method are determined and the tunings and determination that the first candidate machine learning method and a first parameter configuration for the first candidate machine learning method are the best based on a measure of fitness are performed automatically without user - provided information [FEATURE ID: 12] or with user - provided information . 7 . The method of claim 1 , wherein tuning the one or more parameters of the first candidate machine learning method further comprises : setting a prior parameter distribution ; generating a set [FEATURE ID: 6] of sample parameters for the one or more parameters of the first candidate machine learning method based on the prior parameter distribution ; forming a new parameter distribution [FEATURE ID: 10]

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Targeted Patent: Patent: US11238344B1 Filed: 2016-11-02 Issued: 2022-02-01 Patent Holder: (Original Assignee) Individual     (Current Assignee) AUTONOMOUS DEVICES LLC Inventor(s): Jasmin Cosic Title: Artificially intelligent systems, devices, and methods for learning and/or using a device's circumstances for autonomous device operation	Cross Reference / Shared Meaning between the Lines	Charted Against: Patent: US9311283B2 Filed: 2012-08-16 Issued: 2016-04-12 Patent Holder: (Original Assignee) RealNetworks Inc     (Current Assignee) RealNetworks Inc Inventor(s): Jeffrey CHASEN, Niall SMART, Todd OQUIST, Michael Ari Cohen, John Schussler Title: System for clipping webpages by traversing a dom, and highlighting a minimum number of words
[FEATURE ID: 1] system, first device, user, second device, server, third device, fourth device	controller, processor, network, machine, sensor, host, client	[FEATURE ID: 1] computer, memory, first server, user
[TRANSITIVE ID: 2] comprising	including, having, of, containing, comprises, with, includes	[TRANSITIVE ID: 2] comprising, being
[FEATURE ID: 3] processors	entities, instructions, components, elements	[FEATURE ID: 3] video handler objects
[TRANSITIVE ID: 4] accessing, learning	reading, obtaining, providing, storing, recording, selecting, processing	[TRANSITIVE ID: 4] identifying, receiving, rendering, determining
[FEATURE ID: 5] memory	computer, controller, repository, record, database, system, processor	[FEATURE ID: 5] second server
[FEATURE ID: 6] stores, includes, represents	provides, identifies, illustrates, is, records, describes, defines	[FEATURE ID: 6] indicates
[TRANSITIVE ID: 7] correlated, detected	registered, monitored, received, tagged, indexed, stored, identified	[TRANSITIVE ID: 7] recorded
[TRANSITIVE ID: 8] operating	functioning, implementing, executing, performing, running	[TRANSITIVE ID: 8] performed
[FEATURE ID: 9] learning process	process, technique, manner, step, procedure	[FEATURE ID: 9] method
[FEATURE ID: 10] second circumstance representation, fourth circumstance representation	notification, representation, message, first, response, feedback, parameter	[FEATURE ID: 10] first item, first identifier, second identifier, current user selection position, match
[FEATURE ID: 11] response	parallel, order, connection, accordance, addition, correspondence, answer	[FEATURE ID: 11] response, conjunction
[FEATURE ID: 12] claim	preceding claim, clair, figure, paragraph, embodiment, clam, clause	[FEATURE ID: 12] claim
[FEATURE ID: 13] states	parts, objects, elements, attributes, parameters	[FEATURE ID: 13] inline children
[FEATURE ID: 14] artificial intelligence system	input, application, object, identifier	[FEATURE ID: 14] instruction
1 . A system [FEATURE ID: 1] comprising [TRANSITIVE ID: 2] : one or more processors [FEATURE ID: 3] configured to perform at least : accessing [TRANSITIVE ID: 4] a memory [FEATURE ID: 5] that stores [FEATURE ID: 6] at least a knowledgebase that includes [TRANSITIVE ID: 6] a first circumstance representation correlated [TRANSITIVE ID: 7] with a first one or more instruction sets for operating [TRANSITIVE ID: 8] a first device [FEATURE ID: 1] , wherein the first circumstance representation represents [TRANSITIVE ID: 6] a first circumstance detected [TRANSITIVE ID: 7] at least in part by one or more sensors of the first device , and wherein at least a portion of the first circumstance representation or at least a portion of the first one or more instruction sets for operating the first device is learned in a learning process [FEATURE ID: 9] that includes operating the first device at least partially by a user [FEATURE ID: 1] ; generating or receiving a second circumstance representation [FEATURE ID: 10] , wherein the second circumstance representation represents a second circumstance detected at least in part by : the one or more sensors of the first device , or one or more sensors of a second device [FEATURE ID: 1] ; anticipating the first one or more instruction sets for operating the first device based on at least partial match between the second circumstance representation and the first circumstance representation ; and at least in response [FEATURE ID: 11] to the anticipating , executing the first one or more instruction sets for operating the first device , wherein the first device or the second device autonomously performs one or more operations defined by the first one or more instruction sets for operating the first device . 2 . The system of claim [FEATURE ID: 12] 1 , wherein the first one or more instruction sets for operating the first device include one or more information about one or more states [FEATURE ID: 13] of : the first device , or a portion of the first device . 3 . The system of claim 1 , wherein the first circumstance representation includes a first one or more object representations , and wherein the second circumstance representation includes a second one or more object representations . 4 . The system of claim 3 , wherein the second circumstance representation represents the second circumstance detected at least in part by the one or more sensors of the first device , and wherein the first device autonomously performs the one or more operations defined by the first one or more instruction sets for operating the first device . 5 . The system of claim 3 , wherein the second circumstance representation represents the second circumstance detected at least in part by the one or more sensors of the second device , and wherein the second device autonomously performs the one or more operations defined by the first one or more instruction sets for operating the first device . 6 . The system of claim 3 , wherein the system further comprising : a server [FEATURE ID: 1] that receives from the first device at least one of : the first circumstance representation , or the first one or more instruction sets for operating the first device , and wherein the second device receives from the server at least one of : the first circumstance representation , or the first one or more instruction sets for operating the first device , and wherein the second device autonomously performs the one or more operations defined by the first one or more instruction sets for operating the first device . 7 . The system of claim 3 , wherein the one or more processors are further configured to perform at least : modifying : the first one or more instruction sets for operating the first device , or a copy of the first one or more instruction sets for operating the first device , and wherein the anticipating the first one or more instruction sets for operating the first device based on the at least partial match between the second circumstance representation and the first circumstance representation includes : anticipating the modified the first one or more instruction sets for operating the first device based on the at least partial match between the second circumstance representation and the first circumstance representation , or anticipating the modified the copy of the first one or more instruction sets for operating the first device based on the at least partial match between the second circumstance representation and the first circumstance representation , and wherein the executing the first one or more instruction sets for operating the first device includes : executing the modified the first one or more instruction sets for operating the first device , or executing the modified the copy of the first one or more instruction sets for operating the first device , and wherein the autonomously performing , by the first device or by the second device , the one or more operations defined by the first one or more instruction sets for operating the first device includes : autonomously performing , by the first device or by the second device , one or more operations defined by the modified the first one or more instruction sets for operating the first device , or autonomously performing , by the first device or by the second device , one or more operations defined by the modified the copy of the first one or more instruction sets for operating the first device . 8 . The system of claim 3 , wherein the one or more processors are further configured to perform at least : modifying at least one of : the first circumstance representation , a copy of the first circumstance representation , the second circumstance representation , or a copy of the second circumstance representation , and wherein the at least partial match between the second circumstance representation and the first circumstance representation includes : ( i ) at least partial match between the modified the second circumstance representation and the first circumstance representation , ( ii ) at least partial match between the modified the copy of the second circumstance representation and the first circumstance representation , ( iii ) at least partial match between the second circumstance representation and the modified the first circumstance representation , ( iv ) at least partial match between the second circumstance representation and the modified the copy of the first circumstance representation , ( v ) at least partial match between the modified the second circumstance representation and the modified the first circumstance representation , ( vi ) at least partial match between the modified the copy of the second circumstance representation and the modified the copy of the first circumstance representation , ( vii ) at least partial match between the modified the second circumstance representation and the modified the copy of the first circumstance representation , or ( viii ) at least partial match between the modified the copy of the second circumstance representation and the modified the first circumstance representation . 9 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating the first device , and wherein the third circumstance representation represents a third circumstance detected at least in part by the one or more sensors of the first device , and wherein at least a portion of the third circumstance representation or at least a portion of the second one or more instruction sets for operating the first device is learned in another learning process that includes operating the first device at least partially by the user . 10 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating the first device , and wherein the third circumstance representation represents a third circumstance detected at least in part by the one or more sensors of the first device , and wherein at least a portion of the third circumstance representation or at least a portion of the second one or more instruction sets for operating the first device is learned in another learning process that includes operating the first device at least partially by another user . 11 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating a third device [FEATURE ID: 1] , and wherein the third circumstance representation represents a third circumstance detected at least in part by one or more sensors of the third device , and wherein at least a portion of the third circumstance representation or at least a portion of the second one or more instruction sets for operating the third device is learned in another learning process that includes operating the third device at least partially by : the user , or another user . 12 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating a third device , and wherein the third circumstance representation represents a third circumstance detected at least in part by one or more sensors of the third device , and wherein the one or more processors are further configured to perform at least : generating or receiving a fourth circumstance representation [FEATURE ID: 10] , wherein the fourth circumstance representation represents a fourth circumstance detected at least in part by one or more sensors of a fourth device [FEATURE ID: 1] ; anticipating the second one or more instruction sets for operating the third device based on at least partial match between the fourth circumstance representation and the third circumstance representation ; and at least in response to the anticipating the second one or more instruction sets for operating the third device , executing the second one or more instruction sets for operating the third device , wherein the fourth device autonomously performs one or more operations defined by the second one or more instruction sets for operating the third device . 13 . The system of claim 3 , wherein the system further comprising : an artificial intelligence system [FEATURE ID: 14] , and wherein the learning the at least the portion of the first circumstance representation or the at least the portion of the first one or more instruction sets for operating the first device includes learning [FEATURE ID: 4]		1 . A method [FEATURE ID: 9] of identifying [TRANSITIVE ID: 4] content to be recorded [TRANSITIVE ID: 7] , the method performed [TRANSITIVE ID: 8] in a computer [FEATURE ID: 1] comprising [TRANSITIVE ID: 2] a memory [FEATURE ID: 1] , the method comprising : in the computer , receiving [TRANSITIVE ID: 4] from a first server [FEATURE ID: 1] a webpage comprising a first item [FEATURE ID: 10] and rendering [TRANSITIVE ID: 4] the webpage , the first item being [TRANSITIVE ID: 2] a video element or an IMG or block level element ; rendering a first identifier [FEATURE ID: 10] which indicates [TRANSITIVE ID: 6] to a user [FEATURE ID: 1] of the computer that the webpage may be recorded ; in response [FEATURE ID: 11] to a user selection of the first item , determining [TRANSITIVE ID: 4] an element type of the first item and rendering a second identifier [FEATURE ID: 10] which indicates to the user that the first item may be recorded ; in response to a current user selection position [FEATURE ID: 10] , determining that there is not one or more IMG or block level elements at the current user selection position , traversing up the DOM from the first item for an element not of a specified type , determining that the element not of a specified type is valid , counting the number of words in all inline children [FEATURE ID: 13] and keeping the element if it has more than a minimum number of words , and highlighting the words ; and in response to a user selection of at least one of the element not of a specified type or the webpage , transmitting the user selection to a second server [FEATURE ID: 5] in conjunction [FEATURE ID: 11] with an instruction [FEATURE ID: 14] to record the user selection . 2 . The method according to claim [FEATURE ID: 12] 1 , wherein the determined element type is a video element , obtaining video handler objects [FEATURE ID: 3] corresponding to the element type , passing the first item ' s parameter to at least one of the obtaining video handler objects , determining that there is a match [FEATURE ID: 10]

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Targeted Patent: Patent: US11238344B1 Filed: 2016-11-02 Issued: 2022-02-01 Patent Holder: (Original Assignee) Individual     (Current Assignee) AUTONOMOUS DEVICES LLC Inventor(s): Jasmin Cosic Title: Artificially intelligent systems, devices, and methods for learning and/or using a device's circumstances for autonomous device operation	Cross Reference / Shared Meaning between the Lines	Charted Against: Patent: US9274935B1 Filed: 2013-01-15 Issued: 2016-03-01 Patent Holder: (Original Assignee) Google LLC     (Current Assignee) Google LLC Inventor(s): Manish Lachwani, Jay Srinivasan, Pratyus Patnaik, Rahul Jain Title: Application testing system with application programming interface
[FEATURE ID: 1] system, first device, user, second device, fourth device	machine, controller, platform, computer, network, host, server	[FEATURE ID: 1] system, processor, memory, network device, host device
[TRANSITIVE ID: 2] comprising, stores, includes, operating, represents	including, having, defines, implementing, provides, indicates, identifies	[TRANSITIVE ID: 2] comprising, storing, comprises
[FEATURE ID: 3] processors, sensors, operations, object representations	components, modules, elements, nodes, commands, features, processes	[FEATURE ID: 3] instructions, host devices, objects
[TRANSITIVE ID: 4] configured	configure, implemented, disposed, coupled, designed, adapted, arranged	[TRANSITIVE ID: 4] configured
[TRANSITIVE ID: 5] perform	execute, implement, support, provide, complete, control, run	[TRANSITIVE ID: 5] test, initiate
[TRANSITIVE ID: 6] accessing	access, operating, loading, processing, implementing	[TRANSITIVE ID: 6] execution
[FEATURE ID: 7] memory	network, processor, location, component, system	[FEATURE ID: 7] display
[FEATURE ID: 8] knowledgebase, copy	subset, portion, first, list, plurality, configuration, sequence	[FEATURE ID: 8] first network profile, first set, second set
[TRANSITIVE ID: 9] correlated	linked, connected, configured, corresponding	[TRANSITIVE ID: 9] coupled
[FEATURE ID: 10] instruction sets	parameters, information, packets, instructions, settings, codes, rules	[FEATURE ID: 10] network profiles, network profile parameters, assembly code data, assembly code
[FEATURE ID: 11] part	portion, parts, in part, some	[FEATURE ID: 11] part
[FEATURE ID: 12] portion, third circumstance representation, third device	user, first, fourth, second, parameter, combination, subset	[FEATURE ID: 12] portion, communication, second network profile
[FEATURE ID: 13] learning process	procedure, task, transaction, test, simulation, step	[FEATURE ID: 13] input event
[FEATURE ID: 14] response	correspondence, connection, reference, proximity	[FEATURE ID: 14] relationship
[FEATURE ID: 15] claim	item, preceding claim, claimed, aspect, clair, figure, paragraph	[FEATURE ID: 15] claim
[FEATURE ID: 16] information	instructions, communications, messages, details, data, signals	[FEATURE ID: 16] packets, information
[FEATURE ID: 17] states	attributes, characteristics, actions, objects, elements, features, aspects	[FEATURE ID: 17] conditions, screenshots
[FEATURE ID: 18] server	user, provider, network, client	[FEATURE ID: 18] particular service provider
[FEATURE ID: 19] artificial intelligence system	algorithm, object, input, agent, environment, accelerator, identifier	[FEATURE ID: 19] application, assembly code generation module, input virtualization module
1 . A system [FEATURE ID: 1] comprising [TRANSITIVE ID: 2] : one or more processors [FEATURE ID: 3] configured [TRANSITIVE ID: 4] to perform [TRANSITIVE ID: 5] at least : accessing [TRANSITIVE ID: 6] a memory [FEATURE ID: 7] that stores [FEATURE ID: 2] at least a knowledgebase [FEATURE ID: 8] that includes [TRANSITIVE ID: 2] a first circumstance representation correlated [TRANSITIVE ID: 9] with a first one or more instruction sets [FEATURE ID: 10] for operating [TRANSITIVE ID: 2] a first device [FEATURE ID: 1] , wherein the first circumstance representation represents [TRANSITIVE ID: 2] a first circumstance detected at least in part [FEATURE ID: 11] by one or more sensors [FEATURE ID: 3] of the first device , and wherein at least a portion [FEATURE ID: 12] of the first circumstance representation or at least a portion of the first one or more instruction sets for operating the first device is learned in a learning process [FEATURE ID: 13] that includes operating the first device at least partially by a user [FEATURE ID: 1] ; generating or receiving a second circumstance representation , wherein the second circumstance representation represents a second circumstance detected at least in part by : the one or more sensors of the first device , or one or more sensors of a second device [FEATURE ID: 1] ; anticipating the first one or more instruction sets for operating the first device based on at least partial match between the second circumstance representation and the first circumstance representation ; and at least in response [FEATURE ID: 14] to the anticipating , executing the first one or more instruction sets for operating the first device , wherein the first device or the second device autonomously performs one or more operations [FEATURE ID: 3] defined by the first one or more instruction sets for operating the first device . 2 . The system of claim [FEATURE ID: 15] 1 , wherein the first one or more instruction sets for operating the first device include one or more information [FEATURE ID: 16] about one or more states [FEATURE ID: 17] of : the first device , or a portion of the first device . 3 . The system of claim 1 , wherein the first circumstance representation includes a first one or more object representations [FEATURE ID: 3] , and wherein the second circumstance representation includes a second one or more object representations . 4 . The system of claim 3 , wherein the second circumstance representation represents the second circumstance detected at least in part by the one or more sensors of the first device , and wherein the first device autonomously performs the one or more operations defined by the first one or more instruction sets for operating the first device . 5 . The system of claim 3 , wherein the second circumstance representation represents the second circumstance detected at least in part by the one or more sensors of the second device , and wherein the second device autonomously performs the one or more operations defined by the first one or more instruction sets for operating the first device . 6 . The system of claim 3 , wherein the system further comprising : a server [FEATURE ID: 18] that receives from the first device at least one of : the first circumstance representation , or the first one or more instruction sets for operating the first device , and wherein the second device receives from the server at least one of : the first circumstance representation , or the first one or more instruction sets for operating the first device , and wherein the second device autonomously performs the one or more operations defined by the first one or more instruction sets for operating the first device . 7 . The system of claim 3 , wherein the one or more processors are further configured to perform at least : modifying : the first one or more instruction sets for operating the first device , or a copy [FEATURE ID: 8] of the first one or more instruction sets for operating the first device , and wherein the anticipating the first one or more instruction sets for operating the first device based on the at least partial match between the second circumstance representation and the first circumstance representation includes : anticipating the modified the first one or more instruction sets for operating the first device based on the at least partial match between the second circumstance representation and the first circumstance representation , or anticipating the modified the copy of the first one or more instruction sets for operating the first device based on the at least partial match between the second circumstance representation and the first circumstance representation , and wherein the executing the first one or more instruction sets for operating the first device includes : executing the modified the first one or more instruction sets for operating the first device , or executing the modified the copy of the first one or more instruction sets for operating the first device , and wherein the autonomously performing , by the first device or by the second device , the one or more operations defined by the first one or more instruction sets for operating the first device includes : autonomously performing , by the first device or by the second device , one or more operations defined by the modified the first one or more instruction sets for operating the first device , or autonomously performing , by the first device or by the second device , one or more operations defined by the modified the copy of the first one or more instruction sets for operating the first device . 8 . The system of claim 3 , wherein the one or more processors are further configured to perform at least : modifying at least one of : the first circumstance representation , a copy of the first circumstance representation , the second circumstance representation , or a copy of the second circumstance representation , and wherein the at least partial match between the second circumstance representation and the first circumstance representation includes : ( i ) at least partial match between the modified the second circumstance representation and the first circumstance representation , ( ii ) at least partial match between the modified the copy of the second circumstance representation and the first circumstance representation , ( iii ) at least partial match between the second circumstance representation and the modified the first circumstance representation , ( iv ) at least partial match between the second circumstance representation and the modified the copy of the first circumstance representation , ( v ) at least partial match between the modified the second circumstance representation and the modified the first circumstance representation , ( vi ) at least partial match between the modified the copy of the second circumstance representation and the modified the copy of the first circumstance representation , ( vii ) at least partial match between the modified the second circumstance representation and the modified the copy of the first circumstance representation , or ( viii ) at least partial match between the modified the copy of the second circumstance representation and the modified the first circumstance representation . 9 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation [FEATURE ID: 12] correlated with a second one or more instruction sets for operating the first device , and wherein the third circumstance representation represents a third circumstance detected at least in part by the one or more sensors of the first device , and wherein at least a portion of the third circumstance representation or at least a portion of the second one or more instruction sets for operating the first device is learned in another learning process that includes operating the first device at least partially by the user . 10 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating the first device , and wherein the third circumstance representation represents a third circumstance detected at least in part by the one or more sensors of the first device , and wherein at least a portion of the third circumstance representation or at least a portion of the second one or more instruction sets for operating the first device is learned in another learning process that includes operating the first device at least partially by another user . 11 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating a third device [FEATURE ID: 12] , and wherein the third circumstance representation represents a third circumstance detected at least in part by one or more sensors of the third device , and wherein at least a portion of the third circumstance representation or at least a portion of the second one or more instruction sets for operating the third device is learned in another learning process that includes operating the third device at least partially by : the user , or another user . 12 . The system of claim 3 , wherein the knowledgebase further includes a third circumstance representation correlated with a second one or more instruction sets for operating a third device , and wherein the third circumstance representation represents a third circumstance detected at least in part by one or more sensors of the third device , and wherein the one or more processors are further configured to perform at least : generating or receiving a fourth circumstance representation , wherein the fourth circumstance representation represents a fourth circumstance detected at least in part by one or more sensors of a fourth device [FEATURE ID: 1] ; anticipating the second one or more instruction sets for operating the third device based on at least partial match between the fourth circumstance representation and the third circumstance representation ; and at least in response to the anticipating the second one or more instruction sets for operating the third device , executing the second one or more instruction sets for operating the third device , wherein the fourth device autonomously performs one or more operations defined by the second one or more instruction sets for operating the third device . 13 . The system of claim 3 , wherein the system further comprising : an artificial intelligence system [FEATURE ID: 19]		1 . A system [FEATURE ID: 1] comprising [TRANSITIVE ID: 2] : at least one processor [FEATURE ID: 1] ; and at least one memory [FEATURE ID: 1] coupled [TRANSITIVE ID: 9] to the at least one processor and storing [TRANSITIVE ID: 2] instructions [FEATURE ID: 3] configured [TRANSITIVE ID: 4] for execution [FEATURE ID: 6] on the at least one processor , the instructions configured to : receive a test package comprising an application [FEATURE ID: 19] ; configure one or more host devices [FEATURE ID: 3] to test [TRANSITIVE ID: 5] the application ; send the application to one or more of the host devices ; initiate [TRANSITIVE ID: 5] testing and execution of the application on the one or more host devices , wherein the testing comprises [TRANSITIVE ID: 2] instructions to : receive an indication of a first network profile [FEATURE ID: 8] of a plurality of network profiles [FEATURE ID: 10] to be applied to network traffic between at least a portion [FEATURE ID: 12] of the one or more host devices and a network device [FEATURE ID: 1] , the first network profile including a first set [FEATURE ID: 8] of network profile parameters [FEATURE ID: 10] associated with a particular service provider [FEATURE ID: 18] ; receive a plurality of packets [FEATURE ID: 16] included in the network traffic sent from the one or more host devices and addressed to the network device ; modify at least one of the plurality of packets to introduce conditions [FEATURE ID: 17] associated with the particular service provider based at least on the first set of network profile parameters ; send the modified plurality of packets to the network device ; execute an assembly code generation module [FEATURE ID: 19] on at least one of the one or more host devices to generate assembly code data [FEATURE ID: 10] associated with the application ; analyze the assembly code data generated during execution of the application to identify at least one communication [FEATURE ID: 12] of the application ; receive information [FEATURE ID: 16] associated with the execution ; generate one or more test results based at least in part [FEATURE ID: 11] on the information associated with the execution ; and distribute the one or more test results . 2 . The system of claim [FEATURE ID: 15] 1 , the testing comprising instructions configured to : send one or more instructions to an input virtualization module [FEATURE ID: 19] executing on at least a portion of the one or more host devices , wherein the one or more instructions perform at least one input event [FEATURE ID: 13] on the one or more host devices during the execution of the application . 3 . The system of claim 1 , the testing comprising instructions configured to : retrieve , from at least a portion of the one or more host devices , a plurality of screenshots [FEATURE ID: 17] of a display [FEATURE ID: 7] of the one or more host devices generated as the one or more host devices execute the application . 4 . The system of claim 1 , the testing comprising instructions to : receive an indication of a second network profile [FEATURE ID: 12] of the plurality of network profiles to be applied to the network traffic , the second network profile including a second set [FEATURE ID: 8] of network profile parameters that differ from the first set of network profile parameters ; and modify at least another one of the plurality of packets , based at least in part on the second set of network profile parameters , wherein the modification of the at least another one of the plurality of packets is performed after the modification of the at least one of the plurality of packets . 5 . The system of claim 1 , further comprising instructions to : receive assembly code [FEATURE ID: 10] generated from execution of an application on a host device [FEATURE ID: 1] ; analyze the assembly code to identify a plurality of objects [FEATURE ID: 3] associated with the application and to determine at least one relationship [FEATURE ID: 14]

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