Targeted Patent:

Patent: US11055583B1
Filed: 2017-11-26
Issued: 2021-07-06
Patent Holder: (Original Assignee) Individual     (Current Assignee) AUTONOMOUS DEVICES LLC
Inventor(s): Jasmin Cosic

Title: Machine learning for computing enabled systems and/or devices

 
Cross Reference / Shared Meaning between the Lines		 Charted Against:

Patent: US20170339022A1
Filed: 2016-05-17
Issued: 2017-11-23
Patent Holder: (Original Assignee) Extreme Networks Inc     (Current Assignee) Extreme Networks Inc
Inventor(s): Deepak Hegde, Shailender Sharma, Jude Pragash Vedam, Ashwin Naresh

Title: Anomaly detection and prediction in a packet broker
[FEATURE ID: 1] system, first usercomputer, server, controller, processor, client, network, platform[FEATURE ID: 1] packet broker, visibility network, core network, network administrator
[TRANSITIVE ID: 2] comprising, storingincluding, includes, having, with, implementing, of, by[TRANSITIVE ID: 2] comprising
[FEATURE ID: 3] processors, non-transitory machine readable media, microcontrollers, actuators, volatile memories, non-volatile memories, storage devices, storage systemsdevices, servers, controllers, registers, systems, computers, components[FEATURE ID: 3] analytic probes, tools
[TRANSITIVE ID: 4] receiving, generating, operatingproviding, determining, analyzing, identifying, processing, using, transmitting[TRANSITIVE ID: 4] applying, detecting, taking
[TRANSITIVE ID: 5] depictrepresent, define, provide, contain, are[TRANSITIVE ID: 5] include
[FEATURE ID: 6] portion, non-transitory machine readable medium, second learning process, second usersecond, feature, component, first, third, database, characteristic[FEATURE ID: 6] core network parameter, first machine learning model, criterion
[FEATURE ID: 7] instruction setsoperations, parameters, actions, inputs[FEATURE ID: 7] machine learning models
[FEATURE ID: 8] claimclair, paragraph, clause, embodiment, preceding claim, item, figure[FEATURE ID: 8] claim
[FEATURE ID: 9] apparatusdevice, mechanism, system, process[FEATURE ID: 9] method
[FEATURE ID: 10] newlatest, particular, future, next[FEATURE ID: 10] future point
[FEATURE ID: 11] responseorder, proximity, real time, addition, reply, correspondence, parallel[FEATURE ID: 11] response
[FEATURE ID: 12] second deviceserver, component, user, system[FEATURE ID: 12] particular network protocol
[FEATURE ID: 13] operationsactivities, commands, events, steps, tasks, processes, acts[FEATURE ID: 13] predefined actions, flows, additional predefined actions
[FEATURE ID: 14] informationparameters, signals, messages, data, observations, traffic, communications[FEATURE ID: 14] model valid message exchanges, historical traffic data, live traffic data
[FEATURE ID: 15] statesperformance, properties, attributes, parameters, settings[FEATURE ID: 15] value
1 . A system [FEATURE ID: 1]

comprising [TRANSITIVE ID: 2]

: one or more processors [FEATURE ID: 3]

; and one or more non-transitory machine readable media [FEATURE ID: 3]

storing [TRANSITIVE ID: 2]

machine readable code that , when executed by the one or more processors , causes the one or more processors to perform at least : receiving [TRANSITIVE ID: 4]

or generating [TRANSITIVE ID: 4]

a first one or more digital pictures , wherein the first one or more digital pictures depict [TRANSITIVE ID: 5]

at least a portion [FEATURE ID: 6]

of a first device ' s surrounding ; receiving or generating a first one or more instruction sets [FEATURE ID: 7]

for operating [TRANSITIVE ID: 4]

the first device ; and learning the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device . 2 . The system of claim [FEATURE ID: 8]

1 , wherein the receiving the first one or more digital pictures includes receiving the first one or more digital pictures from a picture capturing apparatus [FEATURE ID: 9]

, and wherein the receiving the first one or more instruction sets for operating the first device includes receiving the first one or more instruction sets for operating the first device from : an application for operating the first device , a system for operating the first device , one or more microcontrollers [FEATURE ID: 3]

, another one or more processors , or one or more actuators [FEATURE ID: 3]

, and wherein the learning the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device includes storing the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device into or onto at least one of : at least one non-transitory machine readable medium [FEATURE ID: 6]

of the one or more non-transitory machine readable media , another one or more non-transitory machine readable media , one or more volatile memories [FEATURE ID: 3]

, one or more non-volatile memories [FEATURE ID: 3]

, one or more storage devices [FEATURE ID: 3]

, or one or more storage systems [FEATURE ID: 3]

. 3 . The system of claim 1 , wherein the learning the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device includes determining that the first one or more instruction sets for operating the first device temporally correspond to the first one or more digital pictures . 4 . The system of claim 1 , wherein the machine readable code , when executed by the one or more processors , causes the one or more processors to further perform at least : receiving or generating a new [FEATURE ID: 10]

one or more digital pictures ; determining the first one or more instruction sets for operating the first device based on at least partial match between the new one or more digital pictures and the first one or more digital pictures ; and at least in response [FEATURE ID: 11]

to the determining , causing the first device or a second device [FEATURE ID: 12]

to perform one or more operations [FEATURE ID: 13]

defined by the first one or more instruction sets for operating the first device . 5 . The system of claim 4 , wherein the causing the first device or the second device to perform the one or more operations defined by the first one or more instruction sets for operating the first device includes executing the first one or more instruction sets for operating the first device . 6 . The system of claim 4 , wherein the determining the first one or more instruction sets for operating the first device based on the at least partial match between the new one or more digital pictures and the first one or more digital pictures includes determining at least partial match between one or more portions of the new one or more digital pictures that represent one or more objects and one or more portions of the first one or more digital pictures that represent one or more objects . 7 . The system of claim 4 , wherein the determining the first one or more instruction sets for operating the first device based on the at least partial match between the new one or more digital pictures and the first one or more digital pictures includes determining at least partial match between one or more objects detected in the new one or more digital pictures and one or more objects detected in the first one or more digital pictures . 8 . The system of claim 4 , 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 . 9 . The system of claim 4 , wherein the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device are included in a knowledgebase , and wherein the knowledgebase further includes a second one or more digital pictures correlated with a second one or more instruction sets for operating the first device , and wherein at least a portion of the first one or more digital pictures or at least a portion of the first one or more instruction sets for operating the first device is learned in a first learning process that includes operating the first device at least partially by a first user [FEATURE ID: 1]

, and wherein at least a portion of the second one or more digital pictures or at least a portion of the second one or more instruction sets for operating the first device is learned in a second learning process [FEATURE ID: 6]

that includes operating the first device at least partially by the first user . 10 . The system of claim 4 , wherein the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device are included in a knowledgebase , and wherein the knowledgebase further includes a second one or more digital pictures correlated with a second one or more instruction sets for operating the first device , and wherein at least a portion of the first one or more digital pictures or at least a portion of the first one or more instruction sets for operating the first device is learned in a first learning process that includes operating the first device at least partially by a first user , and wherein at least a portion of the second one or more digital pictures or at least a portion of the second one or more instruction sets for operating the first device is learned in a second learning process that includes operating the first device at least partially by a second user [FEATURE ID: 6]

1 . A method [FEATURE ID: 9]

comprising [TRANSITIVE ID: 2]

: applying [TRANSITIVE ID: 4]

, by a packet broker [FEATURE ID: 1]

in a visibility network [FEATURE ID: 1]

, one or more machine learning models [FEATURE ID: 7]

to network traffic that is replicated from a core network [FEATURE ID: 1]

; detecting [TRANSITIVE ID: 4]

, by the packet broker based on the applying of the one or more machine learning models , that a network traffic anomaly has occurred or is occurring in the core network ; and in response [FEATURE ID: 11]

to the detecting , taking [TRANSITIVE ID: 4]

, by the packet broker , one or more predefined actions [FEATURE ID: 13]

. 2 . The method of claim [FEATURE ID: 8]

1 wherein the one or more machine learning models include [TRANSITIVE ID: 5]

a time - series model adapted to model changes in value [FEATURE ID: 15]

of a core network parameter [FEATURE ID: 6]

over time . 3 . The method of claim 1 wherein the one or more machine learning models include a protocol language model adapted to model valid message exchanges [FEATURE ID: 14]

or flows [FEATURE ID: 13]

with respect to a particular network protocol [FEATURE ID: 12]

in the core network . 4 . The method of claim 1 further comprising , prior to the applying : training , by the packet broker , at least a first machine learning model [FEATURE ID: 6]

in the one or more machine learning models using historical traffic data [FEATURE ID: 14]

collected from the core network . 5 . The method of claim 1 further comprising , prior to the applying : training , by the packet broker , at least a first machine learning model in the one or more machine learning models using live traffic data [FEATURE ID: 14]

replicated from the core network . 6 . The method of claim 1 wherein the applying comprises : determining , from the network traffic replicated from the core network , an actual value of a core network parameter or criterion [FEATURE ID: 6]

that is modeled by one machine learning model in the one or more machine learning models ; and determining , using the machine learning model , an expected value of the core network parameter or criterion . 7 . The method of claim 6 wherein the detecting comprises : determining that a discrepancy exists between the actual value and the expected value that exceeds a predefined threshold or reflects an inconsistency . 8 . The method of claim 1 wherein the one or more predefined actions include : steering network traffic from the core network that is deemed to be related to the network traffic anomaly to one or more analytic probes [FEATURE ID: 3]

or tools [FEATURE ID: 3]

. 9 . The method of claim 1 wherein the one or more predefined actions include : generating an alert for a network administrator [FEATURE ID: 1]

. 10 . The method of claim 1 wherein the one or more predefined actions include : metering network traffic from the core network that is deemed to be related to the network traffic anomaly . 11 . The method of claim 1 further comprising : predicting , by the packet broker based on the applying of the one or more machine learning models , that another network traffic anomaly will occur in the core network at a future point [FEATURE ID: 10]

in time ; and in response to the predicting , taking , by the packet broker , one or more additional predefined actions [FEATURE ID: 13]







Targeted Patent:

Patent: US11055583B1
Filed: 2017-11-26
Issued: 2021-07-06
Patent Holder: (Original Assignee) Individual     (Current Assignee) AUTONOMOUS DEVICES LLC
Inventor(s): Jasmin Cosic

Title: Machine learning for computing enabled systems and/or devices

 
Cross Reference / Shared Meaning between the Lines		 Charted Against:

Patent: US9826338B2
Filed: 2014-11-18
Issued: 2017-11-21
Patent Holder: (Original Assignee) Prophecy Sensorlytics LLC     (Current Assignee) Machinesense LLC
Inventor(s): Biplab Pal

Title: IoT-enabled process control and predective maintenance using machine wearables
[FEATURE ID: 1] system, non-transitory machine readable mediumnetwork, device, module, subsystem, computer, machine, tool[FEATURE ID: 1] system, control module, control panel
[TRANSITIVE ID: 2] comprisingincluding, involving, by, featuring, of, incorporating, containing[TRANSITIVE ID: 2] comprising
[FEATURE ID: 3] processors, non-transitory machine readable media, microcontrollers, actuators, volatile memories, non-volatile memories, storage devices, storage systems, states, first userdevices, sensors, controllers, servers, registers, components, modules[FEATURE ID: 3] temperature sensors, IR sensors
[TRANSITIVE ID: 4] storingproviding, including, implementing, incorporating[TRANSITIVE ID: 4] employing
[FEATURE ID: 5] machine readable codeinstructions, logic, software, data[FEATURE ID: 5] other factors
[TRANSITIVE ID: 6] receiving, generatingacquiring, sending, analyzing, obtaining, processing, collecting, storing[TRANSITIVE ID: 6] receiving
[FEATURE ID: 7] portion, surroundingpart, surface, environment, periphery, body, operation, structure[FEATURE ID: 7] exterior
[FEATURE ID: 8] instruction setsorders, data, operations, information, actions, signals, controls[FEATURE ID: 8] control commands
[TRANSITIVE ID: 9] operatingprocessing, manufacturing, monitoring, controlling[TRANSITIVE ID: 9] process control
[FEATURE ID: 10] claimclair, aspect, figure, item, paragraph, claims, clause[FEATURE ID: 10] claim
[FEATURE ID: 11] picturevideo, motion, light, data, content[FEATURE ID: 11] temperature data
[FEATURE ID: 12] apparatusdevice, system, mechanism, platform, process, unit, sensor[FEATURE ID: 12] based, machine
[FEATURE ID: 13] applicationenvironment, arrangement, organization, architecture, element, interface, infrastructure[FEATURE ID: 13] Internet
[FEATURE ID: 14] partial matchcorrespondence, proximity, registration, alignment[FEATURE ID: 14] operative communication
[FEATURE ID: 15] responseaccordance, parallel, addition, comparison[FEATURE ID: 15] combination
[FEATURE ID: 16] second deviceserver, processor, system, computer[FEATURE ID: 16] algorithm engine
[FEATURE ID: 17] portionsobjects, points, locations, regions[FEATURE ID: 17] temperature
[FEATURE ID: 18] informationinstructions, inputs, messages, commands, outputs[FEATURE ID: 18] data
1 . A system [FEATURE ID: 1]

comprising [TRANSITIVE ID: 2]

: one or more processors [FEATURE ID: 3]

; and one or more non-transitory machine readable media [FEATURE ID: 3]

storing [TRANSITIVE ID: 4]

machine readable code [FEATURE ID: 5]

that , when executed by the one or more processors , causes the one or more processors to perform at least : receiving [TRANSITIVE ID: 6]

or generating [TRANSITIVE ID: 6]

a first one or more digital pictures , wherein the first one or more digital pictures depict at least a portion [FEATURE ID: 7]

of a first device ' s surrounding [TRANSITIVE ID: 7]

; receiving or generating a first one or more instruction sets [FEATURE ID: 8]

for operating [TRANSITIVE ID: 9]

the first device ; and learning the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device . 2 . The system of claim [FEATURE ID: 10]

1 , wherein the receiving the first one or more digital pictures includes receiving the first one or more digital pictures from a picture [FEATURE ID: 11]

capturing apparatus [FEATURE ID: 12]

, and wherein the receiving the first one or more instruction sets for operating the first device includes receiving the first one or more instruction sets for operating the first device from : an application [FEATURE ID: 13]

for operating the first device , a system for operating the first device , one or more microcontrollers [FEATURE ID: 3]

, another one or more processors , or one or more actuators [FEATURE ID: 3]

, and wherein the learning the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device includes storing the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device into or onto at least one of : at least one non-transitory machine readable medium [FEATURE ID: 1]

of the one or more non-transitory machine readable media , another one or more non-transitory machine readable media , one or more volatile memories [FEATURE ID: 3]

, one or more non-volatile memories [FEATURE ID: 3]

, one or more storage devices [FEATURE ID: 3]

, or one or more storage systems [FEATURE ID: 3]

. 3 . The system of claim 1 , wherein the learning the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device includes determining that the first one or more instruction sets for operating the first device temporally correspond to the first one or more digital pictures . 4 . The system of claim 1 , wherein the machine readable code , when executed by the one or more processors , causes the one or more processors to further perform at least : receiving or generating a new one or more digital pictures ; determining the first one or more instruction sets for operating the first device based on at least partial match [FEATURE ID: 14]

between the new one or more digital pictures and the first one or more digital pictures ; and at least in response [FEATURE ID: 15]

to the determining , causing the first device or a second device [FEATURE ID: 16]

to perform one or more operations defined by the first one or more instruction sets for operating the first device . 5 . The system of claim 4 , wherein the causing the first device or the second device to perform the one or more operations defined by the first one or more instruction sets for operating the first device includes executing the first one or more instruction sets for operating the first device . 6 . The system of claim 4 , wherein the determining the first one or more instruction sets for operating the first device based on the at least partial match between the new one or more digital pictures and the first one or more digital pictures includes determining at least partial match between one or more portions [FEATURE ID: 17]

of the new one or more digital pictures that represent one or more objects and one or more portions of the first one or more digital pictures that represent one or more objects . 7 . The system of claim 4 , wherein the determining the first one or more instruction sets for operating the first device based on the at least partial match between the new one or more digital pictures and the first one or more digital pictures includes determining at least partial match between one or more objects detected in the new one or more digital pictures and one or more objects detected in the first one or more digital pictures . 8 . The system of claim 4 , wherein the first one or more instruction sets for operating the first device include one or more information [FEATURE ID: 18]

about one or more states [FEATURE ID: 3]

of : the first device , or a portion of the first device . 9 . The system of claim 4 , wherein the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device are included in a knowledgebase , and wherein the knowledgebase further includes a second one or more digital pictures correlated with a second one or more instruction sets for operating the first device , and wherein at least a portion of the first one or more digital pictures or at least a portion of the first one or more instruction sets for operating the first device is learned in a first learning process that includes operating the first device at least partially by a first user [FEATURE ID: 3]

1 . An Internet [FEATURE ID: 13]

of Things ( IoT ) based [TRANSITIVE ID: 12]

system [FEATURE ID: 1]

for overseeing process control [FEATURE ID: 9]

and predictive maintenance of a machine [FEATURE ID: 12]

by employing [TRANSITIVE ID: 4]

machine wearable sensors , comprising [TRANSITIVE ID: 2]

: a. a plurality of machine - wearable infrared ( IR ) temperature sensors [FEATURE ID: 3]

, each of which being secured to the exterior [FEATURE ID: 7]

of the machine ; each IR sensor transmitting captured temperature data [FEATURE ID: 11]

wirelessly over a communications network ; b. an algorithm engine capable of receiving [TRANSITIVE ID: 6]

data [FEATURE ID: 18]

from the IR sensors [FEATURE ID: 3]

, the algorithm engine [FEATURE ID: 16]

further processing the received data to recognize real - time temperature pattern deviations , and based on the same and , at times , in combination [FEATURE ID: 15]

with other factors [FEATURE ID: 5]

, promptly issuing control commands [FEATURE ID: 8]

pertaining to the machine ; and c. a control module [FEATURE ID: 1]

disposed in operative communication [FEATURE ID: 14]

with a control panel [FEATURE ID: 1]

of the machine , the control module receiving over a communications network the control commands and executing the same resulting in process control or predictive maintenance of the machine or both ; wherein the machine comprises a hopper dryer comprising an elongate , vertical sight glass ; the plurality of infrared ( IR ) temperature sensors being secured over the sight glass for capturing the IR radiation therethrough , each of the plurality of IR sensors being vertically disposed over one another , each IR sensor capturing temperature [FEATURE ID: 17]

within the hopper dryer at the vertical level thereof . 2 . The system of claim [FEATURE ID: 10]







Targeted Patent:

Patent: US11055583B1
Filed: 2017-11-26
Issued: 2021-07-06
Patent Holder: (Original Assignee) Individual     (Current Assignee) AUTONOMOUS DEVICES LLC
Inventor(s): Jasmin Cosic

Title: Machine learning for computing enabled systems and/or devices

 
Cross Reference / Shared Meaning between the Lines		 Charted Against:

Patent: US9825842B2
Filed: 2013-12-23
Issued: 2017-11-21
Patent Holder: (Original Assignee) BAE Systems Information and Electronic Systems Integration Inc     (Current Assignee) BAE Systems Information and Electronic Systems Integration Inc
Inventor(s): Dale A. Rickard

Title: Network test system
[FEATURE ID: 1] system, second device, first usernetwork, device, computer, controller, processor, server, switch[FEATURE ID: 1] network test system, system, spacecraft subsystem, circuit card, spacecraft, signal selector
[TRANSITIVE ID: 2] comprising, storingimplementing, includes, having, of, incorporating, with, containing[TRANSITIVE ID: 2] comprising, including
[FEATURE ID: 3] non-transitory machine readable media, portionslocations, elements, components, aspects, characteristics, areas, sections[FEATURE ID: 3] portions
[FEATURE ID: 4] machine readable code, instruction sets, actuators, informationdata, signals, instructions, hardware, logic, software, inputs[FEATURE ID: 4] signal replication logic, test traffic, signal selector logic
[TRANSITIVE ID: 5] performenable, achieve, realize, control, deliver, facilitate, initiate[TRANSITIVE ID: 5] inject, provide
[TRANSITIVE ID: 6] receiving, generating, operatingprocessing, providing, sending, displaying, monitoring, capturing, transmitting[TRANSITIVE ID: 6] receiving
[FEATURE ID: 7] portion, non-transitory machine readable medium, states, copypart, section, subset, characteristic, component, feature, surface[FEATURE ID: 7] data stream, particular portion, box
[FEATURE ID: 8] claimclair, feature, aspect, figure, item, paragraph, embodiment[FEATURE ID: 8] claim
[FEATURE ID: 9] apparatus, applicationdevice, equipment, interface, port, signal, module, node[FEATURE ID: 9] first digital circuit switch, network activity, network, integrated circuit, second port
[FEATURE ID: 10] second learning processstudy, simulation, test, training[FEATURE ID: 10] test signal injection
1 . A system [FEATURE ID: 1]

comprising [TRANSITIVE ID: 2]

: one or more processors ; and one or more non-transitory machine readable media [FEATURE ID: 3]

storing [TRANSITIVE ID: 2]

machine readable code [FEATURE ID: 4]

that , when executed by the one or more processors , causes the one or more processors to perform [TRANSITIVE ID: 5]

at least : receiving [TRANSITIVE ID: 6]

or generating [TRANSITIVE ID: 6]

a first one or more digital pictures , wherein the first one or more digital pictures depict at least a portion [FEATURE ID: 7]

of a first device ' s surrounding ; receiving or generating a first one or more instruction sets [FEATURE ID: 4]

for operating [TRANSITIVE ID: 6]

the first device ; and learning the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device . 2 . The system of claim [FEATURE ID: 8]

1 , wherein the receiving the first one or more digital pictures includes receiving the first one or more digital pictures from a picture capturing apparatus [FEATURE ID: 9]

, and wherein the receiving the first one or more instruction sets for operating the first device includes receiving the first one or more instruction sets for operating the first device from : an application [FEATURE ID: 9]

for operating the first device , a system for operating the first device , one or more microcontrollers , another one or more processors , or one or more actuators [FEATURE ID: 4]

, and wherein the learning the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device includes storing the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device into or onto at least one of : at least one non-transitory machine readable medium [FEATURE ID: 7]

of the one or more non-transitory machine readable media , another one or more non-transitory machine readable media , one or more volatile memories , one or more non-volatile memories , one or more storage devices , or one or more storage systems . 3 . The system of claim 1 , wherein the learning the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device includes determining that the first one or more instruction sets for operating the first device temporally correspond to the first one or more digital pictures . 4 . The system of claim 1 , wherein the machine readable code , when executed by the one or more processors , causes the one or more processors to further perform at least : receiving or generating a new one or more digital pictures ; determining the first one or more instruction sets for operating the first device based on at least partial match between the new one or more digital pictures and the first one or more digital pictures ; and at least in response to the determining , causing the first device or a second device [FEATURE ID: 1]

to perform one or more operations defined by the first one or more instruction sets for operating the first device . 5 . The system of claim 4 , wherein the causing the first device or the second device to perform the one or more operations defined by the first one or more instruction sets for operating the first device includes executing the first one or more instruction sets for operating the first device . 6 . The system of claim 4 , wherein the determining the first one or more instruction sets for operating the first device based on the at least partial match between the new one or more digital pictures and the first one or more digital pictures includes determining at least partial match between one or more portions [FEATURE ID: 3]

of the new one or more digital pictures that represent one or more objects and one or more portions of the first one or more digital pictures that represent one or more objects . 7 . The system of claim 4 , wherein the determining the first one or more instruction sets for operating the first device based on the at least partial match between the new one or more digital pictures and the first one or more digital pictures includes determining at least partial match between one or more objects detected in the new one or more digital pictures and one or more objects detected in the first one or more digital pictures . 8 . The system of claim 4 , wherein the first one or more instruction sets for operating the first device include one or more information [FEATURE ID: 4]

about one or more states [FEATURE ID: 7]

of : the first device , or a portion of the first device . 9 . The system of claim 4 , wherein the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device are included in a knowledgebase , and wherein the knowledgebase further includes a second one or more digital pictures correlated with a second one or more instruction sets for operating the first device , and wherein at least a portion of the first one or more digital pictures or at least a portion of the first one or more instruction sets for operating the first device is learned in a first learning process that includes operating the first device at least partially by a first user [FEATURE ID: 1]

, and wherein at least a portion of the second one or more digital pictures or at least a portion of the second one or more instruction sets for operating the first device is learned in a second learning process [FEATURE ID: 10]

that includes operating the first device at least partially by the first user . 10 . The system of claim 4 , wherein the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device are included in a knowledgebase , and wherein the knowledgebase further includes a second one or more digital pictures correlated with a second one or more instruction sets for operating the first device , and wherein at least a portion of the first one or more digital pictures or at least a portion of the first one or more instruction sets for operating the first device is learned in a first learning process that includes operating the first device at least partially by a first user , and wherein at least a portion of the second one or more digital pictures or at least a portion of the second one or more instruction sets for operating the first device is learned in a second learning process that includes operating the first device at least partially by a second user . 11 . The system of claim 4 , wherein the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device are included in a knowledgebase , and wherein the knowledgebase further includes a second one or more digital pictures correlated with a second one or more instruction sets for operating a third device , and wherein at least a portion of the first one or more digital pictures or at least a portion of the first one or more instruction sets for operating the first device is learned in a first learning process that includes operating the first device at least partially by a first user , and wherein at least a portion of the second one or more digital pictures or at least a portion of the second one or more instruction sets for operating the third device is learned in a second learning process that includes operating the third device at least partially by : the first user , or a second user . 12 . The system of claim 4 , wherein the new one or more digital pictures depict at least a portion of the first device ' s surrounding , and wherein the first one or more instruction sets for operating the first device are applied to the first device , and wherein the first device is caused to perform the one or more operations defined by the first one or more instruction sets for operating the first device . 13 . The system of claim 4 , wherein the new one or more digital pictures depict at least a portion of the second device ' s surrounding , and wherein the first one or more instruction sets for operating the first device are applied to the second device , and wherein the second device is caused to perform the one or more operations defined by the first one or more instruction sets for operating the first device . 14 . The system of claim 4 , wherein the new one or more digital pictures depict at least a portion of the second device ' s surrounding , and wherein the first one or more instruction sets for operating the first device or a copy [FEATURE ID: 7]

1 . A network test system [FEATURE ID: 1]

comprising [TRANSITIVE ID: 2]

: a Serial / Deserializer ( SERDES ) receiver receiving [TRANSITIVE ID: 6]

network activity therethrough ; a SERDES transmitter downstream from the SERDES receiver receiving network activity therethrough ; a circuit switch operatively coupled to the SERDES receiver and operatively coupled to the SERDES transmitter ; a port operatively coupled to the circuit switch and adapted to receive an observation device or a control device ; and a signal replicator including [TRANSITIVE ID: 2]

signal replication logic [FEATURE ID: 4]

on the SERDES receiver ; wherein the circuit switch is a first digital circuit switch [FEATURE ID: 9]

operatively coupled with the signal replicator on the SERDES receiver and coupled with the port ; wherein the circuit switch is adapted to tap off a data stream [FEATURE ID: 7]

of the network activity [FEATURE ID: 9]

to passively observe network activity with the observation device or to inject [TRANSITIVE ID: 5]

test traffic [FEATURE ID: 4]

into the port from the control device to test a particular portion [FEATURE ID: 7]

of the network [FEATURE ID: 9]

; and wherein the system [FEATURE ID: 1]

is adapted to provide [TRANSITIVE ID: 5]

real - time observation and test capability for network activity without disturbing the network or portions [FEATURE ID: 3]

of the network not under test signal injection [FEATURE ID: 10]

. 2 . The network test system of claim [FEATURE ID: 8]

1 , wherein the port is a data test port . 3 . The network test system of claim 2 , wherein the data test port is integrally formed with one of the following : a box [FEATURE ID: 7]

of a spacecraft subsystem [FEATURE ID: 1]

, a spacecraft system , a circuit card [FEATURE ID: 1]

on a spacecraft [FEATURE ID: 1]

and an integrated circuit [FEATURE ID: 9]

on a spacecraft . 4 . The network test system of claim 1 , further comprising : the signal selector [FEATURE ID: 1]

including signal selector logic [FEATURE ID: 4]

on the SERDES transmitter ; and a second digital circuit switch operatively coupled with the signal selector on the SERDES transmitter . 5 . The network test system of claim 4 , further comprising : a second port [FEATURE ID: 9]







Targeted Patent:

Patent: US11055583B1
Filed: 2017-11-26
Issued: 2021-07-06
Patent Holder: (Original Assignee) Individual     (Current Assignee) AUTONOMOUS DEVICES LLC
Inventor(s): Jasmin Cosic

Title: Machine learning for computing enabled systems and/or devices

 
Cross Reference / Shared Meaning between the Lines		 Charted Against:

Patent: US9824311B1
Filed: 2014-04-23
Issued: 2017-11-21
Patent Holder: (Original Assignee) HRL Laboratories LLC     (Current Assignee) HRL Laboratories LLC
Inventor(s): Jose Cruz-Albrecht, Peter Petre, Randall White

Title: Asynchronous pulse domain processor with adaptive circuit and reconfigurable routing
[FEATURE ID: 1] system, second device, first usermachine, network, controller, computer, method, processor, apparatus[FEATURE ID: 1] liquid state machine pulse domain neural processor circuit, asynchronous input filter
[TRANSITIVE ID: 2] comprising, storingincluding, incorporating, includes, having, with, implementing, of[TRANSITIVE ID: 2] comprising
[FEATURE ID: 3] processors, actuators, volatile memories, non-volatile memories, storage devices, storage systems, statesregisters, devices, sensors, controllers, modules, switches, servers[FEATURE ID: 3] asynchronous nodes, asynchronous pulse domain processing cells, input ports, circuits
[FEATURE ID: 4] non-transitory machine readable mediacircuits, controllers, interfaces, nodes[FEATURE ID: 4] input port switches
[FEATURE ID: 5] machine readable codecommands, instructions, data, information[FEATURE ID: 5] analog input signals
[TRANSITIVE ID: 6] executedconfigured, implemented, operable, used[TRANSITIVE ID: 6] provided
[TRANSITIVE ID: 7] receiving, operatingproviding, using, obtaining, sending, storing, processing, transmitting[TRANSITIVE ID: 7] receiving, generating, outputting
[TRANSITIVE ID: 8] generatingprocessing, storing, rendering, receiving[TRANSITIVE ID: 8] transforming
[FEATURE ID: 9] portion, non-transitory machine readable medium, copysubset, first, part, section, sample, surface, member[FEATURE ID: 9] portion
[FEATURE ID: 10] instruction sets, portions, objectselements, ones, pixels, points, images, inputs, items[FEATURE ID: 10] values
[FEATURE ID: 11] claimof claim, feature, aspect, figure, item, paragraph, embodiment[FEATURE ID: 11] claim
[FEATURE ID: 12] apparatusassembly, interface, operation, element[FEATURE ID: 12] asynchronous trainable readout map circuit
[FEATURE ID: 13] applicationengine, apparatus, interface, architecture, accumulator, interpolator, output[FEATURE ID: 13] asynchronous input filter circuit, input driver circuit
[FEATURE ID: 14] microcontrollerscontrollers, circuits, terminals, networks[FEATURE ID: 14] node switches
[FEATURE ID: 15] newseparate, corresponding, different, first[FEATURE ID: 15] distinct input driver input port
[FEATURE ID: 16] responseorder, parallel, return, accordance, addition, time, correspondence[FEATURE ID: 16] response
[FEATURE ID: 17] informationinputs, outputs, signals, commands[FEATURE ID: 17] pulses
1 . A system [FEATURE ID: 1]

comprising [TRANSITIVE ID: 2]

: one or more processors [FEATURE ID: 3]

; and one or more non-transitory machine readable media [FEATURE ID: 4]

storing [TRANSITIVE ID: 2]

machine readable code [FEATURE ID: 5]

that , when executed [TRANSITIVE ID: 6]

by the one or more processors , causes the one or more processors to perform at least : receiving [TRANSITIVE ID: 7]

or generating [TRANSITIVE ID: 8]

a first one or more digital pictures , wherein the first one or more digital pictures depict at least a portion [FEATURE ID: 9]

of a first device ' s surrounding ; receiving or generating a first one or more instruction sets [FEATURE ID: 10]

for operating [TRANSITIVE ID: 7]

the first device ; and learning the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device . 2 . The system of claim [FEATURE ID: 11]

1 , wherein the receiving the first one or more digital pictures includes receiving the first one or more digital pictures from a picture capturing apparatus [FEATURE ID: 12]

, and wherein the receiving the first one or more instruction sets for operating the first device includes receiving the first one or more instruction sets for operating the first device from : an application [FEATURE ID: 13]

for operating the first device , a system for operating the first device , one or more microcontrollers [FEATURE ID: 14]

, another one or more processors , or one or more actuators [FEATURE ID: 3]

, and wherein the learning the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device includes storing the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device into or onto at least one of : at least one non-transitory machine readable medium [FEATURE ID: 9]

of the one or more non-transitory machine readable media , another one or more non-transitory machine readable media , one or more volatile memories [FEATURE ID: 3]

, one or more non-volatile memories [FEATURE ID: 3]

, one or more storage devices [FEATURE ID: 3]

, or one or more storage systems [FEATURE ID: 3]

. 3 . The system of claim 1 , wherein the learning the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device includes determining that the first one or more instruction sets for operating the first device temporally correspond to the first one or more digital pictures . 4 . The system of claim 1 , wherein the machine readable code , when executed by the one or more processors , causes the one or more processors to further perform at least : receiving or generating a new [FEATURE ID: 15]

one or more digital pictures ; determining the first one or more instruction sets for operating the first device based on at least partial match between the new one or more digital pictures and the first one or more digital pictures ; and at least in response [FEATURE ID: 16]

to the determining , causing the first device or a second device [FEATURE ID: 1]

to perform one or more operations defined by the first one or more instruction sets for operating the first device . 5 . The system of claim 4 , wherein the causing the first device or the second device to perform the one or more operations defined by the first one or more instruction sets for operating the first device includes executing the first one or more instruction sets for operating the first device . 6 . The system of claim 4 , wherein the determining the first one or more instruction sets for operating the first device based on the at least partial match between the new one or more digital pictures and the first one or more digital pictures includes determining at least partial match between one or more portions [FEATURE ID: 10]

of the new one or more digital pictures that represent one or more objects [FEATURE ID: 10]

and one or more portions of the first one or more digital pictures that represent one or more objects . 7 . The system of claim 4 , wherein the determining the first one or more instruction sets for operating the first device based on the at least partial match between the new one or more digital pictures and the first one or more digital pictures includes determining at least partial match between one or more objects detected in the new one or more digital pictures and one or more objects detected in the first one or more digital pictures . 8 . The system of claim 4 , wherein the first one or more instruction sets for operating the first device include one or more information [FEATURE ID: 17]

about one or more states [FEATURE ID: 3]

of : the first device , or a portion of the first device . 9 . The system of claim 4 , wherein the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device are included in a knowledgebase , and wherein the knowledgebase further includes a second one or more digital pictures correlated with a second one or more instruction sets for operating the first device , and wherein at least a portion of the first one or more digital pictures or at least a portion of the first one or more instruction sets for operating the first device is learned in a first learning process that includes operating the first device at least partially by a first user [FEATURE ID: 1]

, and wherein at least a portion of the second one or more digital pictures or at least a portion of the second one or more instruction sets for operating the first device is learned in a second learning process that includes operating the first device at least partially by the first user . 10 . The system of claim 4 , wherein the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device are included in a knowledgebase , and wherein the knowledgebase further includes a second one or more digital pictures correlated with a second one or more instruction sets for operating the first device , and wherein at least a portion of the first one or more digital pictures or at least a portion of the first one or more instruction sets for operating the first device is learned in a first learning process that includes operating the first device at least partially by a first user , and wherein at least a portion of the second one or more digital pictures or at least a portion of the second one or more instruction sets for operating the first device is learned in a second learning process that includes operating the first device at least partially by a second user . 11 . The system of claim 4 , wherein the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device are included in a knowledgebase , and wherein the knowledgebase further includes a second one or more digital pictures correlated with a second one or more instruction sets for operating a third device , and wherein at least a portion of the first one or more digital pictures or at least a portion of the first one or more instruction sets for operating the first device is learned in a first learning process that includes operating the first device at least partially by a first user , and wherein at least a portion of the second one or more digital pictures or at least a portion of the second one or more instruction sets for operating the third device is learned in a second learning process that includes operating the third device at least partially by : the first user , or a second user . 12 . The system of claim 4 , wherein the new one or more digital pictures depict at least a portion of the first device ' s surrounding , and wherein the first one or more instruction sets for operating the first device are applied to the first device , and wherein the first device is caused to perform the one or more operations defined by the first one or more instruction sets for operating the first device . 13 . The system of claim 4 , wherein the new one or more digital pictures depict at least a portion of the second device ' s surrounding , and wherein the first one or more instruction sets for operating the first device are applied to the second device , and wherein the second device is caused to perform the one or more operations defined by the first one or more instruction sets for operating the first device . 14 . The system of claim 4 , wherein the new one or more digital pictures depict at least a portion of the second device ' s surrounding , and wherein the first one or more instruction sets for operating the first device or a copy [FEATURE ID: 9]

1 . A liquid state machine pulse domain neural processor circuit [FEATURE ID: 1]

comprising [TRANSITIVE ID: 2]

: an asynchronous input filter circuit [FEATURE ID: 13]

provided [TRANSITIVE ID: 6]

for , at any given time , receiving [TRANSITIVE ID: 7]

a series of analog input signals [FEATURE ID: 5]

and generating [TRANSITIVE ID: 7]

in response [FEATURE ID: 16]

a set of time - encoded values [FEATURE ID: 10]

that depend on the series of analog input signals received at said given time and before said given time ; and an asynchronous trainable readout map circuit [FEATURE ID: 12]

for transforming [TRANSITIVE ID: 8]

at least a portion [FEATURE ID: 9]

of said set of time encoded values into output signals ; wherein : said generating a set of time - encoded values that depend on the series of analog input signals received at said given time and before said given time comprises receiving each analog input signal on a distinct input driver input port [FEATURE ID: 15]

and outputting a corresponding pulse signal comprising a series of pulses [FEATURE ID: 17]

having a predetermined frequency and having a duty cycle that depends on the value of the analog input signal . 2 . The liquid state machine pulse domain neural processor circuit of claim [FEATURE ID: 11]

1 wherein the asynchronous input filter circuit comprises : an input driver circuit [FEATURE ID: 13]

arranged for said receiving each analog input signal on a distinct input driver input port and for said outputting [FEATURE ID: 7]

, on a corresponding input driver output port , said pulse signal comprising a series of pulses having a predetermined frequency and having a duty cycle that depends on the value of the analog input signal ; a plurality of asynchronous nodes [FEATURE ID: 3]

; a plurality of asynchronous pulse domain processing cells [FEATURE ID: 3]

having each an output port and a plurality of input ports [FEATURE ID: 3]

; a plurality of input port switches [FEATURE ID: 4]

for controllably connecting any input port of any asynchronous pulse domain processing cell to any input driver output port ; a plurality of node switches [FEATURE ID: 14]

for controllably connecting any asynchronous node to any input port of any asynchronous pulse domain processing cell ; and a plurality of output port switches for controllably connecting the output port of any asynchronous pulse domain processing cell to any asynchronous node . 3 . The liquid state machine pulse domain neural processor circuit of claim 2 wherein the input port switches , node switches and output port switches are controllable such that at any given time the asynchronous input filter [FEATURE ID: 1]

generates on the plurality of asynchronous nodes said set of time - encoded values that depends on the series of analog input signals received at said given time and before said given time . 4 . The liquid state machine pulse domain neural processor circuit of claim 2 , wherein each asynchronous pulse domain processing cell comprises : a summing circuit having as many inputs as the asynchronous pulse domain processing cell has input ports ; a plurality of controllable weighing circuits [FEATURE ID: 3]







Targeted Patent:

Patent: US11055583B1
Filed: 2017-11-26
Issued: 2021-07-06
Patent Holder: (Original Assignee) Individual     (Current Assignee) AUTONOMOUS DEVICES LLC
Inventor(s): Jasmin Cosic

Title: Machine learning for computing enabled systems and/or devices

 
Cross Reference / Shared Meaning between the Lines		 Charted Against:

Patent: US20170332049A1
Filed: 2016-05-13
Issued: 2017-11-16
Patent Holder: (Original Assignee) Tijee Corp     (Current Assignee) Tijee Corp
Inventor(s): Yong Zhang

Title: Intelligent sensor network
[FEATURE ID: 1] system, apparatus, first userdevice, network, controller, server, method, control system, network system[FEATURE ID: 1] surveillance network system, controller node, system
[TRANSITIVE ID: 2] comprising, storing, generatingimplementing, of, incorporating, providing, defining, involving, containing[TRANSITIVE ID: 2] comprising, including
[FEATURE ID: 3] machine readable code, instruction setsprograms, data, codes, information, instruction, operations, signals[FEATURE ID: 3] commands
[TRANSITIVE ID: 4] executedoperable, implemented, deployed, used[TRANSITIVE ID: 4] configured
[FEATURE ID: 5] claimclair, feature, aspect, figure, item, paragraph, embodiment[FEATURE ID: 5] claim
[FEATURE ID: 6] second deviceserver, computer, display, controller[FEATURE ID: 6] environmental sensor
[FEATURE ID: 7] informationconditions, messages, parameters, data, observations, indicators[FEATURE ID: 7] environmental data
[FEATURE ID: 8] stateselements, components, devices, parameters[FEATURE ID: 8] environmental sensors
1 . A system [FEATURE ID: 1]

comprising [TRANSITIVE ID: 2]

: one or more processors ; and one or more non-transitory machine readable media storing [TRANSITIVE ID: 2]

machine readable code [FEATURE ID: 3]

that , when executed [TRANSITIVE ID: 4]

by the one or more processors , causes the one or more processors to perform at least : receiving or generating [TRANSITIVE ID: 2]

a first one or more digital pictures , wherein the first one or more digital pictures depict at least a portion of a first device ' s surrounding ; receiving or generating a first one or more instruction sets [FEATURE ID: 3]

for operating the first device ; and learning the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device . 2 . The system of claim [FEATURE ID: 5]

1 , wherein the receiving the first one or more digital pictures includes receiving the first one or more digital pictures from a picture capturing apparatus [FEATURE ID: 1]

, and wherein the receiving the first one or more instruction sets for operating the first device includes receiving the first one or more instruction sets for operating the first device from : an application for operating the first device , a system for operating the first device , one or more microcontrollers , another one or more processors , or one or more actuators , and wherein the learning the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device includes storing the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device into or onto at least one of : at least one non-transitory machine readable medium of the one or more non-transitory machine readable media , another one or more non-transitory machine readable media , one or more volatile memories , one or more non-volatile memories , one or more storage devices , or one or more storage systems . 3 . The system of claim 1 , wherein the learning the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device includes determining that the first one or more instruction sets for operating the first device temporally correspond to the first one or more digital pictures . 4 . The system of claim 1 , wherein the machine readable code , when executed by the one or more processors , causes the one or more processors to further perform at least : receiving or generating a new one or more digital pictures ; determining the first one or more instruction sets for operating the first device based on at least partial match between the new one or more digital pictures and the first one or more digital pictures ; and at least in response to the determining , causing the first device or a second device [FEATURE ID: 6]

to perform one or more operations defined by the first one or more instruction sets for operating the first device . 5 . The system of claim 4 , wherein the causing the first device or the second device to perform the one or more operations defined by the first one or more instruction sets for operating the first device includes executing the first one or more instruction sets for operating the first device . 6 . The system of claim 4 , wherein the determining the first one or more instruction sets for operating the first device based on the at least partial match between the new one or more digital pictures and the first one or more digital pictures includes determining at least partial match between one or more portions of the new one or more digital pictures that represent one or more objects and one or more portions of the first one or more digital pictures that represent one or more objects . 7 . The system of claim 4 , wherein the determining the first one or more instruction sets for operating the first device based on the at least partial match between the new one or more digital pictures and the first one or more digital pictures includes determining at least partial match between one or more objects detected in the new one or more digital pictures and one or more objects detected in the first one or more digital pictures . 8 . The system of claim 4 , wherein the first one or more instruction sets for operating the first device include one or more information [FEATURE ID: 7]

about one or more states [FEATURE ID: 8]

of : the first device , or a portion of the first device . 9 . The system of claim 4 , wherein the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device are included in a knowledgebase , and wherein the knowledgebase further includes a second one or more digital pictures correlated with a second one or more instruction sets for operating the first device , and wherein at least a portion of the first one or more digital pictures or at least a portion of the first one or more instruction sets for operating the first device is learned in a first learning process that includes operating the first device at least partially by a first user [FEATURE ID: 1]

1 . A surveillance network system [FEATURE ID: 1]

, comprising [TRANSITIVE ID: 2]

: a plurality of simple nodes each including [TRANSITIVE ID: 2]

two or more environmental sensors [FEATURE ID: 8]

, wherein individual simple nodes are configured [TRANSITIVE ID: 4]

to transfer environmental data [FEATURE ID: 7]

collected by the two or more environmental sensors to a controller node [FEATURE ID: 1]

via a low - power Internet of Things ( IoT ) communication channel and wherein individual simple nodes join the surveillance network system by sending ; a plurality of complex nodes each including at least one environmental sensor [FEATURE ID: 6]

and at least one video or image sensor , wherein individual complex nodes are configured to transfer environmental data collected by the at least one environmental sensor to the controller node via the low - power IoT communication channel ; and the controller node configured to transmit commands [FEATURE ID: 3]

to individual simple or complex nodes via the low - power IoT communication channel , wherein a command transmitted via the low - power IoT communication channel to at least one complex node causes the at least one complex node to : capture detailed data by the at least one video or image sensor , wherein the detailed data is larger in size than the environmental data ; and transfer the detailed data to the controller node via a high - speed communication channel , wherein the high - speed communication channel has a higher data transfer rate and higher power consuming rate than the low - power IoT communication channel . 2 . The system [FEATURE ID: 1]

of claim [FEATURE ID: 5]







Targeted Patent:

Patent: US11055583B1
Filed: 2017-11-26
Issued: 2021-07-06
Patent Holder: (Original Assignee) Individual     (Current Assignee) AUTONOMOUS DEVICES LLC
Inventor(s): Jasmin Cosic

Title: Machine learning for computing enabled systems and/or devices

 
Cross Reference / Shared Meaning between the Lines		 Charted Against:

Patent: US20170331858A1
Filed: 2016-05-10
Issued: 2017-11-16
Patent Holder: (Original Assignee) Quadrant Information Security     (Current Assignee) Quadrant Information Security
Inventor(s): Champ Clark, III, Robert Alvin Nunley

Title: Method, system, and apparatus to identify and study advanced threat tactics, techniques and procedures
[FEATURE ID: 1] system, non-transitory machine readable medium, first user, second usercomputer, device, server, network, controller, machine, platform[FEATURE ID: 1] system, customer network, virtual honeypot, threat intelligence database, lookup table, host device, secured virtual environment, organization network
[TRANSITIVE ID: 2] comprising, storingincorporating, with, of, includes, having, containing, providing[TRANSITIVE ID: 2] comprising, including, storing
[FEATURE ID: 3] processors, microcontrollers, actuators, storage devices, statesdevices, systems, networks, programs, components, registers, memories[FEATURE ID: 3] tactics, techniques
[FEATURE ID: 4] non-transitory machine readable mediadevices, computers, locations, nodes[FEATURE ID: 4] unsophisticated network attackers
[FEATURE ID: 5] machine readable code, instruction sets, informationcommands, data, instructions, signals, inputs, messages, parameters[FEATURE ID: 5] traffic
[TRANSITIVE ID: 6] receivingcollecting, processing, capturing, acquiring, accepting, transmitting, requesting[TRANSITIVE ID: 6] receiving
[TRANSITIVE ID: 7] generatingcomputing, calculating, detecting, identifying, scanning, analyzing[TRANSITIVE ID: 7] determining
[TRANSITIVE ID: 8] operatingmonitoring, providing, maintaining, controlling[TRANSITIVE ID: 8] implementing
[FEATURE ID: 9] claimclair, aspect, figure, item, paragraph, embodiment, clause[FEATURE ID: 9] claim
[FEATURE ID: 10] apparatussource, device, system, component[FEATURE ID: 10] network
[FEATURE ID: 11] applicationentity, environment, user, agent, enterprise, intranet, organization[FEATURE ID: 11] attacker IP address, IP address, customer network configuration
[FEATURE ID: 12] second devicesecond, user, server, first[FEATURE ID: 12] attack
[FEATURE ID: 13] operationsactivities, applications, transactions, events[FEATURE ID: 13] attacks
1 . A system [FEATURE ID: 1]

comprising [TRANSITIVE ID: 2]

: one or more processors [FEATURE ID: 3]

; and one or more non-transitory machine readable media [FEATURE ID: 4]

storing [TRANSITIVE ID: 2]

machine readable code [FEATURE ID: 5]

that , when executed by the one or more processors , causes the one or more processors to perform at least : receiving [TRANSITIVE ID: 6]

or generating [TRANSITIVE ID: 7]

a first one or more digital pictures , wherein the first one or more digital pictures depict at least a portion of a first device ' s surrounding ; receiving or generating a first one or more instruction sets [FEATURE ID: 5]

for operating [TRANSITIVE ID: 8]

the first device ; and learning the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device . 2 . The system of claim [FEATURE ID: 9]

1 , wherein the receiving the first one or more digital pictures includes receiving the first one or more digital pictures from a picture capturing apparatus [FEATURE ID: 10]

, and wherein the receiving the first one or more instruction sets for operating the first device includes receiving the first one or more instruction sets for operating the first device from : an application [FEATURE ID: 11]

for operating the first device , a system for operating the first device , one or more microcontrollers [FEATURE ID: 3]

, another one or more processors , or one or more actuators [FEATURE ID: 3]

, and wherein the learning the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device includes storing the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device into or onto at least one of : at least one non-transitory machine readable medium [FEATURE ID: 1]

of the one or more non-transitory machine readable media , another one or more non-transitory machine readable media , one or more volatile memories , one or more non-volatile memories , one or more storage devices [FEATURE ID: 3]

, or one or more storage systems . 3 . The system of claim 1 , wherein the learning the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device includes determining that the first one or more instruction sets for operating the first device temporally correspond to the first one or more digital pictures . 4 . The system of claim 1 , wherein the machine readable code , when executed by the one or more processors , causes the one or more processors to further perform at least : receiving or generating a new one or more digital pictures ; determining the first one or more instruction sets for operating the first device based on at least partial match between the new one or more digital pictures and the first one or more digital pictures ; and at least in response to the determining , causing the first device or a second device [FEATURE ID: 12]

to perform one or more operations [FEATURE ID: 13]

defined by the first one or more instruction sets for operating the first device . 5 . The system of claim 4 , wherein the causing the first device or the second device to perform the one or more operations defined by the first one or more instruction sets for operating the first device includes executing the first one or more instruction sets for operating the first device . 6 . The system of claim 4 , wherein the determining the first one or more instruction sets for operating the first device based on the at least partial match between the new one or more digital pictures and the first one or more digital pictures includes determining at least partial match between one or more portions of the new one or more digital pictures that represent one or more objects and one or more portions of the first one or more digital pictures that represent one or more objects . 7 . The system of claim 4 , wherein the determining the first one or more instruction sets for operating the first device based on the at least partial match between the new one or more digital pictures and the first one or more digital pictures includes determining at least partial match between one or more objects detected in the new one or more digital pictures and one or more objects detected in the first one or more digital pictures . 8 . The system of claim 4 , wherein the first one or more instruction sets for operating the first device include one or more information [FEATURE ID: 5]

about one or more states [FEATURE ID: 3]

of : the first device , or a portion of the first device . 9 . The system of claim 4 , wherein the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device are included in a knowledgebase , and wherein the knowledgebase further includes a second one or more digital pictures correlated with a second one or more instruction sets for operating the first device , and wherein at least a portion of the first one or more digital pictures or at least a portion of the first one or more instruction sets for operating the first device is learned in a first learning process that includes operating the first device at least partially by a first user [FEATURE ID: 1]

, and wherein at least a portion of the second one or more digital pictures or at least a portion of the second one or more instruction sets for operating the first device is learned in a second learning process that includes operating the first device at least partially by the first user . 10 . The system of claim 4 , wherein the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device are included in a knowledgebase , and wherein the knowledgebase further includes a second one or more digital pictures correlated with a second one or more instruction sets for operating the first device , and wherein at least a portion of the first one or more digital pictures or at least a portion of the first one or more instruction sets for operating the first device is learned in a first learning process that includes operating the first device at least partially by a first user , and wherein at least a portion of the second one or more digital pictures or at least a portion of the second one or more instruction sets for operating the first device is learned in a second learning process that includes operating the first device at least partially by a second user [FEATURE ID: 1]

1 . A system [FEATURE ID: 1]

implementing [TRANSITIVE ID: 8]

security within a customer network [FEATURE ID: 1]

, comprising [TRANSITIVE ID: 2]

: a virtual honeypot [FEATURE ID: 1]

; a threat intelligence database [FEATURE ID: 1]

including [TRANSITIVE ID: 2]

a lookup table [FEATURE ID: 1]

storing [TRANSITIVE ID: 2]

historical attack information associated with unsophisticated network attackers [FEATURE ID: 4]

, the historical attack information including Internet Protocol ( IP ) address information ; and a host device [FEATURE ID: 1]

in communication with the customer network , the virtual honeypot and the threat intelligence database , the host device : receiving [TRANSITIVE ID: 6]

traffic [FEATURE ID: 5]

from an attacker IP address [FEATURE ID: 11]

via the Internet ; determining [TRANSITIVE ID: 7]

whether the attacker IP address corresponds to an IP address [FEATURE ID: 11]

stored within the threat intelligence database ; ceasing connection with the attacker IP address when the attacker IP address is stored within the threat intelligence database ; and routing data between the attacker IP address and the virtual honeypot when the attack [FEATURE ID: 12]

IP address is not stored within the threat intelligence database . 2 . The system of claim [FEATURE ID: 9]

1 , wherein the host device further presents the attacker IP address with data indicating a compromised network [FEATURE ID: 10]

when the virtual honeypot is compromised . 3 . The system of claim 1 , wherein the virtual honeypot is a secured virtual environment [FEATURE ID: 1]

which mirrors the customer network configuration [FEATURE ID: 11]

, and is located outside of the customer network . 4 . The system of claim 1 , wherein the virtual honeypot is a low - interactive honeypot appearing vulnerable to certain types of attacks [FEATURE ID: 13]

without allowing full interaction with the virtual honeypot . 5 . The system of claim 1 , wherein the virtual honeypot is an interactive honeypot allowing an attacker full control and access to the virtual honeypot , giving an appearance of being vulnerable to certain types of attacks . 6 . The system of claim 5 , said interactive honeypot allowing collection of data on tactics [FEATURE ID: 3]

or techniques [FEATURE ID: 3]

used by the attacker . 7 . The system of claim 1 , wherein during an attack by an attacker , the attacker is given visibility into a virtualized organization network [FEATURE ID: 1]







Targeted Patent:

Patent: US11055583B1
Filed: 2017-11-26
Issued: 2021-07-06
Patent Holder: (Original Assignee) Individual     (Current Assignee) AUTONOMOUS DEVICES LLC
Inventor(s): Jasmin Cosic

Title: Machine learning for computing enabled systems and/or devices

 
Cross Reference / Shared Meaning between the Lines		 Charted Against:

Patent: US9818239B2
Filed: 2015-08-20
Issued: 2017-11-14
Patent Holder: (Original Assignee) Zendrive Inc     (Current Assignee) Zendrive Inc
Inventor(s): Jayanta Pal, Bipul Islam, Romit Roy Choudhury, Pankaj Risbood, Jonathan Matus, Vishal Verma

Title: Method for smartphone-based accident detection
[FEATURE ID: 1] system, second device, second usercontroller, processor, component, user, vehicle, computer, device[FEATURE ID: 1] mobile computing device, location sensor, motion sensor, camera
[TRANSITIVE ID: 2] comprising, storingincluding, includes, having, implementing, of, by, comprise[TRANSITIVE ID: 2] comprising
[TRANSITIVE ID: 3] receiving, generating, operatingdetermining, identifying, monitoring, analyzing, processing, acquiring, capturing[TRANSITIVE ID: 3] detecting, receiving, extracting, comparing, retrieving
[FEATURE ID: 4] portion, copysubset, part, section, vicinity, majority[FEATURE ID: 4] time window
[FEATURE ID: 5] claimclair, feature, aspect, figure, item, paragraph, embodiment[FEATURE ID: 5] claim
[FEATURE ID: 6] picture, new, third devicesecond, first, data, third, motion, data set, sensor data[FEATURE ID: 6] first motion dataset, location dataset, second location dataset, second motion dataset, camera dataset
[FEATURE ID: 7] apparatus, first userdevice, system, computer, process, machine, mechanism, server[FEATURE ID: 7] method
[FEATURE ID: 8] applicationinterface, algorithm, alert, app, accident, instruction, input[FEATURE ID: 8] accident detection model, accident response action, accident confidence metric, emergency service
[FEATURE ID: 9] responseorder, answer, further response, accordance, correlation, reply, due[FEATURE ID: 9] response
[FEATURE ID: 10] operations, statesconfigurations, users, actions, tasks, behaviors, functions, items[FEATURE ID: 10] accident response actions
[FEATURE ID: 11] informationinstructions, messages, details, signals[FEATURE ID: 11] GPS coordinates
1 . A system [FEATURE ID: 1]

comprising [TRANSITIVE ID: 2]

: one or more processors ; and one or more non-transitory machine readable media storing [TRANSITIVE ID: 2]

machine readable code that , when executed by the one or more processors , causes the one or more processors to perform at least : receiving [TRANSITIVE ID: 3]

or generating [TRANSITIVE ID: 3]

a first one or more digital pictures , wherein the first one or more digital pictures depict at least a portion [FEATURE ID: 4]

of a first device ' s surrounding ; receiving or generating a first one or more instruction sets for operating [TRANSITIVE ID: 3]

the first device ; and learning the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device . 2 . The system of claim [FEATURE ID: 5]

1 , wherein the receiving the first one or more digital pictures includes receiving the first one or more digital pictures from a picture [FEATURE ID: 6]

capturing apparatus [FEATURE ID: 7]

, and wherein the receiving the first one or more instruction sets for operating the first device includes receiving the first one or more instruction sets for operating the first device from : an application [FEATURE ID: 8]

for operating the first device , a system for operating the first device , one or more microcontrollers , another one or more processors , or one or more actuators , and wherein the learning the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device includes storing the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device into or onto at least one of : at least one non-transitory machine readable medium of the one or more non-transitory machine readable media , another one or more non-transitory machine readable media , one or more volatile memories , one or more non-volatile memories , one or more storage devices , or one or more storage systems . 3 . The system of claim 1 , wherein the learning the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device includes determining that the first one or more instruction sets for operating the first device temporally correspond to the first one or more digital pictures . 4 . The system of claim 1 , wherein the machine readable code , when executed by the one or more processors , causes the one or more processors to further perform at least : receiving or generating a new [FEATURE ID: 6]

one or more digital pictures ; determining the first one or more instruction sets for operating the first device based on at least partial match between the new one or more digital pictures and the first one or more digital pictures ; and at least in response [FEATURE ID: 9]

to the determining , causing the first device or a second device [FEATURE ID: 1]

to perform one or more operations [FEATURE ID: 10]

defined by the first one or more instruction sets for operating the first device . 5 . The system of claim 4 , wherein the causing the first device or the second device to perform the one or more operations defined by the first one or more instruction sets for operating the first device includes executing the first one or more instruction sets for operating the first device . 6 . The system of claim 4 , wherein the determining the first one or more instruction sets for operating the first device based on the at least partial match between the new one or more digital pictures and the first one or more digital pictures includes determining at least partial match between one or more portions of the new one or more digital pictures that represent one or more objects and one or more portions of the first one or more digital pictures that represent one or more objects . 7 . The system of claim 4 , wherein the determining the first one or more instruction sets for operating the first device based on the at least partial match between the new one or more digital pictures and the first one or more digital pictures includes determining at least partial match between one or more objects detected in the new one or more digital pictures and one or more objects detected in the first one or more digital pictures . 8 . The system of claim 4 , wherein the first one or more instruction sets for operating the first device include one or more information [FEATURE ID: 11]

about one or more states [FEATURE ID: 10]

of : the first device , or a portion of the first device . 9 . The system of claim 4 , wherein the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device are included in a knowledgebase , and wherein the knowledgebase further includes a second one or more digital pictures correlated with a second one or more instruction sets for operating the first device , and wherein at least a portion of the first one or more digital pictures or at least a portion of the first one or more instruction sets for operating the first device is learned in a first learning process that includes operating the first device at least partially by a first user [FEATURE ID: 7]

, and wherein at least a portion of the second one or more digital pictures or at least a portion of the second one or more instruction sets for operating the first device is learned in a second learning process that includes operating the first device at least partially by the first user . 10 . The system of claim 4 , wherein the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device are included in a knowledgebase , and wherein the knowledgebase further includes a second one or more digital pictures correlated with a second one or more instruction sets for operating the first device , and wherein at least a portion of the first one or more digital pictures or at least a portion of the first one or more instruction sets for operating the first device is learned in a first learning process that includes operating the first device at least partially by a first user , and wherein at least a portion of the second one or more digital pictures or at least a portion of the second one or more instruction sets for operating the first device is learned in a second learning process that includes operating the first device at least partially by a second user [FEATURE ID: 1]

. 11 . The system of claim 4 , wherein the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device are included in a knowledgebase , and wherein the knowledgebase further includes a second one or more digital pictures correlated with a second one or more instruction sets for operating a third device [FEATURE ID: 6]

, and wherein at least a portion of the first one or more digital pictures or at least a portion of the first one or more instruction sets for operating the first device is learned in a first learning process that includes operating the first device at least partially by a first user , and wherein at least a portion of the second one or more digital pictures or at least a portion of the second one or more instruction sets for operating the third device is learned in a second learning process that includes operating the third device at least partially by : the first user , or a second user . 12 . The system of claim 4 , wherein the new one or more digital pictures depict at least a portion of the first device ' s surrounding , and wherein the first one or more instruction sets for operating the first device are applied to the first device , and wherein the first device is caused to perform the one or more operations defined by the first one or more instruction sets for operating the first device . 13 . The system of claim 4 , wherein the new one or more digital pictures depict at least a portion of the second device ' s surrounding , and wherein the first one or more instruction sets for operating the first device are applied to the second device , and wherein the second device is caused to perform the one or more operations defined by the first one or more instruction sets for operating the first device . 14 . The system of claim 4 , wherein the new one or more digital pictures depict at least a portion of the second device ' s surrounding , and wherein the first one or more instruction sets for operating the first device or a copy [FEATURE ID: 4]

1 . A method [FEATURE ID: 7]

for detecting [TRANSITIVE ID: 3]

a vehicular accident event with a mobile computing device [FEATURE ID: 1]

located within a vehicle , the method comprising [TRANSITIVE ID: 2]

: receiving [TRANSITIVE ID: 3]

a first location dataset collected at a location sensor [FEATURE ID: 1]

of the mobile computing device during a first time period of movement of the vehicle ; receiving a first motion dataset [FEATURE ID: 6]

collected at a motion sensor [FEATURE ID: 1]

of the mobile computing device during the first time period ; extracting [TRANSITIVE ID: 3]

a vehicle motion characteristic from at least one of the location dataset [FEATURE ID: 6]

and the motion dataset , wherein the vehicle motion characteristic describes the movement of the vehicle within a time window [FEATURE ID: 4]

of the first time period ; comparing [TRANSITIVE ID: 3]

the vehicle motion characteristic to a motion characteristic threshold ; in response [FEATURE ID: 9]

to the vehicle motion characteristic exceeding the motion characteristic threshold : retrieving [TRANSITIVE ID: 3]

an accident detection model [FEATURE ID: 8]

, receiving a second location dataset [FEATURE ID: 6]

collected at the location sensor of the mobile computing device during a second time period of the movement of the vehicle , wherein the second time period is after the first time period , and receiving a second motion dataset [FEATURE ID: 6]

collected at the motion sensor of the mobile computing device during the second time period ; receiving a traffic dataset describing traffic conditions proximal a vehicle location extracted from the second location dataset , wherein the traffic conditions comprise at least one of : a traffic level , a traffic law , and accident data ; detecting the vehicular accident event with the accident detection model , the traffic dataset , and at least one of the second location dataset and the second motion dataset ; in response to detecting the vehicular accident event , automatically initiating an accident response action [FEATURE ID: 8]

at the mobile computing device . 2 . The method of claim [FEATURE ID: 5]

1 , further comprising : calculating an accident confidence metric [FEATURE ID: 8]

indicating a degree of confidence in occurrence of the vehicular accident event , based on at least one of the second location dataset and the second motion dataset ; selecting a personalized accident response action from a set of accident response actions [FEATURE ID: 10]

, based on the accident confidence metric , and wherein the personalized accident response action is the accident response action . 3 . The method of claim 1 , wherein automatically initiating an accident response action comprises : generating an audio sample comprising GPS coordinates [FEATURE ID: 11]

associated with the vehicular accident event , wherein the GPS coordinates are derived from the second location dataset ; and transmitting the audio sample to an emergency service [FEATURE ID: 8]

. 4 . The method of claim 3 , further comprising : receiving a camera dataset [FEATURE ID: 6]

captured at a camera [FEATURE ID: 1]







Targeted Patent:

Patent: US11055583B1
Filed: 2017-11-26
Issued: 2021-07-06
Patent Holder: (Original Assignee) Individual     (Current Assignee) AUTONOMOUS DEVICES LLC
Inventor(s): Jasmin Cosic

Title: Machine learning for computing enabled systems and/or devices

 
Cross Reference / Shared Meaning between the Lines		 Charted Against:

Patent: US9804588B2
Filed: 2014-03-14
Issued: 2017-10-31
Patent Holder: (Original Assignee) Fisher Rosemount Systems Inc     (Current Assignee) Fisher Rosemount Systems Inc
Inventor(s): Terrence L. Blevins, Wilhelm K. Wojsznis, Mark J. Nixon, Paul Richard Muston

Title: Determining associations and alignments of process elements and measurements in a process
[FEATURE ID: 1] system, first device, second device, first user, second usercomputer, machine, controller, processor, server, device, tool[FEATURE ID: 1] method, process plant, data analysis, user, user interface application, application
[TRANSITIVE ID: 2] comprising, storingincluding, includes, having, implementing, of, by, comprise[TRANSITIVE ID: 2] comprising
[FEATURE ID: 3] processors, non-transitory machine readable media, microcontrollers, actuators, volatile memories, non-volatile memories, storage devices, storage systems, objectsdevices, controllers, interfaces, systems, sensors, components, servers[FEATURE ID: 3] process elements, computing devices
[TRANSITIVE ID: 4] executedconfigured, operated, utilized, applied, performed, run, implemented[TRANSITIVE ID: 4] used
[TRANSITIVE ID: 5] performconduct, process, manage, run, implement, execute, provide[TRANSITIVE ID: 5] control
[TRANSITIVE ID: 6] receivingprocessing, capturing, displaying, presenting, communicating, sending, reading[TRANSITIVE ID: 6] receiving, providing
[TRANSITIVE ID: 7] generating, operatingidentifying, indicating, displaying, modeling, creating, processing, forming[TRANSITIVE ID: 7] determining, defining
[FEATURE ID: 8] portionrepresentation, model, state, status, respective, value, first[FEATURE ID: 8] portion, behavior, respective identifier
[FEATURE ID: 9] instruction sets, informationdata, parameters, actions, settings, observations, instructions, characteristics[FEATURE ID: 9] behaviors, diagrams, indications, inputs, input
[FEATURE ID: 10] claimfeature, aspect, figure, item, paragraph, embodiment, clause[FEATURE ID: 10] claim
[FEATURE ID: 11] apparatus, applicationdevice, process, entity, component, system, event, source[FEATURE ID: 11] upstream process element, recipient application, process element, process relative, other process element
[FEATURE ID: 12] non-transitory machine readable mediumportion, member, first, set, second, component[FEATURE ID: 12] subset
[FEATURE ID: 13] portionsareas, patterns, characteristics, locations[FEATURE ID: 13] sources
[FEATURE ID: 14] statesbehaviors, properties, attributes, aspects[FEATURE ID: 14] impacts
[FEATURE ID: 15] first learning processstep, first, procedure, method, system, feature, first process[FEATURE ID: 15] process, target process element
[FEATURE ID: 16] second learning processsequence, process, step, procedure[FEATURE ID: 16] process element alignment map
1 . A system [FEATURE ID: 1]

comprising [TRANSITIVE ID: 2]

: one or more processors [FEATURE ID: 3]

; and one or more non-transitory machine readable media [FEATURE ID: 3]

storing [TRANSITIVE ID: 2]

machine readable code that , when executed [TRANSITIVE ID: 4]

by the one or more processors , causes the one or more processors to perform [TRANSITIVE ID: 5]

at least : receiving [TRANSITIVE ID: 6]

or generating [TRANSITIVE ID: 7]

a first one or more digital pictures , wherein the first one or more digital pictures depict at least a portion [FEATURE ID: 8]

of a first device ' s surrounding ; receiving or generating a first one or more instruction sets [FEATURE ID: 9]

for operating [TRANSITIVE ID: 7]

the first device [FEATURE ID: 1]

; and learning the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device . 2 . The system of claim [FEATURE ID: 10]

1 , wherein the receiving the first one or more digital pictures includes receiving the first one or more digital pictures from a picture capturing apparatus [FEATURE ID: 11]

, and wherein the receiving the first one or more instruction sets for operating the first device includes receiving the first one or more instruction sets for operating the first device from : an application [FEATURE ID: 11]

for operating the first device , a system for operating the first device , one or more microcontrollers [FEATURE ID: 3]

, another one or more processors , or one or more actuators [FEATURE ID: 3]

, and wherein the learning the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device includes storing the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device into or onto at least one of : at least one non-transitory machine readable medium [FEATURE ID: 12]

of the one or more non-transitory machine readable media , another one or more non-transitory machine readable media , one or more volatile memories [FEATURE ID: 3]

, one or more non-volatile memories [FEATURE ID: 3]

, one or more storage devices [FEATURE ID: 3]

, or one or more storage systems [FEATURE ID: 3]

. 3 . The system of claim 1 , wherein the learning the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device includes determining that the first one or more instruction sets for operating the first device temporally correspond to the first one or more digital pictures . 4 . The system of claim 1 , wherein the machine readable code , when executed by the one or more processors , causes the one or more processors to further perform at least : receiving or generating a new one or more digital pictures ; determining the first one or more instruction sets for operating the first device based on at least partial match between the new one or more digital pictures and the first one or more digital pictures ; and at least in response to the determining , causing the first device or a second device [FEATURE ID: 1]

to perform one or more operations defined by the first one or more instruction sets for operating the first device . 5 . The system of claim 4 , wherein the causing the first device or the second device to perform the one or more operations defined by the first one or more instruction sets for operating the first device includes executing the first one or more instruction sets for operating the first device . 6 . The system of claim 4 , wherein the determining the first one or more instruction sets for operating the first device based on the at least partial match between the new one or more digital pictures and the first one or more digital pictures includes determining at least partial match between one or more portions [FEATURE ID: 13]

of the new one or more digital pictures that represent one or more objects [FEATURE ID: 3]

and one or more portions of the first one or more digital pictures that represent one or more objects . 7 . The system of claim 4 , wherein the determining the first one or more instruction sets for operating the first device based on the at least partial match between the new one or more digital pictures and the first one or more digital pictures includes determining at least partial match between one or more objects detected in the new one or more digital pictures and one or more objects detected in the first one or more digital pictures . 8 . The system of claim 4 , 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: 14]

of : the first device , or a portion of the first device . 9 . The system of claim 4 , wherein the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device are included in a knowledgebase , and wherein the knowledgebase further includes a second one or more digital pictures correlated with a second one or more instruction sets for operating the first device , and wherein at least a portion of the first one or more digital pictures or at least a portion of the first one or more instruction sets for operating the first device is learned in a first learning process [FEATURE ID: 15]

that includes operating the first device at least partially by a first user [FEATURE ID: 1]

, and wherein at least a portion of the second one or more digital pictures or at least a portion of the second one or more instruction sets for operating the first device is learned in a second learning process [FEATURE ID: 16]

that includes operating the first device at least partially by the first user . 10 . The system of claim 4 , wherein the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device are included in a knowledgebase , and wherein the knowledgebase further includes a second one or more digital pictures correlated with a second one or more instruction sets for operating the first device , and wherein at least a portion of the first one or more digital pictures or at least a portion of the first one or more instruction sets for operating the first device is learned in a first learning process that includes operating the first device at least partially by a first user , and wherein at least a portion of the second one or more digital pictures or at least a portion of the second one or more instruction sets for operating the first device is learned in a second learning process that includes operating the first device at least partially by a second user [FEATURE ID: 1]

1 . A method [FEATURE ID: 1]

for determining [TRANSITIVE ID: 7]

sources [FEATURE ID: 13]

of variations of behaviors [FEATURE ID: 9]

of process elements [FEATURE ID: 3]

used [TRANSITIVE ID: 4]

in a process plant [FEATURE ID: 1]

to control [TRANSITIVE ID: 5]

a process [FEATURE ID: 15]

, the method comprising [TRANSITIVE ID: 2]

: receiving [TRANSITIVE ID: 6]

, at the one or more computing devices [FEATURE ID: 3]

, an indication of a target process element [FEATURE ID: 15]

included in the plurality of process elements ; defining [TRANSITIVE ID: 7]

, by one or more computing devices and based on a plurality of diagrams [FEATURE ID: 9]

of the process or of the process plant , at least a portion [FEATURE ID: 8]

of a process element alignment map [FEATURE ID: 16]

corresponding to a plurality of process elements used in the process plant to control the process ; determining , by the one or more computing devices and based on the at least the portion of the process element alignment map , an upstream set of process elements corresponding to the target process element ; providing [TRANSITIVE ID: 6]

, by the one or more computing devices , indications [FEATURE ID: 9]

of the upstream set of process elements to a data analysis [FEATURE ID: 1]

to determine a respective strength of an impact of each upstream process element [FEATURE ID: 11]

on a behavior [FEATURE ID: 8]

of the target process element , wherein a set of inputs [FEATURE ID: 9]

to the data analysis includes the indications of the upstream set of process elements and excludes any user [FEATURE ID: 1]

- generated input [FEATURE ID: 9]

; determining , by the one or more computing devices and based on the respective strengths of impacts [FEATURE ID: 14]

of the upstream set of process elements , at least a subset [FEATURE ID: 12]

of the upstream set of process elements to be one or more sources of a variation in the behavior of the target process element ; and causing , by the one or more computing devices , an indication of the one or more sources of the variation in the behavior of the target process element to be provided to a recipient application [FEATURE ID: 11]

, the recipient application being a user interface application [FEATURE ID: 1]

or another application [FEATURE ID: 1]

. 2 . The method of claim [FEATURE ID: 10]

1 , wherein : defining the at least the portion of the process element alignment map of the process comprises defining at least a portion of a process element alignment map that includes , for each process element [FEATURE ID: 11]

included in the plurality of process elements , a respective identifier [FEATURE ID: 8]

of the each process element and an indication of a respective order of an occurrence of a respective event at the each process element to control the process relative [FEATURE ID: 11]

to an occurrence of a respective event at at least one other process element [FEATURE ID: 11]







Targeted Patent:

Patent: US11055583B1
Filed: 2017-11-26
Issued: 2021-07-06
Patent Holder: (Original Assignee) Individual     (Current Assignee) AUTONOMOUS DEVICES LLC
Inventor(s): Jasmin Cosic

Title: Machine learning for computing enabled systems and/or devices

 
Cross Reference / Shared Meaning between the Lines		 Charted Against:

Patent: US9800646B1
Filed: 2014-05-13
Issued: 2017-10-24
Patent Holder: (Original Assignee) Senseware Inc     (Current Assignee) Senseware Inc
Inventor(s): Julien G. Stamatakis, Serene Al-Momen

Title: Modification of a sensor data management system to enable sensors as a service
[FEATURE ID: 1] system, first userdevice, network, server, vehicle, controller, processor, router[FEATURE ID: 1] sensor data control system, wireless node, wireless sensor network, Modbus device
[TRANSITIVE ID: 2] comprisingincluding, includes, having, with, containing, involving, of[TRANSITIVE ID: 2] comprising
[FEATURE ID: 3] processors, non-transitory machine readable media, microcontrollers, actuators, volatile memories, non-volatile memories, storage devices, storage systemscontrollers, devices, interfaces, servers, registers, components, cameras[FEATURE ID: 3] sensors
[TRANSITIVE ID: 4] storingrepresenting, defining, including, providing[TRANSITIVE ID: 4] requesting
[FEATURE ID: 5] machine readable code, informationinstructions, commands, data, messages, characteristics, conditions, parameters[FEATURE ID: 5] Modbus configuration information, periodic Modbus interface command requests, configuration data values reflective, configuration data values
[TRANSITIVE ID: 6] executedoperable, utilized, configured, used[TRANSITIVE ID: 6] designed
[TRANSITIVE ID: 7] causesallow, instruct, command, adapt, cause, control[TRANSITIVE ID: 7] configure
[TRANSITIVE ID: 8] performmake, provide, initiate, execute, conduct, enable, produce[TRANSITIVE ID: 8] transmit, effect
[TRANSITIVE ID: 9] receiving, generatingsending, displaying, providing, requesting, communicating, collecting, capturing[TRANSITIVE ID: 9] transmitting
[TRANSITIVE ID: 10] depictinclude, provide, contain, capture[TRANSITIVE ID: 10] retrieve
[FEATURE ID: 11] portion, first device 'user, space, condition, first, function, person, device[FEATURE ID: 11] resource
[FEATURE ID: 12] instruction setsinformation, parameters, signals, events, operations, samples, reports[FEATURE ID: 12] sensor data, measurements, Modbus interface command requests
[FEATURE ID: 13] claimclair, paragraph, statement, preceding claim, embodiment, clause, item[FEATURE ID: 13] claim
[FEATURE ID: 14] apparatus, applicationdevice, system, entity, operation, element, instrument, interface[FEATURE ID: 14] method, location, sensor, actuator unit
[FEATURE ID: 15] new, third devicesecond, fourth, third, first[FEATURE ID: 15] third hypertext transfer protocol message
[FEATURE ID: 16] responseorder, proximity, correlation, addition, reply, due, parallel[FEATURE ID: 16] response
[FEATURE ID: 17] second devicesecond, first, network, user[FEATURE ID: 17] monitored
[FEATURE ID: 18] statesstate, status, characteristic, performance[FEATURE ID: 18] demand
1 . A system [FEATURE ID: 1]

comprising [TRANSITIVE ID: 2]

: one or more processors [FEATURE ID: 3]

; and one or more non-transitory machine readable media [FEATURE ID: 3]

storing [TRANSITIVE ID: 4]

machine readable code [FEATURE ID: 5]

that , when executed [TRANSITIVE ID: 6]

by the one or more processors , causes [TRANSITIVE ID: 7]

the one or more processors to perform [TRANSITIVE ID: 8]

at least : receiving [TRANSITIVE ID: 9]

or generating [TRANSITIVE ID: 9]

a first one or more digital pictures , wherein the first one or more digital pictures depict [TRANSITIVE ID: 10]

at least a portion [FEATURE ID: 11]

of a first device ' [FEATURE ID: 11]

s surrounding ; receiving or generating a first one or more instruction sets [FEATURE ID: 12]

for operating the first device ; and learning the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device . 2 . The system of claim [FEATURE ID: 13]

1 , wherein the receiving the first one or more digital pictures includes receiving the first one or more digital pictures from a picture capturing apparatus [FEATURE ID: 14]

, and wherein the receiving the first one or more instruction sets for operating the first device includes receiving the first one or more instruction sets for operating the first device from : an application [FEATURE ID: 14]

for operating the first device , a system for operating the first device , one or more microcontrollers [FEATURE ID: 3]

, another one or more processors , or one or more actuators [FEATURE ID: 3]

, and wherein the learning the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device includes storing the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device into or onto at least one of : at least one non-transitory machine readable medium of the one or more non-transitory machine readable media , another one or more non-transitory machine readable media , one or more volatile memories [FEATURE ID: 3]

, one or more non-volatile memories [FEATURE ID: 3]

, one or more storage devices [FEATURE ID: 3]

, or one or more storage systems [FEATURE ID: 3]

. 3 . The system of claim 1 , wherein the learning the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device includes determining that the first one or more instruction sets for operating the first device temporally correspond to the first one or more digital pictures . 4 . The system of claim 1 , wherein the machine readable code , when executed by the one or more processors , causes the one or more processors to further perform at least : receiving or generating a new [FEATURE ID: 15]

one or more digital pictures ; determining the first one or more instruction sets for operating the first device based on at least partial match between the new one or more digital pictures and the first one or more digital pictures ; and at least in response [FEATURE ID: 16]

to the determining , causing the first device or a second device [FEATURE ID: 17]

to perform one or more operations defined by the first one or more instruction sets for operating the first device . 5 . The system of claim 4 , wherein the causing the first device or the second device to perform the one or more operations defined by the first one or more instruction sets for operating the first device includes executing the first one or more instruction sets for operating the first device . 6 . The system of claim 4 , wherein the determining the first one or more instruction sets for operating the first device based on the at least partial match between the new one or more digital pictures and the first one or more digital pictures includes determining at least partial match between one or more portions of the new one or more digital pictures that represent one or more objects and one or more portions of the first one or more digital pictures that represent one or more objects . 7 . The system of claim 4 , wherein the determining the first one or more instruction sets for operating the first device based on the at least partial match between the new one or more digital pictures and the first one or more digital pictures includes determining at least partial match between one or more objects detected in the new one or more digital pictures and one or more objects detected in the first one or more digital pictures . 8 . The system of claim 4 , wherein the first one or more instruction sets for operating the first device include one or more information [FEATURE ID: 5]

about one or more states [FEATURE ID: 18]

of : the first device , or a portion of the first device . 9 . The system of claim 4 , wherein the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device are included in a knowledgebase , and wherein the knowledgebase further includes a second one or more digital pictures correlated with a second one or more instruction sets for operating the first device , and wherein at least a portion of the first one or more digital pictures or at least a portion of the first one or more instruction sets for operating the first device is learned in a first learning process that includes operating the first device at least partially by a first user [FEATURE ID: 1]

, and wherein at least a portion of the second one or more digital pictures or at least a portion of the second one or more instruction sets for operating the first device is learned in a second learning process that includes operating the first device at least partially by the first user . 10 . The system of claim 4 , wherein the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device are included in a knowledgebase , and wherein the knowledgebase further includes a second one or more digital pictures correlated with a second one or more instruction sets for operating the first device , and wherein at least a portion of the first one or more digital pictures or at least a portion of the first one or more instruction sets for operating the first device is learned in a first learning process that includes operating the first device at least partially by a first user , and wherein at least a portion of the second one or more digital pictures or at least a portion of the second one or more instruction sets for operating the first device is learned in a second learning process that includes operating the first device at least partially by a second user . 11 . The system of claim 4 , wherein the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device are included in a knowledgebase , and wherein the knowledgebase further includes a second one or more digital pictures correlated with a second one or more instruction sets for operating a third device [FEATURE ID: 15]

1 . A method [FEATURE ID: 14]

, comprising [TRANSITIVE ID: 2]

: transmitting [TRANSITIVE ID: 9]

, to a sensor data control system [FEATURE ID: 1]

, a first hypertext transfer protocol message via the Internet , the first hypertext transfer protocol message requesting [TRANSITIVE ID: 4]

a change in Modbus configuration information [FEATURE ID: 5]

for a wireless node [FEATURE ID: 1]

in a wireless sensor network [FEATURE ID: 1]

at a monitored [TRANSITIVE ID: 17]

location [FEATURE ID: 14]

, the Modbus configuration information used to configure [TRANSITIVE ID: 7]

the wireless node to transmit [TRANSITIVE ID: 8]

periodic Modbus interface command requests [FEATURE ID: 5]

to a Modbus device [FEATURE ID: 1]

connected to the wireless node via a wired interface , each of the periodic Modbus interface command requests designed [TRANSITIVE ID: 6]

to retrieve [TRANSITIVE ID: 10]

sensor data [FEATURE ID: 12]

based on measurements [FEATURE ID: 12]

by one or more of a plurality of sensors [FEATURE ID: 3]

supported by the Modbus device , the first hypertext transfer protocol message including a wireless node identifier , and one or more configuration data values reflective [FEATURE ID: 5]

of the change ; transmitting , to the sensor data control system , a second hypertext transfer protocol message via the Internet , the second hypertext transfer protocol message requesting a series of sensor data based on measurements by a first of the plurality of sensors , the series of sensor data retrieved from the Modbus device in response [FEATURE ID: 16]

to a transmission of a series of Modbus interface command requests [FEATURE ID: 12]

by the wireless node to the Modbus device , the second hypertext transfer protocol message including the wireless node identifier ; and receiving , from the sensor data control system in response to the second hypertext transfer protocol message , the series of sensor data based on the measurements by the first of the plurality of sensors . 2 . The method of claim [FEATURE ID: 13]

1 , further comprising transmitting , to the sensor data control system , a third hypertext transfer protocol message [FEATURE ID: 15]

via the Internet , the third hypertext transfer protocol message requesting an activation of a sensor [FEATURE ID: 14]

in the wireless node from a deactivated state . 3 . The method of claim 1 , further comprising transmitting , to the sensor data control system , a third hypertext transfer protocol message via the Internet , the third hypertext transfer protocol message requesting a change in configuration in a measurement resolution of a sensor . 4 . The method of claim 1 , further comprising transmitting , to the sensor data control system , a third hypertext transfer protocol message via the Internet , the third hypertext transfer protocol message requesting a reset of the wireless node . 5 . The method of claim 1 , wherein the one or more configuration data values [FEATURE ID: 5]

includes an identification of a device address for the Modbus interface command requests . 6 . The method of claim 1 , wherein the one or more configuration data values includes an identification of a register address for the Modbus interface command requests . 7 . The method of claim 1 , further comprising : analyzing the received series of sensor data to estimate a demand [FEATURE ID: 18]

for a resource [FEATURE ID: 11]

at the monitored location ; and transmitting , to the sensor data control system , a third hypertext transfer protocol message via the Internet , the third hypertext transfer protocol message requesting a change in configuration of an actuator unit [FEATURE ID: 14]

at the monitored location to effect [FEATURE ID: 8]







Targeted Patent:

Patent: US11055583B1
Filed: 2017-11-26
Issued: 2021-07-06
Patent Holder: (Original Assignee) Individual     (Current Assignee) AUTONOMOUS DEVICES LLC
Inventor(s): Jasmin Cosic

Title: Machine learning for computing enabled systems and/or devices

 
Cross Reference / Shared Meaning between the Lines		 Charted Against:

Patent: US9797395B2
Filed: 2015-09-17
Issued: 2017-10-24
Patent Holder: (Original Assignee) Schlumberger Technology Corp     (Current Assignee) Schlumberger Technology Corp
Inventor(s): Carlos Urdaneta, Hao Lam, Rajesh Luharuka

Title: Apparatus and methods for identifying defective pumps
[FEATURE ID: 1] system, application, first userdevice, controller, method, subsystem, assembly, machine, pump[FEATURE ID: 1] apparatus, monitoring system operable, monitoring system
[TRANSITIVE ID: 2] comprising, storingincluding, includes, having, with, comprise, implementing, of[TRANSITIVE ID: 2] comprising, comprises
[FEATURE ID: 3] processors, microcontrollers, actuators, storage devices, storage systemsdevices, servers, controllers, registers, sensors, components, motors[FEATURE ID: 3] pumps
[FEATURE ID: 4] non-transitory machine readable mediasystems, devices, units, reservoirs[FEATURE ID: 4] pump fluid outlets
[TRANSITIVE ID: 5] receiving, generating, operatingmonitoring, analyzing, processing, providing, measuring, identifying, obtaining[TRANSITIVE ID: 5] detecting, generating, determining, comparing
[FEATURE ID: 6] portion, responseposition, characteristic, correspondence, comparison, connection, proximity, condition[FEATURE ID: 6] relationship
[FEATURE ID: 7] claimclair, embodiment, statement, preceding claim, clause, of claim, paragraph[FEATURE ID: 7] claim
[FEATURE ID: 8] apparatus, first learning process, second learning processprocess, system, step, procedure, learning process, training, second[FEATURE ID: 8] method, pumping system
[FEATURE ID: 9] newspecific, particular, corresponding, different[FEATURE ID: 9] defective
[FEATURE ID: 10] partial matchcorrelation, matching, coincidence, difference, alignment, correspondence, phase[FEATURE ID: 10] greatest amplitude, continuous relationship, phase difference
[FEATURE ID: 11] second devicesystem, network, component, user[FEATURE ID: 11] common manifold
[FEATURE ID: 12] informationparameters, messages, signals, data[FEATURE ID: 12] information
[FEATURE ID: 13] leastany, the, more, most, less[FEATURE ID: 13] relative amplitude
1 . A system [FEATURE ID: 1]

comprising [TRANSITIVE ID: 2]

: one or more processors [FEATURE ID: 3]

; and one or more non-transitory machine readable media [FEATURE ID: 4]

storing [TRANSITIVE ID: 2]

machine readable code that , when executed by the one or more processors , causes the one or more processors to perform at least : receiving [TRANSITIVE ID: 5]

or generating [TRANSITIVE ID: 5]

a first one or more digital pictures , wherein the first one or more digital pictures depict at least a portion [FEATURE ID: 6]

of a first device ' s surrounding ; receiving or generating a first one or more instruction sets for operating [TRANSITIVE ID: 5]

the first device ; and learning the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device . 2 . The system of claim [FEATURE ID: 7]

1 , wherein the receiving the first one or more digital pictures includes receiving the first one or more digital pictures from a picture capturing apparatus [FEATURE ID: 8]

, and wherein the receiving the first one or more instruction sets for operating the first device includes receiving the first one or more instruction sets for operating the first device from : an application [FEATURE ID: 1]

for operating the first device , a system for operating the first device , one or more microcontrollers [FEATURE ID: 3]

, another one or more processors , or one or more actuators [FEATURE ID: 3]

, and wherein the learning the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device includes storing the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device into or onto at least one of : at least one non-transitory machine readable medium of the one or more non-transitory machine readable media , another one or more non-transitory machine readable media , one or more volatile memories , one or more non-volatile memories , one or more storage devices [FEATURE ID: 3]

, or one or more storage systems [FEATURE ID: 3]

. 3 . The system of claim 1 , wherein the learning the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device includes determining that the first one or more instruction sets for operating the first device temporally correspond to the first one or more digital pictures . 4 . The system of claim 1 , wherein the machine readable code , when executed by the one or more processors , causes the one or more processors to further perform at least : receiving or generating a new [FEATURE ID: 9]

one or more digital pictures ; determining the first one or more instruction sets for operating the first device based on at least partial match [FEATURE ID: 10]

between the new one or more digital pictures and the first one or more digital pictures ; and at least in response [FEATURE ID: 6]

to the determining , causing the first device or a second device [FEATURE ID: 11]

to perform one or more operations defined by the first one or more instruction sets for operating the first device . 5 . The system of claim 4 , wherein the causing the first device or the second device to perform the one or more operations defined by the first one or more instruction sets for operating the first device includes executing the first one or more instruction sets for operating the first device . 6 . The system of claim 4 , wherein the determining the first one or more instruction sets for operating the first device based on the at least partial match between the new one or more digital pictures and the first one or more digital pictures includes determining at least partial match between one or more portions of the new one or more digital pictures that represent one or more objects and one or more portions of the first one or more digital pictures that represent one or more objects . 7 . The system of claim 4 , wherein the determining the first one or more instruction sets for operating the first device based on the at least partial match between the new one or more digital pictures and the first one or more digital pictures includes determining at least partial match between one or more objects detected in the new one or more digital pictures and one or more objects detected in the first one or more digital pictures . 8 . The system of claim 4 , wherein the first one or more instruction sets for operating the first device include one or more information [FEATURE ID: 12]

about one or more states of : the first device , or a portion of the first device . 9 . The system of claim 4 , wherein the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device are included in a knowledgebase , and wherein the knowledgebase further includes a second one or more digital pictures correlated with a second one or more instruction sets for operating the first device , and wherein at least a portion of the first one or more digital pictures or at least a portion of the first one or more instruction sets for operating the first device is learned in a first learning process [FEATURE ID: 8]

that includes operating the first device at least [FEATURE ID: 13]

partially by a first user [FEATURE ID: 1]

, and wherein at least a portion of the second one or more digital pictures or at least a portion of the second one or more instruction sets for operating the first device is learned in a second learning process [FEATURE ID: 8]

1 . A method [FEATURE ID: 8]

, comprising [TRANSITIVE ID: 2]

: detecting [TRANSITIVE ID: 5]

pump defects in a pumping system [FEATURE ID: 8]

comprising a plurality of pumps [FEATURE ID: 3]

, wherein each of the plurality of pumps comprises [TRANSITIVE ID: 2]

a pump fluid outlet , wherein each of the pump fluid outlets [FEATURE ID: 4]

is fluidly connected to a common manifold [FEATURE ID: 11]

, and wherein detecting pump defects comprises : generating [TRANSITIVE ID: 5]

information [FEATURE ID: 12]

related to fluid pressure fluctuations at each of the pump fluid outlets ; and determining [TRANSITIVE ID: 5]

harmonic frequencies from the information related to fluid pressure fluctuations for each of the plurality of pumps , wherein the amplitude of the harmonic frequencies is indicative of a defective [FEATURE ID: 9]

one of the plurality of pumps . 2 . The method of claim [FEATURE ID: 7]

1 wherein relative amplitude [FEATURE ID: 13]

of the harmonic frequencies of the plurality of pumps is indicative of the defective one of the plurality of pumps . 3 . The method of claim 1 wherein greatest amplitude [FEATURE ID: 10]

of the harmonic frequencies of the plurality of pumps is indicative of the defective one of the plurality of pumps . 4 . The method of claim 1 wherein the amplitude of the harmonic frequencies associated with the defective one of the plurality of pumps is greater than the amplitude of the harmonic frequencies associated with another of the plurality of pumps . 5 . The method of claim 1 wherein detecting pump defects further comprises : determining amplitude of harmonic frequencies for each of the plurality of pumps ; and comparing [TRANSITIVE ID: 5]

the amplitudes of the harmonic frequencies for each of the plurality of pumps to determine the defective one of the plurality of pumps . 6 . The method of claim 5 wherein determining the amplitude of the harmonic frequencies comprises determining the amplitude of first order harmonic frequency from the information related to fluid pressure fluctuations for each of the plurality of pumps , and wherein the amplitude of the first order harmonic frequency is indicative of the defective one of the plurality of pumps . 7 . The method of claim 5 wherein at least one of the plurality of pumps comprises N fluid displacing members , wherein N is an integer equal to at least 2 , wherein determining the amplitude of the harmonic frequencies comprises determining the amplitude of N −1 order harmonic frequency from the information related to fluid pressure fluctuations for each of the plurality of pumps , and wherein the amplitude of the N −1 order harmonic frequency is indicative of the defective one of the plurality of pumps . 8 . The method of claim 1 wherein detecting pump defects further comprises : generating information related to phase of each of the plurality of pumps ; and determining a relationship [FEATURE ID: 6]

between phase of the harmonic frequency and the information related to phase for each of the plurality of pumps , wherein the relationship is indicative of the defective one of the plurality of pumps . 9 . The method of claim 8 wherein a substantially close and / or continuous relationship [FEATURE ID: 10]

between phase of the harmonic frequency and the information related to phase is indicative of the defective one of the plurality of pumps . 10 . The method of claim 8 wherein the relationship comprises phase difference [FEATURE ID: 10]

, phase relationship , and / or phase tracking . 11 . The method of claim 8 wherein a substantially changing , fluctuating , and / or random nature of the relationship is indicative of a healthy one of the plurality of pumps . 12 . The method of claim 1 wherein determining harmonic frequencies from the information related to fluid pressure fluctuations comprises converting the information related to fluid pressure fluctuations from time domain to frequency domain . 13 . An apparatus [FEATURE ID: 1]

, comprising : a monitoring system operable [FEATURE ID: 1]

for detecting pump defects in a pumping system comprising a plurality of pumps , wherein each of the plurality of pumps comprises a pump fluid outlet , wherein each of the pump fluid outlets is fluidly connected to a common manifold , and wherein the monitoring system [FEATURE ID: 1]







Targeted Patent:

Patent: US11055583B1
Filed: 2017-11-26
Issued: 2021-07-06
Patent Holder: (Original Assignee) Individual     (Current Assignee) AUTONOMOUS DEVICES LLC
Inventor(s): Jasmin Cosic

Title: Machine learning for computing enabled systems and/or devices

 
Cross Reference / Shared Meaning between the Lines		 Charted Against:

Patent: US9785719B2
Filed: 2014-07-15
Issued: 2017-10-10
Patent Holder: (Original Assignee) Adobe Systems Inc     (Current Assignee) Adobe Inc
Inventor(s): Wenhui MA, Jun Wan, Hui Li

Title: Generating synthetic data
[FEATURE ID: 1] system, first user, second userserver, controller, computer, person, machine, method, device[FEATURE ID: 1] processor
[TRANSITIVE ID: 2] comprising, storingincluding, having, of, includes, with, containing, involving[TRANSITIVE ID: 2] comprising, describing
[TRANSITIVE ID: 3] receiving, generatinganalyzing, obtaining, providing, determining, storing, computing, capturing[TRANSITIVE ID: 3] generating, identifying
[FEATURE ID: 4] portion, newposition, first, display, node, point, region, second[FEATURE ID: 4] distribution constraint, particular data point, first location, second location
[FEATURE ID: 5] instruction sets, objects, informationsignals, data, observations, parameters, features, statistics, inputs[FEATURE ID: 5] events, data points, synthetic data points, new data points
[TRANSITIVE ID: 6] operatingproviding, implementing, processing, using[TRANSITIVE ID: 6] applying
[FEATURE ID: 7] claimclair, paragraph, statement, preceding claim, embodiment, clause, item[FEATURE ID: 7] claim
[FEATURE ID: 8] apparatus, first learning processprocess, system, device, procedure, step, mechanism[FEATURE ID: 8] method
[FEATURE ID: 9] applicationoperation, algorithm, organization, architecture[FEATURE ID: 9] additive decomposition model
[FEATURE ID: 10] non-transitory machine readable medium, copyportion, subset, record, form, set, second, plurality[FEATURE ID: 10] seasonal component
[FEATURE ID: 11] second deviceuser, system, computer, network, display, database, processor[FEATURE ID: 11] time period, graphical user interface
[FEATURE ID: 12] portionsfragments, sections, features, sets, elements, parts[FEATURE ID: 12] components
[FEATURE ID: 13] statesproperties, components, features, parameters, characteristic[FEATURE ID: 13] dependent characteristics
[FEATURE ID: 14] second learning processmodel, template, method, process, parameter, context, system[FEATURE ID: 14] base dataset, average algorithm
1 . A system [FEATURE ID: 1]

comprising [TRANSITIVE ID: 2]

: one or more processors ; and one or more non-transitory machine readable media storing [TRANSITIVE ID: 2]

machine readable code that , when executed by the one or more processors , causes the one or more processors to perform at least : receiving [TRANSITIVE ID: 3]

or generating [TRANSITIVE ID: 3]

a first one or more digital pictures , wherein the first one or more digital pictures depict at least a portion [FEATURE ID: 4]

of a first device ' s surrounding ; receiving or generating a first one or more instruction sets [FEATURE ID: 5]

for operating [TRANSITIVE ID: 6]

the first device ; and learning the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device . 2 . The system of claim [FEATURE ID: 7]

1 , wherein the receiving the first one or more digital pictures includes receiving the first one or more digital pictures from a picture capturing apparatus [FEATURE ID: 8]

, and wherein the receiving the first one or more instruction sets for operating the first device includes receiving the first one or more instruction sets for operating the first device from : an application [FEATURE ID: 9]

for operating the first device , a system for operating the first device , one or more microcontrollers , another one or more processors , or one or more actuators , and wherein the learning the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device includes storing the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device into or onto at least one of : at least one non-transitory machine readable medium [FEATURE ID: 10]

of the one or more non-transitory machine readable media , another one or more non-transitory machine readable media , one or more volatile memories , one or more non-volatile memories , one or more storage devices , or one or more storage systems . 3 . The system of claim 1 , wherein the learning the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device includes determining that the first one or more instruction sets for operating the first device temporally correspond to the first one or more digital pictures . 4 . The system of claim 1 , wherein the machine readable code , when executed by the one or more processors , causes the one or more processors to further perform at least : receiving or generating a new [FEATURE ID: 4]

one or more digital pictures ; determining the first one or more instruction sets for operating the first device based on at least partial match between the new one or more digital pictures and the first one or more digital pictures ; and at least in response to the determining , causing the first device or a second device [FEATURE ID: 11]

to perform one or more operations defined by the first one or more instruction sets for operating the first device . 5 . The system of claim 4 , wherein the causing the first device or the second device to perform the one or more operations defined by the first one or more instruction sets for operating the first device includes executing the first one or more instruction sets for operating the first device . 6 . The system of claim 4 , wherein the determining the first one or more instruction sets for operating the first device based on the at least partial match between the new one or more digital pictures and the first one or more digital pictures includes determining at least partial match between one or more portions [FEATURE ID: 12]

of the new one or more digital pictures that represent one or more objects [FEATURE ID: 5]

and one or more portions of the first one or more digital pictures that represent one or more objects . 7 . The system of claim 4 , wherein the determining the first one or more instruction sets for operating the first device based on the at least partial match between the new one or more digital pictures and the first one or more digital pictures includes determining at least partial match between one or more objects detected in the new one or more digital pictures and one or more objects detected in the first one or more digital pictures . 8 . The system of claim 4 , wherein the first one or more instruction sets for operating the first device include one or more information [FEATURE ID: 5]

about one or more states [FEATURE ID: 13]

of : the first device , or a portion of the first device . 9 . The system of claim 4 , wherein the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device are included in a knowledgebase , and wherein the knowledgebase further includes a second one or more digital pictures correlated with a second one or more instruction sets for operating the first device , and wherein at least a portion of the first one or more digital pictures or at least a portion of the first one or more instruction sets for operating the first device is learned in a first learning process [FEATURE ID: 8]

that includes operating the first device at least partially by a first user [FEATURE ID: 1]

, and wherein at least a portion of the second one or more digital pictures or at least a portion of the second one or more instruction sets for operating the first device is learned in a second learning process [FEATURE ID: 14]

that includes operating the first device at least partially by the first user . 10 . The system of claim 4 , wherein the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device are included in a knowledgebase , and wherein the knowledgebase further includes a second one or more digital pictures correlated with a second one or more instruction sets for operating the first device , and wherein at least a portion of the first one or more digital pictures or at least a portion of the first one or more instruction sets for operating the first device is learned in a first learning process that includes operating the first device at least partially by a first user , and wherein at least a portion of the second one or more digital pictures or at least a portion of the second one or more instruction sets for operating the first device is learned in a second learning process that includes operating the first device at least partially by a second user [FEATURE ID: 1]

. 11 . The system of claim 4 , wherein the first one or more digital pictures correlated with the first one or more instruction sets for operating the first device are included in a knowledgebase , and wherein the knowledgebase further includes a second one or more digital pictures correlated with a second one or more instruction sets for operating a third device , and wherein at least a portion of the first one or more digital pictures or at least a portion of the first one or more instruction sets for operating the first device is learned in a first learning process that includes operating the first device at least partially by a first user , and wherein at least a portion of the second one or more digital pictures or at least a portion of the second one or more instruction sets for operating the third device is learned in a second learning process that includes operating the third device at least partially by : the first user , or a second user . 12 . The system of claim 4 , wherein the new one or more digital pictures depict at least a portion of the first device ' s surrounding , and wherein the first one or more instruction sets for operating the first device are applied to the first device , and wherein the first device is caused to perform the one or more operations defined by the first one or more instruction sets for operating the first device . 13 . The system of claim 4 , wherein the new one or more digital pictures depict at least a portion of the second device ' s surrounding , and wherein the first one or more instruction sets for operating the first device are applied to the second device , and wherein the second device is caused to perform the one or more operations defined by the first one or more instruction sets for operating the first device . 14 . The system of claim 4 , wherein the new one or more digital pictures depict at least a portion of the second device ' s surrounding , and wherein the first one or more instruction sets for operating the first device or a copy [FEATURE ID: 10]

1 . A method [FEATURE ID: 8]

of generating [TRANSITIVE ID: 3]

synthetic data , comprising [TRANSITIVE ID: 2]

: identifying [TRANSITIVE ID: 3]

, by at least one processor [FEATURE ID: 1]

, a base dataset [FEATURE ID: 14]

describing [TRANSITIVE ID: 2]

a set of events [FEATURE ID: 5]

corresponding to a time period [FEATURE ID: 11]

; decomposing , by the at least one processor , the base dataset into a base dynamic component and a trend component by applying [TRANSITIVE ID: 6]

a decomposition model to the base dataset , the base dynamic component comprising a plurality of data points [FEATURE ID: 5]

that do not correspond to any time - dependent characteristics [FEATURE ID: 13]

of the base dataset and the trend component comprising data points with a time - dependent characteristic that indicates a trend associated with the base dataset ; generating , by the at least one processor , a synthetic dynamic component by randomly resampling the data points from the base dynamic component to create a plurality of synthetic data points [FEATURE ID: 5]

that do not correspond to the time - dependent characteristics of the base dataset ; generating , by the at least one processor , a synthetic dataset comprising new data points [FEATURE ID: 5]

with the time - dependent characteristic that indicates the trend associated with the base dataset by combining the synthetic data points in the synthetic dynamic component with the data points in the trend component ; and predicting , by the at least one processor using the generated synthetic dataset , a future dataset for the base dataset , the predicted future dataset comprising a plurality of predicted data points according to the time - dependent characteristic . 2 . The method as recited in claim [FEATURE ID: 7]

1 , wherein generating the synthetic dynamic component comprises randomly resampling data points from the base dynamic component to create the plurality of synthetic data points according to a distribution constraint [FEATURE ID: 4]

on the base dynamic component . 3 . The method as recited in claim 2 , wherein generating the synthetic dynamic component further comprises : determining a normal distribution for the plurality of data points in the base dynamic component ; and randomly resampling data points from the base dynamic component according to the normal distribution of the plurality of data points in the base dynamic component to create the synthetic data points . 4 . The method as recited in claim 3 , wherein generating the synthetic dynamic component further comprises randomly resampling the data points of the base dynamic component according to a three - sigma rule for the normal distribution of the plurality of data points in the base dynamic component . 5 . The method as recited in claim 1 , wherein decomposing the base dataset further comprises decomposing the base dataset into a seasonal component [FEATURE ID: 10]

comprising data points with a seasonal characteristic indicating a seasonal effect associated with the set of events . 6 . The method as recited in claim 5 , wherein generating the synthetic dataset further comprises combining the data points in the synthetic dynamic component with the data points in the trend component and the data points in the seasonal component to create the new data points that include the trend from the trend component and seasonal information from the seasonal component . 7 . The method as recited in claim 1 , wherein decomposing the base dataset into a plurality of components [FEATURE ID: 12]

comprises decomposing the base dataset according to an additive decomposition model [FEATURE ID: 9]

. 8 . The method as recited in claim 1 , further comprising : providing , in a graphical user interface [FEATURE ID: 11]

, a graph comprising a plurality of data points in the synthetic dynamic component ; receiving , in the graphical user interface , a selection of a particular data point [FEATURE ID: 4]

from the plurality of data points ; moving the particular data point from a first location [FEATURE ID: 4]

in the graph to a second location [FEATURE ID: 4]

in the graph to introduce an anomaly into the synthetic dynamic component ; and merging the synthetic dynamic component comprising the anomaly with the trend component to introduce the anomaly into the generated synthetic dataset . 9 . The method as recited in claim 1 , wherein decomposing the base dataset comprises smoothing the base dataset using an exponential moving average algorithm [FEATURE ID: 14]