Context emotion determination system

• US 9,692,839 B2
• Filed: 03/13/2013
• Issued: 06/27/2017
• Est. Priority Date: 03/13/2013
• Status: Active Grant

• Current Assignee: ARRIS Enterprises LLC (CommScope Holding Company, Inc.)
• Original Assignee: ARRIS Enterprises LLC (CommScope Holding Company, Inc.)
• Inventors: Davis, Paul C., Ali, Mir F., Russell, Dale W., You, Di, Li, Jianguo
• Primary Examiner(s): Park, Jeong S
• Application Number:
  US13/798,367
• Publication Number:
  US 20140280529A1
• Time in Patent Office: 1,567 Days
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/165 Evaluating the state of mind, e.g. depression, anxiety
• CPC Class Codes: A61B 5/6898 Portable consumer electronic devices, e.g. music players, telephones, tablet computers
• CPC Class Codes: G06F 2203/011 Emotion or mood input determined on the basis of sensed human body parameters such as pulse, heart rate or beat, temperature of skin, facial expressions, iris, voice pitch, brain activity patterns
• CPC Class Codes: G06F 40/10 Text processing natural language analysis G06F40/20; semantic analysis G06F40/30; processing or translation of natural language G06F40/40
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34

Abstract

Systems, methods, and devices for determining contexts and determining associated emotion profiles using information received from multiple emotion sensor enabled electronic devices, are disclosed. Contexts can be defined by a description of spatial and/or temporal components. Such contexts can be arbitrarily defined using semantically meaningful and absolute descriptions of times and locations. Emotion sensor data is associated with or includes context data that describes the circumstances under which the data was determined. The emotion sensor data can include emotion sensor readings that are implicit indications of an emotion for the context. The sensor data can also include user reported data with explicit descriptors of an emotion for the context. The emotion sensor data can be filtered by context data according a selected context. The filtered sensor data can then be analyzed to determine an emotion profile for the context that can be output to one or more users or entities.

Claims

1. A method comprising:

receiving, by a computer system, emotion sensor data for a plurality of contexts from a plurality of distributed electronic devices, wherein each of the plurality of contexts corresponds to one or more of a geographical area and a time period, wherein the emotion sensor data comprises implicit emotion indicators based on information sensed by the plurality of distributed electronic devices for the plurality of contexts, wherein the emotion sensor data further comprises explicit emotion descriptors based on user reported descriptions of emotions for the plurality of contexts;

determining, by the computer system, a first context in the plurality of contexts;

determining, by the computer system, a first portion of the emotion sensor data determined to be received from a first portion of the plurality of distributed electronic devices, wherein the first portion of the emotion sensor data is based on information sensed for the first context;

generating, by the computer system, a first emotion profile for the first context based on the first portion of the emotion sensor data, the generating comprising weighting the implicit emotion indicators differently from the explicit emotion descriptors based at least on a reliability,

tracking, using an emotion trend profile, an emotional change associated with a move from one of the plurality of contexts to another of the plurality of contexts; and

predicting, using the emotion trend profile and the first emotion profile, a predicted emotional change associated with a move from one of the plurality of contexts to the first context;

wherein the first emotion profile comprises a description of a first emotion associated with the first context, and wherein the computer system comprises one or more computer processors configured to perform the steps of the method.

2. The method of claim 1, wherein the first portion of the emotion sensor data comprises a plurality of emotion sensor readings from information sensed by the first portion of the plurality of distributed electronic devices, wherein the plurality of emotion sensor readings include an emotion indicator for a plurality of people associated with the first context.

3. The method of claim 1, wherein the plurality of distributed electronic devices comprises a plurality of mobile electronic devices.

4. The method of claim 3, wherein the plurality of distributed electronic devices further comprises a plurality of stationary electronic devices configured to sense emotional data for a plurality of particular locations.

5. The method of claim 4, wherein the first portion of the plurality of distributed electronic devices comprises a portion of the plurality of mobile electronic devices and a portion of the plurality of stationary electronic devices.

6. The method of claim 5, wherein emotion sensor data determined to be received from the plurality of mobile electronic devices is weighted differently from emotion sensor data determined to be received from the plurality of stationary electronic devices in generating the first emotion profile.

7. The method of claim 1, further comprising:

determining, by the computer system, a second context in the plurality of contexts;

determining, by the computer system, a second portion of the emotion sensor data determined to be received from a second portion of the plurality of distributed electronic devices; and

generating, by the computer system, a second emotion profile for the second context based on the second portion of the emotion sensor data,

wherein the second emotion profile comprises a description of a second emotion associated with the second context.

8. The method of claim 7, wherein the first context comprises a first time period during an event, and the second context comprises a second time period during the event.

9. The method of claim 7, wherein the first context comprises a first location, and the second context comprises a second location.

10. The method of claim 7, further comprising:

determining, by the computer system, a difference between the first emotion profile and the second emotion profile, wherein in the difference between the first emotion profile and the second emotion profile describes an emotion profile trend related to the first context and the second context; and

outputting, by the computer system, the difference between the first emotion profile and the second emotion profile.

11. The method of claim 7, wherein the second portion of the plurality of distributed electronic devices is determined from the first portion of the plurality of distributed electronic devices.

12. The method of claim 11, wherein the first context comprises a first location, and the second context comprises a second location, and wherein a portion of the first location comprises the second location.

13. The method of claim 1, wherein the first context comprises event data at which the first portion of the plurality of distributed electronic devices were present to sense the information on which the emotion sensor data is based.

14. The method of claim 13, wherein the event data comprises a particular time period associated with a particular location.

15. The method of claim 1, wherein the first context comprises a dynamically determined geographical region in which the first portion of the plurality of distributed electronic devices were located to sense the information on which the emotion sensor data is based.

16. The method of claim 1, further comprising receiving, by the computer system, event calendar data, wherein the event calendar data comprises a plurality of dates and a plurality of associated locations, and wherein determining the first context comprises referencing the event calendar data.

17. The method of claim 1, further comprising outputting, by the computer system, the first emotion profile.

18. A non-transitory computer-readable storage medium containing instructions that, when executed, control one or more computer processors of an electronic device to be configured for:

receiving emotion sensor data from a plurality of distributed electronic devices for a plurality of contexts, wherein each of the plurality of contexts corresponds to one or more of a geographical area and a time period, wherein the emotion sensor data comprises implicit emotion indicators based on information sensed by the plurality of distributed electronic devices for a plurality of contexts, and wherein the emotion sensor data further comprises explicit emotion descriptors based on user reported descriptions of emotions for the plurality of contexts;

determining a first context in the plurality of contexts;

determining a first portion of the emotion sensor data determined to be received from a first portion of the plurality of distributed electronic devices, wherein the first portion of the emotion sensor data is based on information sensed for the first context;

generating a first emotion profile for the first context based on the first portion of the emotion sensor data, the generating comprising weighting the implicit emotion indicators differently from the explicit emotion descriptors based at least on a reliability,

tracking, using an emotion trend profile, an emotional change associated with a move from one of the plurality of contexts to another of the plurality of contexts; and

predicting, using the emotion trend profile and the first emotion profile, a predicted emotional change associated with a move from one of the plurality of contexts to the first context;

wherein the first emotion profile comprises a description of an emotion associated with the first context.

19. An electronic device comprising:

at least one computer processor;

an emotion sensor communicatively coupled to the at least one computer processor;

an electronic communication interface communicatively coupled to the at least one computer processor; and

a non-transitory computer-readable storage medium containing instructions, that when executed, control the at least one computer processor to be configured to;

activate the emotion sensor to determine an emotion sensor reading;

determine context data for the emotion sensor reading, wherein the context data describes circumstances in which the emotion sensor reading was determined;

generate emotion sensor data comprising implicit emotion indicators based on the context data and the emotion sensor reading;

track, using an emotion trend profile, an emotional change associated with a move from one of the plurality of contexts to another of the plurality of contexts;

predict, using the emotion trend profile and the first emotion profile, a predicted emotional change associated with a move from one of the plurality of contexts to the first context;

send the emotion sensor data to one or more remote service providers through the electronic communication interface; and

receive, from a first remote service provider in the one or more remote service providers through the electronic communication interface, summary emotion sensor data for a particular context, wherein the particular context corresponds to one or more of a geographical area and a time period, wherein the summary emotion sensor data comprises emotion sensor data for the particular context that has been received by the first remote service provider from a plurality of other electronic devices, wherein the summary emotion sensor data further comprises explicit emotion descriptors based on user reported descriptions of emotions for the plurality of contexts, wherein the implicit emotion indicators are weighted differently from the explicit emotion descriptors based at least on a determination of reliability, and wherein the summary emotion sensor data is determined to include context data that matches the particular context.