Sensor and actuator based validation of expected cohort behavior

• US 7,953,686 B2
• Filed: 03/17/2008
• Issued: 05/31/2011
• Est. Priority Date: 03/17/2008
• Status: Active Grant

• Current Assignee: Terrace Licensing LLC (IP Edge LLC)
• Original Assignee: International Business Machines Corporation
• Inventors: Kraemer, James R., Angell, Robert Lee, Friedlander, Robert R.
• Primary Examiner(s): HOLMES, MICHAEL B
• Application Number:
  US12/049,725
• Publication Number:
  US 20090234810A1
• Time in Patent Office: 1,170 Days
• Field of Search: 706/45
• US Class Current: 706/45
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

A computer implemented method, apparatus, and computer-usable program product for validating expected cohort behavior. In one embodiment, sensory data gathered by a set of multimodal sensor devices is processed to form a set of actual cohort behavior data. The sensory data comprises information associated with a cohort group. Each member of the cohort group shares at least one common attribute. The set of actual cohort behavior data is compared to a set of predicted cohort behavior models. The set of actual cohort behavior data comprises information describing actual behavior by members of the cohort group. The set of predicted cohort behavior models comprises information describing an expected behavior of members of the cohort group. A comparison result is generated. The comparison result indicates an accuracy of the set of predicted cohort behavior models.

Claims

1. A computer implemented method for validating expected cohort behavior, the computer implemented method comprising:

processing sensory data associated with a cohort group to form a set of actual cohort behavior data, wherein each member of the cohort group shares at least one common attribute;

comparing the set of actual cohort behavior data to a set of predicted cohort behavior models, wherein the set of actual cohort behavior data comprises information describing actual behavior by members of the cohort group and wherein the set of predicted cohort behavior models comprises information describing an expected behavior of members of the cohort group; and

generating a comparison result, wherein the comparison result indicates an accuracy of the set of predicted cohort behavior models.

2. The computer implemented method of claim 1 wherein the comparison result indicates an accuracy of a given predicted cohort behavior model in the set of predicted cohort behavior models.

3. The computer implemented method of claim 1 wherein the comparison result indicates a number of times a given expected behavior in a single predicted cohort data model corresponds with a given actual behavior of at least one member of the cohort group.

4. The computer implemented method of claim 1 wherein the comparison result indicates a rate of occurrence of each expected behavior in a single predicted cohort data model corresponding with an actual behavior of at least one member of the cohort group.

5. The computer implemented method of claim 1 wherein the comparison result indicates a number of times each expected behavior corresponds with the actual behavior of the members of a set of cohort groups.

6. The computer implemented method of claim 1 wherein processing the sensory data further comprises:

collecting sensory data from a plurality of sensors in a set of multimodal sensors to form aggregated sensory data;

parsing the sensory data to form events;

categorizing the events in accordance with a type of the event; and

processing the events to identify actual behaviors associated with members of cohort groups to form the set of actual cohort behavior data.

7. The computer implemented method of claim 1 further comprising:

identifying a predicted cohort behavior model associated with the cohort group;

parsing the predicted cohort behavior model to identify expected behaviors associated with the members of the cohort group; and

comparing the actual behaviors associated with members of the cohort group to the expected behaviors; and

responsive to a correlation between the actual behaviors and the expected behaviors, identifying a number of occurrences of the actual behaviors corresponding to a given expected behavior.

8. The computer implemented method of claim 1 wherein the set of multimodal sensors comprises at least one of a set of global positioning satellite receivers, a set of infrared sensors, a set of microphones, a set of motion detectors, a set of chemical sensors, a set of biometric sensors, a set of pressure sensors, a set of temperature sensors, a set of metal detectors, a set of radar detectors, a set of photosensors, a set of seismographs, and a set of anemometers.

9. The computer implemented method of claim 1 wherein the set of multimodal sensors comprises a set of digital video cameras, wherein the set of digital video cameras captures a stream of video data associated with the cohort group, and wherein the stream of video data is transmitted to a central data processing system in real time as the stream of video data is generated, and further comprising:

analyzing the stream of video data by a video analytics engine associated with the central data processing system to generate video metadata describing the members of the cohort group and objects in the stream of video data; and

identifying actual behaviors of the members of the cohort group using the video metadata.

10. The computer implemented method of claim 1 wherein the set of multimodal sensors comprises a set of radio frequency identification tag readers, wherein the set of radio frequency identification tag readers receives information from radio frequency identification tags.

11. A computer program product for validating expected cohort behavior, the computer program product comprising:

a computer readable medium;

program code stored on the computer-readable medium for processing sensory data associated with a cohort group to form a set of actual cohort behavior data, wherein each member of the cohort group shares at least one common attribute;

program code stored on the computer-readable medium for comparing the set of actual cohort behavior data to a set of predicted cohort behavior models, wherein the set of actual cohort behavior data comprises information describing actual behavior by members of the cohort group and wherein the set of predicted cohort behavior models comprises information describing an expected behavior of members of the cohort group; and

program code stored on the computer-readable medium for generating a comparison result, wherein the comparison result indicates an accuracy of the set of predicted cohort behavior models.

12. The computer program product of claim 11 wherein the comparison result indicates an accuracy of a given predicted cohort behavior model in the set of predicted cohort behavior models.

13. The computer program product of claim 11 further comprising:

program code stored on the computer-readable medium for collecting sensory data from a plurality of sensors in a set of multimodal sensors to form aggregated sensory data;

program code stored on the computer-readable medium for parsing the sensory data to form events;

program code stored on the computer-readable medium for categorizing the events in accordance with a type of the event; and

program code stored on the computer readable-medium for processing the events to identify actual behaviors associated with members of cohort groups to form the set of actual cohort behavior data.

14. The computer program product of claim 11 further comprising:

program code stored on the computer-readable medium for identifying a predicted cohort behavior model associated with the cohort group;

program code stored on the computer-readable medium for parsing the predicted cohort behavior model to identify expected behaviors associated with the members of the cohort group; and

program code stored on the computer-readable medium for comparing the actual behaviors associated with members of the cohort group to the expected behaviors; and

program code stored on the computer-readable medium for identifying a number of occurrences of the actual behaviors corresponding to a given expected behavior in response to a correlation between the actual behaviors and the expected behaviors.

15. The computer program product of claim 11 wherein the set of multimodal sensors comprises a set of digital video cameras, wherein the set of digital video cameras captures a stream of video data associated with the cohort group, and wherein the stream of video data is transmitted to a central data processing system in real time as the stream of video data is generated, and further comprising:

program code stored on the computer-readable medium for analyzing the stream of video data by a video analytics engine associated with the central data processing system to generate video metadata describing the members of the cohort group and objects in the stream of video data; and

program code stored on the computer-readable medium for identifying actual behaviors of the members of the cohort group using the video metadata.

16. The computer program product of claim 11 wherein the set of multimodal sensors comprises a set of radio frequency identification tag readers, wherein the set of radio frequency identification tag readers receives information from radio frequency identification tags.

17. An apparatus comprising:

a bus system;

a communications system coupled to the bus system;

a memory connected to the bus system, wherein the memory includes computer usable program code; and

a processing unit coupled to the bus system, wherein the processing unit executes the computer-usable program code to process sensory data associated with a cohort group to form a set of actual cohort behavior data, wherein each member of the cohort group shares at least one common attribute;

compare the set of actual cohort behavior data to a set of predicted cohort behavior models, wherein the set of actual cohort behavior data comprises information describing actual behavior by members of the cohort group and wherein the set of predicted cohort behavior models comprises information describing an expected behavior of members of the cohort group; and

generate a comparison result, wherein the comparison result indicates an accuracy of the set of predicted cohort behavior models.

18. The apparatus of claim 17 wherein the comparison result indicates an accuracy of a given predicted cohort behavior model in the set of predicted cohort behavior models.

19. The apparatus of claim 17 wherein the processor unit further executes the computer-usable program code to collect sensory data from a plurality of sensors in a set of multimodal sensors to form aggregated sensory data;

parse the sensory data to form events;

categorize the events in accordance with a type of the event; and

process the events to identify actual behaviors associated with members of cohort groups to form the set of actual cohort behavior data.

20. The apparatus of claim 17 wherein the processor unit further executes the computer-usable program code to identify a predicted cohort behavior model associated with the cohort group;

parse the predicted cohort behavior model to identify expected behaviors associated with the members of the cohort group;

compare the actual behaviors associated with members of the cohort group to the expected behaviors; and

identify a number of occurrences of the actual behaviors corresponding to a given expected behavior in response to a correlation between the actual behaviors and the expected behaviors.

21. A data processing system for validating expected cohort behavior comprising:

a sensory data processing engine, wherein the sensory data processing engine receives sensory data associated with a cohort group from a plurality of multimodal sensor devices and wherein the sensory data processing engine processes the sensory data to form a set of actual cohort behavior data;

a cohort behavior comparison component, wherein the cohort behavior comparison component compares the set of actual cohort behavior data to a set of predicted cohort behavior models, wherein the set of actual cohort behavior data comprises information describing actual behavior by members of the cohort group and wherein the set of predicted cohort behavior models comprises information describing an expected behavior of members of the cohort group, and wherein the cohort behavior comparison component generates a comparison result, wherein the comparison result indicates an accuracy of the set of predicted cohort behavior models.

22. The data processing system of claim 21 further comprising:

the set of multimodal sensors, wherein the set of multimodal sensors comprises a set of digital video cameras, and wherein the set of multimodal sensors transmits a stream of video data associated with the cohort group to the sensory data processing engine in real time as the stream of video data is captured by the set of digital video cameras to form the sensory data.

23. The data processing system of claim 21 further comprising:

the set of multimodal sensors, wherein the set of multimodal sensors comprises a set of radio frequency identification tag receivers.

24. The data processing system of claim 21 wherein the sensory data processing engine further comprises:

a video analysis system, wherein the video analysis system analyzes a stream of video data received from at least one digital video camera in the set of multimodal sensors, wherein the video analysis system generates metadata describing contents of the stream of video data, and wherein the sensory data processing engine identifies events associated with behavior of cohorts using the metadata.

25. The data processing system of claim 21 further comprising:

the set of multimodal sensors, wherein the set of multimodal sensors comprises at least one of a set of global positioning satellite receivers, a set of infrared sensors, a set of microphones, a set of motion detectors, a set of chemical sensors, a set of biometric sensors, a set of pressure sensors, a set of temperature sensors, a set of metal detectors, a set of radar detectors, a set of photosensors, a set of seismographs, and a set of anemometers.