Generating Receptivity Cohorts

• US 20100153180A1
• Filed: 12/16/2008
• Published: 06/17/2010
• Est. Priority Date: 12/16/2008
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Kraemer, James R., Angell, Robert Lee, Friedlander, Robert R.
• Application Number:
  US12/336,488
• Publication Number:
  US 20100153180A1
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G16H 10/20 for electronic clinical trials or questionnaires

Abstract

A computer implemented method, apparatus, and computer program product for generating receptivity cohorts. Digital sensor data associated with a set of individuals is retrieved in response to receiving an identification of a proposed future change in a current set of circumstances associated with the set of individuals. The digital sensor data comprises events metadata describing a set of events associated with the set of individuals. The set of events comprises at least one of body language, facial expressions, vocalizations, and social interactions of the set of individuals. An analysis server selects a set of receptivity analysis models based on the proposed future event and the set of events. Each analysis model in the set of receptivity analysis models analyzes the set of events to identify conduct attributes indicating receptiveness of each individual in the set of individuals to the proposed future change. The events metadata describing the set of events is analyzed in the selected set of receptivity analysis models to form a receptivity cohort. The receptivity cohort comprises a set of conduct attributes indicating receptiveness of each individual in the set of individuals to the proposed future change.

Claims

1. A computer implemented method of generating receptivity cohorts, the computer implemented method comprising:

responsive to receiving an identification of a proposed future change in a current set of circumstances associated with a set of individuals, retrieving digital sensor data associated with the set of individuals, wherein the digital sensor data comprises events metadata describing a set of events associated with the set of individuals, wherein the set of events comprises at least one of body language, facial expressions, vocalizations, and social interactions of the set of individuals;

selecting a set of receptivity analysis models based on the proposed future event and the set of events, wherein each analysis model in the set of receptivity analysis models analyzes the set of events to identify conduct attributes indicating receptiveness of each individual in the set of individuals to the proposed future change; and

analyzing the events metadata describing the set of events in the selected set of receptivity analysis models to form a receptivity cohort, wherein the receptivity cohort comprises a set of conduct attributes indicating receptiveness of the set of individuals to the proposed future change.

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

responsive to determining description data for the individual is available, retrieving the description data, wherein the description data comprises at least one of identification information, past history information, and current status information for the individual;

selecting receptivity analysis models based on any available description data for the set of individuals with the proposed future event and the set of events; and

analyzing the description data with the events metadata in the set of receptivity analysis models to form the receptivity cohort.

3. The computer implemented method of claim 1 wherein the proposed future change is at least one of a proposed future change in work location requiring that an employee relocate or commute across a greater distance, a proposed offer to sell goods or services to a customer at a given price, an offer to purchase goods or services from a person, a proposed request that a person leave a particular location, and a request that a person stop performing a given action.

4. The computer implemented method of claim 1 wherein the digital sensor data comprises attribute metadata describing identification attributes of the individual, wherein the events metadata, attribute metadata, and the description data is analyzed in the set of receptivity analysis models to form the receptivity cohort, and wherein an identification attribute is selected from a group consisting of a fingerprint, a thumbprint, a palm print, a voice pattern, a retinal scan result, an iris scan result, facial recognition, badge reader data, smart card data, a scent recognition, and license plate information.

5. The computer implemented method of claim 1 wherein the set of receptivity analysis models comprises a deportment analysis model, wherein the deportment analysis model analyzes the set of events described in events metadata to identify conduct attributes indicating at least one of an emotional state, demeanor, conduct, manner, social deportment, propriety, impropriety, and flamboyant actions of the set of individuals, wherein an emotional state of an individual comprises at least one of fear, joy, happiness, anger, jealousy, embarrassment, depression, and unemotional.

6. The computer implemented method of claim 1 wherein the set of receptivity analysis models comprises a comportment analysis model, wherein the comportment analysis model analyzes the events metadata to identify conduct attributes indicating an overall level of refinement in movements and conduct of each individual.

7. The computer implemented method of claim 1 wherein the set of receptivity analysis models comprises a social interactions analysis model, wherein the social interactions analysis model analyzes the set of events described in events metadata to identify conduct attributes indicating types social interactions engaged in by the individual and a level of appropriateness of the social interactions.

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

responsive to a determination that new digital sensor data associated with the individual is available, receiving the new digital sensor data, wherein the new digital sensor data comprises updated events metadata describing a new set of events associated with the set of individuals; and

analyzing the updated events metadata in the set of receptivity analysis models to generate an updated result.

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

responsive to receiving cohort data for a set of cohorts associated with the set of individuals, selecting an updated set of receptivity analysis models and analyzing the cohort data with the events metadata in the updated set of receptivity analysis models to generate an updated receptivity cohort, wherein the set of cohorts comprises at least one of a video cohort, an audio cohort, an olfactory cohort, a biometric cohort, a furtive glance cohort, a general risk cohort, a specific risk cohort, a predilection cohort, a comportment cohort, a deportment cohort, and a sensor and actuator cohort.

8. The computer implemented method of claim 8 wherein the type of social interactions comprises identifying interactions of an individual as the interactions typical of at least one of a leader, a follower, a loner, an introvert, an extrovert, a charismatic person, an emotional person, a calm person, a person acting spontaneously, and a person acting according to a plan.

11. A computer program product for generating receptivity cohorts, the computer program product comprising:

a computer usable medium having computer usable program code embodied therewith, the computer usable program code comprising;

computer usable program code configured to retrieve digital sensor data associated with a set of individuals in response to receiving an identification of a proposed future change in a current set of circumstances associated with the set of individuals, wherein the digital sensor data comprises events metadata describing a set of events associated with the set of individuals, wherein the set of events comprises at least one of body language, facial expressions, vocalizations, and social interactions of the set of individuals;

computer usable program code configured to select a set of receptivity analysis models based on the proposed future event and the set of events, wherein each analysis model in the set of receptivity analysis models analyzes the set of events to identify conduct attributes indicating receptiveness of each individual in the set of individuals to the proposed future change; and

computer usable program code configured to analyze the events metadata describing the set of events in the selected set of receptivity analysis models to form a receptivity cohort, wherein the receptivity cohort comprises a set of conduct attributes indicating receptiveness of each individual in the set of individuals to the proposed future change.

12. The computer program product of claim 11 wherein the proposed future change is at least one of a proposed future change in work location requiring that an employee relocate or commute across a greater distance, a proposed offer to sell goods or services to a customer at a given price, an offer to purchase goods or services from a person, a proposed request that a person leave a particular location, and a request that a person stop performing a given action.

13. The computer program product of claim 11 wherein the digital sensor data comprises attribute metadata describing identification attributes of the individual, wherein the events metadata, attribute metadata, and the description data is analyzed in the set of receptivity analysis models to form the receptivity cohort, and wherein an identification attribute is selected from a group consisting of a fingerprint, a thumbprint, a palm print, a voice pattern, a retinal scan result, an iris scan result, facial recognition, badge reader data, smart card data, a scent recognition, and license plate information.

14. The computer program product of claim 11 wherein the set of receptivity analysis models comprises a deportment analysis model, wherein the deportment analysis model analyzes the set of events described in events metadata to identify conduct attributes indicating at least one of an emotional state, demeanor, conduct, manner, social deportment, propriety, impropriety, and flamboyant actions of the set of individuals, wherein an emotional state of an individual comprises at least one of fear, joy, happiness, anger, jealousy, embarrassment, depression, and unemotional.

15. The computer program product of claim 11 wherein the set of receptivity analysis models comprises a social interactions analysis model, wherein the social interactions analysis model analyzes the set of events described in events metadata to identify conduct attributes indicating types social interactions engaged in by the individual and a level of appropriateness of the social interactions, and wherein the type of social interactions comprises identifying interactions of an individual as the interactions typical of at least one of a leader, a follower, a loner, an introvert, an extrovert, a charismatic person, an emotional person, a calm person, a person acting spontaneously, and a person acting according to a plan.

16. 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 retrieve digital sensor data associated with a set of individuals in response to receiving an identification of a proposed future change in a current set of circumstances associated with the set of individuals, wherein the digital sensor data comprises events metadata describing a set of events associated with the set of individuals, wherein the set of events comprises at least one of body language, facial expressions, vocalizations, and social interactions of the set of individuals;

select a set of receptivity analysis models based on the proposed future event and the set of events, wherein each analysis model in the set of receptivity analysis models analyzes the set of events to identify conduct attributes indicating receptiveness of each individual in the set of individuals to the proposed future change; and

analyze the events metadata describing the set of events in the selected set of receptivity analysis models to form a receptivity cohort, wherein the receptivity cohort comprises a set of conduct attributes indicating receptiveness of each individual in the set of individuals to the proposed future change.

17. The apparatus of claim 16 wherein the set of receptivity analysis models comprises a social interactions analysis model, wherein the social interactions analysis model analyzes the set of events described in events metadata to identify conduct attributes indicating types social interactions engaged in by the individual and a level of appropriateness of the social interactions, and wherein the type of social interactions comprises identifying interactions of an individual as the interactions typical of at least one of a leader, a follower, a loner, an introvert, an extrovert, a charismatic person, an emotional person, a calm person, a person acting spontaneously, and a person acting according to a plan.

18. The apparatus of claim 16 wherein the digital sensor data comprises attribute metadata describing identification attributes of the individual, wherein the events metadata, attribute metadata, and the description data is analyzed in the set of receptivity analysis models to form the receptivity cohort, and wherein an identification attribute is selected from a group consisting of a fingerprint, a thumbprint, a palm print, a voice pattern, a retinal scan result, an iris scan result, facial recognition, badge reader data, smart card data, a scent recognition, and license plate information.

19. The apparatus of claim 16 wherein the set of receptivity analysis models comprises a deportment analysis model, wherein the deportment analysis model analyzes the set of events described in events metadata to identify conduct attributes indicating at least one of an emotional state, demeanor, conduct, manner, social deportment, propriety, impropriety, and flamboyant actions of the set of individuals, wherein an emotional state of an individual comprises at least one of fear, joy, happiness, anger, jealousy, embarrassment, depression, and unemotional.

20. A receptivity analysis system comprising:

a set of multimodal sensors, wherein the set of multimodal sensors generates multimodal sensor data associated with a set of individuals;

an analysis server, wherein the analysis server converts the multimodal sensor data into digital sensor data associated with a set of individuals in response to receiving an identification of a proposed future change in a current set of circumstances associated with the set of individuals, wherein the digital sensor data comprises events metadata describing a set of events associated with the set of individuals, wherein the set of events comprises at least one of body language, facial expressions, vocalizations, and social interactions of the set of individuals; and

an analysis server, wherein the analysis server selects a set of receptivity analysis models based on the proposed future event and the set of events, wherein each analysis model in the set of receptivity analysis models analyzes the set of events to identify conduct attributes indicating receptiveness of each individual in the set of individuals to the proposed future change; and

wherein the set of receptivity analysis models analyzes the events metadata describing the set of events in the selected set of receptivity analysis models to form a receptivity cohort, wherein the receptivity cohort comprises a set of conduct attributes indicating receptiveness of each individual in the set of individuals to the proposed future change.