Method and apparatus for identifying unexpected behavior of a customer in a retail environment using detected location data, temperature, humidity, lighting conditions, music, and odors

• US 7,908,237 B2
• Filed: 06/29/2007
• Issued: 03/15/2011
• Est. Priority Date: 06/29/2007
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Kraemer, James R., Angell, Robert Lee
• Primary Examiner(s): VINCENT, DAVID ROBERT
• Application Number:
  US11/771,912
• Publication Number:
  US 20090006286A1
• Time in Patent Office: 1,355 Days
• Field of Search: 706/20, 706/45, 706/47, 706/48, 4554561-457, 342/147, 342/450, 342/451, 705/26, 705/14.37, 705/14.38, 705/14.41, 705/14.53, 705/14.58, 705/14.65, 705/14.67, 348/139, 348/143, 348150-159
• US Class Current: 706/45
• CPC Class Codes: G06Q 30/0238 at point-of-sale [POS]
• CPC Class Codes: G06Q 30/0242 Determining effectiveness of advertisements
• CPC Class Codes: G06Q 30/0255 based on user history
• CPC Class Codes: G06Q 30/0261 based on user location
• CPC Class Codes: G06Q 30/0271 Personalized advertisement
• CPC Class Codes: G06V 20/52 Surveillance or monitoring of activities, e.g. for recognising suspicious objects recognising microscopic objects G06V20/69

Abstract

A computer implemented method, apparatus, and computer usable program product for identifying unexpected behavioral patterns. The process parses event data derived from video data to identify behavioral patterns, wherein the event data comprises metadata describing events occurring in a selected environment. The process analyzes the behavioral patterns to identify a set of expected behavioral patterns occurring in the selected environment, and generates an expected behavioral model using the expected behavioral patterns. Thereafter, the process forms a set of unexpected behavioral patterns from the behavioral patterns inconsistent with the expected behavioral model.

Claims

1. A computer implemented method for identifying unexpected behavioral patterns, the computer implemented method comprising:

parsing event data derived from video data to identify behavioral patterns of a customer in a retail environment, wherein the event data comprises metadata describing events occurring in a selected retail environment;

retrieving a profile of the customer'"'"'s purchases and demographics;

retrieving a set of environmental data associated with a set of environmental conditions of the selected retail environment, wherein the set of environmental conditions include a temperature, a humidity, a set of lighting conditions, a type of music playing, a set of odors, a floor plan, and a set of locations of retail items;

analyzing the customer'"'"'s behavioral patterns, the set of environmental data, and the profile to identify a set of expected behavioral patterns of the customer and a set of expected preferences of the customer occurring in the selected retail environment;

generating an expected behavioral model of the customer using the expected behavioral patterns and the expected preferences; and

forming a set of unexpected behavioral patterns of the customer from the behavioral patterns inconsistent with the expected behavioral model.

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

generating a behavior control strategy for manipulating the set of unexpected behavioral patterns.

3. The computer implemented method of claim 2, wherein the behavior control strategy specifies an action to be performed in response to identifying the set of unexpected behavioral patterns, or a change to a set of environmental variables for modifying environmental conditions of the selected retail environment.

4. The computer implemented method of claim 3, wherein the action comprises generating a notification to the customer or a modification of an environmental variable of the set of environmental variables;

and

wherein the set of unexpected behavioral patterns of the customer at least includes one or more of a behavior associated with frugal shopping, a behavior consistent with theft, and a behavior associated with purchasing baby-related retail items; and

wherein the notification to the customer includes a coupon presented to the customer based on identifying the behavior associated with frugal shopping, or a warning to the customer that the customer is being monitored by security personnel based on identifying the behavior consistent with theft, or advertisements and marketing messages for baby-related products based on identifying the behavior associated with purchasing baby-related retail items; and

wherein the modification of the environmental variable is associated with the expected customer preference for an environmental condition of the selected retail environment.

5. The computer implemented method of claim 2, further comprising:

responsive to detecting a threshold occurrence of the set of unexpected behavioral patterns, classifying the set of unexpected behavioral patterns as one of the set of expected behavioral patterns.

6. The computer implemented method of claim 1, further comprising:

receiving the video data from a set of sensors associated with the selected environment; and

analyzing the video data to identify event data, wherein analyzing the video data comprises generating the metadata describing the events associated with the environment.

7. The computer implemented method of claim 6, wherein the set of sensors comprises a set of digital video cameras.

8. The computer implemented method of claim 1, wherein parsing the event data derived from video data to identify behavioral patterns of the customer in the retail environment further comprises:

identifying behavior patterns including items placed in the customer'"'"'s shopping basket, responses to marketing messages by the customer, retail items viewed by the customer, paths taken by the customer through the retail environment to locate certain retail items, the manner in which the customer compares similar retail items, the amount of time that the customer pauses at a display shelf, and display shelves that the customer looks at; and

processing the event data using at least one of a statistical method, a data mining method, a causal model, a mathematical model, a marketing model, a behavioral model, a psychological model, a sociological model, or a simulation model.

9. A computer program product for identifying unexpected behavioral patterns comprising:

a plurality of instructions stored in a tangible computer readable storage medium configured to cause a processor to perform actions including;

parsing event data derived from video data to identify behavioral patterns of a customer in a retail environment, wherein the event data comprises metadata describing events occurring in a selected retail environment;

retrieving a profile of the customer'"'"'s purchases and demographics;

retrieving a set of environmental data associated with a set of environmental conditions of the selected retail environment, wherein the set of environmental conditions include a temperature, a humidity, a set of lighting conditions, a type of music playing, a set of odors, a floor plan, and a set of locations of retail items;

analyzing the customer'"'"'s behavioral patterns, the set of environmental data, and the profile to identify a set of expected behavioral patterns of the customer and a set of expected preferences of the customer occurring in the selected retail environment;

generating an expected behavioral model of the customer using the expected behavioral patterns and the expected preferences; and

forming a set of unexpected behavioral patterns of the customer from the behavioral patterns inconsistent with the expected behavioral model.

10. The computer program product of claim 9, the plurality of instructions further including:

generating a behavior control strategy for manipulating the set of unexpected behavioral patterns.

11. The computer program product of claim 10, wherein the behavior control strategy specifies an action to be performed in response to identifying the set of unexpected behavioral patterns, or a change to a set of environmental variables for modifying environmental conditions of the selected retail environment.

12. The computer program product of claim 11, wherein the action comprises generating a notification to the customer or a modification of an environmental variable of the set of environmental variables;

and

wherein the set of unexpected behavioral patterns of the customer at least includes one or more of a behavior associated with frugal shopping, a behavior consistent with theft, and a behavior associated with purchasing baby-related retail items; and

wherein the notification to the customer includes a coupon presented to the customer based on identifying the behavior associated with frugal shopping, or a warning to the customer that the customer is being monitored by security personnel based on identifying the behavior consistent with theft, or advertisements and marketing messages for baby-related products based on identifying the behavior associated with purchasing baby-related retail items; and

wherein the modification of the environmental variable is associated with the expected customer preference for an environmental condition of the selected retail environment.

13. The computer program product of claim 10, the plurality of instructions further including:

classifying the set of unexpected behavioral patterns as one of the set of expected behavioral patterns in response to detecting a threshold occurrence of the set of unexpected behavioral patterns.

14. The computer program product of claim 9, the plurality of instructions further comprising:

receiving the video data from a set of sensors associated with the selected environment; and

analyzing the video data to identify event data, wherein analyzing the video data comprises generating the metadata describing the events associated with the environment.

15. The computer program product of claim 14, wherein the set of sensors comprises a set of digital video cameras.

16. The computer program product of claim 9, wherein parsing the event data derived from video data to identify behavioral patterns of the customer in the retail environment further comprises:

identifying behavior patterns including items placed in the customer'"'"'s shopping basket, responses to marketing messages by the customer, retail items viewed by the customer, paths taken by the customer through the retail environment to locate certain retail items, the manner in which the customer compares similar retail items, the amount of time that the customer pauses at a display shelf, and display shelves that the customer looks at; and

processing the event data using at least one of a statistical method, a data mining method, a causal model, a mathematical model, a marketing model, a behavioral model, a psychological model, a sociological model, or a simulation model.

17. A system for identifying unexpected behavioral patterns, the system comprising:

a database, wherein the database stores event data collected by a set of sensors; and

an analysis server, wherein the analysis server parses event data derived from video data to identify behavioral patterns of a customer in a retail environment, wherein the event data comprises metadata describing events occurring in a selected retail environment;

retrieves a profile of the customer'"'"'s purchases and demographics;

retrieves a set of environmental data associated with a set of environmental conditions of the selected retail environment, wherein the set of environmental conditions include a temperature, a humidity, a set of lighting conditions, a type of music playing, a set of odors, a floor plan, and a set of locations of retail items;

analyzes the customer'"'"'s behavioral patterns, the set of environmental data, and the profile to identify a set of expected behavioral patterns of the customer and a set of expected preferences of the customer occurring at the selected retail environment;

generates an expected behavioral model of the customer using the expected behavioral patterns and the expected preferences; and

forms a set of unexpected behavioral patterns of the customer from the behavioral patterns inconsistent with the expected behavioral model.

18. The system of claim 17, wherein the set of sensors comprises a set of digital video cameras.