Real-time in-venue cognitive recommendations to user based on user behavior

Patent metadata

Field	Value
Publication number	US-10909606-B2
Application number	US-201816010952-A
Country	US
Kind code	B2
Filing date	Jun 18, 2018
Priority date	Jun 18, 2018
Publication date	Feb 2, 2021
Grant date	Feb 2, 2021

Abstract

User behaviors are monitored, by machine logic, during a visit to a venue by a user, the user behaviors associated with user interactions with items in the venue, a location of the items being tracked. In real-time, based, at least in part, on the user behaviors and the items, a subsequent behavior of the user is predicted, by machine logic, the predicting resulting in predicted behavior(s). Cognitive recommendations are provided, by machine logic, to the user in real-time during the visit, the cognitive recommendations corresponding to additional item(s) based, at least in part, on the predicted behaviors and the items. Machine learning is used to train a system for facilitating the noted aspects, as well as to update training.

First claim

What is claimed is:

1. A computer-implemented method of providing recommendations, the method comprising:
monitoring, by machine logic, during a visit to a venue by a user, at least one user behavior associated with one or more user interactions with one or more items in the venue, wherein a location of the one or more items is tracked within the venue;
predicting, by machine logic, in real-time, at least one subsequent behavior of the user, the predicting being based on at least two considerations, the at least two considerations comprising the at least one user behavior and the one or more items, and wherein the predicting results in at least one predicted behavior of the user interacting with the one or more items; and
providing, by machine logic, one or more cognitive recommendations in real-time to the user during the visit, the one or more cognitive recommendations corresponding to at least one additional item based on two or more considerations, the two or more considerations comprising the at least one predicted behavior and the one or more items, wherein the providing comprises searching on or more cross co-occurence matrixes based on the one or more user interactions and the one or more items, and wherein results of the searching are used in formulating the one or more cognitive recommendations.

2. The computer-implemented method of claim 1 , further comprising, in response to the monitoring, updating a database of user behaviors with the at least one user behavior.

3. The computer-implemented method of claim 2 , wherein the user is identified in the database of user behaviors.

4. The computer-implemented method of claim 2 , wherein a location for the venue is associated with the at least one user behavior in the database of user behaviors, and wherein the one or more recommendations is based, in part, on the location.

5. The computer-implemented method of claim 2 , further comprising:
identifying one or more characteristics of the user;
updating the user database with the one or more characteristics; and
wherein the one or more characteristics comprise at least one of an identity of the user and an identification of at least one physical characteristic of the user.

6. The computer-implemented method of claim 5 , wherein the one or more characteristics comprise at least one of a user ID and the at least one physical characteristic of the user.

7. The computer-implemented method of claim 1 , wherein providing the one or more cognitive recommendations comprises continually updating the one or more cognitive recommendations.

8. The computer-implemented method of claim 1 , further comprising using machine learning to teach with data and improve the one or more cognitive recommendations.

9. A system for providing recommendations, the system comprising:
a memory storage device; and
a computing device comprising at least one processor in communication with the memory storage device to perform a method, the method comprising:
monitoring, by the system, during a visit to a venue by a user, at least one user behavior associated with one or more user interactions with one or more items in the venue, wherein a location of the one or more items is tracked within the venue;
predicting, by machine logic, in real-time, at least one subsequent behavior of the user, the predicting being based on at least two considerations, the at least two considerations comprising the at least one user behavior and the one or more items, and wherein the predicting results in at least one predicted behavior of the user interacting with the one or more items; and
providing, by machine logic, one or more cognitive recommendations in real-time to the user during the visit, the one or more cognitive recommendations corresponding to at least one additional item based on two or more considerations, the two or more considerations comprising the at least one predicted behavior and the one or more items, wherein the providing comprises searching on or more cross co-occurence matrixes based on the one or more user interactions and the one or more items, and wherein results of the searching are used in formulating the one or more cognitive recommendations.

10. The system of claim 9 , further comprising, in response to the monitoring, updating a database of user behaviors with the at least one user behavior.

11. The system of claim 10 , wherein the user is identified in the database of user behaviors.

12. The system of claim 10 , further comprising:
identifying one or more characteristics of the user;
updating the user database with the one or more characteristics; and
wherein the one or more characteristics comprise at least one of an identity of the user and an identification of at least one physical characteristic of the user.

13. The system of claim 9 , further comprising using machine learning to teach with data and improve the one or more cognitive recommendations.

14. A computer program product for providing recommendations, the computer program product comprising:
a non-transitory storage medium readable by a processor and storing instructions for performing a method of providing recommendations, the method comprising:
monitoring, by machine logic, during a visit to a venue by a user, at least one user behavior associated with one or more user interactions with one or more items in the venue, wherein a location of the one or more items is tracked within the venue;
predicting, by machine logic, in real-time, at least one subsequent behavior of the user, the predicting being based on at least two considerations, the at least two considerations comprising the at least one user behavior and the one or more items, and wherein the predicting results in at least one predicted behavior of the user interacting with the one or more items; and
providing, by machine logic, one or more cognitive recommendations in real-time to the user during the visit, the one or more cognitive recommendations corresponding to at least one additional item based on two or more considerations, the two or more considerations comprising the at least one predicted behavior and the one or more items wherein the providing comprises searching on or more cross co-occurence matrixes based on the one or more user interactions and the one or more items, and wherein results of the searching are used in formulating the one or more cognitive recommendations.

15. The computer program product of claim 14 , further comprising, in response to the monitoring, updating a database of user behaviors with the at least one user behavior.

16. The computer program product of claim 15 , wherein the user is identified in the database of user behaviors.

17. The computer program product of claim 14 , further comprising using machine learning to learn and improve the one or more cognitive recommendations.

Assignees

• IBM

Inventors

• Deluca Lisa Seacat
• Grant Trevor
• Ogle David M

Classifications

• G06F16/23

  Updating · CPC title

• G06Q30/0631 Primary

  Recommending goods or services · CPC title

• G06N5/02

  Knowledge representation; Symbolic representation · CPC title

• G06N20/00

  Machine learning · CPC title