Recommendation engine for aggregated platform data

• US 10,509,806 B2
• Filed: 04/07/2016
• Issued: 12/17/2019
• Est. Priority Date: 08/17/2015
• Status: Active Grant

• Current Assignee: Accenture Global Solutions Limited (Accenture PLC)
• Original Assignee: Accenture Global Solutions Limited (Accenture PLC)
• Inventors: Tiell, Steven C., Liongosari, Edy S., Kundavaram, Chetan R., Modi, Shimon
• Primary Examiner(s): Robinson, Greta L
• Application Number:
  US15/093,374
• Publication Number:
  US 20170053032A1
• Time in Patent Office: 1,349 Days
• CPC Class Codes: G06F 16/113 Details of archiving lifecycle management in storage systems G06F3/0649; backup systems G06F11/1446
• CPC Class Codes: G06F 16/162 Delete operations erasing in storage systems G06F3/0652
• CPC Class Codes: G06F 16/22 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06F 16/24575 using context
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/29 Geographical information databases
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 21/6227 where protection concerns the structure of data, e.g. records, types, queries
• CPC Class Codes: G06F 21/6236 between heterogeneous systems
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0601 Electronic shopping [e-shopping]
• CPC Class Codes: G06Q 30/0631 Item recommendations
• CPC Class Codes: H04L 43/04 Processing captured monitoring data, e.g. for logfile generation
• CPC Class Codes: H04L 43/0876 Network utilisation, e.g. volume of load or congestion level
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34

Abstract

Techniques are described for receiving data generated by multiple platforms of different types, and determining recommendations for end-user(s) of the multiple platforms based on an analysis of the received data. An end-user may interact with multiple individual platforms of different types. The individual platforms may generate data describing, and/or resulting from, such interactions. The data may be received, ingested, stored, analyzed, and/or otherwise processed by a super-platform. The data may be aggregated and the data and/or aggregate data may be analyzed by a recommendation engine executing on the super-platform to determine one or more recommendations for a particular end-user based on an analysis of the data and/or aggregate data associated with that end-user. Such recommendation(s) may be provided to the end-user through an end-user interface and/or search engine provided by the super-platform, or through a third-party entity.

Claims

1. A computer-implemented method performed by at least one processor, the method comprising:

receiving, by the at least one processor, data generated by a plurality of individual platforms associated with a super-platform, wherein the data includes an instance of the data generated by each respective platform of the plurality of individual platforms, and wherein each instance of the data generated by each respective platform is associated with a same end-user of the plurality of individual platforms, at least one individual platform being added and the super-platform dynamically adapting to the at least one individual platform by determining how a respective instance of the data provided by the at least one individual platform fits into a semantic data model that describes previously ingested data from other individual platform;

analyzing, by the at least one processor, the data to determine a time-based correlation between;

i) a first activity of the end-user indicated by a first instance of the data that is generated by a first platform of the plurality of individual platforms, and ii) a second activity of the end-user indicated by a second instance of the data that is generated by a second platform of the plurality of individual platforms;

determining, by the at least one processor, one or more recommendations for the end-user based at least partly on the time-based correlation between the first activity and the second activity; and

providing, by the at least one processor, the one or more recommendations for presentation to the end-user.

2. The method of claim 1, further comprising:

aggregating, by the at least one processor, the data to generate aggregate data associated with the end-user;

wherein the one or more recommendations are determined based on the aggregate data.

3. The method of claim 1, wherein providing the one or more recommendations includes presenting the one or more recommendations in an interface provided by the super-platform in response to a request submitted by the end-user through a search engine executing on the super-platform.

4. The method of claim 1, wherein providing the one or more recommendations includes providing the one or more recommendations to a third party recommendation platform that is external to the super-platform.

5. The method of claim 1, wherein the one or more recommendations are for at least one of a product, a service, or content offered through at least one of the plurality of individual platforms.

6. The method of claim 1, wherein determining the one or more recommendations is further based on determining at least one other correlation between at least two different instances of the data from different ones of the plurality of individual platforms.

7. The method of claim 1, wherein the plurality of individual platforms include at least two of:

an e-commerce platform configured to provide an e-commerce service;

a retail platform configured to support retail operations of a physical store;

a media platform configured to provide an online media service;

a mobile computing device;

an Internet of Things (IoT) computing device;

or

an in-vehicle computer.

8. A system comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor, the memory storing instructions which, when executed by the at least one processor, cause the at least one processor to perform operations comprising;

receiving data generated by a plurality of individual platforms associated with a super-platform, wherein the data includes an instance of the data generated by each respective platform of the plurality of individual platforms, and wherein each instance of the data generated by each respective platform is associated with a same end-user of the plurality of individual platforms, at least one individual platform being added and the super-platform dynamically adapting to the at least one individual platform by determining how a respective instance of the data provided by the at least one individual platform fits into a semantic data model that describes previously ingested data from other individual platform;

analyzing, by the at least one processor, the data to determine a time-based correlation between;

i) a first activity of the end-user indicated by a first instance of the data that is generated by a first platform of the plurality of individual platforms, and ii) a second activity of the end-user indicated by a second instance of the data that is generated by a second platform of the plurality of individual platforms;

determining one or more recommendations for the end-user based at least partly on the time-based correlation between the first activity and the second activity; and

providing the one or more recommendations for presentation to the end-user.

9. The system of claim 8, the operations further comprising:

aggregating the data to generate aggregate data associated with the end-user;

wherein the one or more recommendations are determined based on the aggregate data.

10. The system of claim 8, wherein providing the one or more recommendations includes presenting the one or more recommendations in an interface provided by the super-platform in response to a request submitted by the end-user through a search engine executing on the super-platform.

11. The system of claim 8, wherein providing the one or more recommendations includes providing the one or more recommendations to a third party recommendation platform that is external to the super-platform.

12. The system of claim 8, wherein the one or more recommendations are for at least one of a product, a service, or content offered through at least one of the plurality of individual platforms.

13. The system of claim 8, wherein determining the one or more recommendations is further based on determining at least one other correlation between at least two different instances of the data from different ones of the plurality of individual platforms.

14. The system of claim 8, wherein the plurality of individual platforms include at least two of:

an e-commerce platform configured to provide an e-commerce service;

a retail platform configured to support retail operations of a physical store;

a media platform configured to provide an online media service;

a mobile computing device;

an Internet of Things (IoT) computing device;

or

an in-vehicle computer.

15. One or more computer-readable storage media storing instructions which, when executed by at least one processor, cause the at least one processor to perform operations comprising:

receiving data generated by a plurality of individual platforms associated with a super-platform, wherein the data includes an instance of the data generated by each respective platform of the plurality of individual platforms, and wherein each instance of the data generated by each respective platform is associated with a same end-user of the plurality of individual platforms, at least one individual platform being added and the super-platform dynamically adapting to the at least one individual platform by determining how a respective instance of the data provided by the at least one individual platform fits into a semantic data model that describes previously ingested data from other individual platform;

analyzing, by the at least one processor, the data to determine a time-based correlation between;

i) a first activity of the end-user indicated by a first instance of the data that is generated by a first platform of the plurality of individual platforms, and ii) a second activity of the end-user indicated by a second instance of the data that is generated by a second platform of the plurality of individual platforms;

determining one or more recommendations for the end-user based at least partly on the time-based correlation between the first activity and the second activity; and

providing the one or more recommendations for presentation to the end-user.

16. The one or more computer-readable storage media of claim 15, the operations further comprising:

aggregating the data to generate aggregate data associated with the end-user;

wherein the one or more recommendations are determined based on the aggregate data.

17. The one or more computer-readable storage media of claim 15, wherein providing the one or more recommendations includes presenting the one or more recommendations in an interface provided by the super-platform in response to a request submitted by the end-user through a search engine executing on the super-platform.

18. The one or more computer-readable storage media of claim 15, wherein providing the one or more recommendations includes providing the one or more recommendations to a third party recommendation platform that is external to the super-platform.

19. The one or more computer-readable storage media of claim 15, wherein the one or more recommendations are for at least one of a product, a service, or content offered through at least one of the plurality of individual platforms.

20. The one or more computer-readable storage media of claim 15, wherein determining the one or more recommendations is further based on determining at least one other correlation between at least two different instances of the data from different ones of the plurality of individual platforms.