Systems and methods for user platform based recommendations

Patent metadata

Field	Value
Publication number	US-12008623-B2
Application number	US-202117213395-A
Country	US
Kind code	B2
Filing date	Mar 26, 2021
Priority date	Mar 26, 2021
Publication date	Jun 11, 2024
Grant date	Jun 11, 2024

Abstract

Computer-implemented methods and systems include determining vehicle grades for a user by accessing a plurality of user platforms, identifying user-related content linked to the user via the user platforms, extracting user attributes based on the user-related content, applying weights to vehicle attributes in a vehicle recommendation engine, based on the extracted user attributes, generating the vehicle grades based on the weights, and providing the vehicle grades to the user via a vehicle grading platform.

First claim

What is claimed is:

1. A computer-implemented method for determining vehicle grades for a user, the method comprising:
accessing a plurality of user platforms;
identifying user-related content linked to the user via the user platforms;
extracting user attributes based on the user-related content by:
receiving user images associated with the user-related content;
determining one or more image attributes of the user images, the one or more image attributes including content of the user images determined by performing image recognition on the user images;
determining context associated with the user images;
applying the one or more image attributes of the user images and the context associated with the user images to a machine-learning model, the machine-learning model trained to identify user attributes based on both the one or more image attributes of the user images and the context associated with the user images to output the user attributes; and
receiving, from the machine-learning model, the outputted user attributes;

applying weights to vehicle attributes in a vehicle grading engine, based on the outputted user attributes;
generating the vehicle grades based on the weights; and
providing the vehicle grades to the user via a vehicle grading platform.

2. The computer-implemented method of claim 1 , further comprising generating a user attribute vector based on the outputted user attributes.

3. The computer-implemented method of claim 2 , further comprising identifying a user cluster from a plurality of attribute clusters, wherein the user cluster is most closely related to the user attribute vector when compared to the plurality of attribute clusters.

4. The computer-implemented method of claim 3 , further comprising determining the weights for the vehicle attributes based on vehicle acquisitions by other users in the user cluster.

5. The computer-implemented method of claim 1 , wherein applying the weights comprises determining affinity levels for one or more of the outputted user attributes and determining a weight value based on the affinity levels.

6. The computer-implemented method of claim 5 , wherein determining the affinity level comprises determining at least one of a frequency of engagement, a proportion of engagement, a frequency of content generation, or a proportion of content generation.

7. The computer-implemented method of claim 1 , wherein accessing the plurality of user platforms comprises obtaining user permission to access the plurality of user platforms.

8. The computer-implemented method of claim 7 , wherein obtaining the user permission comprises requesting the user permission via the vehicle grading platform.

9. The computer-implemented method of claim 1 , wherein determining context associated with the user images further comprises:
receiving user text associated with the user; and
determining content of the user text using a contextual engine.

10. The computer-implemented method of claim 9 , further comprising:
generating a correlation score between the content of the user text to the content of a plurality of other users;
identifying one or more other users who's correlation score is higher than a correlation threshold; and
identifying the user attributes further based on attributes of the one or more other users who's correlation score is higher than the correlation threshold.

11. The computer-implemented method of claim 9 , further comprising:
determining a correlation score between the content of the user text to vehicle reviews for a plurality of vehicles;
identifying one or more vehicles with vehicle review correlation scores higher than a correlation threshold; and
applying the weights to the vehicle attributes also based on the vehicles with vehicle correlation scores higher than the correlation threshold.

12. The computer-implemented method of claim 1 , further comprising updating the weights for the vehicle attributes based on user activity.

13. The computer-implemented method of claim 1 , wherein the user-related content comprises associated content related to the user's associates, and further comprising:
extracting associated attributes based on the associated content; and
applying the weights to vehicle attributes based also on the extracted associate attributes.

14. A system, comprising:
a memory storing processor readable instructions; and
at least one processor configured to access the memory and execute the processor readable instructions, which when executed by the at least one processor, configures the at least one processor to perform a plurality of functions, including functions for:
accessing a plurality of user platforms;
identifying user-related content linked to the user via the user platforms;
extracting user attributes based on the user-related content by:
receiving user images associated with the user-related content;
determining one or more image attributes of the user images, the one or more image attributes including content of the user images determined by performing image recognition on the user images;
determining context associated with the user images;
applying the one or more image attributes of the user images and the context associated with the user images to a machine-learning model, the machine-learning model trained to identify user attributes based on both the one or more image attributes of the user images and the context associated with the user images to output the user attributes; and
receiving, from the machine-learning model, the outputted user attributes;

applying weights to vehicle attributes in a vehicle grading engine, based on the outputted user attributes;
generating the vehicle grades based on the weights; and
providing the vehicle grades to the user via a vehicle grading platform.

15. The system of claim 14 , the plurality of functions further including:
generating a user attribute vector based on the outputted user attributes.

16. The system of claim 15 , the plurality of functions further including:
identifying a user cluster from a plurality of attribute clusters, wherein the user cluster is most closely related to the user attribute vector when compared to the plurality of attribute clusters.

17. The system of claim 16 , the plurality of functions further including:
determining the weights for the vehicle attributes based on vehicle acquisitions by other users in the user cluster.

18. A non-transitory computer readable storage medium storing instructions to control one or more processors to perform operations, including:
accessing a plurality of user platforms;
identifying user-related content linked to the user via the user platforms;
extracting user attributes based on the user-related content by:
receiving user images associated with the user-related content;
determining one or more image attributes of the user images, the one or more image attributes including content of the user images determined by performing image recognition on the user images;
determining context associated with the user images;
applying the one or more image attributes of the user images and the context associated with the user images to a machine-learning model, the machine-learning model trained to identify user attributes based on both the one or more image attributes of the user images and the context associated with the user images to output the user attributes; and
receiving, from the machine-learning model, the outputted user attributes;

applying weights to vehicle attributes in a vehicle grading engine, based on

Assignees

• Capital One Services Llc

Inventors

• Huang Jeremy
• Emamdie Michelle
• Bumpas Derek
• Guo Jiaxin
• Tang Qiaochu

Classifications

• G06Q30/0627

  by specifying product or service characteristics, e.g. product dimensions · CPC title

• G06F16/9536

  Search customisation based on social or collaborative filtering · CPC title

• G06Q30/0645

  Rental transactions; Leasing transactions · CPC title

• G06F16/9535

  Search customisation based on user profiles and personalisation · CPC title

• G06Q30/0641

  utilising user interfaces specially adapted for shopping · CPC title