Granted Patent B1

US 12,292,896 · App. 18/956,172 · Granted May 6, 2025

Inventor: Andrew Donald Yates (San Francisco, CA)

Assignee: Promoted.ai, Inc.

G06F16/24578G06F7/08G06F16/2264G06F16/248

Abstract

Claims (72)

1. A computer-implemented method performed by one or more processors, comprising the operations of:

receiving a request from a client device to return a set of content items;

generating, by the one or more processors, a search criterion to search for content items responsive to the request;

generating, by the one or more processors, a first set of relevancy-ranked content items, via a generative Artificial Intelligence (AI) search and retrieval system;

executing, by the one or more processors, one or more multidimensional sorting rules to the first set of relevancy-ranked content items, and generating a second set of relevancy-ranked content items having a different sorted order than the first set of relevancy-ranked content items, wherein one of the multidimensional sorting rules includes a promoted slot selector that modifies a sort order of a promoted content item, wherein one or more of the content items of the first relevancy ranked content items have a display slot position that has been changed to a different display slot position for the same content items of the second set of relevancy-ranked content items;

generating, by the one or more processors, a carousel display structure definition of the second set of relevancy-ranked content items, wherein the carousel display structure definition identifies multiple display groupings and an order of each display grouping for the relevancy-ranked content items, and wherein the second set of relevancy-ranked content items have an associated display grouping value and a display slot position; and

rendering, via a user interface of the client device, at least a portion of the second set of relevancy-ranked content items in the respective multiple display groupings and the display slot positions according to the display structure definition.

2. The computer-implemented method of claim 1 , wherein generating the carousel display structure definition of the second set of relevancy-ranked content items comprises:

assigning the second set relevancy-ranked content items into a predetermined number of display groupings, wherein the predetermined number of display groupings includes 2 or more groupings, and wherein each display grouping includes a predetermined number of display slots, wherein a display slot is an ordered position in a display grouping.

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

determining a group order position for each of the display groupings by:

determining a grouping score of the second set of relevancy-ranked content items assigned to a set of a predetermined number of display slot positions for a respective display grouping; and

sorting the display grouping according to the determined grouping score.

4. The computer-implemented method of claim 1 , wherein generating the first set of relevancy-ranked output comprises:

causing the generated search criterion to be processed, via the generative AI search and retrieval system, comprising one more machine learning models; and

generating by the generative AI search and retrieval system, a relevancy-ranked output listing of content items responsive to the generated search criterion, wherein the content items of the output listing each have a content identifier and a content description.

5. The computer-implemented method of claim 1 , wherein causing the generated search criterion to be processed, via a search system, comprises the operations of:

performing, by the one or more processors, a first stage scoring process, to generate an initial set of content items; and

performing, by the one or more processors, a second stage scoring process, by processing the initial set of content items by an inferencing machine learning model trained to determine content item score values; and

generating, by the inferencing machine learning model, item score values for the initial set of content items.

6. The computer-implemented method of claim 5 , further comprising the operations of:

performing, by the one or more processors, a third stage scoring process that modifies an item score value to adjust a content item position placement in the first set of relevancy-ranked output listing.

7. The computer-implemented method of claim 5 , further comprising the operations of:

performing, by the one or more processors, a blender process that generates annotations for one or more of the content items in the relevancy-ranked output listing, wherein the generated annotations comprise a type of content item for a respective content item in the relevancy-ranked output listing.

8. A system comprising one or more processors configured to perform the operations of:

receiving a request from a client device to return a set of content items;

generating, by the one or more processors, a search criterion to search for content items responsive to the request;

generating, by the one or more processors, a first set of relevancy-ranked content items, via a generative Artificial Intelligence (AI) search and retrieval system;

executing, by the one or more processors, one or more multidimensional sorting rules to the first set of relevancy-ranked content items, and generating a second set of relevancy-ranked content items having a different sorted order than the first set of relevancy-ranked content items, wherein one of the multidimensional sorting rules includes a promoted slot selector that modifies a sort order of a promoted content item, wherein one or more of the content items of the first relevancy ranked content items have a display slot position that has been changed to a different display slot position for the same content items of the second set of relevancy-ranked content items;

generating, by the one or more processors, a carousel display structure definition of the second set of relevancy-ranked content items, wherein the carousel display structure definition identifies multiple display groupings and an order of each display grouping for the relevancy-ranked content items, and wherein the second set of relevancy-ranked content items have an associated display grouping value and a display slot position; and

rendering, via a user interface of the client device, at least a portion of the second set of relevancy-ranked content items in the respective multiple display groupings and the display slot positions according to the display structure definition.

9. The system of claim 8 , wherein generating the carousel display structure definition of the second set of relevancy-ranked content items comprises:

assigning the second set relevancy-ranked content items into a predetermined number of display groupings, wherein the predetermined number of display groupings includes 2 or more groupings, and wherein each display grouping includes a predetermined number of display slots, wherein a display slot is an ordered position in a display grouping.

10. The system of claim 9 , further comprising:

determining a group order position for each of the display groupings by:

determining a grouping score of the second set of relevancy-ranked content items assigned to a set of a predetermined number of display slot positions for a respective display grouping; and

sorting the display grouping according to the determined grouping score.

11. The system of claim 8 , wherein generating the first set of relevancy-ranked output comprises:

causing the generated search criterion to be processed, via the generative AI search and retrieval system, comprising one more machine learning models; and

generating by the generative AI search and retrieval system, a relevancy-ranked output listing of content items responsive to the generated search criterion, wherein the content items of the output listing each have a content identifier and a content description.

12. The system of claim 8 , wherein causing the generated search criterion to be processed, via a search system, comprises the operations of:

performing, by the one or more processors, a first stage scoring process, to generate an initial set of content items; and

performing, by the one or more processors, a second stage scoring process, by processing the initial set of content items by an inferencing machine learning model trained to determine content item score values; and

generating, by the inferencing machine learning model, item score values for the initial set of content items.

13. The system of claim 12 , further comprising the operations of:

performing, by the one or more processors, a third stage scoring process that modifies an item score value to adjust a content item position placement in the first set of relevancy-ranked output listing.

14. The system of claim 12 , further comprising the operations of:

performing, by the one or more processors, a blender process that generates annotations for one or more of the content items in the relevancy-ranked output listing, wherein the generated annotations comprise a type of content item for a respective content item in the relevancy-ranked output listing.

15. A non-transitory computer readable medium storing a software program comprising data and computer implementable instructions that when executed by at least one processor cause the at least one processor to perform operations of:

receiving a request from a client device to return a set of content items;

generating, by the one or more processors, a search criterion to search for content items responsive to the request;

generating, by the one or more processors, a first set of relevancy-ranked content items, via a generative Artificial Intelligence (AI) search and retrieval system;

executing, by the one or more processors, one or more multidimensional sorting rules to the first set of relevancy-ranked content items, and generating a second set of relevancy-ranked content items having a different sorted order than the first set of relevancy-ranked content items, wherein one of the multidimensional sorting rules includes a promoted slot selector that modifies a sort order of a promoted content item, wherein one or more of the content items of the first relevancy ranked content items have a display slot position that has been changed to a different display slot position for the same content items of the second set of relevancy-ranked content items;

generating, by the one or more processors, a carousel display structure definition of the second set of relevancy-ranked content items, wherein the carousel display structure definition identifies multiple display groupings and an order of each display grouping for the relevancy-ranked content items, and wherein the second set of relevancy-ranked content items have an associated display grouping value and a display slot position; and

rendering, via a user interface of the client device, at least a portion of the second set of relevancy-ranked content items in the respective multiple display groupings and the display slot positions according to the display structure definition.

16. The non-transitory computer readable medium of claim 15 , wherein generating the carousel display structure definition of the second set of relevancy-ranked content items comprises:

assigning the second set relevancy-ranked content items into a predetermined number of display groupings, wherein the predetermined number of display groupings includes 2 or more groupings, and wherein each display grouping includes a predetermined number of display slots, wherein a display slot is an ordered position in a display grouping.

17. The non-transitory computer readable medium of claim 16 , further comprising:

determining a group order position for each of the display groupings by:

determining a grouping score of the second set of relevancy-ranked content items assigned to a set of a predetermined number of display slot positions for a respective display grouping; and

sorting the display grouping according to the determined grouping score.

18. The non-transitory computer readable medium of claim 15 , wherein generating the first set of relevancy-ranked output comprises:

causing the generated search criterion to be processed, via the generative AI search and retrieval system, comprising one more machine learning models; and

generating by the generative AI search and retrieval system, a relevancy-ranked output listing of content items responsive to the generated search criterion, wherein the content items of the output listing each have a content identifier and a content description.

19. The non-transitory computer readable medium of claim 15 , wherein causing the generated search criterion to be processed, via a search system, comprises the operations of:

performing, by the one or more processors, a first stage scoring process, to generate an initial set of content items; and

performing, by the one or more processors, a second stage scoring process, by processing the initial set of content items by an inferencing machine learning model trained to determine content item score values; and

generating, by the inferencing machine learning model, item score values for the initial set of content items.

20. The non-transitory computer readable medium of claim 19 , further comprising the operations of:

performing, by the one or more processors, a third stage scoring process that modifies an item score value to adjust a content item position placement in the first set of relevancy-ranked output listing.

21. The non-transitory computer readable medium 19 , further comprising the operations of:

performing, by the one or more processors, a blender process that generates annotations for one or more of the content items in the relevancy-ranked output listing, wherein the generated annotations comprise a type of content item for a respective content item in the relevancy-ranked output listing.

Assignments (3)

Continuity (7)

References Cited (13)