SYSTEM AND METHOD FOR FINDING AND PRIORITIZING CONTENT BASED ON USER SPECIFIC INTEREST PROFILES

• US 20140156681A1
• Filed: 12/05/2013
• Published: 06/05/2014
• Est. Priority Date: 12/05/2012
• Status: Active Grant

• Current Assignee: Grapevine6 Incorporated (Seismic Software, Inc.)
• Original Assignee: Grapevine6 Incorporated (Seismic Software, Inc.)
• Inventors: LEE, JONATHAN MICHAEL, Gomes, Wayne Luis, Orr, Michael Thomas, Desancic, Danko, Saini, Pankaj
• Granted Patent: US 10,133,812 B2
• Time in Patent Office: Days
• US Class Current: 707/754
• CPC Class Codes: G06F 16/335 Filtering based on additional data, e.g. user or group profiles filtering in web context G06F16/9535, G06F16/9536
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation

Abstract

A personalized content delivery computer system is provided comprising: (a) one or more server computers; (b) a server computer program which when executed provides: a content interest profile builder; and a content matching utility; wherein the content interest profile builder and content matching utility are linked so as to enable users of the platform interested in targeting (“targeting users”) one or more other users (“consumer” or “consumers”), using content that is likely to be of interest to the consumer; wherein the content interest profile builder intelligently harvests interest parameters for consumers, and stores the interest parameters iteratively into a content interest profile maintained for each consumer; and wherein the content matching utility determines whether content is likely to be of significant interest to a consumer, using the content interest profile for the consumer. A related method is also provided.

Claims

1. A personalized content delivery computer system is provided comprising:

(a) at least one computing device, and

(b) a content interest profile and a content matching utility executable by the computing device;

wherein the content interest profile builder and content matching utility are linked so as to enable users of the platform interested in targeting (“

targeting user”

) one or more other users (the “

consumers”

), using content that is likely to be of interest to the consumer;

wherein the content interest profile builder includes an inference engine that (i) intelligently collects content interest parameters for the consumer, by extracting from one or more target entities associated with the consumer, such as a profile maintained by or for the consumer or articles or documents associated with the consumer, one or more data elements that are relevant to content interest, and that (ii) analyzes the data elements so as to construct, including iteratively, a content interest profile that includes a series of topics that the consumer is substantially likely to have a significant interest in;

wherein the content matching utility is responsive to a request to target the consumer with content likely to be of significant interest to the consumer by analyzing a collection of content items within an information domain that is relevant to the consumer based on their content interest profile (“

candidate entity collection”

) so as to predict a subset of content items from the candidate entity collection that are most likely to be of significant interest to the consumer (“

matched entities”

), and generate a list of such matched entities.

2. The computer system of claim 1, wherein the system generates matched entities that are substantially likely to be of interest to the consumer in real time or near real time.

3. The computer system of claim 1, wherein content interest parameters include extracted key words and interests, and wherein the interest profile builder also includes or links to a semantic engine to analyze and supplement the meaning of the key terms and interests, which the interest profile builder builds into the applicable content interest profile.

4. The computer system of claim 1, wherein the candidate entity collection consists of a variety of content objects such as social media profiles, news articles, or whitepapers.

5. The computer system of claim 1, wherein the system includes a dashboard that enables targeting users to supplement content interest profiles with their own information and insights regarding the consumer, thus enriching the content interest profile with personal insight.

6. The computer system of claim 5, wherein the dashboard enables a targeting user to deselect matched entities.

7. The computer system of claims 5 and 6, wherein the content interest profile builder automatically updates an applicable content interest profile based on personal insights or insights generated from deselection of matched entities.

8. The computer system of claim 1, wherein the content matching utility dynamically generates for the consumer a matching profile for optimizing the matching of matched entities to the current content interest profile for the consumer.

9. The computer system of claim 1, wherein the structure of the content interest profile and of the matching profile is complementary, thereby enabling close matching of content to interests, and also enabling matching of different types of content.

10. The computer system of claim 1, wherein the system identifies common interests between the targeting user and the consumer thereby providing commonality between the targeting user and the consumer for effective engagement between them.

11. The computer system of claim 1, further comprising a content delivery platform that when executed enables the delivery of matched entities to the consumer.

12. The computer system of claim 11, wherein the content delivery platform includes a suggestion utility that suggests content for communicating one or more matched entities to the consumer, based on the content interest profile, and based optionally also on communication history for the consumer and the targeting user.

13. The computer system of claim 1, wherein the targeting user has a plurality of contacts, and the system prioritizes contacts based on opportunities for targeting user to engage with contacts based on content interests.

14. The computer system of claim 1, wherein the targeting user is interested in targeting a plurality of consumers, and the system clusters consumers automatically, and iteratively, into groups of consumers with similar interests.

15. The computer system of claim 14, wherein groups of consumers are clustered based on common interests amongst a cluster, or common interests amongst a cluster and additionally the targeting user.

16. The computer system of claim 1, wherein the inference engine is configured to extract content interest parameters from different types of target entities, including different online profiles associated with different Internet platforms (including social networking platforms), data feeds from micro-blogging sites, content websites, and online news services.

17. The computer system of claim 14, wherein the content interest profile builder automatically connects to sources of target entities, looks for updates, extracts new content interest parameters from updates, and automatically updates the applicable content interest profile.

18. The computer system of claim 3, wherein the semantic engine is configured to (i) process the key words and interests based on context dependent standard terminology, (ii) identify related concepts, and (iii) categorize the concepts or define key word relationships, in order to generate insights regarding topics of interest to the consumer.

19. The computer system of claim 1 wherein the inference engine is configured to analyze a current content interest profile, and based on this analysis selectively access or dynamically generate one or more inference models for augmenting the content interest profile so as to predict parameters for additional target entities that will enable enrichment of the content interest profile.

20. The computer system of claim 17, wherein the system is configured to iteratively analyze and the content interest profiles, and search for and acquire information from additional target entities, and process such additional target entities using the content interest profile builder, and based on this information update the applicable content interest profile.

21. The computer system of claim 1, wherein the inference engine implements event dependent rules for varying interest levels, and applies applicable rules upon detection of an associated event.

22. The computer system of claim 1, wherein the system implements a series of rules for scoring content items, and based on such scoring rules the content matching scores content items and ranks content items, applies a content item number threshold, and presents a number of top ranked content items based on the number threshold as matched entities.

23. A method performed by at least one computing device for targeting one or more entities using content comprising:

(a) identifying one or more content consumers (“

consumer”

);

(b) generating a content interest profile for at least one consumer at the at least one computing device by;

(I) collecting content interest parameters for the consumer, by extracting from one or more target entities associated with the consumer, such as a profile maintained by or for the consumer or articles or documents associated with the consumer, one or more data elements that are relevant to content interest;

(ii) analyzing the data elements so as to construct, including iteratively, a content interest profile that includes a series of topics that the consumer is substantially likely to have a significant interest in;

(iii) processing a request to target the consumer with content likely to be of significant interest to the consumer, and based on such request;

(A) analyzing a collection of content items within an information domain that is relevant to the consumer based on their content interest profile (“

candidate entity collection”

) so as to predict a subset of content items from the candidate entity collection that are most likely to be of significant interest to the consumer (“

matched entities”

), and

(B) generating a list such matched entities.