DIGITAL CONTENT PERSONALIZATION METHOD AND SYSTEM

• US 20100250341A1
• Filed: 06/07/2010
• Published: 09/30/2010
• Est. Priority Date: 03/16/2006
• Status: Active Grant

• Current Assignee: Cricket Media, Inc. (Cricket Media Group Ltd.)
• Original Assignee: 4000 Hollywood Holdings, Inc. (Cricket Media Group Ltd.)
• Inventors: Hauser, Eduardo A.
• Granted Patent: US 8,019,777 B2
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0243 Comparative campaigns
• CPC Class Codes: G06Q 30/0282 Rating or review of business operators or products
• CPC Class Codes: H04L 67/02 based on web technology, e.g. hypertext transfer protocol [HTTP]
• CPC Class Codes: H04L 67/306 User profiles
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34

Abstract

A system and method for predicting what content a user wants to view based on such user'"'"'s previous behavior and actions, comprising: receiving a cookie for every content page template in a web site; receiving a request for service of a content page; sending the content requested to a requester; for each content page sent, retrieving the cookie from the user; assigning a unique identifier (ID) to each new requester and storing the ID in the cookie; recording each ID, IP address, referrer, and time of request from the server; and storing the data recorded in a buffer for a period of time before storing it more permanently in a client-specific database. The system can be monetized by receiving fees from end users for presenting the content preferences or by receiving fees form content providers that include advertising related to the content preferences.

Claims

1. A method of producing and storing a content interest profile, said method comprising:

using a processor device for;

determining what content a user is most likely to want to view based on a plurality of data points comprising content previously viewed, said determining step comprising steps of;

receiving from the user a request for service of one or more content items;

determining whether the user has been provided a unique identifier (ID);

if the user has not been provided the unique ID, assigning the unique ID to each user;

placing software code in the user'"'"'s web site to read and record the URL (uniform resource locator) of the web pages or other address form which digital content assets can be retrieved;

assigning a unique identity to each user and storing the identity in a database; and

receiving and storing each user'"'"'s ID, URLs, and IP address.

2. The method of claim 1 wherein the assigning step comprises inserting JavaScript into the user'"'"'s computer'"'"'s memory to assign a cookie to the user.

3. The method of claim 1 further comprising a step of receiving and storing the user'"'"'s longitude and latitude.

4. The method of claim 1 further comprising a step of receiving and storing the user'"'"'s device type.

5. The method of claim 1 further comprising a step of receiving and storing the user'"'"'s referrer.

6. The method of claim 1 further comprising analyzing the subject matter of each digital content previously viewed by the user, using a plurality of content analysis processes to determine the meaning and substance of each content article.

7. The method of claim 6 wherein the content analysis processes comprise semantic analysis of content, software for entity extraction, knowledge maps and other methods of determining topical matter.

8. The method of claim 1 wherein the content viewed comprises text, audio, and video of graphical file.

9. The method of claim 1 wherein the content comprises a news story.

10. The method of claim 1 wherein where the content viewed comprises video or audio and the content is processed with speech recognition in order to generate ancillary text to be data mined and semantically analyzed.

11. The method of claim 9 where the system stores a relevance score of the various data points collected relative to the frequency and relative weight in articles viewed.

12. The method of claim 9 further comprising storing a relevance score of the data points relative to frequency and relative weight in articles viewed.

13. The method of claim 9 further comprising dynamically and continuously changing each user'"'"'s changes in interest based on the news articles viewed.

14. The method of claim 1 further comprising providing the user the ability to declare interest in a topic.

15. A method of applying statistical, probabilistic, and predictive methods to data contained in a user'"'"'s profile in order to determine the likely interest of a user in content not yet viewed, said method comprising steps of:

using an information processing device for;

using a plurality of calculations to determine a pattern, in a subject matter of articles previously viewed.

16. The method of claim 15 further comprising determining similarities in substance between articles previously viewed and a set of articles not yet viewed, by comparing a plurality of data points, where the data points include a content'"'"'s author, source, time of creation, time of publication, length whereas in the case of text assets the length maybe measured in terms of character, word or paragraph count.

17. The method of claim 16 further comprising ranking assets not yet viewed according to a likelihood of interest for each user.

18. The method of claim 16 further comprising including an ability of a publisher to access a dashboard where statistical matches between user profiles and available content may be viewed at an aggregate level to provide a more comprehensive perspective of the audience content preferences.

19. A method for making personalized content recommendations to users, said method comprising:

using an information processing device for;

scoring each available asset against a content preference of each user.

20. The method of claim 19 further comprising a step of using a matching function to determine the highest matches between available assets and a user'"'"'s profile.

21. The method of claim 19 further comprising ranking recommendable assets based on a series of signals including the content relevance, collaborative filtering scores, social tags, user actions, prior domain knowledge, a measure of recency, editorial rules and the commercial value of the asset.

22. The method of claim 19 further comprising:

preparing a set of recommended content,

wherein the recommended content comprises an entire new page of content created solely for a user.

23. The method of claim 19 wherein the recommended content further comprises:

elements within a page.

24. The method of claim 19 wherein the recommended content further comprises:

an advertisement or a commercial good or service available for sale.

25. The method of claim 19 wherein the recommended content further comprises:

the recommendations populating the screens of a mobile application with data relevant to the user.

26. The method of claim 19 further comprising:

making the system supporting this method in exchange for a fee to be paid by a content publisher, a vendor of goods and a provider of services,

wherein the fee comprises a fixed, recurring payment or a variable payment proportionate to certain other variables measuring the effectiveness of the recommendations.