Systems and Methods for Targeted Advertising

• US 20120136723A1
• Filed: 10/28/2011
• Published: 05/31/2012
• Est. Priority Date: 04/06/2007
• Status: Active Grant

• Current Assignee: Convergent Assets LLC (Jeffrey M. Gross)
• Original Assignee: Andy Moeck, Wendell Brown
• Inventors: Larner, Marcus G., Moeck, Andrew M.
• Granted Patent: US 8,443,384 B2
• Time in Patent Office: Days
• US Class Current: 705/14.54
• CPC Class Codes: G06F 40/221 Parsing markup language streams streaming G06F40/149
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06F 40/40 Processing or translation of natural language natural language analysis G06F40/20; semantic analysis G06F40/30
• CPC Class Codes: G06Q 30/0255 based on user history
• CPC Class Codes: G06Q 30/0256 User search
• CPC Class Codes: H04N 21/25 Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication, learning user preferences for recommending movies maintenance or administration in data networks H04L41/00
• CPC Class Codes: H04N 21/251 Learning process for intelligent management, e.g. learning user preferences for recommending movies details of learning user preferences for the retrieval of video data in a video database G06F16/739; computer systems using learning methods G06N3/08
• CPC Class Codes: H04N 21/252 Processing of multiple end-users' preferences to derive collaborative data
• CPC Class Codes: H04N 21/2542 for selling goods, e.g. TV shopping payment schemes, payment architectures or payment protocols for electronic shopping systems G06Q20/12
• CPC Class Codes: H04N 21/258 Client or end-user data management, e.g. managing client capabilities, user preferences or demographics, processing of multiple end-users preferences to derive collaborative data arrangements for services using the result on the distributing side of broadcast systems H04H60/66; profiles in network data switching protocols H04L67/30
• CPC Class Codes: H04N 21/25891 being end-user preferences retrieval of video data in a video database based on user preferences G06F16/739; arrangements for recognizing users' preferences H04H60/46; user profiles in network data switching protocols H04L67/306; processing of user preferences or user profiles in wireless networks H04W8/18
• CPC Class Codes: H04N 21/2668 Creating a channel for a dedicated end-user group, e.g. insertion of targeted commercials based on end-user profiles information retrieval from the Internet by querying with filtering and personalisation G06F16/9535; arrangements for replacing or switching information during the broadcast H04H20/10; push services over packet-switching network H04L12/1859; adaptation of message content in packet-switching networks H04L51/063
• CPC Class Codes: H04N 21/44213 Monitoring of end-user related data arrangements for monitoring the users' behaviour or opinions in broadcast systems H04H60/33
• CPC Class Codes: H04N 21/44226 on social networks
• CPC Class Codes: H04N 21/45 Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
• CPC Class Codes: H04N 21/4532 involving end-user characteristics, e.g. viewer profile, preferences monitoring of user activities for profile generation for accessing a video database G06F16/739; user profiles in network data switching protocols H04L67/306; processing of user preferences or user profiles in wireless networks H04W8/18
• CPC Class Codes: H04N 21/4661 Deriving a combined profile for a plurality of end-users of the same client, e.g. for family members within a home user profiles in network data switching protocols H04L67/306
• CPC Class Codes: H04N 21/4667 Processing of monitored end-user data, e.g. trend analysis based on the log file of viewer selections
• CPC Class Codes: H04N 21/4668 for recommending content, e.g. movies
• CPC Class Codes: H04N 21/6582 Data stored in the client, e.g. viewing habits, hardware capabilities, credit card number arrangements where receivers interact with the broadcast H04H20/38
• CPC Class Codes: H04N 21/812 : involving advertisement data advertising per se G06Q30/02
• CPC Class Codes: H04N 7/16 : Analogue secrecy systems; Analogue subscription systems

Abstract

Methods of generating recommendations may include obtaining social network data from one or more network resources. Word relationships may be created between selected words in the social network data to produce relationship data. Advertisement or other asset recommendations may be generated for a target user by analyzing browse information of the target user to identify one or more words. Other words in the relationship data may be identified that are related to the words in the target user'"'"'s browse information. One or more advertisements may be identified having at least one keyword that corresponds to the other words. At least a portion of these advertisements may be selected from a data repository to provide to the target user.

Claims

1. A system for generating targeted advertisement recommendations, the system comprising:

a data aggregation module configured to obtain word data from social-network data of one or more network resources, the word data comprising a plurality of words in the social-network data;

a relationship mining module in communication with the data aggregation module, the relationship mining module configured to create word relationships between selected ones of the plurality of words in the word data to produce relationship data, each of the word relationships reflecting a degree of association between two or more of the selected words, wherein the degree of association is based at least in part on at least one of;

a frequency of the selected words in the social network data,

a recency of a subset of the social-network data that comprises the two or more selected words, and

an authority factor of the subset of the social-network data; and

a recommender module in communication with the relationship module, the recommender module configured to;

access browse information of a target user to identify one or more first words in the target user'"'"'s browse information,

identify the one or more first words in the relationship data,

identify one or more second words in the relationship data that have one or more of said word relationships with the one or more first words, and

identify one or more advertisements having at least one keyword that corresponds to the one or more second words.

2. The system of claim 1, wherein the relationship mining module is further configured to reassess the degree of association of the word relationships over time.

3. The system of claim 1, wherein the browse information of the target user comprises a network resource currently accessed by the target user.

4. The system of claim 1, wherein the browse information of the target user comprises a plurality of network resources accessed by the target user.

5. The system of claim 1, wherein the recommender module is further configured to weight the one or more first words based at least in part on a frequency of which the one or more first words occur in the browse information of the target user.

6. The system of claim 1, wherein a relationship mining module is further configured to create word relationships by creating one or more graphs of word relationships.

7. The system of claim 1, further comprising a data repository for storing the word data.

8. The system of claim 1, wherein the data aggregation module is further configured to obtain the word data from reference data of one or more network resources.

9. The system of claim 1, wherein the data aggregation module is configured to obtain word data by using one or more natural language processing techniques to extract signal words from the social-network data.

10. A computer-implemented method of generating targeted advertisement recommendations, the method comprising:

obtaining word data from one or more network resources, the word data comprising words in content of the one or more network resources;

creating word relationships between selected words in the word data to produce relationship data, each word relationship indicating a degree of association between two or more of the selected words;

analyzing browse information of a target user to identify one or more first words in the target user'"'"'s browse information;

identifying, based on the word relationships, second words in the relationship data that are related to the first words in the target user'"'"'s browse information;

identifying one or more advertisements having at least one keyword that corresponds to the second words; and

selecting at least a portion of the one or more advertisements from a data repository to provide to the target user.

11. The method of claim 10, further comprising inferring category relationships from the word relationships and inferring asset relationships from the category relationships.

12. The method of claim 11, further comprising creating additional asset relationships by relating assets based at least in part on browse behavior relationships of a plurality of users.

13. The method of claim 12, further comprising inferring additional word relationships from the additional asset relationships.

14. The method of claim 10, wherein creating word relationships between two or more of selected words further comprises detecting the proximity of the selected words in a network resource.

15. The method of claim 10, wherein creating word relationships between two or more of selected words further comprises analyzing behavioral relationships between different users of one or more network resources.

16. The method of claim 10, wherein obtaining word data further comprises detecting signal words using one or more natural language processing techniques.

17. The method of claim 16, wherein detecting signal words comprises generating graphs of words in the word data, the graphs comprising overlapping categories or related words of the words in the word data.

18. A computer-implemented method of determining meanings of ambiguous words, the method comprising:

obtaining word data from one or more network resources, the word data comprising words in content of the one or more network resources;

for a subset of the word data, determining parts of speech of selected words in the subset of the word data;

determining definitions of the selected words to determine forms of the selected words;

determining one or more categories or related words for each of the word forms;

for two selected word forms, connecting the two selected word forms in one or more graphs in response to determining that categories or related words of the two selected word forms overlap, wherein connecting the two selected word forms in the one or more graphs comprises creating weighted word relationships between the two selected word forms; and

determining that a selected graph having a highest total weight of word relationships compared to one or more other graphs indicates definitions of one or more of the selected words in the subset of the word data.

19. The method of claim 18, further comprising:

creating word relationships between selected words in the word data to produce relationship data, the relationships indicating a degree of association between the selected words; and

recommending one or more assets to a target user based at least in part on the relationship data.

20. A system for dynamically generating targeted asset recommendations, the system comprising:

means for obtaining word data from one or more network resources, the word data comprising words in content of the one or more network resources;

means for creating word relationships between selected words in the word data to produce relationship data, each word relationship indicating a degree of association between two or more of the selected words;

means for analyzing browse information of a target user to identify one or more first words in the target user'"'"'s browse information;

means for identifying second words in the relationship data that are related to the first words in the target user'"'"'s browse information;

means for identifying one or more assets that are related to the second words; and

means for selecting at least a portion of the one or more advertisements from a data repository to provide at least one recommendation for the target user.