Systems and methods for targeted advertising

• US 9,129,305 B2
• Filed: 04/21/2014
• Issued: 09/08/2015
• Est. Priority Date: 04/06/2007
• Status: Expired due to Fees

• Current Assignee: Convergent Assets LLC (Jeffrey M. Gross)
• Original Assignee: AWEL, LLC
• Inventors: Larner, Marcus G., Moeck, Andrew Michael
• Primary Examiner(s): SHEPARD, JUSTIN E
• Application Number:
  US14/257,394
• Publication Number:
  US 20140229280A1
• Time in Patent Office: 505 Days
• Field of Search: None
• US Class Current: 1/1
• 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:

under control of a hardware processor;

a data aggregation module configured to obtain a plurality of words from a social network;

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 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 an amount of social momentum between said selected words; and

a recommender configured to generate targeted advertising based at least in part on said relationship data, wherein the recommender module is further configured to generate the targeted advertising by at least accessing user data to identify one or more first words in the relationship data, identifying one or more second words in the relationship data that have one or more of the word relationships with the one or more first words, and identifying 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 degree of association is further 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, or

an authority factor of the subset of the social-network data.

4. The system of claim 1, wherein said recommender module selects an advertisement from the one or more advertisements based on the degree of association between said keyword and the one or more second words.

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 user data.

6. The system of claim 1, wherein the user data includes at least one of user behavioral data and user tracking data.

7. The system of claim 1, wherein the user behavioral data includes at least one of data on the user'"'"'s browse behavior, the user'"'"'s asset selection behavior, and the user'"'"'s purchase behavior.

8. The system of claim 1, wherein the user tracking data includes at least one or more of the following:

data regarding a browse history of the user, a purchase history of the user, demographic data on the user, and geographic data related to the user.

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