Semantic Advertiser System for Contextual Content Targeting
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Solution Overview
Problem
Conventional online advertising systems rely on keyword analysis, which can be unreliable and lead to counterproductive ad placements, failing to effectively target users' interests and needs based on the content of webpages.
Innovation Solution
A semantic advertiser system that analyzes electronic text to identify topic categories, psychological states, and demographic profiles, linking them to relevant content, such as ads, using a contextual taxonomy, demographic profile database, and psychological state map to provide highly relevant content to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If keyword analysis is used to place advertisements, then ad placement automation is achieved, but ad relevance and effectiveness deteriorate
Solution Approach 1:
The system transitions from simple keyword matching to multi-dimensional semantic analysis by changing the parameters of text analysis. It extracts entities, relationships, and psychological states from text, transforming the automation approach from surface-level keyword comparison to deep semantic understanding, thereby maintaining automation while significantly improving ad relevance
Solution Approach 2:
The patent introduces an intermediary layer of semantic analysis between the ad placement system and the content. This intermediary extracts psychological states, entities, and relationships from text, serving as a bridge that enables more accurate matching between advertisements and content context without sacrificing automation
2Speed
If keyword analysis is used to place advertisements, then processing speed is maintained, but measurement precision of user interests deteriorates
Solution Approach 1:
The system segments the text analysis process into distinct components: entity extraction, relationship identification, and psychological state detection. This segmentation allows each component to be optimized independently, maintaining processing speed while improving the precision of user interest identification through focused analysis of specific text features
3Device complexity
If ads are placed based on surface-level content matching, then ad placement complexity is reduced, but ad effectiveness worsens
Solution Approach 1:
The system performs preliminary semantic analysis of the text content before ad placement, extracting entities, relationships, and psychological states in advance. This preliminary action creates a rich semantic representation that guides subsequent ad matching, reducing the complexity of the placement decision while improving effectiveness through pre-processed semantic understanding
Data Source
AI summary
The exemplary embodiments provide methods and systems for automatically identifying content related to an electronic text. Aspects of exemplary embodiments include linking topic categories, psychological states, demographic profiles, and additional content using one or more databases; in response to receiving content of an electronic text, analyzing by a software component executing on a computer the content and assigning one or more of topic categories to the content; automatically identifying at least one the psychological states of a user caused by the content and the demographic profiles whose members would be interested in the content that are linked to the one or more topic categories assigned to the content; and presenting a portion of the additional content that is linked to at least one of the identified demographic profiles and the psychological states.