Content Interest Determination via Keyword Network Analysis
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Solution Overview
Problem
Conventional analytics systems rely on manual rule-based techniques for extracting relevant information from vast datasets, leading to inaccurate and inefficient user interest determination, particularly in business-to-business interactions, due to limitations in contextual data collection and manual tagging of digital content.
Innovation Solution
The system automatically determines content interest by extracting keywords from digital content, creating network representations that capture both statistical and semantic significance, and combining interaction weights to generate interest values, enabling more accurate and dynamic user interest analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual rule-based techniques are used to extract relevant information from data, then the system operation is simple and maintainable, but the accuracy and reliability of user interest determination deteriorates
Solution Approach 1:
The patent replaces manual rule-based techniques with automated machine learning models that process digital content and interaction data. The system automatically extracts features, determines topics, and calculates interest values using neural networks and computational algorithms, eliminating the need for manual content tagging and rule configuration while improving accuracy and reliability of user interest determination.
Solution Approach 2:
The system performs self-service by automatically extracting topics from digital content, determining user interests, and generating recommendations without requiring manual intervention. The machine learning models continuously process interaction datasets and update interest values autonomously, allowing the system to maintain high accuracy while reducing operational complexity through automation.
2Productivity
If manual tagging of digital content is performed, then the labeling process is controllable and reviewable, but the time required for processing and the quantity of accurately tagged content deteriorates
Solution Approach 1:
The patent replaces manual content tagging with automated machine learning systems that process digital content at scale. The system automatically extracts topics, determines relevance, and assigns interest values using computational algorithms, enabling rapid processing of vast quantities of content without the time constraints of manual tagging while maintaining accuracy through sophisticated models.
Solution Approach 2:
The system performs preliminary actions by pre-processing digital content to extract features, identify topics, and prepare interaction data before final interest determination. This preliminary processing enables the system to handle large volumes of content efficiently and reduces the time required for subsequent analysis and recommendation generation.
3Quantity of substance
If conventional analytics systems process vast quantities of data, then the system can analyze more comprehensive datasets, but the computational resources required and operational efficiency deteriorates
Solution Approach 1:
The patent extracts only the most relevant features and topics from vast quantities of digital content and interaction data. By using machine learning models to identify and select key characteristics, the system processes comprehensive datasets while reducing computational requirements compared to analyzing all raw data, thereby maintaining high operational efficiency despite processing large volumes of information.
Solution Approach 2:
The system applies local quality by focusing computational resources on the most relevant portions of data. The machine learning models identify and process only the critical features and interaction patterns that contribute to accurate interest determination, rather than uniformly processing all data, which optimizes operational efficiency while maintaining comprehensive analysis capability.
Data Source
AI summary
Techniques and systems are described for content interest from interaction information. Keywords are extracted from digital content, and relevance values are determined based on the keywords that captures both the statistical and semantic significance of topics in the digital content through use of a network representation. Interest values for an entity are determined based on the relevance values and an interaction dataset, which capture both the statistical and semantic significance of the topics with respect to the entity. The interest values may be utilized to control output of digital content to a client device.