Hidden Connection Detection for Phrase Proximity Analysis
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Solution Overview
Problem
Current search systems and advertising technologies fail to accurately distinguish between relevant and irrelevant information, particularly in identifying user intent and trends, leading to inefficient targeting of advertisements and lack of real-time analysis of human behavior.
Innovation Solution
A method and system for identifying hidden connections among non-sentiment phrases by analyzing their proximity, determining direct and indirect correlations, and generating term taxonomies to associate sentiment and non-sentiment phrases, enabling real-time detection of trends and user intent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If search systems provide comprehensive information access, then information availability is improved, but information relevance deteriorates due to large amounts of unwanted hits
Solution Approach 1:
The patent segments the information retrieval process into multiple stages: initial broad search followed by progressive filtering and refinement. The system divides the large result set into relevant and irrelevant portions through multiple processing steps, applying segmentation to separate wanted information from unwanted hits.
Solution Approach 2:
The system applies local quality by enhancing the precision of information matching in specific regions of the search process. It uses targeted analysis of user intent, sentiment detection, and contextual relevance assessment to improve the quality of specific result subsets rather than treating all information uniformly.
2Adaptability or versatility
If advertising targets are expanded to include more demographic parameters, then targeting coverage is improved, but targeting accuracy deteriorates due to inability to capture true user intent
Solution Approach 1:
The patent introduces intermediary elements such as sentiment analysis, intent detection algorithms, and contextual understanding mechanisms that mediate between basic demographic data and actual user intent. These intermediaries translate surface-level demographic information into deeper insights about true user needs and preferences.
Solution Approach 2:
The system replaces mechanical demographic matching with intelligent analysis mechanisms. Instead of relying solely on rigid demographic criteria, it substitutes algorithmic analysis of user behavior, sentiment, and contextual patterns to achieve more accurate intent-based targeting.
3Loss of information
If analysis of user behavior data is deepened to understand true intent, then understanding quality is improved, but processing complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-processing user data to extract key intent indicators before main analysis. It performs preliminary sentiment classification, intent categorization, and pattern recognition on raw data, preparing refined inputs for subsequent deeper analysis and reducing the complexity of later processing stages.
Data Source
AI summary
A system and method for identifying hidden connections among non-sentiment phrases are presented. The method includes identifying all connections among a plurality of non-sentiment phrases based on at least one proximity rule; determining direct connections among the identified connections, wherein each direct connection meets a predetermined correlation; filtering out the determined direct connections from the identified connections to yield hidden connections among the identified connections; analyzing the hidden connections to identify a common phrase, wherein the common phrase is associated with at least two hidden connections; generating a new hidden connection among the plurality of non-sentiment phrases based on the common phrase; and associating a sentiment phrase with at least two non-sentiment phrases having a hidden connection, wherein the association is a term taxonomy.