Semantic Search Vector Space Intent Analysis
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Solution Overview
Problem
Conventional search mechanisms fail to accurately account for the user's intent behind a query, leading to irrelevant search results due to their reliance on keywords rather than understanding the underlying concepts and semantic relationships.
Innovation Solution
The system performs a semantic analysis of the query to determine the user's intent, representing it as a query vector in a multi-dimensional semantic space, allowing for the identification of relevant results based on semantic similarities and relationships, and uses machine learning techniques to refine the search results interactively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional keyword-based search mechanisms are used, then the search system is simple and fast, but the search results are not accurate or relevant to user intent
Solution Approach 1:
The patent replaces the mechanical keyword-matching system with a semantic analysis system that uses natural language understanding and machine learning models to interpret user intent. This substitution enables the system to comprehend the meaning behind queries rather than merely matching keywords, thereby improving search accuracy while accepting increased system complexity.
Solution Approach 2:
The patent transforms the search approach by changing the fundamental parameter from keyword exactness to semantic similarity. By using vector representations of text and calculating cosine similarity between query vectors and document vectors, the system evaluates relevance based on meaning rather than literal word matching, significantly improving result accuracy.
2Adaptability or versatility
If keyword-based categorization is used, then the categorization process is straightforward, but it fails to account for user intent and semantic relationships
Solution Approach 1:
The patent replaces simple keyword-based categorization with a semantic analysis mechanism that uses natural language processing and machine learning. This substitution enables the system to detect and measure semantic relationships and user intent by analyzing the meaning and context of queries, thereby improving adaptability to user needs.
Solution Approach 2:
The patent introduces vector representations as an intermediary between user queries and search results. These vectors serve as a mediator that captures semantic meaning, allowing the system to interpret user intent and perform semantic similarity comparisons without directly processing complex natural language structures.
3Measurement precision
If precise keyword matching is required, then the search system is simple to implement, but it produces irrelevant results when users use different wording
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing vector representations of search documents in advance. When a user query arrives, the system only needs to compute the query vector and perform similarity comparisons, rather than performing full semantic analysis on each document in real-time. This significantly reduces computational processing time while maintaining high semantic similarity accuracy.
Data Source
AI summary
A semantic analysis can be performed to determine an intent of a received query. The intent can relate to a primary object of the query, which can be identified through the semantic analysis. Other attributes can be determined from the query that help to focus the object of the intent. A query vector is generated, based on the intent and primary object, and used to search a multi-dimensional semantic space including semantic representations of possible matches. The attributes are used to adjust the query vector in the semantic space. Objects having vectors ending proximate the query vector are identified as potential search results, with the distance from the query vector being used as a ranking mechanism. If refinement is needed, a dialog is used to obtain additional information from the user. Once results are obtained with sufficient confidence, results can be returned as search results for the query.