Context-Driven Search Using Semantic Vectors Beyond Keywords

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Solution Overview

Problem

Keyword-based information retrieval systems fail to provide accurate search results when the search corpus lacks shared keywords with the query, leading to irrelevant or missing information.

Innovation Solution

Implementing an AI-based context search controller that tokenizes and encodes text into vectors, applies natural language processing to determine semantic similarity, and generates context-driven search results using machine learning models like neural networks to bridge the gap between query and document vocabulary.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If keyword-based search techniques are used, then search queries can be compared against a corpus of documents using simple matching, but search results become irrelevant or missing when the search corpus does not feature the specific keywords

Engineering Contradiction:

Improveease of implementationVSAvoidsearch accuracy

Core Design Contradiction:

Ease of manufactureVSReliability

Solution Approach 1:

The patent replaces the mechanical keyword-matching system with an AI-based natural language processing system. Instead of comparing search queries against documents using simple string matching, the system uses trained AI models to understand semantic meaning, context, and intent behind queries and documents, enabling accurate retrieval even when exact keywords are absent.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent changes the fundamental parameter of search from keyword presence/absence to semantic similarity scores. By transforming the search mechanism from binary keyword matching to continuous semantic scoring based on contextual understanding, the system can retrieve relevant documents that don't share exact keywords with the query.

Inventive Principle:

Principle #35Parameter changes

2Speed

If keyword-based matching is used, then the search process is fast and simple, but the system fails to capture contextual relationships between queries and documents

Engineering Contradiction:

Improvesearch speedVSAvoidcontextual information

Core Design Contradiction:

SpeedVSLoss of information

Solution Approach 1:

The patent performs preliminary processing of documents by training AI models on the corpus beforehand. This pre-training enables the system to understand contextual relationships in advance, so that during actual search operations, the system can quickly compare query semantics against pre-processed document representations without losing contextual information.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20250298823A1Systems, methods, and apparatus for context-driven search

Publication Date: 2025.09.25 ENCYCLOPEDIA BRITANNICA

AI summary

Systems, methods, and apparatus for context-drive search are disclosed. An example apparatus includes memory to store machine-readable instructions, and at least one processor to execute the machine-readable instructions to at least tokenize text included in a query for content into text portions, encode the text portions into respective vectors, organize the text portions based on natural language similarity of the text portions, the natural language similarity based on the respective vectors, and generate one or more search results based on the organized text portions, and rank the one or more search results for presentation on a computing device.