Knowledge Q&A Retrieval Using Semantic Matching for Business Documents

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Solution Overview

Problem

Existing methods for retrieving business knowledge are inefficient and prone to missing relevant information due to the need for manual document browsing, which is time-consuming and lacks accuracy.

Innovation Solution

A knowledge question-answering method utilizing a machine learning model to retrieve and determine answers from a knowledge base based on user input, employing semantic recall and matching retrieval to enhance accuracy and efficiency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of time

If manual document browsing is used to retrieve business knowledge, then users can access documents, but the process is time-consuming and prone to missing relevant information

Engineering Contradiction:

Improvetime required to find informationVSAvoidefficiency of knowledge retrieval

Core Design Contradiction:

Loss of timeVSProductivity

Solution Approach 1:

The patent replaces manual document browsing with an automated machine learning-based retrieval system. The system uses natural language processing to understand user queries and automatically searches through the knowledge base, eliminating the need for manual document inspection and significantly reducing time loss.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system performs self-service by automatically retrieving and filtering relevant documents based on user questions. The machine learning model autonomously processes queries, searches the knowledge base, and presents relevant information without requiring user intervention in the search process.

Inventive Principle:

Principle #25Self-service

2Measurement precision

If manual document browsing is used to retrieve business knowledge, then users can review documents, but accuracy is reduced due to manual inspection limitations

Engineering Contradiction:

Improveaccuracy of knowledge retrievalVSAvoidease of document searching

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The patent replaces manual document inspection with automated machine learning-based information extraction. The system uses NLP models to understand query semantics and automatically identifies relevant information, improving accuracy compared to manual browsing while maintaining ease of operation through natural language interfaces.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Extent of automation

If traditional document retrieval is used, then users can access business documents, but the process lacks automation and requires manual intervention

Engineering Contradiction:

Improveautomation level of knowledge retrievalVSAvoidcomplexity of retrieval system

Core Design Contradiction:

Extent of automationVSDevice complexity

Solution Approach 1:

The patent implements automation by replacing manual retrieval operations with machine learning-based automated systems. The NLP models and automated search algorithms handle the retrieval process autonomously, achieving high automation levels while managing system complexity through integrated AI components.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20260065095A1Knowledge question-answering method, apparatus, readable medium, electronic device, and program product

Publication Date: 2026.03.05 BEIJING YOUZHUJU NETWORK TECH CO LTD

AI summary

The present disclosure relates to a knowledge question-answering method, an apparatus, a readable medium, an electronic device, and a program product. The method includes: acquiring a target question input by a user in a natural language; retrieving, through a machine learning model, in a knowledge base according to the target question to obtain a target knowledge document for answering the target question, and determining, based on the target knowledge document, a target answer for the target question, wherein the knowledge base is used to store a business knowledge document; and displaying the target answer to the user.