Automated Technical Need Identification System Using Text Mining
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Solution Overview
Problem
Identifying unmet customer needs in technical areas is daunting due to the vast amount of documents and complexity of technical systems, making it difficult to distinguish important and valuable problems that can be addressed by organizations or technologies.
Innovation Solution
A computer-implemented method that receives user input, associates it with a technological field, generates suggested terms, and analyzes documents to identify technical needs and problems by using textual analysis, sentiment analysis, and machine learning classifiers to score and tag problem kernels, thereby facilitating the identification of unmet technical needs and opportunities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual search and analysis of technical documents is performed, then comprehensive coverage of technical needs can be achieved, but the time and resources required become prohibitively large
Solution Approach 1:
The patent replaces manual mechanical search and analysis processes with automated computer-based text mining, natural language processing, and machine learning algorithms. The system automatically extracts technical needs, problems, and solutions from documents, eliminating the need for human reviewers to manually examine each document while maintaining comprehensive coverage through systematic automated analysis of entire document corpora.
2Measurement precision
If comprehensive document analysis is conducted to identify all technical needs, then identification accuracy improves, but the complexity of the system increases
Solution Approach 1:
The patent segments the complex task of technical need identification into distinct modular components: document retrieval module, text preprocessing module, entity recognition module, relationship extraction module, and result ranking module. Each module performs a specific function and can be independently optimized, maintained, and scaled, reducing overall system complexity while achieving comprehensive and accurate identification through coordinated operation of these specialized components.
3Difficulty of detecting and measuring
If advanced text mining and NLP techniques are applied, then the ability to identify unmet needs improves, but computational resources and processing time increase
Solution Approach 1:
The patent applies partial action by selectively extracting only the most relevant technical information from documents using targeted entity recognition and relationship extraction algorithms. Rather than analyzing every aspect of each document in equal detail, the system focuses computational resources on identifying and extracting specific technical needs, problems, and solutions that are most likely to represent unmet market requirements, thereby reducing overall computational burden while maintaining high detection capability.
Data Source
AI summary
Systems and methods described herein comprise a user interface for searching, analyzing, and interpreting documents obtained from computer databases. Exemplary systems and methods receive a user input and automatically identify, analyze, and interpret unmet technical needs and/or technical problems in specific areas of technology based on that input. Other exemplary systems and methods automatically identify, analyze, and interpret unmet technical needs and/or technical problems across numerous areas of technology based on similar user input. Other exemplary systems and methods receive user input and automatically identify, analyze, and interpret documents associated with a company to determine one or more technical capabilities of that company.