NLP Question Generation From Communication Data for Searchable Knowledge Bases
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Solution Overview
Problem
Digital communications data often lack definitive scope or boundaries, making it difficult to identify answers to potential future online queries and result in large, cumbersome datasets that are hard to search effectively.
Innovation Solution
A computer-implemented method using natural language processing (NLP) tools to analyze digital communications data, generate context-based questions and answers, and update a dynamic knowledge base automatically, eliminating the need for manual input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If digital communications data is stored without processing, then the complete communication history is preserved, but the data becomes large and difficult to search effectively
Solution Approach 1:
The patent extracts relevant information from digital communications data by generating summaries and identifying key content through NLP analysis. This extraction process creates a condensed representation that maintains the essential communication history while reducing the volume of data that needs to be searched, thereby improving search effectiveness.
Solution Approach 2:
The patent segments the digital communications data into manageable units by creating individual summaries for each communication or communication thread. This segmentation allows the system to process and search through smaller, organized portions of data rather than handling the entire large dataset at once, improving both processing efficiency and search capability.
2Ease of operation
If manual query input is used for searching communications data, then user-specific needs can be addressed, but time and productivity are reduced
Solution Approach 1:
The patent performs preliminary action by automatically generating summaries and organizing communications data before user queries are received. The system pre-processes the data structure, identifies key content, and creates search-ready representations, so when users need to search, the data is already optimized for efficient retrieval, significantly reducing search time.
Solution Approach 2:
The system provides self-service capabilities by automatically generating its own search queries and summaries without requiring manual input. The NLP-based system autonomously analyzes communications data, creates relevant search terms, and organizes results, eliminating the need for users to manually formulate complex search queries while still addressing their specific information needs.
3Measurement precision
If comprehensive search options are provided, then search thoroughness is improved, but the system complexity increases
Solution Approach 1:
The patent introduces an intermediary layer in the form of AI-powered NLP processing that sits between the raw communications data and the search interface. This intermediary automatically translates user search needs into optimized queries, processes the data through intelligent analysis, and presents relevant results, thereby achieving thorough search capabilities without exposing users to or requiring complex search system infrastructure.
Data Source
AI summary
Systems and methods are provided to generate context-based questions from communications data. In embodiments, a method includes: determining, by a processor set, context of text under study obtained from digital communications data; generating, by the processor set, a text-based summary of the text under study using a natural language processing (NLP) tool to eliminate text from the text under study based on keywords of a dynamic knowledge base; constructing, by the processor set, a question phrase in sentence form based statements in the text under study, wherein the one or more statements constitute an answer to the question phrase; outputting, by the processor set, a question and answer set including the question phrase and the statements; and automatically updating, by the processor set, the dynamic knowledge base based on the question and answer set, thereby generating an updated dynamic knowledge base.