Natural Language Query Answering via Metadata-Driven Sentence Construction
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Solution Overview
Problem
Current search engines and intelligent personal assistants often provide lists of documents or web pages containing the answer, rather than directly answering queries, which can lead to user confusion due to potential misinterpretation of queries and lack of clarity in answer presentation.
Innovation Solution
A system and method that receives natural language queries, determines answers, and formats them with metadata to construct syntactically correct sentences or statements, enabling clear presentation and confirmation of intended answers, using techniques such as entity databases and mathematical calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If search engines provide lists of documents or web pages containing the answer, then the system can return multiple potential answers, but the user may experience confusion due to potential misinterpretation of queries and lack of clarity in answer presentation
Solution Approach 1:
The patent introduces an intermediary system that sits between the search engine and the user. This intermediary parses the query, identifies the user's intent, and transforms the raw search results into a structured, natural language answer. The intermediary acts as a mediator that translates complex search data into clear, unambiguous responses, resolving the contradiction between providing multiple potential answers and ensuring clarity.
Solution Approach 2:
The system employs self-service mechanisms by automatically analyzing the query, determining the appropriate answer format, and generating the response without requiring user interaction to clarify ambiguities. The system serves itself by internally resolving query interpretation issues and presenting the most appropriate answer directly, eliminating user confusion while maintaining answer accuracy.
2Manufacturing precision
If the system provides detailed metadata and structured information, then the answer construction can be more accurate, but the system complexity increases
Solution Approach 1:
The patent segments the answer generation process into distinct components: query parsing, metadata extraction, answer construction, and natural language generation. Each component handles a specific aspect of the task, working independently but coordinating through standardized interfaces. This segmentation allows the system to achieve high answer construction accuracy through specialized processing while managing complexity through modular architecture.
Solution Approach 2:
The system changes parameters by transforming structured metadata into natural language parameters. Instead of presenting raw metadata, the system converts it into grammatical structures, syntactic patterns, and semantic parameters that enable accurate answer construction. This parameter transformation allows precise control over answer generation while abstracting away the underlying system complexity.
Data Source
AI summary
A natural language query is received, and an answer to the natural language query is determined. A message is formatted such that the message includes the answer, and metadata corresponding to the answer, the metadata including information to enable construction, using the metadata, of a sentence that rephrases the query and recites the answer.