Natural Language Input Quality Scoring via Intent Entity Context Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current computing systems that interact with users, such as ticketing tools and chatbots, struggle to analyze unstructured user input effectively due to the lack of detailed insights from high-level, generic identifiers in structured data, leading to low-quality training and user adoption.
Innovation Solution
A system that analyzes and scores natural language input by identifying intent, entities, and context, preventing users from proceeding until a sufficient quality score is met, ensuring adequate information is provided for accurate semantic analysis and improved insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If structured ticket data fields are used for analysis, then high-level insights are provided, but detailed insights and holistic analysis are unachievable
Solution Approach 1:
The patent segments unstructured natural language input into distinct components including intent, entities, parameters, and context. This segmentation allows detailed analysis of each component separately while maintaining the overall meaning, thereby achieving holistic analysis without requiring complex structured data fields
Solution Approach 2:
The patent introduces an intermediary processing layer that translates unstructured natural language into a standardized semantic representation. This intermediary layer includes intent identification, entity extraction, and context analysis, enabling detailed insights without directly manipulating complex structured data
2Productivity
If periodic reviews of operations delivery data are conducted, then preliminary information is obtained, but user adoption and automation opportunities are limited
Solution Approach 1:
The patent performs preliminary analysis of natural language input by identifying intent, entities, and context before processing. This preliminary action ensures that high-quality, detailed information is extracted and structured upfront, enabling better automation opportunities and reducing information loss in subsequent processing stages
Solution Approach 2:
The patent implements feedback mechanisms where the analyzed semantic representation is evaluated against expected patterns and requirements. This feedback loop ensures information quality by identifying gaps or ambiguities in the extracted intent, entities, or context, and enables continuous improvement of the analysis process
Data Source
AI summary
Embodiments for managing natural language user input are provided. Natural language input is received from a user utilizing a computing node. The natural language input is analyzed. The analyzing of the natural language input includes attempting to identify at least one of an intent associated with the natural language input, an entity associated with the natural language input, and context data associated with the natural language input. The natural language input is evaluated against a pre-trained model based on the analyzing of the natural language input. A quality score for the natural language input is calculated based on the evaluating of the natural language input. An action is caused to be performed utilizing the computing node based on the calculated quality score.