Empathy Model for Detecting Closed-Ended Queries
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing querying methods often result in closed-ended questions that lead to short answers, inadequate information collection, and empathy gaps, causing extended query sessions and misdirected data collection.
Innovation Solution
A computer method and system for training an empathy model to detect closed-ended queries using machine learning, which generates a model to classify queries based on training data, incorporating vivid grammar and domain-specific grammars to elicit more informative and empathetic responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If closed-ended queries are used, then query sessions are extended and more queries are generated, but information collection becomes inadequate and less information is gathered
Solution Approach 1:
The system changes the parameter of query structure from closed-ended to open-ended by detecting closed-ended queries and transforming them. This parameter change allows the same query session to elicit more comprehensive information without extending duration, resolving the contradiction between session length and information quality
Solution Approach 2:
The system implements feedback by analyzing the structure of generated queries and providing real-time transformations when closed-ended patterns are detected. This feedback loop ensures that information collection quality is maintained or improved while managing query session efficiency
2Loss of information
If more queries are generated to collect information, then information coverage increases, but query sessions become extended and less efficient
Solution Approach 1:
The system transforms the parameter of query openness from closed to open, enabling single queries to cover broader information ground. This reduces the total number of queries needed while maintaining or improving information coverage, thereby enhancing query session efficiency
Solution Approach 2:
The system segments the query generation process into detection and transformation phases, allowing efficient handling of information collection by addressing closed-ended patterns as they arise rather than requiring multiple separate queries
3Ease of operation
If closed-ended queries are used, then queries are simple and direct, but empathy gaps occur and data collection becomes misdirected
Solution Approach 1:
The system introduces an intermediary transformation layer that converts simple closed-ended queries into more empathetic open-ended queries. This intermediary process maintains the simplicity intent while improving the quality and direction of data collection through empathetic framing
Solution Approach 2:
The system changes the parameter of query empathy by transforming closed-ended structures into open-ended ones, thereby improving the reliability of data collection while preserving the essential simplicity and directness of the original query intent
Data Source
AI summary
A computer method, system, and device of training an empathy model for detecting a type of query, the method including defining an intent for detecting closed ended queries, providing a plurality of queries that are closed ended queries to a machine learning model generator, said plurality of queries comprising training data, providing a plurality of corresponding labels identifying the plurality of queries as closed ended queries, and generating a model that classifies closed ended queries as a function of the training data.