Natural Language Query Modeling With User Confirmation
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Solution Overview
Problem
Existing voice-based user interaction systems for devices such as PCs, PDAs, and smartphones are often inaccurate, imprecise, and unreliable, requiring time-consuming voice training, which limits their adoption and use in applications like e-commerce and e-learning.
Innovation Solution
A system and method that allows users to input natural language queries, which are modeled and presented to the user for confirmation before execution, enabling modification, and executed to extract data from databases, with user identity-based access and adaptive learning to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If voice-based user interaction systems are implemented, then user interaction convenience is improved, but accuracy and reliability deteriorate
Solution Approach 1:
The system performs preliminary actions by presenting the modeled query to the user before execution. This allows the user to review and verify the interpreted query, ensuring accuracy before it is submitted to the database, thus resolving the contradiction between convenience and reliability.
Solution Approach 2:
The system implements feedback by displaying the modeled query back to the user for confirmation. This feedback loop allows the user to detect and correct any misinterpretations, improving accuracy while maintaining the convenience of voice-based interaction.
2Measurement precision
If voice training is implemented to improve accuracy, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system uses self-service by automatically modeling the query from the user's voice input without requiring external training. The query modeling engine processes the speech input and generates a structured query automatically, eliminating the need for time-consuming voice training while maintaining accuracy.
Solution Approach 2:
The system performs preliminary query modeling and presentation before execution, allowing accurate query interpretation without requiring prior voice training. This preliminary processing step ensures precision while avoiding the time loss associated with training procedures.
3Reliability
If query modeling and user confirmation are implemented, then reliability is improved, but loss of time increases
Solution Approach 1:
The system performs the query modeling and user confirmation as a preliminary action before query execution. By presenting the modeled query to the user beforehand, the system ensures reliability and accuracy while minimizing time loss, as the user only needs to review and confirm rather than manually construct the entire query.
Data Source
AI summary
A computer method and system for providing information results in response to a natural language information request. The system and method include receiving a natural language information request from a user and compiling a computer executable query from the natural language information request from a user wherein the query is formatted to extract data from one or more computer databases. The query is then presented to the user prior to execution of the query so as to enable the user to change the query prior to its execution. The query is then executed to extract data from one or more computer databases whereby extracted data is presented to the user in a certain presentation format.