Natural Language Query Editing via Variable Segmentation
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Solution Overview
Problem
Existing natural language processing systems often misinterpret user queries due to slight nuances or single-word errors, requiring users to repeat entire statements to achieve the desired result, which is inefficient and sometimes detrimental.
Innovation Solution
A system and method that parse natural language queries into categories and variables, allowing users to modify these variables through spoken or typed inputs, enabling efficient editing and refinement of queries without requiring users to restate the entire query, and providing a user interface to filter and flag results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system immediately executes the natural language query without allowing modification, then the processing speed is fast, but the accuracy of the query interpretation may be wrong due to single-word errors or nuances
Solution Approach 1:
The patent segments the natural language query into discrete components (categories and variables) that can be individually examined and modified. This allows the system to maintain fast processing by only requiring minimal user input for corrections rather than full query repetition, while improving accuracy through precise targeting of erroneous segments.
Solution Approach 2:
The patent introduces an intermediary interface that displays parsed categories and variables between the user's original query and the final execution. This intermediary layer allows users to make targeted corrections to specific variables without restate the entire query, resolving the contradiction between speed and accuracy.
2Measurement precision
If the system requires users to repeat the entire statement to correct errors, then the query accuracy can be improved, but the ease of operation deteriorates due to inefficiency
Solution Approach 1:
By segmenting the query into modifiable variables and categories, the system enables users to correct only the specific portions that contain errors rather than repeating the entire statement. This dramatically improves operational ease while maintaining accuracy.
Solution Approach 2:
The patent implements partial action by allowing users to modify only the necessary portions of the query (specific variables or categories) rather than requiring complete repetition. This partial modification approach significantly reduces user effort while achieving the desired accuracy.
3Ease of operation
If the system displays and allows modification of multiple categories and variables, then the ease of operation improves for query refinement, but the device complexity increases
Solution Approach 1:
The segmentation of queries into standardized categories and variables creates a modular structure that, while appearing complex, actually simplifies the editing process. Users interact with discrete, well-defined elements rather than attempting to modify unstructured natural language, making the system easier to operate despite the underlying complexity.
Solution Approach 2:
The patent creates a universal interface framework that handles multiple categories and variables through a single consistent interaction model. This multi-functional approach allows the same editing mechanisms to work across different query types, reducing the perceived complexity for users while maintaining comprehensive functionality.
Data Source
AI summary
Systems and methods are disclosed herein for processing a natural language query. A receiver circuitry receives the natural language query from a user. A natural language interpreter circuitry parses the natural language query to convert the natural language query into a plurality of categories and a plurality of variables, each variable in the plurality of variables corresponding to one category in the plurality of categories. A user interface displays to the user the plurality of categories' and the plurality of variables, and allows the user to modify at least one variable in the plurality of variables by providing a natural language utterance.