Feature-Sensitive Query Processing With Executable Data Constraints
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Solution Overview
Problem
Existing systems struggle to efficiently respond to time-sensitive and feature-sensitive queries by accurately identifying relevant data subsets within large datasets.
Innovation Solution
Utilizing a first language model to generate executable constraint determination commands based on time-sensitive or feature-sensitive queries, analyzing a data structure to identify relevant data subsets, and generating responses based on these subsets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a language model generates executable constraint determination commands to identify relevant data subsets, then query response accuracy is improved, but system complexity increases
Solution Approach 1:
The patent introduces a language model as an intermediary component that generates executable constraint determination commands. This mediator translates natural language queries into structured commands that can be executed to identify relevant data subsets, thereby improving query response accuracy while managing system complexity through modular design
Solution Approach 2:
The system segments the query processing into distinct components: the language model that generates commands, the command execution engine that runs constraints, and the data subset identification module. This segmentation allows each component to specialize in specific tasks, improving overall accuracy while making the complex system more manageable and maintainable
2Reliability
If the system analyzes the entire data structure to identify relevant data, then completeness of results is improved, but processing time increases
Solution Approach 1:
The patent applies preliminary action by generating and executing constraint determination commands before analyzing the entire data structure. These commands pre-filter the data based on identified constraints (such as time, category, or other attributes), ensuring that only relevant data subsets are processed in detail, thus maintaining result completeness while reducing processing time
Solution Approach 2:
The system extracts only the relevant data subsets that satisfy the generated constraints from the larger data structure. By taking out and focusing only on the pertinent portions of data rather than processing everything, the system maintains result reliability while significantly reducing the time required for analysis
Data Source
AI summary
One or more computing devices and/or methods are provided. In an example, a feature-sensitive query may be received. A first language model may be used to generate an executable feature constraint determination command based upon a set of information including the feature-sensitive query. The executable feature constraint determination command may be executed to determine a feature constraint associated with the feature-sensitive query. The data structure may be analyzed based upon the feature constraint to identify a subset of data, of the data structure, relevant to the feature constraint. A response to the feature-sensitive query may be generated based upon the subset of data.