Constrained Natural Language Processing for Dataset Access
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Solution Overview
Problem
Existing natural language processing systems struggle to accurately and efficiently process user inputs in conversational queries due to ambiguity and variability in natural language expressions, particularly in accessing multi-dimensional datasets.
Innovation Solution
The implementation of constrained natural language processing (CNLP) techniques that expose language sub-surfaces in a controlled manner, allowing users to interact with datasets using conversational queries which are then transformed into formal statements adhering to the dataset's syntax.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If natural language processing is used to process user inputs, then ease of operation is improved, but measurement precision deteriorates due to ambiguity and variability in natural language expressions
Solution Approach 1:
The patent introduces an intermediary layer between natural language input and dataset access that translates conversational queries into formal query statements. This intermediary translation layer preserves the ease of natural language input while ensuring precise interpretation by converting ambiguous user intent into structured, unambiguous query syntax that accurately reflects the desired data retrieval operation.
Solution Approach 2:
The system dynamically adjusts processing parameters based on the detected intent type and dataset characteristics. By changing parameters such as query transformation rules, model selection criteria, and processing depth according to the specific query context, the system maintains high precision across diverse natural language inputs while preserving ease of use.
2Measurement precision
If multiple machine learning models are applied to analyze query results, then measurement precision is improved, but use of energy increases due to additional processing requirements
Solution Approach 1:
The patent applies partial action by selectively invoking machine learning models based on the specific query type, dataset characteristics, and confidence thresholds. Instead of always applying all available models, the system applies only the necessary subset of models required to achieve sufficient analysis precision for each particular query, thereby reducing unnecessary energy consumption while maintaining accuracy where needed.
Solution Approach 2:
The analysis process is segmented into multiple stages with different model complexities. Simple queries use lightweight models for quick processing, while complex queries progressively engage more sophisticated models only when needed. This segmentation allows the system to optimize energy usage by matching model computational intensity to the actual complexity of the analysis task.
3Measurement precision
If formal syntax is required for database access, then measurement precision is improved, but ease of operation deteriorates due to rigid syntax requirements
Solution Approach 1:
The patent employs an intermediary translation mechanism that sits between the user interface and database engine. This intermediary automatically converts informal natural language queries into formal database query syntax, allowing users to benefit from the precision of formal syntax while experiencing the ease of informal language input. The translation layer handles syntax conversion transparently without requiring user expertise in formal query languages.
4Measurement precision
If additional processing steps are performed to transform queries, then measurement precision is improved, but loss of time increases due to extra processing operations
Solution Approach 1:
The system performs partial processing by applying transformation steps selectively based on query characteristics. Simple queries that clearly map to standard dataset operations undergo minimal or no transformation, while ambiguous or complex queries receive more extensive processing. This partial action approach maintains high accuracy for well-formed queries while minimizing processing time through selective application of transformation operations.
Solution Approach 2:
The system performs preliminary analysis of the incoming query to determine the appropriate processing path before executing full transformation sequences. By pre-assessing query structure, intent clarity, and dataset compatibility, the system can bypass unnecessary processing steps for straightforward queries while preparing appropriate transformation pipelines in advance for complex queries, thereby reducing overall processing time while maintaining precision.
Data Source
AI summary
In general, techniques are described for various aspects of accessing datasets. A device comprising a memory configured to store the dataset, and a processor may be configured to perform the techniques. The processor may expose a language sub-surface specifying a natural language containment hierarchy defining a grammar for a natural language as a hierarchical arrangement of a plurality of language sub-surfaces. The processor may receive a query to access the dataset, the query conforming to a portion of the natural language provided by the exposed language sub-surface. The processor may transform the query into one or more statements that conform to a formal syntax associated with the dataset, access, based on the one or more statements, the dataset to obtain a query result, and output the query result.