Natural Language Data Query System with Context Propagation
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Solution Overview
Problem
Current data analytics tools are challenging for non-technical users to perform complex data analyses and explorations due to their high-level, inflexible nature, often requiring specialized technical individuals and manual data manipulation in spreadsheets, and lack the ability to easily answer new and complex data questions.
Innovation Solution
A method and system for querying and analyzing datasets via natural language processing (NLP) with context propagation, allowing users to interact with datasets using natural language, translating utterances into structured query language (SQL) statements, and enabling context-based higher-level actions to build complex queries from previous conversation steps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional data analytics tools are used, then data analysis capability is provided, but ease of operation deteriorates for non-technical users
Solution Approach 1:
The patent introduces natural language processing as an intermediary layer between users and the data query engine. Users interact through natural language utterances instead of directly writing SQL queries, while the system automatically translates these utterances into executable queries. This mediator resolves the contradiction by making the system easy to operate for non-technical users while maintaining full adaptability to complex data analysis needs through the translation capability.
Solution Approach 2:
The patent replaces the mechanical system of manual SQL query writing and spreadsheet manipulation with an automated natural language processing system. Instead of requiring users to manually construct queries using technical syntax, the system uses NLP to automatically translate user intent into executable queries, thereby improving ease of operation while preserving analytical versatility.
2Measurement precision
If specialized technical individuals are used for data analysis, then data analysis precision is improved, but productivity deteriorates due to manual processes
Solution Approach 1:
The patent enables self-service data analysis by allowing any user to perform complex data queries through natural language without requiring specialized technical knowledge. The system automatically handles query translation, execution, and result generation, eliminating the need for manual intervention by technical specialists while maintaining analysis precision and significantly improving productivity through automation.
Solution Approach 2:
The patent substitutes the manual mechanical process of query construction and data manipulation performed by technical specialists with an automated NLP-driven system. This replacement maintains the precision of data analysis by accurately translating natural language intent into correct queries while dramatically improving productivity by eliminating manual processes and enabling any user to perform complex analyses.
3Adaptability or versatility
If conventional tools are used for data exploration, then basic analysis is possible, but adaptability deteriorates for complex and new data questions
Solution Approach 1:
The patent creates a universal interface that handles multiple types of data analysis tasks through a single natural language processing system. Instead of requiring different tools for different analysis types, the system can adapt to various query complexities and data exploration needs through the same NLP interface, thereby improving adaptability while managing system complexity through a unified approach.
Solution Approach 2:
The patent implements a dynamic query translation system that adapts to different user needs and query complexities. The NLP system can dynamically adjust its translation approach based on the specific utterance, conversation context, and data schema, allowing the system to handle everything from simple queries to complex data explorations without requiring separate tools for each scenario.
4Ease of operation
If Text-to-SQL translation is implemented for single questions, then ease of operation is improved, but adaptability deteriorates for iterative connected questions
Solution Approach 1:
The patent implements feedback mechanisms that allow the system to learn from and adapt to conversational context. By tracking previous questions and answers, the system can use this feedback to improve subsequent translations, maintaining ease of operation while gaining the adaptability to handle iterative and connected questions through context-aware translation that builds upon previous interactions.
Solution Approach 2:
The patent performs preliminary actions by maintaining conversation state and context information from previous interactions. Before translating new utterances, the system prepares by incorporating relevant context from the conversation history, enabling it to handle iterative questions that depend on previous answers while maintaining the simplicity of natural language interaction.
Data Source
AI summary
A method for querying and analyzing datasets via natural language processing (NLP) with context propagation is disclosed. In one embodiment, a computer-implemented method includes receiving, by a user interface, at least one of an utterance or a structured query language statement. The method includes identifying zero or more previous data conversation steps indicated by the utterance. The method includes determining an effective schema targeted by the utterance. The method includes generating, based on the utterance and the effective schema, an intermediate structured query language statement that is representative of the utterance. The method includes generating an executable structured query language statement based on the intermediate structured query language statement and zero or more previous structured query language statements. The method includes executing the executable structured query language statement for the data query engine schema. The method includes communicating, via the user interface, a result set and metadata.