Natural Language Interface for Data Visualization Analysis
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Solution Overview
Problem
Current data visualization tools are difficult to use for complex datasets, requiring expert modeling and limited to basic interactive visualizations or single text answers, and often fail to provide intuitive navigation through large or complex data sets.
Innovation Solution
A natural language interface is integrated into data visualization applications to allow users to interact with datasets using ordinary questions, applying principles of pragmatics for improved interaction, enabling graphical answers and efficient data exploration by understanding grammatical and lexical structures, and resolving ambiguity through targeted feedback and widgets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a natural language interface is integrated into data visualization applications, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The patent introduces a natural language processing system as an intermediary layer between the user and the complex data visualization backend. This mediator translates ordinary user questions into structured queries and contextual instructions, enabling users to interact with complex datasets without needing to understand the underlying system complexity. The natural language interface acts as a buffer that simplifies user interaction while managing the complexity internally.
Solution Approach 2:
The system employs contextual analysis and pragmatic inference to automatically understand user intent without requiring explicit parameter specification. The natural language interface self-adjusts by analyzing conversation history, identifying entities, and inferring missing information, thereby serving itself to bridge the gap between simple user input and complex data retrieval operations.
2Ease of operation
If contextual analysis and pragmatic inference are used to understand user intent, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The system applies partial contextual analysis by focusing on the most relevant pragmatic features needed to understand user intent, rather than attempting to analyze all possible linguistic aspects. This selective approach maintains measurement precision by concentrating computational resources on critical inference elements while ignoring redundant information, thereby balancing ease of operation with accurate intent recognition.
Solution Approach 2:
The natural language interface performs preliminary contextual analysis by pre-processing user input to identify entities, extract key concepts, and establish conversation context before executing the main data query. This preliminary action prepares the system to handle ambiguous references more accurately by having advance knowledge of the conversational context, thereby maintaining precision while improving ease of operation.
3Ease of operation
If the system supports incomplete utterances and synonyms, then ease of operation is improved, but reliability deteriorates
Solution Approach 1:
The system employs feedback mechanisms by analyzing conversation history and user responses to continuously refine its understanding of incomplete utterances and synonyms. When encountering ambiguous or incomplete input, the system uses feedback from contextual analysis to disambiguate meaning and confirm intent before executing queries, thereby maintaining reliability while supporting flexible user input formats.
Solution Approach 2:
The natural language interface dynamically adapts its interpretation of user input based on conversational context and previous interactions. Rather than relying on rigid keyword matching, the system dynamically adjusts its understanding of synonyms and incomplete utterances based on the evolving conversation state, thereby maintaining reliability through context-aware interpretation while improving ease of operation.
4Productivity
If the natural language interface provides graphical answers and creates new visualizations, then productivity is improved, but device complexity increases
Solution Approach 1:
The natural language interface is designed as a multi-functional system that can perform multiple operations through a single unified interface. It can interpret various types of user intents (filtering, aggregation, comparison), generate different visualization types, and update existing visualizations all through natural language processing. This universality improves productivity by consolidating multiple functions into one interface while managing complexity through a unified architectural approach.
Solution Approach 2:
The system merges the natural language processing functionality with the existing data visualization engine, allowing the same system to handle both query interpretation and visualization generation. By combining these functions into an integrated workflow where the NLP interface directly drives the visualization engine, the system improves productivity through streamlined operations while managing complexity through functional integration rather than separate systems.
Data Source
AI summary
A method uses natural language for visual analysis of a dataset. A data visualization application displays a data visualization, at a computer, based on a dataset retrieved from a database using a set of one or more queries. A user specifies a natural language command related to the displayed data visualization, and the computer extracts an analytic phrase from the natural language command. The computer computes semantic relatedness between the analytic phrase and numeric data fields in the dataset. The computer identifies numeric data fields having highest semantic relatedness to the analytic phrase, and also selects a relevant numerical function. The numerical function compares data values in the numeric data fields to a threshold value. The computer retrieves an updated dataset that filters the identified numeric data fields according to the numeric function. The computer then displays an updated data visualization using the updated dataset.