Message Threading for Ambiguous Natural Language Data Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional conversational interfaces for data analysis focus primarily on natural language input, lacking effective integration of visualization responses and struggle with precise interpretation of user intent in complex interactions.
Innovation Solution
A method that parses natural language inputs into tokens, generates intermediate responses, and displays data visualizations, handling ambiguous or underspecified utterances through clarification requests and affordances, while leveraging anaphora detection and context awareness to manage conversational flow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional conversational interfaces focus primarily on natural language input, then the interface simplicity is maintained, but the precision of user intent interpretation deteriorates in complex interactions
Solution Approach 1:
The patent introduces an intermediary natural language processing layer that translates user inputs into structured query formats. This intermediary layer maintains interface simplicity for users while enabling precise intent interpretation by converting ambiguous natural language into structured data representations that can be accurately processed by the system.
Solution Approach 2:
The system dynamically adjusts processing parameters based on the complexity and ambiguity of user inputs. When simple queries are detected, the system uses direct natural language interpretation; when complex or ambiguous queries are detected, it transitions to structured query generation and disambiguation processes, thereby maintaining both simplicity for straightforward tasks and precision for complex interactions.
2Device complexity
If conventional interfaces lack integration of visualization responses, then the system complexity is reduced, but the effectiveness of data exploration deteriorates
Solution Approach 1:
The patent merges the natural language processing component with the data visualization component into an integrated system. The natural language interface directly generates queries that connect to data sources, and visualization responses are seamlessly integrated into the conversational flow, allowing users to explore data effectively without encountering the limitations of separate, un integrated components.
3Measurement precision
If the system handles ambiguous or underspecified utterances through clarification requests, then the accuracy of query interpretation is improved, but the interaction time increases
Solution Approach 1:
The system performs preliminary analysis of user inputs to detect ambiguity or underspecification before generating queries. By identifying potentially problematic inputs in advance, the system can proactively request clarifications using natural language follow-up questions, preventing erroneous query generation and reducing the need for corrective iterations, thereby managing interaction time more efficiently.
Solution Approach 2:
The system implements feedback mechanisms where clarification requests are generated in natural language that maintains the conversational flow. The feedback loop allows users to provide additional information or confirm their intent, and the system adjusts its query generation accordingly, improving interpretation accuracy while keeping interaction time reasonable through efficient feedback cycles.
Data Source
AI summary
A method uses natural language processing for visual analysis of a dataset, in a messaging application. The method includes receiving a first natural language (NL) input directed to a data source, in a messaging application. The first NL input includes at least one underspecified or ambiguous utterance. The method also includes parsing the first NL input into tokens based on a grammar and the data source. The method also includes generating and displaying an intermediate NL response, based on the tokens. In response to receiving a user input to provide missing information in the at least one underspecified or ambiguous utterance, the method includes generating an input query based on the user input and querying the data source using the input query, to obtain a result set. The method also includes generating and displaying a first NL output and a snapshot of a data visualization based on the result set.