Line Chart Trend Search Using Quantified Semantic Queries
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing search tools lack the ability to effectively interpret and leverage the precise, quantified semantics of natural language to identify and visualize temporal trends in time-series data, limiting their support for complex analytical tasks such as identifying specific trends and correlations.
Innovation Solution
A system and interface, SlopeSeeker, that utilizes a labeled dataset of semantic concepts describing trends and their quantifiable properties, leveraging quantified semantics to detect and visualize trends in line charts, integrating text with search results for enhanced navigation and expressivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing search tools are used for data exploration, then basic analytical tasks such as document search and fact-finding are supported, but the ability to interpret and leverage precise quantified semantics of natural language for identifying temporal trends is insufficient
Solution Approach 1:
The patent transforms natural language trend descriptors into quantified semantic parameters by mapping linguistic expressions (e.g., 'slow increase', 'steady increase', 'exploding') to numerical slope values and trend characteristics. This parameter transformation enables precise measurement of temporal trends while maintaining the system's ability to handle diverse analytical queries through natural language interfaces.
Solution Approach 2:
The patent introduces a semantic interpretation layer that acts as an intermediary between natural language queries and data analysis operations. This intermediary component translates expressive language into structured semantic representations, enabling the system to bridge the gap between basic search capabilities and complex trend analysis without requiring users to learn specialized query languages.
2Adaptability or versatility
If natural language interfaces are enhanced to support diverse trend descriptions, then the expressive power for capturing user intent is improved, but the complexity of interpreting and processing these expressions increases
Solution Approach 1:
The patent segments natural language expressions into distinct semantic components (e.g., separating trend direction, rate of change, and temporal characteristics). By dividing complex language expressions into manageable semantic units, the system can process diverse trend descriptions without requiring a monolithic complex interpretation engine, thus reducing overall processing complexity while maintaining expressive power.
Solution Approach 2:
The patent converts diverse natural language expressions into a standardized set of semantic parameters representing trend characteristics. This parameterization approach allows the system to handle expressive language input by transforming it into a unified numerical representation, simplifying the processing complexity while preserving the full range of expressive capabilities.
3Ease of operation
If existing tools provide basic data retrieval functionality, then ease of operation is maintained, but the ability to perform specific analytic tasks such as computing derived values and finding correlations is limited
Solution Approach 1:
The patent enables the system to automatically perform complex analytical operations such as computing derived values and identifying correlations without requiring explicit user programming. Through natural language queries, users can request sophisticated analyses that the system executes autonomously, maintaining ease of operation while significantly improving the reliability and accuracy of trend analysis through programmatic precision.
Data Source
AI summary
A computing device receives a natural language input specifying a first search term and a second search term, directed to a dataset. The device (i) executes, for the first search term, first queries against a search index to retrieve a first set of labeled trend events; and (ii) executes for the second search term, second queries against the search index to retrieve a second set of labeled trend events. Each labeled trend event has a respective chart identifier. The device constructs sequences of labeled trend events based on the retrieved sets of labeled trend events, assigns each sequence into groups according to the respective chart identifier, and ranks the groups. The device retrieves data corresponding to a subset of line charts having the respective chart identifiers of the ranked groups, generates the subset of line charts, and displays one or more line charts of the subset.