Visual Analytic System Infers User Reasoning for Text Retrieval

Solution Overview

Problem

Traditional approaches for visual analytics in big data require users to explicitly understand and control underlying models, which becomes impractical as datasets grow in size and complexity, limiting the ability to effectively train and steer these models for information retrieval and visualization.

Innovation Solution

The TexTonic visual analytic system infers user reasoning from interactions within a spatial visualization metaphor, allowing users to explore and manipulate text data at multiple levels of aggregation, steering the underlying data model through semantic interactions for information retrieval and visualization.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If users explicitly control model parameters to steer analytic models, then information retrieval accuracy is improved, but system complexity and difficulty of operation increase

Engineering Contradiction:

Improveinformation retrieval accuracyVSAvoiduser operation difficulty

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The system performs self-service by automatically inferring user reasoning from interaction patterns and using this inferred reasoning to steer the analytic models. This eliminates the need for users to explicitly understand or control model parameters, as the system adapts to user needs autonomously through observed interactions with the visual analytic tool.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system introduces an intermediary layer that translates user interactions into model steering commands. Instead of directly controlling complex model parameters, users interact with the visual analytic interface, and the system mediates by inferring reasoning from these interactions and translating them into appropriate model adjustments, simplifying the user experience while maintaining retrieval accuracy.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If users explicitly control model parameters, then analytical precision is improved, but device complexity increases

Engineering Contradiction:

Improveanalytical precisionVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system performs self-service by automatically inferring user reasoning from interaction patterns and using this inferred reasoning to steer the analytic models. This eliminates the need for users to explicitly understand or control model parameters, as the system adapts to user needs autonomously through observed interactions with the visual analytic tool.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system introduces an intermediary layer that translates user interactions into model steering commands. Instead of directly controlling complex model parameters, users interact with the visual analytic interface, and the system mediates by inferring reasoning from these interactions and translating them into appropriate model adjustments, simplifying the user experience while maintaining retrieval accuracy.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS10255355B2Method and system for information retrieval and aggregation from inferred user reasoning

Publication Date: 2019.04.09 BATTELLE MEMORIAL INST

AI summary

The visual analytic system enables information retrieval within large text collections. Typically, users have to directly and explicitly query information to retrieve it. With this system and process, the reasoning of the user is inferred from the user interaction they perform in a visual analytic tool, and the appropriate information to query, process, and visualize is systematically determined.