Visual Analytics Intent Language for Ambiguous User Requests
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current visual analytics tools face challenges in interpreting users' higher-level analytical intents due to vague expressions, mismatched terminology, and the need for complex hard-coded implementations, which limits their ability to infer underspecified or ambiguous intents effectively across various platforms and devices.
Innovation Solution
The Visual Analytics Intent Language (VAIL) is introduced, which translates expressive higher-level intents into lower-level representations, incorporating intent specifications, data semantics, and rules for editing and inferring intent, enabling the generation of effective output specifications and reducing the cognitive burden on users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complex hard-coded implementations are used to interpret user intent, then the system can handle specific analytical tasks, but the system becomes difficult to fine-tune and adapt to different platforms
Solution Approach 1:
The patent introduces an intermediary layer (natural language processing interface and intent specification language) between the user and the visual analytics system. This intermediary translates vague user expressions into structured intent specifications, resolving the contradiction by maintaining reliability through structured interpretation while reducing complexity by avoiding hard-coded implementations for each task.
Solution Approach 2:
The system changes parameters by introducing configurable intent specification parameters (intent type, data fields, visualization type) that can be adjusted through natural language input. This allows the system to adapt to different platforms and tasks by modifying these parameters rather than requiring complex hard-coded implementations, thereby maintaining reliability while reducing system complexity.
2Measurement precision
If the system requires explicit specification of all analytical parameters, then the output is precise, but the user cognitive burden increases
Solution Approach 1:
The system performs preliminary action by providing pre-defined intent types and template-based intent specifications that capture common analytical patterns. Users only need to specify high-level parameters while the system automatically fills in standard configurations, achieving precise intent specification without requiring users to manually define every parameter, thus easing operation.
Solution Approach 2:
The system enables self-service by using natural language processing to automatically interpret user expressions and generate intent specifications. The system serves itself by translating vague inputs into structured parameters through automated NLP techniques, maintaining precision while reducing the cognitive burden on users who don't need to understand the underlying parameter structures.
3Reliability
If bespoke business logic is implemented for each visual analytics platform, then the platform handles its specific conventions, but the implementation cannot be generalized across platforms
Solution Approach 1:
The patent implements universality by creating a platform-agnostic intent specification language and NLP interface that works across different visual analytics platforms. The core intent interpretation logic is generalized to handle various platforms through configurable parameters rather than bespoke implementations, enabling the same system to adapt to multiple platforms while maintaining reliable platform-specific functionality through parameter configuration.
Data Source
AI summary
An electronic device has one or more processors and memory. The memory stores one or more programs configured for execution by the one or more processors. The device receives a request directed to a data source. The request includes one or more intent types and one or more predefined attributes associated with the intent types. Each of the predefined attributes limits a respective data analysis operation of a respective intent type. In response to the request, for each of the intent types, the device formulates a respective intent specification according to the request. The device also applies one or more respective rules corresponding to the respective intent type to determine whether the respective intent type is ambiguous and/or underspecified. When the respective intent type is ambiguous and/or underspecified, the device infers information to resolve the ambiguous and/or underspecified intent.