Activity-Centric Domain Scoping for NLP Accuracy
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Solution Overview
Problem
Conventional computer systems lack the ability to dynamically understand user activities and the necessary steps or resources required to complete them, leading to inefficient human-machine interaction and resource management.
Innovation Solution
An activity-centric system that uses natural language processing, machine learning, and environmental sensors to automatically scope and adapt to the current activity, reducing the search space and improving recognition accuracy by defining relevant grammar and lexicon based on user and environmental contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional systems use broad grammar and lexicon for natural language processing, then recognition coverage is improved, but processing power and battery life consumption increase
Solution Approach 1:
The patent segments the grammar and lexicon into activity-specific subsets. Instead of using a single comprehensive language model, the system divides the language understanding space into multiple smaller, context-specific grammars and lexicons that are activated based on the detected activity state, thereby reducing processing requirements while maintaining recognition coverage for the current activity.
Solution Approach 2:
The patent implements dynamic adaptation of grammar and lexicon scope based on detected activity states. The system dynamically adjusts the recognition scope to match the current activity context, expanding coverage when needed and reducing it during idle or well-understood states, optimizing the balance between recognition capability and energy consumption.
2Adaptability or versatility
If conventional systems manually manage activity components, then flexibility is improved, but user burden and time consumption increase
Solution Approach 1:
The patent enables the system to automatically detect, track, and manage activity states without requiring explicit user input or manual configuration. The system self-adjusts the grammar and lexicon scope based on observed user behavior and contextual cues, eliminating the need for users to manually manage activity components while maintaining system flexibility.
Solution Approach 2:
The patent implements feedback mechanisms that continuously monitor user interactions and activity patterns, using this information to automatically adjust the recognition scope and activity management. The system learns from user behavior and refines its activity detection and language model selection over time, providing flexibility without requiring user intervention.
3Device complexity
If conventional systems use single desktop metaphor, then system simplicity is improved, but user-friendly access to activity resources deteriorates
Solution Approach 1:
The patent introduces an activity-based organizational dimension alongside the traditional file-based desktop metaphor. Instead of only organizing resources by location (files and folders), the system adds an activity context dimension that groups and prioritizes resources based on what the user is currently doing, providing intuitive access without complicating the underlying system structure.
Data Source
AI summary
A system that can automatically narrow the search space or recognition scope within an activity-centric environment based upon a current activity or set of activities is provided. In addition, the activity and context data can also be used to rank the results of the recognition or search activity. In accordance with the domain scoping, natural language processing (NLP) as well as other types of conversion and recognition systems can dynamically adjust to the scope of the activity or group of activities thereby increasing the recognition systems accuracy and usefulness. In operation, a user context, activity context, environment context and/or device profile can be employed to effectuate the scoping. As well, the system can combine context with extrinsic data, including but not limited to, calendar, profile, historical activity data, etc. in order to define the parameters for an appropriate scoping.