User Attribute Resolution for Offline Action Queries
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Solution Overview
Problem
Existing computing devices struggle to accurately interpret user requests due to insufficient context or multiple interpretations, especially in the absence of network connectivity.
Innovation Solution
Utilize user attribute data stored on the device to resolve unresolved entities in action queries by identifying action and entity terms, combining with location data and local or remote databases to perform computer-based actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If context-based interpretation techniques are used to infer user intent, then the ability to interpret user requests is improved, but the system fails when context is incomplete or subject to multiple interpretations
Solution Approach 1:
The system pre-collects and stores user attribute data (preferences, history, characteristics) before they are needed for query resolution. This preliminary preparation of user profiles enables the system to quickly resolve ambiguous entities without requiring additional real-time context or user input, thereby maintaining reliability when context is insufficient.
Solution Approach 2:
User attribute data serves as an intermediary between the ambiguous query and the intended meaning. When context-based interpretation fails to resolve an entity uniquely, the system uses stored user attributes (such as preferred restaurants, music artists, or communication contacts) as a mediator to disambiguate the entity term and determine the correct interpretation.
2Measurement precision
If the system requests additional user input to clarify ambiguous queries, then interpretation accuracy is improved, but user interaction complexity and time increase
Solution Approach 1:
The system performs self-service by automatically resolving ambiguous entities using pre-stored user attribute data without requiring user intervention. Instead of prompting the user to clarify ambiguous terms, the system independently queries its own user profile database, matches potential entities against user attributes, and selects the most likely intended entity, thereby maintaining ease of operation.
Solution Approach 2:
The system uses previously collected user interaction data as feedback to improve real-time query resolution. User attributes are continuously updated based on past behavior, and this feedback loop enables the system to increasingly accurately predict user intent without additional input requests, balancing accuracy with operational simplicity.
3Adaptability or versatility
If the system stores and processes extensive user attribute data, then the ability to resolve ambiguous queries is improved, but device storage and processing requirements increase
Solution Approach 1:
The system extracts only the essential and most frequently used user attributes from the complete user profile for real-time query resolution. Instead of processing all stored user data, the system identifies and utilizes only the relevant attributes needed for the current disambiguation task, thereby reducing processing overhead and effective data volume while maintaining high resolution capability.
Solution Approach 2:
The system applies different levels of data processing and storage to different types of user attributes. Frequently accessed attributes are maintained in readily accessible formats, while less frequently used attributes are stored more compactly or processed only when relevant. This local quality differentiation optimizes the balance between adaptability and storage requirements.
Data Source
AI summary
User attribute data associated with a user may be used to resolve unresolved entities in connection with the performance of computer-based actions, e.g., actions resulting from action queries generated based at least in part on content being accessed on a computing device.