Cross-Assistant Context Sharing for AI Voice Commands
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Solution Overview
Problem
Existing AI voice assistance systems fail to maintain context continuity when users interact with multiple providers across different locations, requiring users to repeat commands due to lack of context carryover.
Innovation Solution
A system utilizing a common distributed computing platform with a context database and context assigning engine to analyze and share context between multiple AI voice assistants, employing user-defined triggers and virtual accounts to maintain context across interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users interact with multiple AI voice assistants across different locations, then service coverage and accessibility are improved, but context continuity is lost requiring users to repeat commands
Solution Approach 1:
The system segments context management by creating location-specific context databases for each voice assistant while maintaining a unified user profile that links them. This allows each assistant to have localized context storage while preserving overall context continuity through the user profile association.
Solution Approach 2:
A context database acts as an intermediary between multiple voice assistants, storing and managing context information centrally. This intermediary enables context to be retrieved and shared across different assistants and locations, preventing context loss when users switch between services.
2Loss of information
If a centralized context database is implemented across multiple voice assistants, then context carryover is improved, but system complexity increases
Solution Approach 1:
The context database is designed as a universal system that serves multiple voice assistants across different locations and service providers. This multi-functional database structure enables context management for numerous assistants without requiring separate complex systems for each, reducing overall system complexity while maintaining comprehensive context carryover.
3Ease of operation
If context information is stored and shared across multiple assistants, then user convenience is improved, but security and privacy risks increase
Solution Approach 1:
The system implements location-specific context databases that store context information locally at each assistant's location rather than in a single centralized repository. This distributed storage approach maintains user convenience through context availability while reducing security risks by limiting the attack surface and isolating potential security breaches to specific locations.
Data Source
AI summary
An artificial intelligence that registers users to a common distributed computing platform that provides access to a plurality of voice assistants. A first command issued by a user is received by a first voice assistant of the plurality of voice assistants at a first location. A context database is built by storing the at least the first command on the distributed computing platform. A second command issued by the user is received by a second voice assistant of the plurality of first assistants at a second location. A context assigning engine on the common distributed computing platform analyzes the second command in comparison with the at least the first command on the context database. Similarity between the first command and the second command provides context. The second voice assistant receiving the context from the context assigning engine employs the context to answer the second command.