Digital Personal Assistant Infrastructure Service for Action Provider Management
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Solution Overview
Problem
Current digital personal assistants are limited in extensibility and resource utilization, as they require each action provider to implement their own digital personal assistant functionality, leading to increased computing resources and memory usage, and users have varying preferences for action providers for specific tasks.
Innovation Solution
A digital personal assistant infrastructure service that allows multiple action providers to share resources and register with the service, enabling task assignment based on stored registrations and preferences, with machine learning to determine the preferred provider, reducing resource consumption and enhancing user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If each action provider implements their own digital personal assistant functionality, then the extensibility and number of action providers increases, but the computing resources and memory usage increase
Solution Approach 1:
The patent merges the digital personal assistant functionality into a single shared service that multiple action providers can utilize. Instead of each provider implementing their own DPA functionality, they all connect to the centralized infrastructure service, which handles speech recognition, natural language understanding, and task management. This consolidation reduces redundant computing resources while maintaining extensibility across multiple providers.
Solution Approach 2:
The digital personal assistant infrastructure service is designed as a universal platform that serves multiple action providers simultaneously. The service provides multi-functional capabilities including speech-to-text conversion, intent recognition, task routing, and provider management, making it applicable to diverse action providers without requiring each to implement these functions independently.
2Adaptability or versatility
If each action provider implements their own digital personal assistant functionality, then the number of action providers increases, but the memory usage increases
Solution Approach 1:
The patent consolidates memory-intensive DPA components into a shared infrastructure service. Speech recognition models, natural language processing engines, and task management data structures are loaded once in the centralized service rather than being duplicated in each action provider's memory, enabling more providers to participate while controlling overall memory consumption.
Solution Approach 2:
Instead of each action provider maintaining full copies of the digital personal assistant functionality in their own memory, the system uses lightweight client implementations that reference the centralized service. This allows multiple providers to access the same DPA capabilities without duplicating the heavy memory footprint of speech recognition and natural language processing components.
3Ease of operation
If users have varying preferences for action providers for specific tasks, then user experience customization increases, but the complexity of task assignment increases
Solution Approach 1:
The infrastructure service implements feedback mechanisms where user preferences and task completion outcomes are continuously monitored. The system learns from user selections and task success rates, automatically adjusting provider recommendations and assignments. This feedback loop manages the complexity of task assignment by using observed patterns rather than requiring explicit complex configuration rules.
Solution Approach 2:
The system provides self-service capabilities where users can directly express preferences for specific action providers on particular tasks through simple interfaces. The infrastructure service automatically processes these preferences and configures task routing accordingly, eliminating the need for users to manually manage complex assignment rules while still providing customization.
Data Source
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AI summary
An electronic device can receive user input via voice or text that includes tasks to be performed. A digital personal assistant infrastructure service can control to which registered action provider the task is assigned. Per-task action provider preferences can be stored. If a preferred action provider is not able to complete the task, the task can still be performed by a registered action provider that has appropriate capabilities. Machine learning can determine a user's preferences. Resource conservation and effective user interaction can result.