Skill Session Resource Manager for Speech Multitasking
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Solution Overview
Problem
Current speech processing systems lack the ability to efficiently manage resource allocation across multiple skills, leading to limitations in multitasking and seamless interaction between different applications, as they struggle to interrupt or share resources effectively during user interactions.
Innovation Solution
The system employs a skill session management framework that tracks the state of interactions and allocates resources accordingly, allowing for the interruption and sharing of resources between skills, enabling multitasking by determining when and how resources can be used or shared based on priority and scope.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a speech processing system allocates resources to a single skill at a time, then resource conflicts are avoided, but the system cannot perform multiple tasks simultaneously
Solution Approach 1:
The system dynamically adjusts resource allocation based on skill priority and interaction state. The resource manager continuously monitors skill sessions and reallocates resources as skills enter or exit active states, enabling seamless transitions between single-task and multi-task modes without permanent complexity
Solution Approach 2:
A resource manager component acts as an intermediary between multiple skills and shared resources. This mediator tracks which skills are actively using resources and coordinates allocation decisions, preventing direct conflicts while enabling controlled multitasking through centralized management
2Adaptability or versatility
If the system allows multiple skills to share resources simultaneously, then multitasking is enabled, but resource conflicts and interference occur
Solution Approach 1:
The system dynamically determines resource sharing based on skill priority levels and current interaction states. High-priority skills can preempt resources from lower-priority skills, while the system monitors for conflicts and adjusts allocation in real-time to maintain reliability
Solution Approach 2:
The resource manager changes allocation parameters such as priority weights and sharing permissions based on the active skill session state. This allows the system to adapt resource distribution dynamically, enabling sharing when appropriate while preventing conflicts through parameter-adjusted control mechanisms
3Speed
If the system interrupts a skill's resource usage to allocate to another skill, then responsive multitasking is achieved, but the original skill's continuity is disrupted
Solution Approach 1:
The system implements dynamic interruption policies where skill sessions can be suspended and resumed based on user input patterns. The resource manager tracks skill state and enables quick resumption of interrupted skills, maintaining continuity while allowing rapid task switching when users provide new inputs
Data Source
AI summary
Described herein is a system for allocating resources among multiple skills to enable multitasking. The system tracks use of resources using skill sessions. In one case, the system suspends a skill session to release a resource for allocation to another resource. In another case, the system determines if multiple skill sessions can remain active and use resources to provide output to the user at the same time.