Multi-Agent Audio Coordination via Location Context
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Solution Overview
Problem
Multiple virtual assistant agents simultaneously recording and responding to user requests often lead to inaccurate recognition and user frustration due to overlapping audio inputs, necessitating a system to accurately identify and process audio requests in a coordinated manner.
Innovation Solution
A processor-based system that uses location context and secondary trait analysis to determine which agent should actively process audio requests, incorporating features like attention tokens, sound categorization, and state machines to enhance request recognition and execution accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple virtual assistant agents simultaneously record and respond to user requests, then the coverage and availability of virtual assistant services are improved, but the accuracy of audio request recognition deteriorates due to overlapping audio inputs
Solution Approach 1:
The patent introduces a coordination system that acts as an intermediary between multiple virtual assistant agents. This coordinator receives audio inputs from multiple agents, processes them through a unified decision-making framework using location context and secondary trait analysis, and determines which agent should actively process each request. This mediator prevents direct conflict between agents and maintains recognition accuracy while preserving multi-agent coverage.
Solution Approach 2:
The system performs preliminary analysis of audio requests by evaluating location context and secondary traits before assigning them to agents. By pre-processing and categorizing audio inputs based on contextual factors, the system determines the most appropriate agent for each request in advance, preventing overlapping processing and maintaining accuracy while allowing multiple agents to remain available.
2Productivity
If multiple agents process audio requests simultaneously, then the responsiveness and availability of the system are improved, but user frustration increases due to inaccurate recognition
Solution Approach 1:
The coordination system implements feedback mechanisms that continuously monitor audio request patterns, location context, and agent performance. This feedback loop allows the system to learn from previous interactions and improve its decision-making about which agent should process which request, thereby maintaining high responsiveness while reducing recognition errors that cause user frustration.
Solution Approach 2:
The system dynamically changes processing parameters by adjusting which agent is active based on location context, secondary traits, and request characteristics. This parameter adjustment allows the system to maintain high productivity by keeping multiple agents available while improving ease of operation by ensuring the most appropriate agent processes each request, thereby reducing user frustration.
3Measurement precision
If the system uses location context and secondary trait analysis to coordinate agents, then the accuracy of request processing is improved, but the device complexity increases
Solution Approach 1:
The coordination system implements a universal framework that handles multiple types of audio requests, location contexts, and secondary traits through a single unified decision-making process. This multi-functional approach improves accuracy by considering all relevant factors while managing complexity through a consolidated system rather than separate mechanisms for each factor.
Solution Approach 2:
The system segments the complex coordination task into distinct analytical components: location context analysis, secondary trait analysis, and agent selection decision-making. By dividing the overall process into these manageable segments, the system achieves high accuracy through comprehensive analysis while keeping device complexity manageable through modular organization of the coordination logic.
Data Source
AI summary
Multi-agent input coordination can be used to for acoustic collaboration of multiple listening agents deployed in smart devices on a premises, improving the accuracy of identifying requests and specifying where that request should be honored, improving quality of detection, and providing better understanding of user commands and user intent throughout the premises. A processor or processors such as those in a smart speaker can identify audio requests received through at least two agents in a network and determine at which of the agents to actively process a selected audio request. The identification can make use of techniques such as location context and secondary trait analysis. The audio request can include simultaneous audio requests received through at least two agents, differing audio requests received from different requesters, or both.