Speech Processing System Mitigating Unintended Automation Events
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Solution Overview
Problem
Speech processing systems face issues with performing unwanted actions due to sensor malfunctions or misinterpretations, leading to undesirable user experiences and inefficient resource allocation, resulting in increased computing resource consumption and potential latency.
Innovation Solution
A system that detects and mitigates the performance of undesired actions by analyzing execution history and sensor data to determine if actions should be performed, using machine learning techniques to identify patterns and prevent unnecessary actions, and notifying users to override decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If speech processing systems perform actions based on sensor data and user input, then system functionality and user interaction capability are improved, but unintended automation events occur due to sensor malfunctions or misinterpretations
Solution Approach 1:
The system implements feedback by monitoring execution history of actions and using this information to determine whether to execute future actions. The policy engine continuously learns from past executions and adjusts its behavior to prevent unintended automation events while maintaining system functionality.
Solution Approach 2:
The system performs preliminary analysis of execution history and sensor data before executing actions. By evaluating patterns from past executions and current sensor readings, the policy engine determines in advance whether an action should be executed, preventing unintended actions before they occur.
2Speed
If the system executes actions based on sensor triggers, then responsiveness and user experience are improved, but computing resources are consumed by unnecessary actions
Solution Approach 1:
The system performs partial execution by analyzing only the necessary portions of execution history and sensor data required to make a go/no-go decision. This selective approach prevents full processing of all possible action scenarios, reducing computing resource consumption while maintaining responsiveness.
Solution Approach 2:
The policy engine serves itself by automatically learning from execution history and making decisions about action execution without requiring external intervention. This self-service mechanism optimizes resource allocation by independently determining when actions should be executed based on learned patterns.
3Measurement precision
If the system monitors and analyzes execution history to prevent unintended actions, then accuracy in action execution is improved, but system complexity increases
Solution Approach 1:
The system extracts only the essential features from execution history that are relevant to determining action execution. By focusing on key patterns and metrics rather than analyzing all possible aspects of past executions, the policy engine achieves high accuracy while maintaining manageable system complexity.
Solution Approach 2:
The policy engine serves multiple functions: it monitors execution history, analyzes sensor data, determines action execution decisions, and learns from patterns. This multi-functional approach consolidates complexity into a single component rather than requiring separate systems for each function.
Data Source
AI summary
Described are techniques for minimizing the performance of sets of actions in undesired situations. For example, a set of actions may be triggered based on a sensor detecting the presence of an individual, but the sensor may be faulty and trigger the set of actions to be performed based on false detections of an individual. A system may maintain a record of past executions of sets of actions. The system may analyze the past executions to determine a frequency with which a set of actions was executed, whether the past executions resulted in failed processing, etc. If the system determines, based on these determinations, that a set of actions should not be performed in the present instance, the system prevents the set of actions from being performed. The system may notify a user of such and provide the user with the ability to override the system's determination.