Reactive Voice Device Management for Anomaly Correction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Voice-based devices often operate on rigid or specific commands, leading to limitations in functionality and user interaction, especially when users provide unexpected or anomalous inputs.
Innovation Solution
The Reactive Voice Device Management (RVM) system uses artificial intelligence techniques, including machine learning models, to detect user interactions, determine potential additional inputs based on an activity model, monitor for deviations, identify activity anomalies, and perform corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If voice-based devices operate based on rigid or specific commands, then device complexity is reduced and ease of operation is improved, but adaptability and versatility deteriorate
Solution Approach 1:
The patent applies dynamics by transitioning from rigid, fixed command structures to adaptive, learning-based command interpretation. The system dynamically adjusts its understanding of user inputs based on contextual information from multiple sources (audio, text, environmental sensors) and continuously learns from user interactions to improve its response accuracy over time, resolving the contradiction between operational simplicity and adaptability
Solution Approach 2:
The patent changes parameters by incorporating multiple input modalities (audio commands, text inputs, environmental context) and adjusting the system's interpretation based on varying contextual parameters. This allows the device to maintain ease of operation while adapting to different situations and user needs through parameter-based flexibility in command processing
2Device complexity
If voice-based devices use rigid commands, then device complexity is reduced, but functionality and handling of unexpected inputs deteriorate
Solution Approach 1:
The patent segments the command processing system into multiple independent modules: audio processing, text processing, environmental sensing, contextual analysis, and response generation. This segmentation allows the system to handle unexpected inputs through specialized modules while keeping overall device complexity manageable through modular architecture
Solution Approach 2:
The patent introduces contextual analysis and environmental sensors as intermediary layers between user input and device response. These intermediaries process and interpret unexpected inputs by providing additional context, enabling the system to handle diverse situations without requiring complex direct command-processing logic
Data Source
AI summary
One or more user interactions directed to a set of one or more voice-controlled devices in an environment are received by a first connected device. A first input to a first voice-controlled device of the set of voice-controlled devices is detected based on the user interactions. A potential second input to the set of voice-controlled devices is determined in response to the first input and based on an activity model. A deviation from the potential second input is monitored for, in response to the first input and from the user interactions. An activity anomaly in the environment is identified based on the monitoring. A correction action is performed in response to the activity anomaly.