Context-Aware Device Selection for Ambiguous User Inputs
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Solution Overview
Problem
In environments with multiple smart devices, it is challenging to determine which device should receive and execute user inputs, such as voice commands or gestures, when the intended device is not explicitly identified, leading to ambiguity and potential misexecution of commands.
Innovation Solution
The proposed solution involves processing user input in conjunction with contextual information, including device status and proximity, using algorithms and lookup tables to compute a confidence score for each device, which helps associate the input with the most relevant device based on predefined criteria and priorities, ensuring accurate command execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple smart devices are deployed in an environment, then device functionality and versatility are improved, but ambiguity in user input association increases
Solution Approach 1:
The system uses feedback from multiple sources including device status, proximity detection, and interaction history to continuously refine device selection. The processor receives feedback about user actions and environmental context, then adjusts which device is selected to receive user inputs based on this accumulated feedback information.
Solution Approach 2:
The patent introduces an intermediary processing system that acts as a mediator between user inputs and multiple devices. This intermediary processor analyzes contextual information from various sources and determines the most appropriate device to receive each input, preventing direct ambiguous routing to multiple devices simultaneously.
2Reliability
If device selection is based on multiple contextual factors, then input association accuracy is improved, but processing complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-establishing proximity relationships, device status profiles, and interaction patterns before user inputs occur. This preparation work is done in advance so that when a user input is received, the processor can quickly reference pre-computed contextual information rather than analyzing everything from scratch.
Solution Approach 2:
The contextual information processing is segmented into distinct components: proximity detection, device status monitoring, interaction history analysis, and input type classification. Each component operates independently and contributes its findings to the overall device selection process, making the complex system manageable and efficient.
3Reliability
If explicit device identification is required in user inputs, then input association accuracy is improved, but ease of operation deteriorates
Solution Approach 1:
The system provides self-service by automatically determining the intended device without requiring explicit user identification. The processor analyzes contextual cues such as proximity, device status, and usage patterns to autonomously select which device should receive the user input, freeing the user from the burden of specifying device names or IDs.
Solution Approach 2:
The system dynamically changes parameters such as selection criteria weightings and sensitivity thresholds based on contextual conditions. For example, proximity may be given higher weight when the user is physically near a device, while interaction history may be more influential for recurring command patterns, allowing flexible adaptation without requiring explicit user specification.
Data Source
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AI summary
A method includes determining a status of a first device, receiving a first user input, and associating the first user input with the first device, wherein the first user input and the status of the first device is used to associate the first user input with the first device.