Context-Aware Device Selection for Connected Assistant Ecosystems
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Solution Overview
Problem
Determining which device within an ecosystem of connected devices should provide a response to a device-agnostic user request is challenging, as multiple devices may be capable of responding, leading to suboptimal user experiences.
Innovation Solution
A user specifies a scenario and contextual features are identified from ecosystem devices, which are stored with an association to a device for handling responses. Current contextual features are compared to these stored scenario features to select the appropriate device for responding to user requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple devices are configured to respond to user requests, then device versatility and user choice are improved, but device selection complexity and response accuracy deteriorate
Solution Approach 1:
The system performs preliminary actions by collecting contextual features (location, activity, device state) before a user request is made, and pre-storing these features along with the user's preferred device associations. When a request arrives, the system quickly matches current context against pre-stored scenario features, avoiding complex real-time decision-making and enabling fast, accurate device selection.
2Measurement precision
If device selection is based on real-time contextual analysis, then response accuracy is improved, but processing time and system complexity increase
Solution Approach 1:
The system performs preliminary analysis by collecting and storing contextual features (location, activity, device state) in advance, creating a library of scenario-feature associations. When a user request arrives, the system performs a simple matching operation against pre-stored features rather than conducting full real-time analysis, significantly reducing processing time while maintaining high accuracy through the use of relevant contextual data.
3Measurement precision
If contextual features from multiple sensors are collected, then scenario recognition accuracy is improved, but data processing complexity and energy consumption increase
Solution Approach 1:
The system applies partial action by selectively collecting and processing only the most relevant contextual features needed for scenario recognition, rather than continuously processing all available sensor data. The system identifies key features (such as location, activity type, and device state) and focuses computational resources on these critical parameters, reducing overall energy consumption while maintaining high scenario recognition accuracy.
Data Source
AI summary
Implementations relate to selecting a particular device, from an ecosystem of devices, to provide responses to a device-agnostic request of the user while a scenario is occurring. The user specifies a scenario and contextual features are identified from one or more devices of the ecosystem to generate scenario features indicative of the scenario occurring. The scenario features are stored with a correlation to a device that is specified by the user to handle responses while the scenario is occurring. When a subsequent device-agnostic request is received, current contextual features are identified and compared to the scenario features. Based on the comparison, the specified assistant device is selected to respond to the device-agnostic request.