Multi-Modal Context Modeling for Smart Home Intent Detection
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Solution Overview
Problem
Current smart home automation systems, particularly those relying on smart televisions, lack comprehensive contextual information, leading to inaccurate user intent understanding and limited functionality beyond media consumption, as they do not effectively utilize multi-modal sensor inputs and fail to communicate with other devices.
Innovation Solution
Implement a multi-task dynamic machine learning model with sub-models for tasks like room type classification, user pose estimation, and voice identification, dynamically controlling their use based on available inputs and user preferences, and enable communication between smart devices to share contextual information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sub-models are used to process multi-modal inputs, then contextual information accuracy is improved, but device complexity increases
Solution Approach 1:
The patent divides the machine learning system into multiple independent sub-models, each specialized for processing specific input modalities (e.g., image processing sub-model, audio processing sub-model, text processing sub-model). This segmentation allows the system to handle complex multi-modal inputs accurately while maintaining manageable complexity through modular architecture, where each sub-model can be independently trained and optimized.
Solution Approach 2:
The patent creates a universal machine learning system that can process multiple types of inputs (images, audio, text, sensor data) through a unified architecture comprising multiple sub-models. This multi-functional system serves various purposes including user identification, activity recognition, and environmental understanding, replacing the need for separate specialized systems while improving overall contextual accuracy.
2Productivity
If dynamic sub-model control is implemented, then processing efficiency is improved, but control complexity increases
Solution Approach 1:
The patent implements dynamic control of sub-model activation based on the specific processing task and available computational resources. The system can adaptively enable or disable certain sub-models depending on the input type, required output, and device capacity, optimizing processing efficiency for different scenarios while managing control complexity through rule-based or learned selection criteria.
Solution Approach 2:
The system dynamically adjusts operational parameters such as which sub-models are active, the level of processing detail, and resource allocation based on contextual factors including device capabilities, user preferences, and task requirements. This parameter adjustment enables efficient resource utilization while maintaining accuracy when needed.
3Loss of information
If comprehensive contextual information is collected, then user intent understanding is improved, but information processing load increases
Solution Approach 1:
The patent extracts only the most relevant contextual information from multi-modal inputs based on the current task and user needs. Rather than processing all available data uniformly, the system selectively extracts and processes key features using specialized sub-models, reducing processing load while maintaining accurate user intent understanding by focusing on salient information.
Solution Approach 2:
The system performs partial processing of contextual information by activating only the necessary sub-models required for the current task. When full contextual analysis is not needed, the system uses a subset of processing capabilities, reducing energy consumption and computational load while still achieving sufficient user intent understanding for the given context.
Data Source
AI summary
A method includes obtaining one or more inputs at a first electronic device. The first electronic device includes a multi-task dynamic machine learning model that includes multiple sub-models configured to perform different machine learning functions. The method also includes dynamically controlling the sub-models used to process the one or more inputs based on at least one of: (i) the one or more inputs that are available for use or (ii) one or more outputs to be generated. The method further includes generating a set of contextual information associated with the first electronic device. At least a portion of the set of contextual information is associated with at least one user of the first electronic device. The method may additionally include sharing the set of contextual information with at least a second electronic device in a specified environment.