Context Information Platform for Mobile Device Automation
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Solution Overview
Problem
Service providers and device manufacturers face challenges in automating user interactions on mobile devices due to difficulties in accurately correlating complex user contexts, limiting the ability to associate user, object, or device context information with a user-defined context.
Innovation Solution
A system that enables devices to respond to context information by using a context information platform to analyze and associate labeled or unlabeled context data with a user-defined context model, allowing for the automation of interactions based on perceived context, utilizing sensors and a client-server model for data processing and storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional context correlation methods are used, then device complexity is reduced, but measurement precision of user context deteriorates
Solution Approach 1:
The patent introduces a context information platform as an intermediary component that mediates between sensors and the mobile device. This platform performs context inference, feature extraction, and data correlation centrally, allowing the mobile device to maintain simplicity while achieving high measurement precision through the intermediary's sophisticated processing capabilities.
Solution Approach 2:
The system segments context processing into distinct functional modules: sensor data acquisition, feature extraction, context inference, and publication. This segmentation allows each component to specialize in specific tasks, improving overall measurement precision while distributing complexity across multiple manageable modules rather than concentrating it in a single complex system.
2Extent of automation
If sophisticated context analysis is implemented, then automation capability is improved, but device complexity increases
Solution Approach 1:
The context information platform serves as an intermediary that handles sophisticated context analysis and automation logic centrally. This allows the mobile device to achieve high automation capability for user interactions without embedding complex processing systems within the device itself, thus improving automation extent while controlling device complexity.
Solution Approach 2:
The system performs preliminary context inference and feature extraction in advance through the context information platform, preparing processed context data before it reaches the mobile device. This preliminary action enables the device to execute automated interactions with simpler processing requirements, improving automation capability while reducing the complexity of real-time processing needed at the device level.
3Loss of information
If comprehensive sensor data is collected, then context information completeness is improved, but loss of time in processing increases
Solution Approach 1:
The context information platform performs preliminary feature extraction and data filtering on sensor inputs before full context inference is conducted. This preliminary action processes comprehensive sensor data in advance, organizing and pre-processing information so that complete context analysis can be performed more efficiently, thereby maintaining information completeness while reducing the time loss associated with processing vast amounts of raw sensor data.
Data Source
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AI summary
An approach is provided for training a device to associate user, object or device context information with a user defined context. The context information platform receives recorded context information from a device. The associated recorded context information is then associated with the context to enable training of a context model associated with the context.