Social Interaction Detection via Dynamic Sensor State Analysis
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Solution Overview
Problem
Current social network interaction recommendations do not effectively consider the actual circumstances and availability of users, leading to suboptimal interaction initiation and acceptance, as they rely on static assumptions rather than dynamic user states and interaction histories.
Innovation Solution
A method and system that acquire and analyze sensor data to generate classifier data, predicting an interaction score and generating an interaction identifier based on user and recipient states, incorporating location, activity, emotion, and interaction history to recommend and initiate interactions at suitable times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If recommendation systems use static assumptions about user interests, then implementation complexity is reduced, but interaction quality and acceptance rate deteriorate
Solution Approach 1:
The patent implements dynamic user state tracking using mobile device sensors (accelerometer, gyroscope, microphone, camera) to continuously monitor user activity, location, and contextual information. This replaces static assumptions with real-time dynamic data about user availability and willingness to interact, directly improving interaction acceptance rates while managing complexity through targeted sensor usage.
Solution Approach 2:
The system automatically collects sensor data from users' own devices and uses machine learning models to generate interaction recommendations without requiring manual user input or configuration. Users simply provide sensor data access, and the system self-manages the complex tasks of data collection, processing, and recommendation generation, improving interaction quality without increasing perceived user burden.
2Measurement precision
If the system collects and analyzes multiple sensor data types, then interaction prediction accuracy improves, but data processing complexity and energy consumption increase
Solution Approach 1:
The patent segments the data processing task by implementing separate machine learning models for different sensor types and user states. The system processes accelerometer data for motion detection, microphone data for audio analysis, camera data for visual context, and location data for spatial awareness independently, then integrates these segmented results to form comprehensive interaction recommendations. This modular approach manages processing complexity while maintaining high detection accuracy.
Solution Approach 2:
The system selectively activates specific sensor types and processing algorithms based on the current interaction context and user state rather than continuously processing all available sensor data. For example, the microphone is activated primarily during conversation detection, and the camera is used selectively for visual context, reducing overall processing complexity while maintaining precision when needed.
3Speed
If interaction recommendations are generated in real-time based on current sensor data, then interaction timeliness improves, but computational load and processing time increase
Solution Approach 1:
The system performs preliminary processing of sensor data by continuously monitoring basic user states (motion, location, audio levels) in the background and pre-computing features that will be needed for interaction detection. When interaction events are detected, the pre-processed data is already available, enabling rapid real-time recommendations without requiring intensive computation at the moment of interaction, thus reducing energy consumption while maintaining responsiveness.
Data Source
AI summary
A method and corresponding system for improving an interaction process in a social network for a user with at least one other person is provided. The method comprises a step of, in a training phase, acquiring sensor data on at least a user state and interaction data on a social interaction of a human person with the at least one other person. The acquired sensor data and the interaction data is then analyzed in order to generate classifier data from the acquired sensor data and the acquired interaction data. In a subsequent application phase, the method acquires current sensor data on at least the user state of the at least one other person, predicts an interaction score for the user based on the acquired current sensor data and the classifier data, and generates an interaction identifier for the user for an interaction with the at least one other person in the social network based on the predicted interaction score.