Vision-Based Associative Interaction Framework
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Solution Overview
Problem
Current human-computer interaction technologies, particularly in ambient computing, lack intuitive and scalable solutions for user input in environments with multiple users, relying on voice or explicit gestures towards specific devices, and do not effectively utilize natural interactions for user interfaces in commercial settings.
Innovation Solution
A vision-based associative interaction framework that enables users to interact with objects in their environment using natural actions like touching, pointing, or gesturing, allowing for multiple users to input data without directing interactions towards a specific device, using computer vision to detect and interpret these interactions and trigger responses across connected systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If voice or explicit gestures are used for user input, then user interaction is possible, but the system requires users to explicitly target specific devices, reducing natural interaction
Solution Approach 1:
The patent introduces cameras and computer vision systems as intermediaries between users and computing devices. Instead of users directly interacting with devices through voice or gestures, the camera captures visual data of user actions (touching, pointing at objects), and the computer vision system interprets these actions to trigger device responses. This intermediary layer enables natural interaction without requiring users to explicitly target specific devices.
Solution Approach 2:
The patent replaces traditional mechanical or direct user-device interaction mechanisms (voice commands, explicit gestures toward devices) with a visual-based system. Cameras capture visual information, and computer vision algorithms process this data to understand user intentions, substituting the need for direct device targeting with indirect visual observation and interpretation.
2Adaptability or versatility
If traditional user interfaces are used, then user input is possible, but scalability to multiple users in commercial settings is limited
Solution Approach 1:
The patent creates a universal interaction framework where a single camera system can serve multiple users simultaneously in commercial settings. The computer vision system processes visual data from various users and objects, triggering appropriate device responses without requiring separate interaction mechanisms for each user. This multi-functional system adapts to different users and scenarios using the same infrastructure.
Solution Approach 2:
The patent transitions from traditional one-to-one user-device interaction to a many-to-many interaction model enabled by visual data capture. By capturing spatial and contextual information from the environment, the system can identify multiple users, their actions, and relevant objects simultaneously, adding dimensional complexity to the interaction model while maintaining infrastructure simplicity.
3Ease of operation
If ambient computing is implemented, then hands-free interaction is possible, but intuitive user interfaces for general video surveillance environments do not exist
Solution Approach 1:
The patent changes the fundamental parameters of interaction by using visual data capture and computer vision interpretation instead of traditional input methods. This enables hands-free interaction where users can touch or point at objects in the environment, and the system interprets these actions through visual processing. The approach adapts to different environmental contexts by processing visual information from various scenarios.
Data Source
AI summary
A system and method for an associative interaction framework used for user input within an interaction platform used in an environment that includes collecting image data in the environment; through computer vision analysis of the image data, classifying objects in the environment wherein a plurality of the objects are detected users; for at least one user, detecting an associative interaction event, which includes: through computer vision analysis of the image data, detecting a first object association of the one user with a first object, and initiating an associative interaction event with a set of interaction properties including properties of the user and the first object association; and executing an action response based on the associative interaction event.