Intent Detection via Gesture and Environment Analysis
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Solution Overview
Problem
Current computing devices lack an effective method to detect user intent without traditional input devices like mice or keyboards, especially in scenarios where screens are absent, and existing technologies are limited in interpreting real-world objects and gestures.
Innovation Solution
A system that captures images using a single non-depth sensing camera, employs machine learned models to detect hand gestures and identify user intent based on both the gesture and environment, allowing for the execution of tasks such as searching or interacting with digital assistants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional input devices like mice or keyboards are used, then user intent detection is reliable, but device complexity and physical hardware requirements increase
Solution Approach 1:
The patent replaces mechanical input devices (mouse, keyboard) with an optical system consisting of a camera and machine learning model. The camera captures images of hand gestures, and the ML model processes these images to detect gestures and determine user intent, eliminating the need for physical input hardware while maintaining reliable intent detection.
2Device complexity
If a single non-depth sensing camera is used, then device complexity is reduced, but gesture detection precision deteriorates
Solution Approach 1:
The patent changes the processing parameters by applying machine learning algorithms that can interpret 2D camera data to detect 3D hand gestures. The ML model learns to infer depth and gesture meaning from single-camera 2D images through training on labeled gesture data, achieving accurate gesture detection without depth sensing hardware.
3Ease of operation
If machine learned models are used to detect hand gestures, then ease of operation is improved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary action by pre-training the machine learning model on large datasets of hand gestures before deployment. This offline training phase prepares the model to quickly and accurately classify gestures in real-time during operation, reducing processing time during actual use while maintaining high accuracy for natural gesture interaction.
Data Source
AI summary
A method can perform a process with a method including capturing an image, determining an environment that a user is operating a computing device, detecting a hand gesture based on an object in the image, determining, using a machine learned model, an intent of a user based on the hand gesture and the environment, and executing a task based at least on the determined intent.