Apple inc. (20240331447). Pinch Recognition and Rejection simplified abstract

Pinch Recognition and Rejection

Organization Name

apple inc.

Inventor(s)

Itay Bar Yosef of Sunnyvale CA (US)

Bhavin Vinodkumar Nayak of San Jose CA (US)

Chao-Ming Yen of San Francisco CA (US)

Chase B. Lortie of San Francisco CA (US)

Daniel J. Brewer of San Jose CA (US)

Dror Irony of Rishon Le Zion (IL)

Eslam A. Mostafa of San Jose CA (US)

Guy Engelhard of Kiryat Ono (IL)

Ian R. Fasel of San Francisco CA (US)

Julian K. Shutzberg of San Francisco CA (US)

Liuhao Ge of San Jose CA (US)

Lucas Soffer of Sunny Isles Beach FL (US)

Matthias M. Schroeder of Cupertino CA (US)

Mohammadhadi Kiapour of San Francisco CA (US)

Victor Belyaev of San Jose CA (US)

Yirong Tang of Munich (DE)

Pinch Recognition and Rejection - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240331447 titled 'Pinch Recognition and Rejection

Simplified Explanation: The patent application describes a method for processing gesture input by tracking hand movements, determining hand poses, classifying the intentionality of gestures, enabling input actions based on hand poses and intentionality, and determining occlusion classifications for hands to further refine gesture recognition.

• Hand tracking data is obtained from camera frames.
• Hand poses are determined based on the tracking data.
• Intentionality classifications for gestures are assigned based on hand poses.
• Input actions are enabled based on hand poses and intentionality classifications.
• Occlusion classifications for hands are determined to improve gesture recognition accuracy.

Potential Applications: 1. Virtual reality and augmented reality systems for intuitive hand gesture controls. 2. Sign language recognition and translation applications. 3. Interactive gaming experiences with gesture-based controls. 4. Human-computer interaction in smart devices and touchless interfaces.

Problems Solved: 1. Enhances the accuracy and efficiency of gesture recognition systems. 2. Enables more natural and intuitive user interactions in various applications. 3. Improves the user experience in virtual environments and interactive systems.

Benefits: 1. Increased accuracy in recognizing and interpreting hand gestures. 2. Enhanced user experience through intuitive gesture-based controls. 3. Improved accessibility for users with physical limitations. 4. Potential for new and innovative applications in various industries.

Commercial Applications: The technology can be applied in industries such as virtual reality, gaming, communication devices, and smart home systems to provide more intuitive and efficient user interactions, leading to enhanced user experiences and market competitiveness.

Questions about Gesture Recognition Technology: 1. How does the occlusion classification improve gesture recognition accuracy? 2. What are the potential challenges in implementing this technology in real-world applications?

Original Abstract Submitted

processing gesture input includes obtaining hand tracking data based on a set of camera frames, determining a hand pose based on the hand tracking data, and determining an intentionality classification for a gesture based on the hand pose. an input action corresponding to the gesture is enabled based on the hand pose and the intentionality classification. an occlusion classification is determined for the hand based on the hand pose and the input gesture can be determined based on the occlusion classification.