Google llc (20240126384). Selective Gesture Recognition for Handheld Devices simplified abstract

Selective Gesture Recognition for Handheld Devices

Organization Name

google llc

Inventor(s)

Dev Bhargava of San Francisco CA (US)

Alejandro Kauffmann of San Francisco CA (US)

Selective Gesture Recognition for Handheld Devices - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240126384 titled 'Selective Gesture Recognition for Handheld Devices

Simplified Explanation

The present disclosure is directed to selective gesture recognition for handheld device gestures. An example method includes receiving movement information descriptive of a gesture performed with the handheld interactive object. The method includes selecting a local and/or remote machine-learned model for processing the movement information. The movement information can be processed to identify a gesture action corresponding to the movement information. The local and/or remote machine-learned model can be selected based on user input data and/or a complexity of the movement information. In response to selecting the local machine-learned model, the method includes processing the movement information according to the local machine-learned model and communicating a message to a remote device based on the result. In response to selecting the remote machine-learned model, the method includes communicating the movement information to the remote device for processing in accordance with the remote machine-learned model.

• Handheld device gestures are recognized through movement information received by the device.
• A machine-learned model is selected to process the movement information and identify the corresponding gesture action.
• The selected model can be either local or remote, based on user input data and complexity of the movement information.
• The method involves processing the movement information and communicating with a remote device based on the selected model.

Potential Applications

This technology can be applied in:

• Virtual reality and augmented reality applications
• Gaming consoles and interactive entertainment systems
• Gesture-based control systems for smart devices

Problems Solved

This technology solves the following problems:

• Accurate recognition of handheld device gestures
• Efficient communication between devices based on gesture actions
• Adaptation to user input data and complexity of gestures

Benefits

The benefits of this technology include:

• Enhanced user experience with handheld interactive objects
• Seamless integration of gesture recognition in various applications
• Improved efficiency in processing and communicating gesture actions

Potential Commercial Applications

This technology has potential commercial applications in:

• Consumer electronics industry
• Gaming and entertainment sector
• Virtual reality and augmented reality markets

Possible Prior Art

One possible prior art for this technology could be:

• Gesture recognition systems in gaming consoles and smart devices

How does this technology ensure data privacy and security when processing movement information?

This article does not address the specific measures taken to protect user data and ensure privacy during the processing of movement information.

What are the limitations of using machine-learned models for gesture recognition in handheld devices?

The article does not discuss any potential drawbacks or limitations of relying on machine-learned models for gesture recognition in handheld devices.

Original Abstract Submitted

the present disclosure is directed to selective gesture recognition for handheld device gestures. an example method includes receiving, by a handheld interactive object, movement information descriptive of a gesture performed with the handheld interactive object. the method includes selecting a local and/or remote machine-learned model for processing the movement information. the movement information can be processed to identify a gesture action corresponding to the movement information. the local and/or remote machine-learned model can be selected based on user input data and/or a complexity of the movement information. in response to selecting the local machine-learned model, the method includes processing the movement information according to the local machine-learned model and communicating a message to a remote device based on the result. in response to selecting the remote ma-chine-learned model, the method includes communicating the movement information to the remote device for processing in accordance with the remote machine-learned model.