公开(公告)号：US20240231505A1

公开(公告)日：2024-07-11

申请号：US18554337

申请日：2022-04-08

Applicant: Google LLC

Inventor： Eiji Hayashi ,  Jaime Lien ,  Nicholas Edward Gillian ,  Andrew C. Felch ,  Jin Yamanaka ,  Blake Charles Jacquot

IPC: G06F3/01 ,  H04L27/10

CPC classification number: G06F3/017 ,  H04L27/103

Abstract: Techniques and apparatuses are described that facilitate ambient computing using a radar system. Compared to other smart devices that rely on a physical user interface, a smart device with a radar system can support ambient computing by providing an eye-free interaction and less cognitively demanding gesture-based user interface. The radar system can be designed to address a variety of challenges associated with ambient computing, including power consumption, environmental variations, background noise, size, and user privacy. The radar system uses an ambient-computing machine-learned module to quickly recognize gestures performed by a user up to at least two meters away. The ambient-computing machine-learned module is trained to filter background noise and have a sufficiently low false positive rate to enhance the user experience.

公开(公告)号：US12265666B2

公开(公告)日：2025-04-01

申请号：US18554337

申请日：2022-04-08

Applicant: Google LLC

Inventor： Eiji Hayashi ,  Jaime Lien ,  Nicholas Edward Gillian ,  Andrew C. Felch ,  Jin Yamanaka ,  Blake Charles Jacquot

IPC: G06F3/01 ,  H04L27/10

Abstract: Techniques and apparatuses are described that facilitate ambient computing using a radar system. Compared to other smart devices that rely on a physical user interface, a smart device with a radar system can support ambient computing by providing an eye-free interaction and less cognitively demanding gesture-based user interface. The radar system can be designed to address a variety of challenges associated with ambient computing, including power consumption, environmental variations, background noise, size, and user privacy. The radar system uses an ambient-computing machine-learned module to quickly recognize gestures performed by a user up to at least two meters away. The ambient-computing machine-learned module is trained to filter background noise and have a sufficiently low false positive rate to enhance the user experience.