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.

公开(公告)号：US12055656B2

公开(公告)日：2024-08-06

申请号：US17932461

申请日：2022-09-15

Applicant: Google LLC

Inventor： Nicholas Edward Gillian

IPC: G01S13/53 ,  G01S7/295 ,  G01S13/88 ,  G06F3/01

CPC classification number: G01S7/2955 ,  G01S13/53 ,  G01S13/88 ,  G06F3/017

Abstract: Techniques and apparatuses are described that implement a smart-device-based radar system capable of detecting a frame-of-reference change. In particular, a radar system includes a frame-of-reference machine-learned module trained to recognize whether or not the radar system's frame of reference changes. The frame-of-reference machine-learned module analyzes complex radar data generated from at least one chirp of a reflected radar signal to analyze a relative motion of at least one object over time. By analyzing the complex radar data directly using machine learning, the radar system can operate as a motion sensor without relying on non-radar-based sensors, such as gyroscopes, inertial sensors, or accelerometers. With knowledge of whether the frame-of-reference is stationary or moving, the radar system can determine whether or not a gesture is likely to occur and, in some cases, compensate for the relative motion of the radar system itself.

公开(公告)号：US11906619B2

公开(公告)日：2024-02-20

申请号：US18175753

申请日：2023-02-28

Applicant: Google LLC

Inventor： Changzhan Gu ,  Jaime Lien ,  Nicholas Edward Gillian ,  Jian Wang

IPC: G01S7/41 ,  G01S7/48 ,  G01S13/08 ,  G01S13/62 ,  G01S13/58 ,  G06F3/01

CPC classification number: G01S13/584 ,  G01S7/417 ,  G01S7/4808 ,  G01S13/08 ,  G01S13/62 ,  G06F3/017

Abstract: Techniques and apparatuses are described that implement a smart-device-based radar system capable of detecting user gestures in the presence of saturation. In particular, a radar system employs machine learning to compensate for distortions resulting from saturation. This enables gesture recognition to be performed while the radar system's receiver is saturated. As such, the radar system can forgo integrating an automatic gain control circuit to prevent the receiver from becoming saturated. Furthermore, the radar system can operate with higher gains to increasing sensitivity without adding additional antennas. By using machine learning, the radar system's dynamic range increases, which enables the radar system to detect a variety of different types of gestures having small or large radar cross sections, and performed at various distances from the radar system.

公开(公告)号：US11698439B2

公开(公告)日：2023-07-11

申请号：US17500747

申请日：2021-10-13

Applicant: Google LLC

Inventor： Jaime Lien ,  Nicholas Edward Gillian ,  Ivan Poupyrev

IPC: G09G5/00 ,  G01S7/41 ,  G01S13/56 ,  G01S13/86 ,  H04Q9/00 ,  G01S13/88 ,  G06F21/32 ,  G06F3/04815 ,  G01S7/40 ,  H04W4/80 ,  G06N20/00 ,  H04W16/28 ,  G01S13/90 ,  G06V20/64 ,  G06V40/20 ,  G06F18/25 ,  G06F18/28 ,  G06F18/40 ,  G06F18/21 ,  G06V10/80 ,  G06F16/245 ,  G06F3/01 ,  G06F21/62 ,  A63F13/21 ,  A63F13/24 ,  G01S13/66 ,  G08C17/02 ,  G06T7/73 ,  G01S13/931 ,  G06F1/16 ,  G06F3/0484 ,  G01S19/42 ,  G06F3/0346 ,  G06F3/16

CPC classification number: G01S7/415 ,  A63F13/21 ,  A63F13/24 ,  G01S7/4004 ,  G01S7/41 ,  G01S7/412 ,  G01S13/56 ,  G01S13/66 ,  G01S13/86 ,  G01S13/867 ,  G01S13/88 ,  G01S13/888 ,  G01S13/90 ,  G01S13/904 ,  G06F3/011 ,  G06F3/017 ,  G06F3/04815 ,  G06F16/245 ,  G06F18/217 ,  G06F18/25 ,  G06F18/253 ,  G06F18/28 ,  G06F18/41 ,  G06F21/32 ,  G06F21/6245 ,  G06N20/00 ,  G06V10/806 ,  G06V20/64 ,  G06V40/28 ,  H04Q9/00 ,  H04W4/80 ,  H04W16/28 ,  A63F2300/8082 ,  G01S13/865 ,  G01S13/931 ,  G01S19/42 ,  G01S2013/9322 ,  G06F1/163 ,  G06F3/0346 ,  G06F3/0484 ,  G06F3/165 ,  G06F2203/0384 ,  G06F2221/2105 ,  G06T7/75 ,  G08C17/02 ,  G08C2201/93 ,  H04Q2209/883

Abstract: Various embodiments wirelessly detect micro gestures using multiple antenna of a gesture sensor device. At times, the gesture sensor device transmits multiple outgoing radio frequency (RF) signals, each outgoing RF signal transmitted via a respective antenna of the gesture sensor device. The outgoing RF signals are configured to help capture information that can be used to identify micro-gestures performed by a hand. The gesture sensor device captures incoming RF signals generated by the outgoing RF signals reflecting off of the hand, and then analyzes the incoming RF signals to identify the micro-gesture.

公开(公告)号：US11592547B2

公开(公告)日：2023-02-28

申请号：US16772566

申请日：2019-02-28

Applicant: Google LLC

Inventor： Changzhan Gu ,  Jaime Lien ,  Nicholas Edward Gillian ,  Jian Wang

IPC: G01S13/58 ,  G01S7/41 ,  G01S7/48 ,  G01S13/08 ,  G01S13/62 ,  G06F3/01

Abstract: Techniques and apparatuses are described that implement a smart-device-based radar system capable of detecting user gestures in the presence of saturation. In particular, a radar system 104 employs machine learning to compensate for distortions resulting from saturation. This enables gesture recognition to be performed while the radar system 104's receiver 304 is saturated. As such, the radar system 104 can forgo integrating an automatic gain control circuit to prevent the receiver 304 from becoming saturated. Furthermore, the radar system 104 can operate with higher gains to increasing sensitivity without adding additional antennas. By using machine learning, the radar system 104's dynamic range increases, which enables the radar system 104 to detect a variety of different types of gestures having small or large radar cross sections, and performed at various distances from the radar system 104.

公开(公告)号：US20210326642A1

公开(公告)日：2021-10-21

申请号：US17361824

申请日：2021-06-29

Applicant: Google LLC

Inventor： Nicholas Edward Gillian ,  Jaime Lien ,  Patrick M. Amihood ,  Ivan Poupyrev

IPC: G06K9/62 ,  G06K9/00 ,  G06F3/01

Abstract: Various embodiments dynamically learn user-customizable input gestures. A user can transition a radar-based gesture detection system into a gesture-learning mode. In turn, the radar-based gesture detection system emits a radar field configured to detect a gesture new to the radar-based gesture detection system. The radar-based gesture detection system receives incoming radio frequency (RF) signals generated by the outgoing RF signal reflecting off the gesture, and analyzes the incoming RF signals to learn one or more identifying characteristics about the gesture. Upon learning the identifying characteristics, the radar-based gesture detection system reconfigures a corresponding input identification system to detect the gesture when the one or more identifying characteristics are next identified, and transitions out of the gesture-learning mode.