公开(公告)号：US11693092B2

公开(公告)日：2023-07-04

申请号：US17523051

申请日：2021-11-10

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：US20230008681A1

公开(公告)日：2023-01-12

申请号：US17932461

申请日：2022-09-15

Applicant: Google LLC

Inventor： Nicholas Edward Gillian

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

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：US11481040B2

公开(公告)日：2022-10-25

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: G06F3/01 ,  G01S7/41 ,  G01S13/56 ,  G01S13/86 ,  H04Q9/00 ,  G06K9/62 ,  G01S13/88 ,  G06F21/32 ,  G06F3/04815 ,  G01S7/40 ,  H04W4/80 ,  G06N20/00 ,  H04W16/28 ,  G01S13/90 ,  G06V20/64 ,  G06V40/20 ,  G06F16/245 ,  G06F21/62 ,  A63F13/21 ,  A63F13/24 ,  G01S13/66 ,  G08C17/02 ,  G06T7/73 ,  G01S13/931 ,  G06F1/16 ,  G06F3/0484 ,  G01S19/42 ,  G06F3/0346 ,  G06F3/16

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.

公开(公告)号：US11175743B2

公开(公告)日：2021-11-16

申请号：US17005207

申请日：2020-08-27

Applicant: Google LLC

Inventor： Jaime Lien ,  Nicholas Edward Gillian ,  Ivan Poupyrev

IPC: G09G5/00 ,  G06F3/01 ,  G01S7/41 ,  G01S13/56 ,  G01S13/86 ,  H04Q9/00 ,  G06K9/00 ,  G06K9/62 ,  G01S13/88 ,  G06F21/32 ,  G06F3/0481 ,  G01S7/40 ,  H04W4/80 ,  G06N20/00 ,  H04W16/28 ,  G01S13/90 ,  G06F16/245 ,  G06F21/62 ,  A63F13/21 ,  A63F13/24 ,  G01S13/66 ,  G08C17/02 ,  G06T7/73 ,  G01S13/931 ,  G06F1/16 ,  G06F3/0484 ,  G01S19/42 ,  G06F3/0346 ,  G06F3/16

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：US20190321719A1

公开(公告)日：2019-10-24

申请号：US16503234

申请日：2019-07-03

Applicant: Google LLC

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

IPC: A63F13/21 ,  G06F3/01 ,  H04Q9/00 ,  G06K9/00 ,  G06K9/62 ,  G01S7/41 ,  G01S13/86 ,  G01S13/56 ,  G06F16/245 ,  G01S13/90 ,  G01S13/66 ,  A63F13/24 ,  G06F21/62

Abstract: This document describes apparatuses and techniques for radar-enabled sensor fusion. In some aspects, a radar field is provided and reflection signals that correspond to a target in the radar field are received. The reflection signals are transformed to provide radar data, from which a radar feature indicating a physical characteristic of the target is extracted. Based on the radar features, a sensor is activated to provide supplemental sensor data associated with the physical characteristic. The radar feature is then augmented with the supplemental sensor data to enhance the radar feature, such as by increasing an accuracy or resolution of the radar feature. By so doing, performance of sensor-based applications, which rely on the enhanced radar features, can be improved.

公开(公告)号：US12158991B2

公开(公告)日：2024-12-03

申请号：US17634857

申请日：2020-10-20

Applicant: Google LLC

Inventor： Michal Matuszak ,  Abel Seleshi Mengistu ,  Nicholas Edward Gillian ,  Abhijit Aroon Shah

IPC: G06F3/01 ,  G01S13/06 ,  G06V10/82 ,  G06V10/94 ,  G06V40/20

Abstract: Techniques and apparatuses are described that implement a smart-device-based radar system capable of performing gesture recognition using a space time neural network. The space time neural network employs machine learning to recognize a user's gesture based on complex radar data. The space time neural network is implemented using a multi-stage machine-learning architecture, which enables the radar system to conserve power and recognize the user's gesture in real time (e.g., as the gesture is performed). The space time neural network is also adaptable and can be expanded to recognize multiple types of gestures, such as a swipe gesture and a reach gesture, without significantly increasing size, computational requirements, or latency.

公开(公告)号：US11573311B2

公开(公告)日：2023-02-07

申请号：US16772760

申请日：2019-04-02

Applicant: Google LLC

Inventor： Nicholas Edward Gillian ,  Michal Matuszak ,  Octavio Ponce Madrigal ,  Jaime Lien ,  Patrick M. Amihood ,  Ivan Poupyrev

IPC: G01S13/44 ,  G01S13/34 ,  G01S13/72 ,  G06N3/04

Abstract: Techniques and apparatuses are described that implement a smart-device-based radar system capable of performing angular estimation using machine learning. In particular, a radar system 102 includes an angle-estimation module 504 that employs machine learning to estimate an angular position of one or more objects (e.g., users). By analyzing an irregular shape of the radar system 102's spatial response across a wide field of view, the angle-estimation module 504 can resolve angular ambiguities that may be present based on the angle to the object or based on a design of the radar system 102 to correctly identify the angular position of the object. Using machine-learning techniques, the radar system 102 can achieve a high probability of detection and a low false-alarm rate for a variety of different antenna element spacings and frequencies.

公开(公告)号：US20210365124A1

公开(公告)日：2021-11-25

申请号：US17394241

申请日：2021-08-04

Applicant: Google LLC

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

IPC: G06F3/01 ,  G01S7/41 ,  G01S13/56 ,  G01S13/86 ,  H04Q9/00 ,  G06K9/00 ,  G06K9/62 ,  G01S13/88 ,  G06F21/32 ,  G06F3/0481 ,  G01S7/40 ,  H04W4/80 ,  G06N20/00 ,  H04W16/28 ,  G01S13/90 ,  G06F16/245 ,  G06F21/62 ,  A63F13/21 ,  A63F13/24 ,  G01S13/66

Abstract: This document describes apparatuses and techniques for radar-enabled sensor fusion. In some aspects, a radar field is provided and reflection signals that correspond to a target in the radar field are received. The reflection signals are transformed to provide radar data, from which a radar feature indicating a physical characteristic of the target is extracted. Based on the radar features, a sensor is activated to provide supplemental sensor data associated with the physical characteristic. The radar feature is then augmented with the supplemental sensor data to enhance the radar feature, such as by increasing an accuracy or resolution of the radar feature. By so doing, performance of sensor-based applications, which rely on the enhanced radar features, can be improved.

公开(公告)号：US11698438B2

公开(公告)日：2023-07-11

申请号：US17488015

申请日：2021-09-28

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：US20220326367A1

公开(公告)日：2022-10-13

申请号：US17634857

申请日：2020-10-20

Applicant: Google LLC

Inventor： Michal Matuszak ,  Abel Seleshi Mengistu ,  Nicholas Edward Gillian ,  Abhijit Aroon Shah

IPC: G01S13/06 ,  G06F3/01

Abstract: Techniques and apparatuses are described that implement a smart-device-based radar system capable of performing gesture recognition using a space time neural network. The space time neural network employs machine learning to recognize a user's gesture based on complex radar data. The space time neural network is implemented using a multi-stage machine-learning architecture, which enables the radar system to conserve power and recognize the user's gesture in real time (e.g., as the gesture is performed). The space time neural network is also adaptable and can be expanded to recognize multiple types of gestures, such as a swipe gesture and a reach gesture, without significantly increasing size, computational requirements, or latency.