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公开(公告)号：US20220066568A1
公开(公告)日：2022-03-03
申请号：US17523051
申请日：2021-11-10
Applicant: Google LLC
Inventor： Jaime Lien , Nicholas Edward Gillian , 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: 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.
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公开(公告)号：US11132065B2
公开(公告)日：2021-09-28
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: 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: 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.
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公开(公告)号：US20210156957A1
公开(公告)日：2021-05-27
申请号：US16911116
申请日：2020-06-24
Applicant: Google LLC
Inventor： Nicholas Edward Gillian
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.
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公开(公告)号：US20240345212A1
公开(公告)日：2024-10-17
申请号：US18753749
申请日：2024-06-25
Applicant: Google LLC
Inventor： Nicholas Edward Gillian
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.
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公开(公告)号：US12117560B2
公开(公告)日：2024-10-15
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: G01S7/41 , A63F13/21 , A63F13/24 , G01S7/40 , G01S13/56 , G01S13/66 , G01S13/86 , G01S13/88 , G01S13/90 , G06F3/01 , G06F3/04815 , G06F16/245 , G06F18/21 , G06F18/25 , G06F18/28 , G06F18/40 , G06F21/32 , G06F21/62 , G06N20/00 , G06V10/80 , G06V20/64 , G06V40/20 , H04Q9/00 , H04W4/80 , H04W16/28 , G01S13/931 , G01S19/42 , G06F1/16 , G06F3/0346 , G06F3/0484 , G06F3/16 , G06T7/73 , G08C17/02
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 , G01S2013/9322 , G01S19/42 , G06F1/163 , G06F3/0346 , G06F3/0484 , G06F3/165 , G06F2203/0384 , G06F2221/2105 , G06T7/75 , G08C17/02 , G08C2201/93 , H04Q2209/883
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.
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Patent Agency Ranking
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
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.
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公开(公告)号：US20230204754A1
公开(公告)日：2023-06-29
申请号：US18175753
申请日：2023-02-28
Applicant: Google LLC
Inventor： Changzhan Gu , Jaime Lien , Nicholas Edward Gillian , Jian Wang
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.
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公开(公告)号：US11460538B2
公开(公告)日：2022-10-04
申请号：US16911116
申请日：2020-06-24
Applicant: Google LLC
Inventor： Nicholas Edward Gillian
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.
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公开(公告)号：US20220066567A1
公开(公告)日：2022-03-03
申请号：US17500747
申请日：2021-10-13
Applicant: Google LLC
Inventor： Jaime Lien , Nicholas Edward Gillian , Ivan Poupyrev
IPC: G06F3/01 , G01S13/56 , G01S13/88 , G01S13/86 , G06N20/00 , A63F13/21 , G06K9/62 , A63F13/24 , G01S7/41 , G01S13/90 , G06F16/245 , G06K9/00 , H04W16/28 , H04W4/80 , G01S13/66 , H04Q9/00 , G06F3/0481 , G06F21/62 , G01S7/40 , G06F21/32 , G06F1/16 , G06F3/0484 , G01S13/931 , G08C17/02 , G06F3/0346 , G06T7/73 , G01S19/42 , 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.
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公开(公告)号：US20220019291A1
公开(公告)日：2022-01-20
申请号：US17488015
申请日：2021-09-28
Applicant: Google LLC
Inventor： Jaime Lien , Nicholas Edward Gillian , 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: 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.
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