# US 2026/0093333 A1 · Radar-Based Gesture Determination at Long Ranges > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0093333 A1 - Document ID: US20260093333A1 - Status: Published application - Kind: A1 - Application number: 19/112,562 - Filed: 2022-09-30 - Published: 2026-04-02 - Earliest claimed: 2022-09-30 - Assignees: Google Inc. - Applicants: Google LLC - Inventors: Eiji Hayashi; Zhuo Wang; Andrew C. Felch; Jin Yamanaka; Blake Charles Jacquot; Ivan Poupyrev; Leonardo Giusti; Will R. Walker; Hideaki Matsui; Lauren Marie Bedal; Lawrence Au; Jaime Lien - Web page: https://patentgrep.com/patent/US20260093333A1 ## CPC - G06F 3/017: {Gesture based interaction, e.g. based on a set of recognized hand gestures (interaction based on gestures traced on a digitiser G06F3/04883)} - G01S 7/415: {Identification of targets based on measurements of movement associated with the target} - G01S 7/417: {involving the use of neural networks} - G01S 13/582: {adapted for simultaneous range and velocity measurements} - G01S 13/584: {adapted for simultaneous range and velocity measurements} - G06N 3/0442: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/0475: Generative networks - G06N 3/048: Activation functions - G06N 3/065: Analogue means - G06N 3/088: Non-supervised learning, e.g. competitive learning - G06N 3/09: Supervised learning - G06N 3/094: Adversarial learning - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G01S: RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract Techniques and devices for radar-based gesture determination at long ranges are described in this document. The techniques described herein enable a computing device to detect and recognize gestures at long-range extents of up to eight meters. The computing device of this disclosure does not require the user to perform a gestural command at a specific location, in a specific orientation, contingent upon a wake-up trigger, or at a specific time, enabling the user to freely provide commands whenever and wherever is most convenient. This continual recognition of gestures may be enabled by a machine-learned model, generation of augmented data, and inclusion of negative data. ## Claims ### Claim 1 A method comprising: transmitting radar-transmit signals from a radar system associated with a computing device; receiving, at the radar system or another radar system associated with the computing device, radar-receive signals; continually attempting to determine, based on one or more of the radar-receive signals, that a user has performed a gesture at a long-range extent, the continually attempting performed without segmenting or narrowing a time-frame in which to analyze the one or more radar-receive signals based on an indication that the user intends to perform the gesture; responsive to determining the user has performed the gesture, correlating a radar-signal characteristic of the one or more radar-receive signals to one or more stored radar-signal characteristics of a known gesture; and responsive to determining that the performed gesture is the known gesture, directing the computing device, an application associated with the computing device, or another device associated with the computing device to execute a command associated with the known gesture. ### Claim 2 (depends on 1) The method as recited by claim 1, wherein the one or more radar-receive signals are reflected off the user performing the gesture at a linear displacement of two to four meters relative to the radar system associated with the computing device. ### Claim 3 (depends on 1) The method as recited by claim 1, wherein the indication includes a sensed action that the user intends to perform a gesture, the sensed action performed by the user and recognized by one or more sensors associated with the computing device. ### Claim 4 (depends on 1) The method as recited by claim 1, wherein the computing device is configured to recognize the gesture performed by the user at one or more locations within a proximate region of the radar system, the recognition conducted without requiring the user to perform the gesture at a specified location or orientation within the proximate region. ### Claim 5 (depends on 1) The method as recited by claim 1, the method further comprising: responsive to the correlating of the radar-signal characteristic to the one or more stored radar-signal characteristics, verifying a preliminary result of the correlation using a gesture debouncer, the preliminary result comprising a classification of the performed gesture as the known gesture associated with the command, the preliminary result satisfying at least one of the following requirements of the gesture debouncer: a minimum threshold requirement of elapsed time between gesture performances; a maximum threshold requirement regarding a level of confidence; or one or more heuristics. ### Claim 6 (depends on 1) The method as recited by claim 1, the method further comprising: receiving, at the radar system or the other radar system, a second radar-receive signal reflected off another gesture of the user; determining, from the second radar-receive signal, a second radar-signal characteristic; comparing the second radar-signal characteristic to the one or more stored radar-signal characteristics, the comparing effective to determine the other gesture of the user is not the known gesture or another known gesture; and responsive to the determination that the other gesture of the user is not the known gesture or the other known gesture: classifying the second radar-signal characteristic as negative data, the negative data corresponding to a class of background motions; and storing the second radar-signal characteristic, the stored second radar-signal characteristic usable to correlate, at a future time, a future-received radar-receive signal to the other gesture of the user, the future-received radar-receive signal reflected off an additional gesture of the user and having a similar or identical radar-signal characteristic to the second radar-signal characteristic, the correlation indicating the additional gesture of the user corresponds to the class of background motions and not the known gesture or the other known gesture. ### Claim 7 (depends on 1) The method as recited by claim 1, the method further comprising: responsive to the determining that the performed gesture is the known gesture, augmenting the radar-signal characteristic of the performed gesture prior to storing the radar-signal characteristic, the augmenting including an interpolation or extrapolation of the radar-signal characteristic without requiring one or more additional radar-receive signals. ### Claim 8 (depends on 7) The method as recited by claim 7, wherein the radar-signal characteristic is associated with at least one complex-range Doppler map, the at least one complex-range Doppler map comprising: a range dimension corresponding to a displacement of the performed gesture relative to the radar system, the displacement taken at a scattering center of the performed gesture; and a Doppler dimension corresponding to a velocity of the performed gesture relative to the radar system. ### Claim 9 (depends on 8) The method as recited by claim 8, wherein: the augmentation of the radar-signal characteristic includes a rotation of the at least one complex-range Doppler map; the rotation is achieved by substituting a random or predetermined phase value into the at least one complex-range Doppler map to generate an augmented radar-signal characteristic of the performed gesture, the random or predetermined phase value being distinct from a phase value associated with the radar-signal characteristic; and the augmented radar-signal characteristic represents an additional angular displacement of the performed gesture relative to the radar system, the augmented radar-signal characteristic determined without requiring the user to perform the gesture at the additional angular displacement. ### Claim 10 (depends on 8) The method as recited by claim 8, wherein: the augmentation of the radar-signal characteristic includes one or more scalings of a magnitude of the at least one complex-range Doppler map; the one or more scalings are achieved using one or more random or predetermined scaling values of a normal distribution to generate an augmented radar-signal characteristic of the performed gesture; and the augmented radar-signal characteristic represents an additional linear displacement of the performed gesture relative to the radar system, the augmented radar-signal characteristic determined without requiring the user to perform the gesture at the additional linear displacement. ### Claim 11 (depends on 1) The method as recited by claim 1, the method further comprising: augmenting the radar-signal characteristic associated with the gesture being performed at the long-range extent and for a short-range extent, the short-range extent corresponding to a smaller linear displacement from the radar system than the long-range extent; and storing the augmented radar-signal characteristic to enable recognition, at a future time, of the gesture being performed at the short-range extent. ### Claim 12 (depends on 1) The method as recited by claim 1, wherein the correlation of the radar-signal characteristic to the one or more stored radar-signal characteristics is performed using a machine-learned model, the machine-learned model associated with one or more convolutional neural networks, the machine-learned model comprising a frame model, the frame model configured to modify one or more frames of the radar-signal characteristic using one or more convolution layers, the one or more frames collected by the radar system at one or more times, and the frame model comprising one or more of: a separable convolution layer; or a residual neural network. ### Claim 13 (depends on 12) The method as recited by claim 12, wherein: the machine-learned model comprises a temporal model configured to correlate one or more modified frames along a time domain, the temporal model including one or more residual neural networks; and the correlation of the one or more modified frames is effective to determine a probability of the performed gesture being associated with a set of possible classes, the set of possible classes comprising at least one of: a gesture class corresponding to the known gesture or another known gesture; or a background class corresponding to one or more background motions that are not the known gesture or the other known gesture. ### Claim 14 A computing device comprising: at least one antenna; a radar system configured to transmit a radar-transmit signal and receive a radar-receive signal using the at least one antenna; at least one processor; and a computer-readable storage media comprising instructions that, responsive to execution by the processor, are configured to direct the computing device to perform operations including: transmitting radar-transmit signals from the radar system; receiving, at the radar system or another radar system associated with the computing device, radar-receive signals; continually attempting to determine, based on one or more of the radar-receive signals, that a user has performed a gesture at a long-range extent, the continually attempting performed without segmenting or narrowing a time-frame in which to analyze the one or more radar-receive signals based on an indication that the user intends to perform the gesture; responsive to determining the user has performed the gesture, correlating a radar-signal characteristic of the one or more radar-receive signals to one or more stored radar-signal characteristics of a known gesture; and responsive to determining that the performed gesture is the known gesture, directing an application associated with the computing device or another device associated with the computing device to execute a command associated with the known gesture. ### Claim 15 (canceled) ### Claim 16 (depends on 14) The computing device of claim 14, wherein the one or more radar-receive signals are reflected off the user performing the gesture at a linear displacement of two to four meters relative to the radar system associated with the computing device. ### Claim 17 (depends on 14) The computing device of claim 14, further comprising: responsive to the correlating of the radar-signal characteristic to the one or more stored radar-signal characteristics, verifying a preliminary result of the correlation using a gesture debouncer, the preliminary result comprising a classification of the performed gesture as the known gesture associated with the command, the preliminary result satisfying at least one of the following requirements of the gesture debouncer: a minimum threshold requirement of elapsed time between gesture performances; a maximum threshold requirement regarding a level of confidence; or one or more heuristics. ### Claim 18 (depends on 14) The computing device of claim 14, further comprising: receiving, at the radar system or the other radar system, a second radar-receive signal reflected off another gesture of the user; determining, from the second radar-receive signal, a second radar-signal characteristic; comparing the second radar-signal characteristic to the one or more stored radar-signal characteristics, the comparing effective to determine the other gesture of the user is not the known gesture or another known gesture; and responsive to the determination that the other gesture of the user is not the known gesture or the other known gesture: classifying the second radar-signal characteristic as negative data, the negative data corresponding to a class of background motions; and storing the second radar-signal characteristic, the stored second radar-signal characteristic usable to correlate, at a future time, a future-received radar-receive signal to the other gesture of the user, the future-received radar-receive signal reflected off an additional gesture of the user and having a similar or identical radar-signal characteristic to the second radar-signal characteristic, the correlation indicating the additional gesture of the user corresponds to the class of background motions and not the known gesture or the other known gesture. ### Claim 19 (depends on 14) The computing device of claim 14, further comprising: responsive to the determining that the performed gesture is the known gesture, augmenting the radar-signal characteristic of the performed gesture prior to storing the radar-signal characteristic, the augmenting including an interpolation or extrapolation of the radar-signal characteristic without requiring one or more additional radar-receive signals. ### Claim 20 (depends on 14) The computing device of claim 14, further comprising: augmenting the radar-signal characteristic associated with the gesture being performed at the long-range extent and for a short-range extent, the short-range extent corresponding to a smaller linear displacement from the radar system than the long-range extent; and storing the augmented radar-signal characteristic to enable recognition, at a future time, of the gesture being performed at the short-range extent. ### Claim 21 (depends on 14) The computing device of claim 14, wherein the correlation of the radar-signal characteristic to the one or more stored radar-signal characteristics is performed using a machine-learned model, the machine-learned model associated with one or more convolutional neural networks, the machine-learned model comprising a frame model, the frame model configured to modify one or more frames of the radar-signal characteristic using one or more convolution layers, the one or more frames collected by the radar system at one or more times, and the frame model comprising one or more of: a separable convolution layer; or a residual neural network.