# US 12,003,858 B2 · System and method for gunshot detection > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,003,858 B2 - Document ID: US12003858B2 - Status: Granted - Kind: B2 - Application number: 18/380,134 - Filed: 2023-10-13 - Published: 2024-04-18 - Granted: 2024-06-04 - Earliest claimed: 2022-10-13 - Assignees: Auris, LLC - Applicants: Auris, LLC - Inventors: Brian C. Fitzpatrick; Robert McGrath - Web page: https://patentgrep.com/patent/US12003858B2 ## CPC - H04N 7/188: {Capturing isolated or intermittent images triggered by the occurrence of a predetermined event, e.g. an object reaching a predetermined position (signal generation from motion picture films H04N5/253)} - H04N 23/695: Control of camera direction for changing a field of view, e.g. pan, tilt or based on tracking of objects - G01S 5/22: Position of source determined by co-ordinating a plurality of position lines defined by path-difference measurements (G01S5/28 takes precedence) - G08B 13/1672: {using sonic detecting means, e.g. a microphone operating in the audio frequency range} - G08B 13/1963: {Arrangements allowing camera rotation to change view, e.g. pivoting camera, pan-tilt and zoom [PTZ]} - G08B 13/19697: {Arrangements wherein non-video detectors generate an alarm themselves} - H04N 7/183: {for receiving images from a single remote source} - H04R 1/406: {microphones} - H04R 3/005: {for combining the signals of two or more microphones (specially adapted for hearing aids H04R25/407)} - H04R 1/028: {associated with devices performing functions other than acoustics, e.g. electric candles} - H04R 1/04: Structural association of microphone with electric circuitry therefor (in electric hearing aids H04R25/00) - H04R 1/08: Mouthpieces; {Microphones;} Attachments therefor - H04R 2201/401: 2D or 3D arrays of transducers - H04R 2430/20: Processing of the output signals of the acoustic transducers of an array for obtaining a desired directivity characteristic (H04R2203/12 takes precedence) - H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION - 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 - G08B: SIGNALLING SYSTEMS, e.g. PERSONAL CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS - H04R: LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; ELECTRIC HEARING AIDS; PUBLIC ADDRESS SYSTEMS (producing sounds with frequency not determined by supply frequency G10K) ## Abstract An apparatus may include a device housing; a camera; a plurality of microphones inside or mounted to the device housing; and a processor inside the device housing and electrically coupled to the plurality of microphones. The processor can be configured to receive a set of audio data from each of the plurality of microphones; execute a machine learning model using each of the sets of audio data as input to determine whether the set of audio data corresponds to an actionable sound; determine a location of the actionable sound relative to a location of the camera based on a plurality of sets of audio data determined to correspond to the actionable sound; and rotate the camera towards the determined location of the actionable sound. ## Claims ### Claim 1 A system, comprising: one or more processors configured by machine readable instructions to: receive a set of audio data from each of a plurality of microphones; execute one or more sound detection machine learning models using each of the sets of audio data as input to determine whether the set of audio data corresponds to an actionable sound, each set of audio data comprising a plurality of sounds; responsive to determining at least one set of audio data corresponds to the actionable sound, execute a time detection machine learning model using the at least one set of audio data as input to determine each instance and time of the actionable sound; and responsive to determining each instance and time of the actionable sound, determine a location of the actionable sound based on the time of each instance of the actionable sound in each of the at least one set of audio data. ### Claim 2 (depends on 1) The system of claim 1, wherein the one or more processors are further configured to: perform an automated action based on the determined location of the actionable sound. ### Claim 3 (depends on 1) The system of claim 1, wherein the one or more processors are further configured to: rotate a camera towards the determined location of the actionable sound. ### Claim 4 (depends on 3) The system of claim 3, wherein the one or more processors are configured to: determine the location of the actionable sound by determining the location of the actionable sound relative to the camera. ### Claim 5 (depends on 1) The system of claim 1, wherein the one or more processors are configured to: transmit an indication of the location of the actionable sound to a remote computing device. ### Claim 6 (depends on 5) The system of claim 5, wherein receipt of the indication of the location by the remote computing device causes the remote computing device to direct a camera to the location and activate a livestream with the camera. ### Claim 7 (depends on 1) The system of claim 1, comprising: a first computing device comprising a first processor of the one or more processors, wherein the first processor is configured to: receive a first set of audio data of the sets of audio data from a first microphone of the plurality of microphones; execute a first sound detection machine learning model of the one or more sound detection machine learning models using the first set of audio data of the sets of audio data as input to determine whether the first set of audio data corresponds to the actionable sound, and transmit the first set of audio data to a second computing device responsive to determining, based on the execution of the first sound detection machine learning model, the first set of audio data corresponds to the actionable sound; and the second computing device comprising a second processor of the one or more processors, wherein the second processor is configured to: execute the time detection machine learning model using the first set of audio data as input to determine a first instance and first time of the actionable sound within the first set of audio data; and determine the location of the actionable sound based at least on the first time of the first instance of the actionable sound in the first set of audio data. ### Claim 8 (depends on 7) The system of claim 7, wherein the second processor of the one or more processors is configured to: receive a second set of audio data of the sets of audio data from a third computing device comprising a third processor of the one or more processors; execute the time detection machine learning model using the second set of audio data as input to determine a second instance and second time of the actionable sound within the second set of audio data; and determine the location of the actionable sound based at least on the second time of the second instance of the actionable sound in the second set of audio data. ### Claim 9 (depends on 8) The system of claim 8, wherein the third processor of the third computing device is configured to: receive the second set of audio data of the sets of audio data from a second microphone of the plurality of microphones; execute a second sound detection machine learning model of the one or more sound detection machine learning models using the second set of audio data of the sets of audio data as input to determine whether the second set of audio data corresponds to the actionable sound, and transmit the second set of audio data to the second computing device responsive to determining, based on the execution of the second sound detection machine learning model, the second set of audio data corresponds to the actionable sound. ### Claim 10 (depends on 1) The system of claim 1, wherein the one or more processors are configured to determine the location of the actionable sound using multilateration techniques. ### Claim 11 A method, comprising: receiving, by one or more processors, a set of audio data from each of a plurality of microphones; executing, by the one or more processors, one or more sound detection machine learning models using each of the sets of audio data as input to determine whether the set of audio data corresponds to an actionable sound, each set of audio data comprising a plurality of sounds; responsive to determining at least one set of audio data corresponds to the actionable sound, executing, by the one or more processors, a time detection machine learning model using the at least one set of audio data as input to determine each instance and time of the actionable sound; and responsive to determining each instance and time of the actionable sound, determining, by the one or more processors, a location of the actionable sound based on the time of each instance of the actionable sound in each of the at least one set of audio data. ### Claim 12 (depends on 11) The method of claim 11, further comprising: performing, by the one or more processors, an automated action based on the determined location of the actionable sound. ### Claim 13 (depends on 11) The method of claim 11, further comprising: rotating, by the one or more processors, a camera towards the determined location of the actionable sound. ### Claim 14 (depends on 13) The method of claim 13, wherein determining the location of the actionable sound comprises determining, by the one or more processors, the location of the actionable sound relative to the camera. ### Claim 15 (depends on 11) The method of claim 11, further comprising: transmitting, by the one or more processors, an indication of the location of the actionable sound to a remote computing device. ### Claim 16 (depends on 15) The method of claim 15, wherein receipt of the indication of the location by the remote computing device causes the remote computing device to direct a camera to the location and activate a livestream with the camera. ### Claim 17 (depends on 11) The method of claim 11, further comprising: receiving, by a first processor of the one or more processors, a first set of audio data of the sets of audio data from a first microphone of the plurality of microphones; executing, by the first processor of the one or more processors, a first sound detection machine learning model of the one or more sound detection machine learning models using the first set of audio data of the sets of audio data as input to determine whether the first set of audio data corresponds to the actionable sound; transmitting, by the first processor of the one or more processors, the first set of audio data to a second computing device responsive to determining, based on the execution of the first sound detection machine learning model, the first set of audio data corresponds to the actionable sound; executing, by a second processor of the one or more processors and the second computing device, the time detection machine learning model using the first set of audio data as input to determine a first instance and first time of the actionable sound within the first set of audio data; and determining, by the second processor of the one or more processors, the location of the actionable sound based at least on the first time of the first instance of the actionable sound in the first set of audio data. ### Claim 18 (depends on 17) The method of claim 17, comprising: receiving, by the second processor of the one or more processors and the second computing device, a second set of audio data of the sets of audio data from a third computing device comprising a third processor of the one or more processors; executing, by the second processor of the one or more processors and the second computing device, the time detection machine learning model using the second set of audio data as input to determine a second instance and second time of the actionable sound within the second set of audio data; and determining, by the second processor of the one or more processors and the second computing device, the location of the actionable sound based at least on the second time of the second instance of the actionable sound in the second set of audio data. ### Claim 19 (depends on 18) The method of claim 18, further comprising: receiving, by the third processor of the one or more processors and the third computing device, the second set of audio data of the sets of audio data from a second microphone of the plurality of microphones; executing, by the third processor of the one or more processors and the third computing device, a second sound detection machine learning model of the one or more sound detection machine learning models using the second set of audio data of the sets of audio data as input to determine whether the second set of audio data corresponds to the actionable sound, and transmitting, by the third processor of the one or more processors and the third computing device, the second set of audio data to the second computing device responsive to determining, based on the executing of the second sound detection machine learning model, the second set of audio data corresponds to the actionable sound. ### Claim 20 (depends on 11) The method of claim 11, wherein determining the location of the actionable sound comprising determining, by the one or more processors, the location of the actionable sound using multilateration techniques.