# US 12,225,354 B2 · Hearing aid personalization using machine leaning > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,225,354 B2 - Document ID: US12225354B2 - Status: Granted - Kind: B2 - Application number: 18/171,023 - Filed: 2023-02-17 - Published: 2024-08-22 - Granted: 2025-02-11 - Earliest claimed: 2023-02-17 - Assignees: Anhui Huami Health Technology Co., Ltd. - Applicants: Anhui Huami Health Technology Co., Ltd. - Inventors: Shaghayegh Zihajehzadeh; Gabrielle Toupin; Artem Galeev; Yan Vule; Kongqiao Wang - Web page: https://patentgrep.com/patent/US12225354B2 ## CPC - H04R 25/505: {using digital signal processing} - H04R 25/70: {Adaptation of deaf aid to hearing loss, e.g. initial electronic fitting} - H04R 25/507: {implemented by neural network or fuzzy logic} - H04R 2225/39: Aspects relating to automatic logging of sound environment parameters and the performance of the hearing aid during use, e.g. histogram logging, or of user selected programs or settings in the hearing aid, e.g. usage logging - H04R 2225/41: Detection or adaptation of hearing aid parameters or programs to listening situation, e.g. pub, forest - 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 Training data are obtained. Each training datum includes environment characteristics obtained based on sensor data. Respective user settings corresponding to the training data are obtained. At least one respective user setting corresponds to one training datum, and a respective user setting is indicative of a user preference of at least one parameter of a hearing aid device. A machine-learning model for the hearing aid device is trained to output values for the at least one parameter. The hearing aid device is reconfigured based on an output of the machine-learning model. Reconfiguring the hearing aid device includes using current environment characteristics as an input to the machine-learning model to obtain at least one current value for the at least one parameter and configuring the hearing aid device to use at least one current value. ## Claims ### Claim 1 A method, comprising: obtaining training data, wherein each training datum comprises environment characteristics obtained based on sensor data; obtaining respective user settings corresponding to the training data, wherein at least one respective user setting corresponds to one training datum, and wherein a respective user setting is indicative of a user preference of at least one parameter of a hearing aid device; training a machine-learning model for the hearing aid device to output values for the at least one parameter; and reconfiguring the hearing aid device based on an output of the machine-learning model, wherein reconfiguring the hearing aid device comprises: using current environment characteristics as an input to the machine-learning model to obtain at least one current value for the at least one parameter; and configuring the hearing aid device to use at least one current value. ### Claim 2 (depends on 1) The method of claim 1, wherein the sensor data are obtained from sensors of the hearing aid device or a companion device associated with the hearing aid device. ### Claim 3 (depends on 1) The method of claim 1, wherein the training data are playback recording of stored sensor data. ### Claim 4 (depends on 1) The method of claim 1, further comprising: receiving, after reconfiguring, a training command of the hearing aid device in response to are obtaining, based on the current environment characteristics, user settings corresponding to at least one parameter of the hearing aid device; and initiating the training the machine-learning model based on the current environment characteristics and the user settings responsive to the training command. ### Claim 5 (depends on 1) The method of claim 1, further comprising: determining whether to initiate the reconfiguring of the hearing aid device based on a difference between previous environment characteristics used to configure the hearing aid device and the current environment characteristics. ### Claim 6 (depends on 1) The method of claim 1, wherein the sensor data comprise at least one of microphone data, motion data, global positioning system (GPS) data, barometric pressure data, or luminosity data. ### Claim 7 (depends on 1) The method of claim 1, wherein the at least one parameter of the hearing aid device is one of a volume level, a gain-frequency response shape, a noise suppression, a selection of microphone directionality, and frequency compression. ### Claim 8 A system, comprising: a hearing aid device, comprising a first processor configured to: receive a parameter value; and configure the hearing aid device to use the parameter value; and a device communicatively connected to the hearing aid device, the device comprising a second processor configured to execute instructions to: receive sensor data; extract environment characteristics from the sensor data; input the environment characteristics to a machine-learning model to obtain the parameter value for a parameter of the hearing aid device; and transmit a command to the hearing aid device to use the parameter value. ### Claim 9 (depends on 8) The system of claim 8, wherein the sensor data are playback recording of stored sensor data. ### Claim 10 (depends on 9) The system of claim 9, wherein the sensor data comprise at least one of microphone data, motion data, global positioning system (GPS) data, barometric pressure data, or luminosity data. ### Claim 11 (depends on 8) The system of claim 8, wherein: the first processor is further configured to: receive a training command; and transmit the training command to the device; and the second processor is further configured to execute instructions to: receive the training command from the first processor of the hearing aid device; and train, using current sensor data, the machine-learning model responsive to the training command. ### Claim 12 (depends on 11) The system of claim 11, wherein the training command is determined to be received in response to receiving, by the first processor, at least one of a command of a parameter value from the user. ### Claim 13 (depends on 11) The system of claim 11, wherein the training command is determined based on a difference between previous environment characteristics used to configure the hearing aid device and current environment characteristics. ### Claim 14 (depends on 8) The system of claim 8, wherein the parameter is one of a volume level, a gain-frequency response shape, a noise suppression, a selection of microphone directionality, and a frequency compression. ### Claim 15 A non-transitory computer-readable storage medium, comprising executable instructions that, when executed by a processor, perform operations to: obtain training data, wherein each training datum comprises environment characteristics obtained based on sensor data; obtain respective user settings corresponding to the training data, wherein at least one respective user setting corresponds to one training datum, and wherein a respective user setting is indicative of a user preference of at least one parameter of a hearing aid device; train a machine-learning model for the hearing aid device to output values for the at least one parameter; and reconfigure the hearing aid device based on an output of the machine-learning model, wherein reconfiguring the hearing aid device comprises: using current environment characteristics as an input to the machine-learning model to obtain at least one current value for the at least one parameter; and configuring the hearing aid device to use at least one current value. ### Claim 16 (depends on 15) The non-transitory computer-readable storage medium of claim 15, wherein the sensor data are obtained from sensors of the hearing aid device or a companion device associated with the hearing aid device. ### Claim 17 (depends on 15) The non-transitory computer-readable storage medium of claim 15, wherein the training data are playback recording of stored sensor data. ### Claim 18 (depends on 15) The non-transitory computer-readable storage medium of claim 15, wherein the operations further comprise operations to: receive, after reconfiguring, a training command of the hearing aid device in response to user settings corresponding to at least one parameter of the hearing aid device being obtained based on the current environment characteristics; and initiate the training the machine-learning model based on the current environment characteristics and the user settings responsive to the training command. ### Claim 19 (depends on 15) The non-transitory computer-readable storage medium of claim 15, wherein the sensor data comprise at least one of microphone data, motion data, global positioning system (GPS) data, barometric pressure data, or luminosity data. ### Claim 20 (depends on 15) The non-transitory computer-readable storage medium of claim 15, wherein the at least one parameter of the hearing aid device is one of a volume level, a gain-frequency response shape, a noise suppression, a selection of microphone directionality, and frequency compression.