IP Library Granted Patent US 12,363,489
Granted Patent B2
US 12,363,489 · App. 18/496,723 · Granted Jul 15, 2025

Method, apparatus and system for neural network hearing aid

Inventors: Andrew J. Casper (Inver Grove Heights, MN); Igor Lovchinsky (New York, NY); Nicholas Morris (Brooklyn, NY); Matthew de Jonge (Brooklyn, NY); Jonathan Macoskey (Pittsburgh, PA); Philip Meyers, IV (Brooklyn, NY)
Assignee: Chromatic Inc.
H04R25/507H04R2225/43
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,363,489
App. No.
18/496,723
Granted
Jul 15, 2025
Kind
B2
Abstract

The disclosure generally relates to a method, system and apparatus to improve a user's understanding of speech in real-time conversations by processing the audio through a neural network contained in a hearing device. The hearing device may be a headphone or hearing aid. In one embodiment, the disclosure relates to an apparatus to enhance incoming audio signal. The apparatus includes a controller to receive an incoming signal and provide a controller output signal; a neural network engine (NNE) circuitry in communication with the controller, the NNE circuitry activatable by the controller, the NNE circuitry configured to generate an NNE output signal from the controller output signal; and a digital signal processing (DSP) circuitry to receive one or more of controller output signal or the NNE circuitry output signal to thereby generate a processed signal; wherein the controller determines a processing path of the controller output signal through one of the DSP or the NNE circuitries as a function of one or more of predefined parameters, incoming signal characteristics and NNE circuitry feedback.

Claims (33)

1. An ear-worn device comprising:

a physical user control configured to receive input from a user of the ear-worn device;

neural network engine (NNE) circuitry comprising:

a source separation module configured to receive an input audio signal and output one or more intermediate signals representing one or more portions of the input audio signal corresponding to one or more respective sound sources;

a relative gain module configured to receive the one or more intermediate signals and apply a respective gain or gains to the one or more intermediate signals; and

a recombiner module configured to receive the one or more intermediate signals from the relative gain module after application of the respective gain or gains by the relative gain module, the recombiner module further configured to combine the one or more intermediate signals into a combined signal;

wherein the respective gain or gains are set based on the input from the user received by the physical user control on the ear-worn device.

2. The ear-worn device of claim 1 , wherein the input from the user received by the physical user control on the ear-worn device is configured to control a signal-to-noise ratio (SNR) of the combined signal.

3. The ear-worn device of claim 1 , wherein the input from the user received by the physical user control on the ear-worn device is configured to control a background noise level of the combined signal.

4. The ear-worn device of claim 1 , wherein the one or more intermediate signals comprise a speech component and a noise component of the input audio signal, and wherein the relative gain module is configured to increase a relative gain of the speech component compared to the noise component.

5. The ear-worn device of claim 1 , wherein the NNE circuitry is configured to provide the combined signal in about 32 milliseconds or less of receipt of the input audio signal by the ear-worn device.

6. The ear-worn device of claim 1 , wherein the NNE circuitry is configured to perform at least 1 billion operations per second.

7. The ear-worn device of claim 1 , wherein the NNE circuitry is configured to achieve at least 2 billion operations per milliwatt.

8. The ear-worn device of claim 1 , wherein the NNE circuitry is configured to process a digitized version of the input audio signal with an associated power consumption of about 2 milliwatts or less.

9. The ear-worn device of claim 1 , wherein the neural network engine (NNE) circuitry is implemented on a single chip in the ear-worn device.

10. The ear-worn device of claim 1 , wherein the ear-worn device comprises a hearing aid.

11. A system comprising:

an ear-worn device comprising:

neural network engine (NNE) circuitry comprising:

a source separation module configured to receive an input audio signal and output one or more intermediate signals representing one or more portions of the input audio signal corresponding to one or more respective sound sources;

a relative gain module configured to receive the one or more intermediate signals and apply a respective gain or gains to the one or more intermediate signals; and

a recombiner module configured to receive the one or more intermediate signals from the relative gain module after application of the respective gain or gains by the relative gain module, the recombiner module further configured to combine the one or more intermediate signals into a combined signal; and

a mobile device in communication with the ear-worn device, the mobile device configured to display a graphical user interface comprising a user-adjustable control, the user-adjustable control configured to receive input from a user of the ear-worn device;

wherein the respective gain or gains are set based on the input from the user received by the user-adjustable control.

12. The system of claim 11 , wherein the input from the user received by the user-adjustable control on the ear-worn device is configured to control a signal-to-noise ratio (SNR) of the combined signal.

13. The system of claim 11 , wherein the input from the user received by the user-adjustable control on the ear-worn device is configured to control a background noise level of the combined signal.

14. The system of claim 11 , wherein the one or more intermediate signals comprise a speech component and a noise component of the input audio signal, and wherein the relative gain module is configured to increase a relative gain of the speech component compared to the noise component.

15. The system of claim 11 , wherein the NNE circuitry is configured to provide the combined signal in about 32 milliseconds or less of receipt of the input audio signal by the ear-worn device.

16. The system of claim 11 , wherein the NNE circuitry is configured to perform at least 1 billion operations per second.

17. The system of claim 11 , wherein the NNE circuitry is configured to achieve at least 2 billion operations per milliwatt.

18. The system of claim 11 , wherein the NNE circuitry is configured to process a digitized version of the input audio signal with an associated power consumption of about 2 milliwatts or less.

19. The system of claim 11 , wherein the neural network engine (NNE) circuitry is implemented on a single chip in the ear-worn device.

20. The system of claim 11 , wherein the ear-worn device comprises a hearing aid.

Assignments (3)
CHANGE OF NAME Recorded Oct 9, 2025
From: CHROMATIC INC.
To: FORTELL RESEARCH INC.
Reel/Frame 073057/0966 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 2, 2024
From: MACOSKEY, JONATHAN; MEYERS, PHILIP, IV
To: CHROMATIC INC.
Reel/Frame 068161/0535 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 2, 2024
From: CASPER, ANDREW J.; LOVCHINSKY, IGOR; MORRIS, NICHOLAS; DE JONGE, MATTHEW
To: CHROMATIC INC.
Reel/Frame 068161/0539 →
Continuity (3)
Continuation 18137970 · Apr 21, 2023
Continuation 17576893 · Jan 14, 2022
Related Publication 20240056747A1 · Feb 15, 2024
References Cited (89)
US 7804973B2 · De Vries et al. · 2010 [cited by applicant]
US 9716939B2 · Censo et al. · 2017 [cited by applicant]
US 9881631B2 · Erdogan et al. · 2018 [cited by applicant]
US 10199047B1 · Clark · 2019 [cited by applicant]
US 10516934B1 · Solbach · 2019 [cited by applicant]
US 10536775B1 · Sen · 2020 [cited by examiner]
US 10659893B2 · Pedersen et al. · 2020 [cited by applicant]
US 10721571B2 · Crow et al. · 2020 [cited by applicant]
US 10805748B2 · Fichtl et al. · 2020 [cited by applicant]
US 10812915B2 · Santos et al. · 2020 [cited by applicant]
US 10957301B2 · Hoby et al. · 2021 [cited by applicant]
US 11245993B2 · Andersen et al. · 2022 [cited by applicant]
US 11270198B2 · Busch et al. · 2022 [cited by applicant]
US 11330378B1 · Jelcicováet al. · 2022 [cited by applicant]
US 11375325B2 · Froehlich et al. · 2022 [cited by applicant]
US 11445307B2 · Pandey et al. · 2022 [cited by applicant]
US 11553286B2 · Sabin et al. · 2023 [cited by applicant]
US 11620977B2 · Birmingham · 2023 [cited by examiner]
US 11647344B2 · Chen et al. · 2023 [cited by applicant]
US 11678120B2 · Nyayate et al. · 2023 [cited by applicant]
US 11696079B2 · Jelcicova et al. · 2023 [cited by applicant]
US 11812225B2 · Casper et al. · 2023 [cited by applicant]
US 11818523B2 · Lovchinsky et al. · 2023 [cited by applicant]
US 11818547B2 · Casper et al. · 2023 [cited by applicant]
US 11832061B2 · Casper et al. · 2023 [cited by applicant]
US 11877125B2 · Casper et al. · 2024 [cited by applicant]
US 11950056B2 · Casper et al. · 2024 [cited by applicant]
US 12075215B2 · Casper et al. · 2024 [cited by applicant]
US 20070172087A1 · Olsen · 2007 [cited by applicant]
US 20100027820A1 · Kates · 2010 [cited by applicant]
US 20140064529A1 · Jang · 2014 [cited by applicant]
US 20150078575A1 · Selig et al. · 2015 [cited by applicant]
US 20170229117A1 · Van der Made et al. · 2017 [cited by applicant]
US 20200043499A1 · Basye et al. · 2020 [cited by applicant]
US 20200204928A1 · Fichtl · 2020 [cited by applicant]
US 20210105565A1 · Pedersen et al. · 2021 [cited by applicant]
US 20210274296A1 · Rohde et al. · 2021 [cited by applicant]
US 20210281958A1 · Diehl et al. · 2021 [cited by applicant]
US 20210289299A1 · Durrieu · 2021 [cited by applicant]
US 20220095061A1 · Diehl et al. · 2022 [cited by applicant]
US 20220124444A1 · Andersen et al. · 2022 [cited by applicant]
US 20220159403A1 · Sporer et al. · 2022 [cited by applicant]
US 20220223161A1 · Fuchs et al. · 2022 [cited by applicant]
US 20220230048A1 · Li et al. · 2022 [cited by applicant]
US 20220232321A1 · Wexler et al. · 2022 [cited by applicant]
US 20220256294A1 · Diehl et al. · 2022 [cited by applicant]
US 20230037356A1 · Pontoppidan et al. · 2023 [cited by applicant]
US 20230087486A1 · Pennies-Hochmuth et al. · 2023 [cited by applicant]
US 20230209283A1 · Wagner et al. · 2023 [cited by applicant]
US 20230232169A1 · Casper et al. · 2023 [cited by applicant]
US 20230232170A1 · Casper et al. · 2023 [cited by applicant]
US 20230232171A1 · Casper et al. · 2023 [cited by applicant]
US 20230232172A1 · Casper et al. · 2023 [cited by applicant]
US 20230254650A1 · Lovchinsky et al. · 2023 [cited by applicant]
US 20230254651A1 · Casper et al. · 2023 [cited by applicant]
US 20230292074A1 · Marquardt et al. · 2023 [cited by applicant]
US 20230306982A1 · Lovchinsky et al. · 2023 [cited by applicant]
US 20230319492A1 · Corey · 2023 [cited by examiner]
US 20230388725A1 · Casper et al. · 2023 [cited by applicant]
US 20230402055A1 · Kulasekaran · 2023 [cited by examiner]
US 20240048922A1 · Casper et al. · 2024 [cited by applicant]
US 20240129674A1 · Casper et al. · 2024 [cited by applicant]
US 20240194213A1 · Wichern · 2024 [cited by examiner]
US 20240221769A1 · Philipsson et al. · 2024 [cited by applicant]
US 20240292165A1 · Lovchinsky et al. · 2024 [cited by applicant]
US 20240381039A1 · Casper et al. · 2024 [cited by applicant]
US 20240422484A1 · Casper et al. · 2024 [cited by applicant]
CN 105611477A · 2016 [cited by applicant]
EP 0357212A2 · 1990 [cited by applicant]
KR 102316626B1 · 2021 [cited by applicant]
WO WO2020079485A2 · 2020 [cited by applicant]
WO WO2022079848A1 · 2022 [cited by applicant]
WO WO2022107393A1 · 2022 [cited by applicant]
WO WO2022191879A1 · 2022 [cited by applicant]
WO WO2023010014A1 · 2023 [cited by applicant]
WO WO2023110836A1 · 2023 [cited by examiner]
U.S. Appl. No. 18/658,814, filed May 8, 2024, Lovchinsky et al. [cited by applicant]
U.S. Appl. No. 18/778,822, filed Jul. 19, 2024, Casper et al. [cited by applicant]
U.S. Appl. No. 18/814,431, filed Aug. 23, 2024, Casper et al. [cited by applicant]
PCT/US2023/010837, Jul. 25, 2024, International Preliminary Report on Patentability. [cited by applicant]
PCT/US2022/012567, Jul. 25, 2024, International Preliminary Report on Patentability. [cited by applicant]
International Preliminary Report on Patentability mailed Jul. 25, 2024 in connection with International Application No. PCT/US2023/010837. [cited by applicant]
International Preliminary Report on Patentability mailed Jul. 25, 2024 in connection with International Application No. PCT/US2022/012567. [cited by applicant]
International Search Report and Written Opinion mailed Jun. 16, 2022 in connection with International Application No. PCT/US2022/012567. [cited by applicant]
International Search Report and Written Opinion mailed Apr. 28, 2023 in connection with International Application No. PCT/US2023/010837. [cited by applicant]
Gerlach et al., A Survey on Application Specific Processor Architectures for Digital Hearing Aids. Journal of Signal Processing Systems. Mar. 20, 2021;94:1293-1308. https://link.springer.com/rticle/10.1007/s11265-021-01… [cited by applicant]
Giri et al., Personalized Percepnet: Real-time, Low-complexity Target Voice Separation and Enhancement. Amazon Web Service, Jun. 8, 2021, arXiv preprint arXiv:2106.04129. 5 pages. [cited by applicant]
[No Author Listed], An introduction to MoreSound Intelligence. Tech Paper 2020. Oticon life-changing technology. 2020, 12 pages. [cited by applicant]
[No Author Listed], GAP9 next generation processor for hearables and smart sensors. Greenwaves Technologies. 2021, 2 pages. [cited by applicant]
Cited By (4)
US 12,574,691 US 12,610,200 US 12,634,642 US 12,713,188