CTNF 18/815,424 CTNF 73109 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Double Patenting 08-33 AIA The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg , 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman , 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi , 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum , 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel , 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington , 418 F.2d 528, 163 USPQ 644 (CCPA 1969). A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b). The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13. The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA/25, or PTO/AIA/26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer. 08-34 AIA Claim s 1-16 and 18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 1-6, 9-11, 13-16 and 19 of U.S. Patent No. 11,605,392 . Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-16 and 18 of the instant application are similar in scope and content of the patented claims 1-6, 9-11, 13-16 and 19 of the patent issued to the same Applicant . It is clear that all the elements of the application claims 1-16 and 18 are to be found in patented claims 1-6, 9-11, 13-16 and 19 (as the application claims 1-16 and 18 fully encompasses patented claims 1-6, 9-11, 13-16 and 19 ). The difference between the application claims and the patent claims lies in the fact that the patent claim includes many more elements and is thus much more specific. Thus the invention of claims 1-6, 9-11, 13-16 and 19 of the patent is in effect a “species” of the “generic” invention of the application claims 1-16 and 18 . It has been held that the generic invention is “anticipated” by the “species”. See In re Goodman , 29 USPQ2d 2010 (Fed. Cir. 1993). Since application claims 1-16 and 18 is anticipated by claims 1-6, 9-11, 13-16 and 19 of the patent, it is not patentably distinct from of the patented claims. Application No: 18/815,424 Patent No: 11,605,392 1. A system comprising: a memory; and a processing device communicably coupled to the memory, the processing device to: identify audio data associated with a plurality of input devices; determine, each input device of the plurality of input devices, a speech energy level by providing the audio data as input to a model that is trained to determine the speech energy level of given audio data; for each input device, determine a statistical value associated with the speech energy level of the input device; identify a strongest input device, wherein the strongest input device has highest statistical value associated with the speech energy level; and responsive to determining that the statistical value associated with the speech energy level of the strongest input device satisfies a threshold condition, update, for a respective input device, a gain value to an estimated target gain value based on the statistical value associated with the speech energy level of the respective input device. 11. A system comprising: a memory; and a processing device communicably coupled to the memory, the processing device to: receive, through a plurality of channels, audio data, wherein the audio data of each channel corresponds to a plurality of frequency ranges; determine, for each of the plurality of frequency ranges for each channel, at least one of a speech audio energy level or a noise energy level by providing audio data corresponding to each frequency range as input to a model that is trained to determine at least one of a speech audio energy level of given audio data or a noise energy level of the given audio data in the corresponding frequency range of the plurality of frequency ranges; determine, based on at least one of the speech audio energy level or the noise energy level for each of the plurality of frequency ranges, a speech signal with removed noise for each channel associated with the audio data; for each channel, determine one or more statistical values associated with an energy level of a channel's speech signal with the removed noise; determine a strongest channel, wherein the strongest channel has highest one or more statistical values associated with an energy level of a speech signal of a respective channel; determine that the one or more statistical values associated with the energy level of the speech signal of the strongest channel satisfy a threshold condition; compare one or more statistical values associated with an energy level of a speech signal of each channel other than the strongest channel with the corresponding one or more statistical values associated with the energy level of the speech signal of the strongest channel; and depending on the comparing, determine whether to update, for a respective channel, a gain value to an estimated target gain value based on the one or more statistical values associated with the energy level of the respective channel. 2. The system of claim 1, wherein the processing device is further to: compare the statistical value associated with the speech energy level of each input device other than the strongest input device with the statistical value associated with the speech energy level of the strongest input device. 11. A system comprising: a memory; and a processing device communicably coupled to the memory, the processing device to: receive, through a plurality of channels, audio data, wherein the audio data of each channel corresponds to a plurality of frequency ranges; determine, for each of the plurality of frequency ranges for each channel, at least one of a speech audio energy level or a noise energy level by providing audio data corresponding to each frequency range as input to a model that is trained to determine at least one of a speech audio energy level of given audio data or a noise energy level of the given audio data in the corresponding frequency range of the plurality of frequency ranges; determine, based on at least one of the speech audio energy level or the noise energy level for each of the plurality of frequency ranges, a speech signal with removed noise for each channel associated with the audio data; for each channel, determine one or more statistical values associated with an energy level of a channel's speech signal with the removed noise; determine a strongest channel, wherein the strongest channel has highest one or more statistical values associated with an energy level of a speech signal of a respective channel; determine that the one or more statistical values associated with the energy level of the speech signal of the strongest channel satisfy a threshold condition; compare one or more statistical values associated with an energy level of a speech signal of each channel other than the strongest channel with the corresponding one or more statistical values associated with the energy level of the speech signal of the strongest channel; and depending on the comparing, determine whether to update, for a respective channel, a gain value to an estimated target gain value based on the one or more statistical values associated with the energy level of the respective channel. 3. The system of claim 1, wherein the threshold condition requires that the statistical value associated with the speech energy level of the strongest input device be above a respective threshold value for a threshold period of time. 13. The system of claim 11, wherein the threshold condition requires that the one or more statistical values associated with the energy level of the strongest channel be above a respective threshold value for a threshold period of time. 4. The system of claim 1, wherein to update the gain value for the respective input device, the processing device is further to: determine whether the statistical value associated with the speech energy level of the respective input device has been within a predefined range of the statistical value associated with the speech energy level of the strongest input device for a period of time. 14. The system of claim 11, wherein to determine whether to update the gain value for the respective channel, the processing device is further to: determine whether the one or more statistical values associated with the energy level of the respective channel have been within a predefined range from a corresponding one or more statistical values associated with the energy level of the strongest channel for a period of time. 5. The system of claim 1, wherein the processing device is further to: based on the speech energy level, update a state of a state machine that includes a speech state, a noise state, a silence state, and an uncertain state. 15. The system of claim 11, wherein the processing device is further to: based on the speech audio energy level and the noise energy level, update a state of a state machine that includes a speech state, a noise state, a silence state, and an uncertain state. 6. The system of claim 5, wherein to update the gain value for the respective device, the processing device is further to: determine whether the state of the state machine is speech state for a threshold amount of time; responsive to determining that the state of the state machine is speech state for the threshold amount of time, update the gain value by no more than a first number of decibels per second; determine whether the state of the state machine is uncertain state for the threshold amount of time; and responsive to determining that the state of the state machine is uncertain state for the threshold amount of time, update the gain value by no more than a second number of decibels per second. 16. The system of claim 15, wherein the processing device is further to: update the gain value for the respective channel, wherein to update the gain value for the respective channel, the processing device is further to: determine whether the state of the state machine is speech state for a threshold amount of time; responsive to determining that the state of the state machine is speech state for the threshold amount of time, update the gain value by no more than a first number of decibels per second; determine whether the state of the state machine is uncertain state for the threshold amount of time; and responsive to determining that the state of the state machine is uncertain state for the threshold amount of time, update the gain value by no more than a second number of decibels per second. 7. The system of claim 1, wherein the processing device is further to: receive speech audio segments and noise segments; determine a noise energy level of each noise segment and a speech energy level of each speech audio segment; generate noisy speech audio segments by: overlapping each noise segment and each audio segment in a time domain, and summing each noise segment and each audio segment; and train, using machine learning, the model using the noise energy level of each noise segment, a speech audio energy level of each speech audio segment, and the noisy speech audio segments. 19. The system of claim 11, wherein the processing device is further to: receive speech audio segments and noise segments; determine a noise energy level of each noise segment and a speech energy level of each speech audio segment; generate noisy speech audio segments by combining each noise segment and each speech audio segment; and train, using machine learning, the model using the noise energy level of each noise segment, a speech audio energy level of each speech audio segment, and the noisy speech audio segments. 8. A method comprising: receiving, by a server device, audio data associated with a plurality of input device; determining, for each input device of the plurality of input devices, a speech energy level by providing the audio data as input to a model that is trained to determine the speech energy level of given audio data; for each input device, determining a statistical value associated with the speech energy level of the input device; identifying a strongest input device, wherein the strongest input device has highest statistical value associated with the speech energy level; and responsive to determining that the statistical value associated with the speech energy level of the strongest input device satisfies a threshold condition, updating, for a respective input device, a gain value to an estimated target value based on the statistical value associated with the speech energy level of the respective input device. 1. A method comprising: receiving, by a processing device through a plurality of channels, audio data, wherein the audio data of each channel corresponds to a plurality of frequency ranges; determining, for each of the plurality of frequency ranges for each channel, at least one of a speech audio energy level or a noise energy level by providing audio data corresponding to each frequency range as input to a model that is trained to determine at least one of a speech audio energy level of given audio data or a noise energy level of the given audio data in the corresponding frequency range of the plurality of frequency ranges; determining, based on at least one of the speech audio energy level or the noise energy level for each of the plurality of frequency ranges, a speech signal with removed noise for each channel associated with the audio data; for each channel, determining one or more statistical values associated with an energy level of a channel's speech signal with the removed noise; determining a strongest channel, wherein the strongest channel has highest one or more statistical values associated with an energy level of a speech signal of a respective channel; determining that the one or more statistical values associated with the energy level of the speech signal of the strongest channel satisfy a threshold condition; comparing one or more statistical values associated with an energy level of a speech signal of each channel other than the strongest channel with the corresponding one or more statistical values associated with the energy level of the speech signal of the strongest channel; and depending on the comparing, determining whether to update, for a respective channel, a gain value to an estimated target gain value based on the one or more statistical values associated with the energy level of the respective channel. 9. The method of claim 8, further comprising: comparing the statistical value associated with the speech energy level of each input device other than the strongest input device with the statistical value associated with the speech energy level of the strongest input device. 3. The method of claim 1, wherein the threshold condition requires that the one or more statistical values associated with the energy level of the strongest channel be above a respective threshold value for a threshold period of time. 10. The method of claim 8, wherein the threshold condition requires that the statistical value associated with the speech energy level of the strongest input device be above a respective threshold value for a threshold period of time. 4. The method of claim 1, wherein determining whether to update the gain value for the respective channel comprises: determining whether the one or more statistical values associated with the energy level of the respective channel have been within a predefined range from a corresponding one or more statistical values associated with the energy level of the strongest channel for a period of time. 11. The method of claim 8, wherein updating the gain value for the respective input device comprises: determining whether the statistical value associated with the speech energy level of the respective input device has been within a predefined range of the statistical value associated with the speech energy level of the strongest input device for a period of time. 4. The method of claim 1, wherein determining whether to update the gain value for the respective channel comprises: determining whether the one or more statistical values associated with the energy level of the respective channel have been within a predefined range from a corresponding one or more statistical values associated with the energy level of the strongest channel for a period of time. 12. The method of claim 8, further comprising: based on the speech energy level, updating a state of a state machine that includes a speech state, a noise state, a silence state, and an uncertain state. 5. The method of claim 1, comprising: based on the speech audio energy level and the noise energy level, updating a state of a state machine that includes a speech state, a noise state, a silence state, and an uncertain state. 13. The method of claim 12, wherein updating the gain value for the respective device further comprises: determining whether the state of the state machine is speech state for a threshold amount of time; responsive to determining that the state of the state machine is speech state for the threshold amount of time, updating the gain value by no more than a first number of decibels per second; determining whether the state of the state machine is uncertain state for the threshold amount of time; and responsive to determining that the state of the state machine is uncertain state for the threshold amount of time, updating the gain value by no more than a second number of decibels per second. 6. The method of claim 5, further comprising: updating the gain value for the respective channel, wherein updating the gain value for the respective channel further comprises: determining whether the state of the state machine is speech state for a threshold amount of time; responsive to determining that the state of the state machine is speech state for the threshold amount of time, updating the gain value by no more than a first number of decibels per second; determining whether the state of the state machine is uncertain state for the threshold amount of time; and responsive to determining that the state of the state machine is uncertain state for the threshold amount of time, updating the gain value by no more than a second number of decibels per second. 14. The method of claim 8, further comprising: receiving speech audio segments and noise segments; determining a noise energy level of each noise segment and a speech energy level of each speech audio segment; generating noisy speech audio segments by combining each noise segment and each speech audio segment; and training, using machine learning, the model using the noise energy level of each noise segment, a speech audio energy level of each speech audio segment, and the noisy speech audio segments. 9. The method of claim 1, comprising: receiving speech audio segments and noise segments; determining a noise energy level of each noise segment and a speech energy level of each speech audio segment; generating noisy speech audio segments by combining each noise segment and each speech audio segment; and training, using machine learning, the model using the noise energy level of each noise segment, a speech audio energy level of each speech audio segment, and the noisy speech audio segments. 15. The method of claim 14, wherein combining each noise segment and each speech audio segment comprises overlapping each noise segment and each audio segment in a time domain and summing each noise segment and each audio segment. 10. The method of claim 9, wherein combining each noise segment and each speech audio segment comprises overlapping each noise segment and each audio segment in a time domain and summing each noise segment and each audio segment. 16. A non-transitory machine-readable storage medium comprising instructions for a server that, when executed by a processing device, cause the processing device to perform operations comprising: receiving audio data associated with a plurality of input devices; determining, for each input device of the plurality of input devices, a speech energy level by providing the audio data as input to a model that is trained to determine the speech energy level of given audio data; for each input device, determining a statistical value associated with the speech energy level of the input device; identifying a strongest input device, wherein the strongest input device has highest statistical value associated with the speech energy level; and responsive to determining that the statistical value associated with the speech energy level of the strongest input device satisfies a threshold condition, updating, for a respective input device, a gain value to an estimated target gain value based on the statistical value associated with the speech energy level of the respective input device. 21. A non-transitory machine-readable storage medium comprising instructions that cause a processing device to: receive, through a plurality of channels, audio data, wherein the audio data of each channel corresponds to a plurality of time-related portions; determine, for each of the plurality of time-related portions for each channel, a speech signal with removed noise by providing audio data corresponding to each time-related portion as input to a model that is trained to determine a speech signal with removed noise of given audio data; for each channel, determine one or more statistical values associated with an energy level of a channel's speech signal with the removed noise; determine a strongest channel, wherein the strongest channel has highest one or more statistical values associated with an energy level of a speech signal of a respective channel; determine that the one or more statistical values associated with the energy level of the speech signal of the strongest channel satisfy a threshold condition; compare one or more statistical values associated with an energy level of a speech signal of each channel other than the strongest channel with the corresponding one or more statistical values associated with the energy level of the speech signal of the strongest channel; and depending on the comparing, determine whether to update, for a respective channel, a gain value to an estimated target gain value based on the one or more statistical values associated with the energy level of the respective channel. 17. The non-transitory machine-readable storage medium of claim 16, the operations further comprising: comparing the statistical value associated with the speech energy level of each input device other than the strongest input device with the statistical value associated with the speech energy level of the strongest input device. 18. The non-transitory machine-readable storage medium of claim 16, wherein the threshold condition requires that the statistical value associated with the speech energy level of the strongest input device be above a respective threshold value for a threshold period of time. 23. The non-transitory machine-readable storage medium of claim 21, wherein the threshold condition requires that the one or more statistical values associated with the energy level of the strongest channel be above a respective threshold value for a threshold period of time. 19. The non-transitory machine-readable storage medium of claim 16, wherein updating the gain value for the respective input device comprises: determining whether the statistical value associated with the speech energy level of the respective input device has been within a predefined range of the statistical value associated with the speech energy level of the strongest input device for a period of time. 20. The non-transitory machine-readable storage medium of claim 16, the operations further comprising: based on the speech energy level, updating a state of a state machine that includes a speech state, a noise state, a silence state, and an uncertain state; and updating the gain value for the respective input device by: determining whether the state of the state machine is speech state for a threshold amount of time; responsive to determining that the state of the state machine is speech state for the threshold amount of time, updating the gain value by no more than a first number of decibels per second; determining whether the state of the state machine is uncertain state for the threshold amount of time; and responsive to determining that the state of the state machine is uncertain state for the threshold amount of time, updating the gain value by no more than a second number of decibels per second . 08-34 AIA Claim s 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 1-20 of U.S. Patent No. 12,073,845 . Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-20 of the instant application are similar in scope and content of the patented claims 1-20 of the patent issued to the same Applicant . It is clear that all the elements of the application claims 1-20 are to be found in patented claims 1-20 (as the application claims 1-20 fully encompasses patented claims 1-20 ). The difference between the application claims and the patent claims lies in the fact that the patent claim includes many more elements and is thus much more specific. Thus the invention of claims 1-20 of the patent is in effect a “species” of the “generic” invention of the application claims 1-20 . It has been held that the generic invention is “anticipated” by the “species”. See In re Goodman , 29 USPQ2d 2010 (Fed. Cir. 1993). Since application claims 1-20 is anticipated by claims 1-20 of the patent, it is not patentably distinct from of the patented claims. Application No: 18/815,424 Patent No: 12,073,845 1. A system comprising: a memory; and a processing device communicably coupled to the memory, the processing device to: identify audio data associated with a plurality of input devices; determine, each input device of the plurality of input devices, a speech energy level by providing the audio data as input to a model that is trained to determine the speech energy level of given audio data; for each input device, determine a statistical value associated with the speech energy level of the input device; identify a strongest input device, wherein the strongest input device has highest statistical value associated with the speech energy level; and responsive to determining that the statistical value associated with the speech energy level of the strongest input device satisfies a threshold condition, update, for a respective input device, a gain value to an estimated target gain value based on the statistical value associated with the speech energy level of the respective input device. 1. A system comprising: a memory; and a processing device communicably coupled to the memory, the processing device to: receive, from a plurality of input devices, audio data, wherein the audio data of each input device corresponds to a plurality of frequency ranges; determine, for each of the plurality of frequency ranges for each input device of the plurality of input devices, a speech energy level by providing audio data corresponding to each frequency range as input to a model that is trained to determine a speech energy level of given audio data in the corresponding frequency range of the plurality of frequency ranges; for each input device, determine a statistical value associated with the speech energy level of the input device; identify a strongest input device, wherein the strongest input device has highest statistical value associated with the speech energy level; compare the statistical value associated with the speech energy level of each input device other than the strongest input device with the statistical value associated with the speech energy level of the strongest input device; and depending on the comparing, determine whether to update, for a respective input device, a gain value to an estimated target gain value based on the statistical value associated with the speech energy level of the respective input device. 2. The system of claim 1, wherein the processing device is further to: compare the statistical value associated with the speech energy level of each input device other than the strongest input device with the statistical value associated with the speech energy level of the strongest input device. 2. The system of claim 1, wherein the processing device is further to: determine that the statistic value associated with the speech energy level of the strongest input device satisfies a threshold condition. 3. The system of claim 2, wherein the threshold condition requires that the statistical value associated with the speech energy level of the strongest input device be above a respective threshold value for a threshold period of time. 3. The system of claim 1, wherein the threshold condition requires that the statistical value associated with the speech energy level of the strongest input device be above a respective threshold value for a threshold period of time. 4. The system of claim 1, wherein to determine whether to update the gain value for the respective input device, the processing device is further to: determine whether the statistical value associated with the speech energy level of the respective input device has been within a predefined range of the statistical value associated with the speech energy level of the strongest input device for a period of time. 4. The system of claim 1, wherein to update the gain value for the respective input device, the processing device is further to: determine whether the statistical value associated with the speech energy level of the respective input device has been within a predefined range of the statistical value associated with the speech energy level of the strongest input device for a period of time. 4. The system of claim 1, wherein to determine whether to update the gain value for the respective input device, the processing device is further to: determine whether the statistical value associated with the speech energy level of the respective input device has been within a predefined range of the statistical value associated with the speech energy level of the strongest input device for a period of time. 5. The system of claim 1, wherein the processing device is further to: based on the speech energy level, update a state of a state machine that includes a speech state, a noise state, a silence state, and an uncertain state. 5. The system of claim 1, wherein the processing device is further to: based on the speech energy level, update a state of a state machine that includes a speech state, a noise state, a silence state, and an uncertain state. 6. The system of claim 5, wherein to update the gain value for the respective device, the processing device is further to: determine whether the state of the state machine is speech state for a threshold amount of time; responsive to determining that the state of the state machine is speech state for the threshold amount of time, update the gain value by no more than a first number of decibels per second; determine whether the state of the state machine is uncertain state for the threshold amount of time; and responsive to determining that the state of the state machine is uncertain state for the threshold amount of time, update the gain value by no more than a second number of decibels per second. 6. The system of claim 5, wherein the processing device is further to: update the gain value for the respective input device, wherein to update the gain value for the respective input device, the processing device is further to: determine whether the state of the state machine is speech state for a threshold amount of time; responsive to determining that the state of the state machine is speech state for the threshold amount of time, update the gain value by no more than a first number of decibels per second; determine whether the state of the state machine is uncertain state for the threshold amount of time; and responsive to determining that the state of the state machine is uncertain state for the threshold amount of time, update the gain value by no more than a second number of decibels per second. 7. The system of claim 1, wherein the processing device is further to: receive speech audio segments and noise segments; determine a noise energy level of each noise segment and a speech energy level of each speech audio segment; generate noisy speech audio segments by: overlapping each noise segment and each audio segment in a time domain, and summing each noise segment and each audio segment; and train, using machine learning, the model using the noise energy level of each noise segment, a speech audio energy level of each speech audio segment, and the noisy speech audio segments. 7. The system of claim 1, wherein the processing device is further to: receive speech audio segments and noise segments; determine a noise energy level of each noise segment and a speech energy level of each speech audio segment; generate noisy speech audio segments by: overlapping each noise segment and each audio segment in a time domain, and summing each noise segment and each audio segment; and train, using machine learning, the model using the noise energy level of each noise segment, a speech audio energy level of each speech audio segment, and the noisy speech audio segments. 8. A method comprising: receiving, by a server device, audio data associated with a plurality of input device; determining, for each input device of the plurality of input devices, a speech energy level by providing the audio data as input to a model that is trained to determine the speech energy level of given audio data; for each input device, determining a statistical value associated with the speech energy level of the input device; identifying a strongest input device, wherein the strongest input device has highest statistical value associated with the speech energy level; and responsive to determining that the statistical value associated with the speech energy level of the strongest input device satisfies a threshold condition, updating, for a respective input device, a gain value to an estimated target value based on the statistical value associated with the speech energy level of the respective input device. 8. A method comprising: receiving, by a server device from a plurality of input devices, audio data, wherein the audio data of each input device corresponds to a time-related portion of the audio data; determining, for each input device of the plurality of input devices, a speech energy level by providing the time-related portion of audio data as input to a model that is trained to determine at least one of a speech energy level of given audio data; for each input device, determining a statistical value associated with the speech energy level; identifying a strongest input device, wherein the strongest input device has highest statistical value associated with the speech energy level; comparing the statistical value associated with the speech energy level of each input device other than the strongest input device with the statistical value associated with the speech energy level of the strongest input device; and depending on the comparing, determining whether to update, for a respective input device, a gain value to an estimated target value based on the statistical value associated with the speech energy level of the respective input device. 9. The method of claim 8, further comprising: comparing the statistical value associated with the speech energy level of each input device other than the strongest input device with the statistical value associated with the speech energy level of the strongest input device. 9. The method of claim 8, further comprising: determining that the statistical value associated with the speech energy level of the strongest input device satisfies a threshold condition. 10. The method of claim 9, wherein the threshold condition requires that the statistical value associated with the speech energy level of the strongest input device be above a respective threshold value for a threshold period of time. 10. The method of claim 8, wherein the threshold condition requires that the statistical value associated with the speech energy level of the strongest input device be above a respective threshold value for a threshold period of time. 11. The method of claim 8, wherein determining whether to update the gain value for the respective input device comprises: determining whether the statistical value associated with the speech energy level of the respective input device has been within a predefined range of the statistical value associated with the speech energy level of the strongest input device for a period of time. 11. The method of claim 8, wherein updating the gain value for the respective input device comprises: determining whether the statistical value associated with the speech energy level of the respective input device has been within a predefined range of the statistical value associated with the speech energy level of the strongest input device for a period of time. 11. The method of claim 8, wherein determining whether to update the gain value for the respective input device comprises: determining whether the statistical value associated with the speech energy level of the respective input device has been within a predefined range of the statistical value associated with the speech energy level of the strongest input device for a period of time. 12. The method of claim 8, further comprising: based on the speech energy level, updating a state of a state machine that includes a speech state, a noise state, a silence state, and an uncertain state. 12. The method of claim 8, comprising: based on the speech energy level, updating a state of a state machine that includes a speech state, a noise state, a silence state, and an uncertain state. 13. The method of claim 12, wherein updating the gain value for the respective device further comprises: determining whether the state of the state machine is speech state for a threshold amount of time; responsive to determining that the state of the state machine is speech state for the threshold amount of time, updating the gain value by no more than a first number of decibels per second; determining whether the state of the state machine is uncertain state for the threshold amount of time; and responsive to determining that the state of the state machine is uncertain state for the threshold amount of time, updating the gain value by no more than a second number of decibels per second. 13. The method of claim 12, further comprising: updating the gain value for the respective input device, wherein updating the gain value for the respective input device further comprises: determining whether the state of the state machine is speech state for a threshold amount of time; responsive to determining that the state of the state machine is speech state for the threshold amount of time, updating the gain value by no more than a first number of decibels per second; determining whether the state of the state machine is uncertain state for the threshold amount of time; and responsive to determining that the state of the state machine is uncertain state for the threshold amount of time, updating the gain value by no more than a second number of decibels per second. 14. The method of claim 8, further comprising: receiving speech audio segments and noise segments; determining a noise energy level of each noise segment and a speech energy level of each speech audio segment; generating noisy speech audio segments by combining each noise segment and each speech audio segment; and training, using machine learning, the model using the noise energy level of each noise segment, a speech audio energy level of each speech audio segment, and the noisy speech audio segments. 14. The method of claim 8, comprising: receiving speech audio segments and noise segments; determining a noise energy level of each noise segment and a speech energy level of each speech audio segment; generating noisy speech audio segments by combining each noise segment and each speech audio segment; and training, using machine learning, the model using the noise energy level of each noise segment, a speech audio energy level of each speech audio segment, and the noisy speech audio segments. 15. The method of claim 14, wherein combining each noise segment and each speech audio segment comprises overlapping each noise segment and each audio segment in a time domain and summing each noise segment and each audio segment. 15. The method of claim 14, wherein combining each noise segment and each speech audio segment comprises overlapping each noise segment and each audio segment in a time domain and summing each noise segment and each audio segment. 16. A non-transitory machine-readable storage medium comprising instructions for a server that, when executed by a processing device, cause the processing device to perform operations comprising: receiving audio data associated with a plurality of input devices; determining, for each input device of the plurality of input devices, a speech energy level by providing the audio data as input to a model that is trained to determine the speech energy level of given audio data; for each input device, determining a statistical value associated with the speech energy level of the input device; identifying a strongest input device, wherein the strongest input device has highest statistical value associated with the speech energy level; and responsive to determining that the statistical value associated with the speech energy level of the strongest input device satisfies a threshold condition, updating, for a respective input device, a gain value to an estimated target gain value based on the statistical value associated with the speech energy level of the respective input device. 16. A non-transitory machine-readable storage medium comprising instructions for a server that, when executed by a processing device, cause the processing device to perform operations comprising: receiving, from a plurality of input devices, audio data, wherein the audio data of each input device corresponds to a plurality of frequency ranges; determining, for each of the plurality of frequency ranges for each input device of the plurality of input devices, a speech energy level by providing audio data corresponding to each frequency range as input to a model that is trained to determine a speech energy level of given audio data in the corresponding frequency range of the plurality of frequency ranges; for each input device, determining a statistical value associated with the speech energy level of the input device; identifying a strongest input device, wherein the strongest input device has highest statistical value associated with the speech energy level; comparing the statistical value associated with the speech energy level of each input device other than the strongest input device with the statistical value associated with the speech energy level of the strongest input device; and depending on the comparing, determining whether to update, for a respective input device, a gain value to an estimated target gain value based on the statistical value associated with the speech energy level of the respective input device. 17. The non-transitory machine-readable storage medium of claim 16, the operations further comprising: comparing the statistical value associated with the speech energy level of each input device other than the strongest input device with the statistical value associated with the speech energy level of the strongest input device. 17. The non-transitory machine-readable storage medium of claim 16, further comprising: determining that the statistic value associated with the speech energy level of the strongest input device satisfies a threshold condition. 18. The non-transitory machine-readable storage medium of claim 16, wherein the threshold condition requires that the statistical value associated with the speech energy level of the strongest input device be above a respective threshold value for a threshold period of time. 18. The non-transitory machine-readable storage medium of claim 17, wherein the threshold condition requires that the statistical value associated with the speech energy level of the strongest input device be above a respective threshold value for a threshold period of time. 19. The non-transitory machine-readable storage medium of claim 16, wherein updating the gain value for the respective input device comprises: determining whether the statistical value associated with the speech energy level of the respective input device has been within a predefined range of the statistical value associated with the speech energy level of the strongest input device for a period of time. 19. The non-transitory machine-readable storage medium of claim 16, wherein determining whether to update the gain value for the respective input device comprises: determining whether the statistical value associated with the speech energy level of the respective input device has been within a predefined range of the statistical value associated with the speech energy level of the strongest input device for a period of time. 20. The non-transitory machine-readable storage medium of claim 16, the operations further comprising: based on the speech energy level, updating a state of a state machine that includes a speech state, a noise state, a silence state, and an uncertain state; and updating the gain value for the respective input device by: determining whether the state of the state machine is speech state for a threshold amount of time; responsive to determining that the state of the state machine is speech state for the threshold amount of time, updating the gain value by no more than a first number of decibels per second; determining whether the state of the state machine is uncertain state for the threshold amount of time; and responsive to determining that the state of the state machine is uncertain state for the threshold amount of time, updating the gain value by no more than a second number of decibels per second. 20. The non-transitory machine-readable storage medium of claim 16, further comprising: based on the speech energy level, updating a state of a state machine that includes a speech state, a noise state, a silence state, and an uncertain state; and updating the gain value for the respective input device by: determining whether the state of the state machine is speech state for a threshold amount of time; responsive to determining that the state of the state machine is speech state for the threshold amount of time, updating the gain value by no more than a first number of decibels per second; determining whether the state of the state machine is uncertain state for the threshold amount of time; and responsive to determining that the state of the state machine is uncertain state for the threshold amount of time, updating the gain value by no more than a second number of decibels per second . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see attached form PTO-892 . The following is closest applicable prior art. Matheja et al., (US 2016/0261951 A1) teach methods and apparatus for frequency selective signal mixing for speech enhancement. In one embodiment frequency-based channel selection is performed for signal magnitude, signal energy, and noise estimate using speaker activity detection information, signal-to-noise ratio, and/or signal level, Frequency-based channel selection is performed for a dynamic spectral floor to adjust the noise estimate using speaker dominance information. Noise reduction is performed on the signal for the selected channel. Alvarez et al., (US 2016/0099007 A1) describe, among other things, a computer-implemented method. The method can include receiving a stream of audio data at a computing device. The stream of audio data can be segmented into a plurality of audio segments. Respective intensity levels are determined for each of the plurality of audio segments. For each of the plurality of audio segments and based on the respective intensity levels, a determination can be made as to whether the audio segment includes a speech signal. Selective gain control can be performed on the stream of audio data by automatically adjusting a gain of particular ones of the plurality of audio segments that are determined to include a speech signal. Wu et al., (US 11,164,592 B1) teach a system that performs automatic gain control (AGC) using different decay rates. The system may select a slow decay rate to track a loudness level within speech (e.g., within an utterance), improving audio quality and maintaining dynamic range for an individual voice, while selecting a fast decay rate to track the loudness level after a gap of silence (e.g., no voice activity detected for a duration of time) or during large level changes (e.g., actual speech loudness is lower than estimated speech loudness for a duration of time). This improves an accuracy of the loudness estimate and therefore a responsiveness of the automatic gain control, resulting in an improved user experience. Mitchell et al., (US 10,878,840 B1) teach a method for recognising at least one of a non-verbal sound event and a scene in an audio signal comprising a sequence of frames of audio data, the method comprising: for each frame of the sequence: receiving at least one sound class score, wherein each sound class score is representative of a degree of affiliation of the frame with a sound class of a plurality of sound classes; for a sound class score of the at least one sound class scores: determining a confidence that the sound class score is representative of a degree of affiliation of the frame with the sound class by processing a value for a property associated with the frame, wherein the value is processed using a learned model for the property; adjusting the sound class score for the frame based at least on the determined confidence. Marinov (US 9,325,853 B1) teaches a method for equalizing silence levels includes receiving, at a selective forwarding unit (SFU), media protocol packets corresponding to an audio stream, wherein each particular media protocol packet comprises a packet header comprising an audio level value identifying an audio level of an audio sample that is carried in that particular media protocol packet. The method further includes equalizing, by the SFU, a silence level of the audio stream by determining a minimum audio level of the audio stream over a time period using the audio level from each of a subset of the media protocol packets corresponding to the time period, setting the silence level for the audio stream to the minimum audio level by updating a stored silence level value in digital memory, performing, by the SFU, an action using the silence level and using packets communicated from the SFU toward client devices. Any inquiry concerning this communication or earlier communications from the examiner should be directed to VIJAY B CHAWAN whose telephone number is (571)272-7601. The examiner can normally be reached 7-5 Monday thru Thursday. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Richemond Dorvil can be reached at 571-272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /VIJAY B CHAWAN/Primary Examiner, Art Unit 2658 Application/Control Number: 18/815,424 Page 2 Art Unit: 2658 Application/Control Number: 18/815,424 Page 3 Art Unit: 2658 Application/Control Number: 18/815,424 Page 5 Art Unit: 2658 Application/Control Number: 18/815,424 Page 6 Art Unit: 2658 Application/Control Number: 18/815,424 Page 7 Art Unit: 2658 Application/Control Number: 18/815,424 Page 8 Art Unit: 2658 Application/Control Number: 18/815,424 Page 9 Art Unit: 2658 Application/Control Number: 18/815,424 Page 10 Art Unit: 2658 Application/Control Number: 18/815,424 Page 11 Art Unit: 2658 Application/Control Number: 18/815,424 Page 12 Art Unit: 2658 Application/Control Number: 18/815,424 Page 13 Art Unit: 2658 Application/Control Number: 18/815,424 Page 14 Art Unit: 2658 Application/Control Number: 18/815,424 Page 15 Art Unit: 2658 Application/Control Number: 18/815,424 Page 16 Art Unit: 2658 Application/Control Number: 18/815,424 Page 17 Art Unit: 2658 Application/Control Number: 18/815,424 Page 18 Art Unit: 2658 Application/Control Number: 18/815,424 Page 19 Art Unit: 2658 Application/Control Number: 18/815,424 Page 20 Art Unit: 2658 Application/Control Number: 18/815,424 Page 21 Art Unit: 2658 Application/Control Number: 18/815,424 Page 22 Art Unit: 2658 Application/Control Number: 18/815,424 Page 23 Art Unit: 2658 Application/Control Number: 18/815,424 Page 24 Art Unit: 2658 Application/Control Number: 18/815,424 Page 25 Art Unit: 2658 Application/Control Number: 18/815,424 Page 26 Art Unit: 2658 Application/Control Number: 18/815,424 Page 27 Art Unit: 2658 Application/Control Number: 18/815,424 Page 28 Art Unit: 2658 Application/Control Number: 18/815,424 Page 29 Art Unit: 2658 Application/Control Number: 18/815,424 Page 30 Art Unit: 2658 Application/Control Number: 18/815,424 Page 31 Art Unit: 2658 Application/Control Number: 18/815,424 Page 32 Art Unit: 2658 Application/Control Number: 18/815,424 Page 33 Art Unit: 2658 Application/Control Number: 18/815,424 Page 34 Art Unit: 2658 Application/Control Number: 18/815,424 Page 35 Art Unit: 2658 Application/Control Number: 18/815,424 Page 36 Art Unit: 2658 Application/Control Number: 18/815,424 Page 37 Art Unit: 2658 Application/Control Number: 18/815,424 Page 38 Art Unit: 2658 Application/Control Number: 18/815,424 Page 39 Art Unit: 2658 Application/Control Number: 18/815,424 Page 40 Art Unit: 2658 Application/Control Number: 18/815,424 Page 41 Art Unit: 2658 Application/Control Number: 18/815,424 Page 42 Art Unit: 2658 Application/Control Number: 18/815,424 Page 43 Art Unit: 2658 Application/Control Number: 18/815,424 Page 44 Art Unit: 2658 Application/Control Number: 18/815,424 Page 45 Art Unit: 2658 Application/Control Number: 18/815,424 Page 46 Art Unit: 2658 Application/Control Number: 18/815,424 Page 47 Art Unit: 2658 Application/Control Number: 18/815,424 Page 48 Art Unit: 2658 Application/Control Number: 18/815,424 Page 49 Art Unit: 2658 Application/Control Number: 18/815,424 Page 50 Art Unit: 2658 Application/Control Number: 18/815,424 Page 51 Art Unit: 2658 Application/Control Number: 18/815,424 Page 52 Art Unit: 2658 Application/Control Number: 18/815,424 Page 53 Art Unit: 2658 Application/Control Number: 18/815,424 Page 54 Art Unit: 2658 Application/Control Number: 18/815,424 Page 55 Art Unit: 2658 Application/Control Number: 18/815,424 Page 56 Art Unit: 2658
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