DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The Office Action is responsive to amendments filed for application
17/544,749 on 08/14/2026. Please note claims 1, 3-15, and 17-25 remain in the application.
Response to Arguments
Applicant's arguments filed 08/14/2026 have been fully considered but they are not persuasive.
In response to applicant's argument (Pg. 10, ¶4) that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., wherein the parameter values of the tinnitus masking signal adjustment network are provided by the classification network) are not recited in the rejected claim(s), only wherein the parameter values are associated (by the second deep neural network) with the specified acoustic environment (Claim 1, lines 21-22; Claim 23, lines 14-15). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicant’s remaining arguments with respect to claim(s) 1 and 23 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1, 3, 9-11, 13-15, 17-21, and 23-25 are rejected under 35 U.S.C. 103 as being unpatentable over Von Brasch et al (hereinafter Von Brasch), US-PG-PUB No. 2023/0329912 (previously cited) in view of Kamkar-Parsi et al (hereinafter Kamkar-Parsi), US-PG-PUB No. 2022/0201406.
Regarding claim 1, Von Brasch discloses an ear-wearable electronic device (A hearing prosthesis.....¶[0014], lines 11-13) comprising: a housing configured to be about an ear of a wearer (Shown in Fig. 2, a behind-the-ear device.....¶[0038], lines 1-3);
a sound generator (A speaker.....¶[0039], lines 10-12) disposed in the housing (Shown in Fig. 2, speaker (250) is part of the hearing aid housing) and configured to produce at least a tinnitus masking sound (The sound provided is a tinnitus masking sound.....¶[0066], lines 10-12);
a physiologic sensor arrangement (Sensors for monitoring physiological conditions.....¶[0219], lines 15-18) disposed in the housing (The sensor arrangement may be included in the hearing prosthesis (i.e. the housing).....¶[0219], lines 9-14) and configured to measure a plurality of physiologic parameters of the wearer (The sensor captures monitoring (measurement) data of various physiological conditions.....¶[0219], lines 16-18), the physiologic sensor arrangement configured to produce physiologic sensor signals in response to the physiologic sensor measurements (The captured data comprises a physiological sensor signal responsive to physiological sensor measurements);
a controller (The prosthesis may include a deep neural network (DNN) based device which controls an output based on inputs.....¶[0181], line 4, ¶[0185], lines 1-3) operatively coupled to the sound generator (The device provides an output signal for tinnitus mitigation (the masking signal from the speaker recited earlier).....¶[0181], lines 13-15) and the physiologic sensor arrangement (The device receives the monitoring signals from the physiologic sensors as input.....¶[0182], lines 4-6), the controller configured to detect the presence of tinnitus (The device comprising said controller uses the input to determine the presence of a current tinnitus event.....¶[0060], lines 1-4) of the wearer (The signals correspond to the wearer of the hearing prosthetic) using the physiologic sensor signals (The signals obtained are the physiological features of the user.....¶[0059], lines 13-16) ; and
a microphone arrangement supported by the housing (The hearing aid may comprise a microphone.....¶[0026], lines 1-5) and coupled to the controller (Microphone output is received by the controller.....¶[0182], lines 1-2), the microphone arrangement comprising a configured to generate a microphone signal indicative of sound (¶[0026], lines 1-2) within the wearer's current acoustic environment (The microphone is on the hearing aid, and therefore the output signal corresponds the environment of the hearing aid’s wearer);
wherein the controller comprises, or is operatively coupled to, a processor configured with instructions to (The controller may comprise a processor capable of performing the cited methods.....¶[0181], lines 18-21):
classify, via a deep neural network (Audio classification may be embodied via neural networks, which are optimally deep neural networks.....¶[0210], lines 8-10, ¶[0055], lines 6-11), the acoustic environment of the wearer as a specified one of a plurality of disparate acoustic environments (Microphone audio may be classified as music, speech, and general scene(environment)-based classification…..¶[0123], lines 12-15, ¶[0212], lines 7-9); and
process the physiologic sensor signals (Shown in Fig. 4, the device utilizes the physiological sensor data.....¶[0122], lines 8-13) via a deep neural network (The method correlates the data using a machine learning system, ideally implemented as a deep neural network.....¶[0122], lines 6-8, ¶[0178]), to adjust the tinnitus masking sound produced by the sound generator (The masking sound produced may be determined by a machine learning system and algorithm, adjusting over time.....¶[0143], lines 1-3 and 9-11, ¶[0148], lines 1-5) using one or more of the physiologic sensor signals (Shown in Fig. 4, the device utilizes the physiological sensor data.....¶[0122], lines 8-13) and a (classification) parameter value associated with the specified acoustic environment (The detection and mitigation functions of are in-part determined based on classification of the acoustic environment.....¶[0099], lines 30-33).
Von Brasch fails to explicitly disclose wherein the deep neural network (DNN) for audio classification and the deep neural network for adjusting the masking sound are two separate neural networks, instead broadly describing wherein the disclosed operations may be performed by neural networks, and wherein the output from the classification process may be used as input for the masking signal adjusting process. Additionally, Von Brasch fails to teach wherein parameter values (plural) used by the second deep neural network are associated with the specified acoustic environment classified via the first deep neural network, instead only broadly teaching wherein the classification result is used.
Kamkar-Parsi teaches a hearing instrument (optionally implemented as a tinnitus masker.....¶[0005]) wherein an audio classifier (Shown in Fig. 3, an analysis module (56) analogous to the DNN-enabled audio classifier of Von Brasch identifies a listening situation (audio classification).....¶[0058], lines 1-5) is different from a (second) deep neural network (A DNN (30) for modifying signal components, analogous to the masking signal adjusting DNN of Von Brasch.....¶[0017], lines 3-5); and
wherein parameter values used by the deep neural network (Weights and topologies of the DNN.....¶[0058], lines 4-12) are associated with the specified acoustic environment classified via the first deep neural network (Said weights and topologies are adjusted and updated according to the determined listening situation.....¶[0058], lines 4-12).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Von Brasch by Kamkar-Parsi to provide the benefit of rapid adaptation of the DNN responsible for (masking) signal adjustment (Kamkar-Parsi, ¶[0036], lines 13-16), as well as more reliable outputs due to specialized architecture using dedicated resources (Kamkar-Parsi, ¶[0007], lines 10-16). Such modification would make obvious the feature(s) wherein the deep neural network of Von Brasch may be embodied as a first deep neural network for audio classification and a second deep neural network, different from the first deep neural network, used for adjusting the tinnitus masking sound, as well as wherein the parameter values used by the second deep neural network are associated with the specified acoustic environment classified via the first deep neural network.
Regarding claim 3, the combination of Von Brasch and Kamkar-Parsi teaches the device of claim 1.
Von Brasch additionally teaches wherein: the physiologic sensor arrangement comprises a sensor configured to produce an electroencephalography (EEG) signal (An EEG monitor.....¶[0102], lines 1-3); and the controller is configured to: produce EEG spectral power data (Brain wave activity data.....¶[0102], line 4) using the EEG signal; and detect presence of tinnitus of the wearer using the EEG spectral power data (All collected physiological data may be used to detect the presence of an occurring tinnitus event.....¶[0102], lines 7-11).
Regarding claim 9, the combination of Von Brasch and Kamkar-Parsi teaches the device of claim 1.
Von Brasch additionally teaches wherein the device comprises: a motion sensor (A movement monitor.....¶[0172], lines 10-11) disposed in or supported by the housing and coupled to the controller (As previously established, sensors may be provided to the hearing prosthetic and coupled to the controller..... ¶[0219], lines 9-14), the motion sensor configured to generate a motion sensor signal indicative of wearer motion (The motion sensor indicates wearer movement.....¶[0172], lines 10-11); wherein the controller is configured to detect presence of tinnitus (The controller determines if tinnitus is presently occurring.....¶[0189], lines 5-10) of the wearer using the motion sensor signal (The controller is able to detect tinnitus using all input signals, including that from the motion sensor.....¶[0189], lines 1-3).
Regarding claim the combination of Von Brasch and Kamkar-Parsi teaches the device of claim 1.
Von Brasch additionally teaches wherein the device comprises: an optical sensor (A light sensor.....¶[0101], lines 14-16) supported by the housing and coupled to the controller (As previously established, sensors may be provided to the hearing prosthetic and coupled to the controller..... ¶[0219], lines 9-14), the optical sensor configured to generate an optical sensor signal indicative of ambient light intensity (The sensor outputs a data signal regarding detected light or dark conditions.....¶[0091], line 12); wherein the controller is configured to detect one or more of absence, presence, and severity of tinnitus of the wearer using the optical sensor signal (The controller is able to detect tinnitus using all input signals, including that from the optical sensor.....¶[0189], lines 1-3).
Regarding claim 10 the combination of Von Brasch and Kamkar-Parsi teaches the device of claim 1, as explained above.
Von Brasch additionally teaches wherein the device comprises: an optical sensor (A light sensor.....¶[0101], lines 14-16) supported by the housing and coupled to the controller (As previously established, sensors may be provided to the hearing prosthetic and coupled to the controller..... ¶[0219], lines 9-14), the optical sensor configured to generate an optical sensor signal indicative of ambient light intensity (The sensor outputs a data signal regarding detected light or dark conditions.....¶[0091], line 12); wherein the controller is configured to detect one or more of absence, presence, and severity of tinnitus of the wearer using the optical sensor signal (The controller is able to detect tinnitus using all input signals, including that from the optical sensor.....¶[0189], lines 1-3).
Regarding claim 11, the combination of Von Brasch and Kamkar-Parsi teaches the device of claim 1 as explained above.
Von Brasch additionally teaches wherein the controller is configured to detect presence of tinnitus of the wearer using the microphone signal (As cited in the rejection of claim 1, the microphone signal feeds an audio classifier, the result of which is used in determination of presence of tinnitus).
Regarding claim 13, the combination of Von Brasch and Kamkar-Parsi teaches the device of claim 1, as explained above.
Von Brasch additionally teaches wherein the controller is configured to adjust the tinnitus masking sound (The masking sound produced may be determined by a machine learning system, adjusting over time.....¶[0143], lines 1-3, ¶[0148], lines 1-5) produced by the sound generator by producing a tinnitus masking sound that substantially matches the wearer's tinnitus (The machine learning system over time may learn to match the masking signal frequency such that the frequency masks the frequencies of tinnitus.....¶[0148], lines 11-14).
Regarding claim 14, the combination of Von Brasch and Kamkar-Parsi teaches the device of claim 1, as explained above.
Von Brasch additionally teaches wherein: the controller is configured to adjust the tinnitus masking sound produced by the sound generator (The masking sound produced may be determined by a machine learning system, adjusting over time.....¶[0143], lines 1-3, ¶[0148], lines 1-5) using the physiologic sensor signals; and the physiologic sensor signals are produced by an EEG sensor and an EKG (ECG) sensor (¶[0102], lines 1-3, ¶[0143], lines 9-13).
Regarding claim 15, the combination of Von Brasch and Kamkar-Parsi teaches the device of claim 1, as explained above.
Von Brasch additionally teaches the device of claim 15, wherein: wherein the controller is configured to adjust (The masking sound produced may be determined by a machine learning system, adjusting over time.....¶[0143], lines 1-3, ¶[0148], lines 1-5) a frequency composition of the tinnitus masking sound produced by the sound generator (The machine learning system over time may learn to match the masking signal frequency such that the frequency masks the frequencies of tinnitus.....¶[0148], lines 11-14).
Regarding claim 17, the combination of Von Brasch and Kamkar-Parsi teaches the device of claim 1, as explained above.
Von Brasch additionally teaches wherein the device is comprising (As previously established, sensors may be provided to the hearing prosthetic and coupled to the controller..... ¶[0219], lines 9-14): a non-physiologic sensor arrangement comprising one or more non-physiologic sensors (A light sensor.....¶[0101], lines 14-16) configured to sense a least one non-physiologic parameter (The sensor detects light or dark conditions.....¶[0091], line 12), the non-physiologic sensor arrangement configured to produce non-physiologic sensor signals in response to the non-physiologic sensor measurement (The sensor outputs a data signal regarding detected light or dark conditions.....¶[0101], line 14-16);
and wherein the controller is configured to detect one or more of presence, absence, and severity of tinnitus of the wearer using the physiologic sensor signals and the non-physiologic sensor signals (The controller is able to detect tinnitus using all input signals, including that from the optical sensor.....¶[0189], lines 1-3).
Regarding claim 18, the combination of Von Brasch and Kamkar-Parsi teaches the device of claim 17, as explained above.
Von Brasch additionally teaches wherein the device is comprising: a communication device disposed in or supported by the housing and configured to wirelessly communicate with an external electronic device (The hearing aid may include means for (a communication device) wireless linkage with external devices such as portable handheld devices or a body-carry device for dedicated data logging.....¶[0042], lines 12-14, ¶[0182], lines 20-27) and
to receive, from the external electronic device or system (The body carry device transmits data to the hearing aid.....¶[0182], lines 27-31), contextual factor data indicative of factors impacting a current context (The data logged by the body-carry device includes ambient environmental data, including time of day (a contextual factor)…..¶[0182], lines 20-24, ¶[0091], lines 12-13); wherein the controller is configured to detect presence of tinnitus of the wearer using the physiologic sensor signals, the non-physiologic sensor signals, and the contextual factor data (The controller is able to detect tinnitus using all input signals, including contextual factors from a body-carry device.....¶[0189], lines 1-10, ¶[0182], lines 7-11).
Regarding claim 19, the combination of Von Brasch and Kamkar-Parsi teaches the device of claim 19, as explained above.
Von Brasch additionally teaches wherein the contextual factor data includes time of day data…..¶[0182], lines 20-24, ¶[0091], lines 12-13).
Regarding claim 20, the combination of Von Brasch and Kamkar-Parsi teaches the device of claim 18, as explained above.
Von Brasch additionally teaches wherein the processor is further configured with instructions to process one or more of the physiologic sensor signals, the non-physiologic sensor signals, and the contextual factor data (The device processes all physiological and ambient/environmental (which includes aforementioned non-physiological and contextual factor data) in tinnitus therapy processes.....¶[0059], lines 13-18) via a machine learning algorithm to adjust the tinnitus masking sound produced by the sound generator (The masking sound produced may be determined by a machine learning system and algorithm, adjusting over time.....¶[0143], lines 1-3 and 9-11, ¶[0148], lines 1-5).
Regarding claim 21, the combination of Von Brasch and Kamkar-Parsi teaches the device of claim 18, as explained above.
Von Brasch additionally teaches wherein the processor is further configured with instructions to process the physiologic sensor signals and one or more of the non-physiologic sensor signals and the contextual factor data, via the second deep neural network (The device processes all physiological and ambient/environmental (which includes aforementioned non-physiological and contextual factor data) in tinnitus therapy processes.....¶[0059], lines 13-18), to adjust the tinnitus masking sound produced by the sound generator (The masking sound produced may be determined by a machine learning system and algorithm, adjusting over time.....¶[0143], lines 1-3 and 9-11, ¶[0148], lines 1-5) using the one or more of the physiologic sensor signals and the parameter values associated with the specified acoustic environment (The tinnitus mitigation processes are also affected by the sound scene classification.....¶[0159], lines 9-11), and one or more of the non-physiologic sensor signals and the contextual factor data .
Regarding claim 23, Von Brasch discloses a method implemented by an ear-wearable electronic device (A hearing prosthesis.....¶[0014], lines 11-13) configured for deployment about an ear of a wearer (Shown in Fig. 2, a behind-the-ear device.....¶[0038], lines 1-3), comprising: measuring (Sensors arrangement captures monitoring (measurement) data of various physiological conditions.....¶[0219], lines 16-18), using a physiologic sensor arrangement of the device (The sensor arrangement may be included in the hearing prosthesis (i.e. the housing).....¶[0219], lines 9-14), a plurality of one or both of physiologic parameters and physiologic conditions of the wearer (The sensor captures monitoring (measurement) data of various physiological conditions.....¶[0219], lines 16-18);
producing, by the physiologic sensor arrangement, physiologic sensor signals in response to the physiologic sensor measurements (The captured data comprises a physiological sensor signal responsive to physiological sensor measurements); and
detecting, using a controller of the device (The prosthesis may include a deep neural network (DNN) based device which controls an output based on inputs.....¶[0181], line 4, ¶[0185], lines 1-3), presence of tinnitus of the wearer using the physiologic sensor signals (Shown in Fig. 3, method 399, step 392 utilizes the controller to process all received inputs (including physiological sensor signals) to determine the presence of an occurring tinnitus event.....¶[0181], lines 1-5);
classifying, via a deep neural network (Audio classification may be embodied via neural networks, which are optimally deep neural networks.....¶[0210], lines 8-10, ¶[0055], lines 6-11), and using a microphone arrangement of the device, a current acoustic environment of the wearer as a specified one of a plurality of disparate acoustic environments (Microphone audio may be classified as music, speech, and general scene(environment)-based classification…..¶[0123], lines 12-15, ¶[0212], lines 7-9); and
process the physiologic sensor signals (Shown in Fig. 4, the device utilizes the physiological sensor data.....¶[0122], lines 8-13) via a deep neural network (The method correlates the data using a machine learning system, ideally implemented as a deep neural network.....¶[0122], lines 6-8, ¶[0178]), to adjust a tinnitus masking sound produced by a sound generator of the device (The masking sound produced may be determined by a machine learning system and algorithm, adjusting over time.....¶[0143], lines 1-3 and 9-11, ¶[0148], lines 1-5) using one or more of the physiologic sensor signals (Shown in Fig. 4, the device utilizes the physiological sensor data.....¶[0122], lines 8-13) and a (classification) parameter value associated with the specified acoustic environment (The detection and mitigation functions of are in-part determined based on classification of the acoustic environment.....¶[0099], lines 30-33).
Von Brasch fails to explicitly disclose wherein the deep neural network (DNN) for audio classification and the deep neural network for adjusting the masking sound are two separate neural networks, instead broadly describing wherein the disclosed operations may be performed by neural networks, and wherein the output from the classification process may be used as input for the masking signal adjusting process. Additionally, Von Brasch fails to teach wherein parameter values (plural) used by the second deep neural network are associated with the specified acoustic environment classified via the first deep neural network, instead only broadly teaching wherein the classification result is used.
Kamkar-Parsi teaches a hearing instrument (optionally implemented as a tinnitus masker.....¶[0005]) wherein an audio classifier (Shown in Fig. 3, an analysis module (56) analogous to the DNN-enabled audio classifier of Von Brasch identifies a listening situation (audio classification).....¶[0058], lines 1-5) is different from a (second) deep neural network (A DNN (30) for modifying signal components, analogous to the masking signal adjusting DNN of Von Brasch.....¶[0017], lines 3-5); and
wherein parameter values used by the deep neural network (Weights and topologies of the DNN.....¶[0058], lines 4-12) are associated with the specified acoustic environment classified via the first deep neural network (Said weights and topologies are adjusted and updated according to the determined listening situation.....¶[0058], lines 4-12).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Von Brasch by Kamkar-Parsi to provide the benefit of rapid adaptation of the DNN responsible for (masking) signal adjustment (Kamkar-Parsi, ¶[0036], lines 13-16), as well as more reliable outputs due to specialized architecture using dedicated resources (Kamkar-Parsi, ¶[0007], lines 10-16). Such modification would make obvious the feature(s) wherein the deep neural network of Von Brasch may be embodied as a first deep neural network for audio classification and a second deep neural network, different from the first deep neural network, used for adjusting the tinnitus masking sound, as well as wherein the parameter values used by the second deep neural network are associated with the specified acoustic environment classified via the first deep neural network.
Regarding claim 24, the combination of Von Brasch and Kamkar-Parsi teaches the device of claim 23.
Von Brasch additionally teaches measuring, using a non-physiologic sensor arrangement (A light sensor.....¶[0101], lines 14-16) of the device (As previously established, sensors may be provided to the hearing prosthetic and coupled to the controller..... ¶[0219], lines 9-14), at least one non- physiologic parameter (The sensor detects light or dark conditions.....¶[0091], line 12);
producing, by the non-physiologic sensor arrangement, non-physiologic sensor signals in response to the non-physiologic sensor measurement (The sensor outputs a data signal regarding detected light or dark conditions.....¶[0101], line 14-16); and detecting, using the controller (The prosthesis may include a deep neural network (DNN) based device which controls an output based on inputs.....¶[0181], line 4, ¶[0185], lines 1-3), one or more of presence, absence, and severity of tinnitus of the wearer using the physiologic sensor signals and the non-physiologic sensor signals (Shown in Fig. 3, method 399, step 392 utilizes the controller to process all received inputs (including physiological and ambient/environmental (non-physiological) signals) to determine the presence of an occurring tinnitus event.....¶[0181], lines 1-5).
Regarding claim 25, the combination of Von Brasch and Kamkar-Parsi teaches the device of claim 24.
Von Brasch additionally teaches receiving, from an external electronic device or system (The hearing aid may include means for (a communication device) wireless linkage with external devices such as portable handheld devices or a body-carry device for dedicated data logging.....¶[0042], lines 12-14, ¶[0182], lines 20-27), contextual factor data indicative of one or more factors impacting a current context or wellbeing of the wearer (The data logged by the body-carry device includes ambient environmental data, including time of day (a contextual factor)…..¶[0182], lines 20-24, ¶[0091], lines 12-13); and
detecting, using the controller (The prosthesis may include a deep neural network (DNN) based device which controls an output based on inputs.....¶[0181], line 4, ¶[0185], lines 1-3), one or more of presence, absence, and severity of tinnitus of the wearer using the physiologic sensor signals, the non-physiologic sensor signals, and the contextual factor data (Shown in Fig. 3, method 399, step 392 utilizes the controller to process all received inputs (including physiological and ambient/environmental (non-physiological and contextual factor data) signals) to determine the presence of an occurring tinnitus event.....¶[0181], lines 1-5).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Von Brasch in view of Kamkar-Parsi in further view of Cho, US-PG-PUB 2022/0408205 (previously cited).
Regarding claim 4, the combination of Von Brasch and Kamkar-Parsi teaches the device of claim 1.
Von Brasch further teaches including wherein the physiologic sensor arrangement comprises a sensor configured to produce an electrocardiogram (ECG) signal.
This further combination fails to teach wherein the controller is configured to detect tinnitus of the wearer using the heart rate variability data derived from the ECG signal.
Cho teaches a tinnitus treatment system wherein a controller is configured to: produce heart rate variability data (A tinnitus treatment program providing unit (controller) acquires (produces) HRV information…..¶ [0096], lines 1-5) using the ECG signal (Biometric information (ECG, as stated above) may be used to provide HRV data for stress analysis…..¶ [0164], lines 1-5); and detect the severity of tinnitus of the wearer using the heart rate variability data (Heart rate variability data is correlated with user stress levels, which correspond to the intensity of tinnitus treatment, establishing a correlation between stress levels and the severity of user tinnitus…..¶ [0164], lines 1-11).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Von Brasch and Kamkar-Parsi to incorporate the teachings of Cho and provide wherein: the physiologic sensor arrangement comprises a sensor configured to produce an ECG signal; and the controller is configured to: produce heart rate variability data using the ECG signal; and detect severity of tinnitus of the wearer using the heart rate variability data. This would provide the benefit of a system which can match intensity of treatment to the intensity of user symptoms (Cho, ¶ [0164], lines 1-3).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Von Brasch in view of Kamkar-Parsi in further view of Cho and Hu et al (hereinafter Hu), US-PG-PUB 2010/0274144 (previously cited).
Regarding claim 5, the combination of Von Brasch and Kamkar-Parsi, as explained above, teaches the device of claim 1, but fails to teach a photoplethysmography (PPG) sensor producing a PPG signal; and the controller is configured to: produce heart rate variability data using the PPG signal; and identify tinnitus of the wearer using the heart rate variability data.
Cho teaches detecting the severity of tinnitus of the wearer using heart rate variability data (Heart rate variability data is correlated with user stress levels, which correspond to the intensity of tinnitus treatment, implying a further correlation between stress levels and the severity of user tinnitus…..¶ [0164], lines 1-11).
Hu teaches an earpiece comprising a sensor configured to produce a photoplethysmography (PPG) signal (An earpiece accommodates a measurement unit for PPG signal…..¶ [0025], lines 10-12); and a controller (A control and processing unit (controller)…..¶ [0030], lines 1-3) configured to: produce heart rate variability data using the PPG signal (The controller analyzes PPG signal and outputs a heart rate variability signal…..¶ [0030], lines 1-6).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Von Brasch and Kamkar-Parsi to incorporate the teachings of Cho and Hu to provide wherein the physiologic sensor arrangement comprises a sensor configured to produce a photoplethysmography (PPG) signal; and the controller is configured to: produce heart rate variability data using the PPG signal. This would provide a system which can match intensity of treatment to the intensity of user symptoms (Cho, ¶ [0164], lines 1-3), as well as a system that provides HRV measurement data utilizing compact and cheap-to-produce methods (Hu, ¶ [0014], lines 1-6).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Von Brasch in view of Kamkar-Parsi in further view of Popelka et al (hereinafter Popelka), US-PG-PUB 2019/0201657 (previously cited).
Regarding claim 6, the combination of Von Brasch and Kamkar-Parsi, as explained above, teaches the device of claim 1, but fails to teach wherein the physiologic sensor arrangement comprises an EMG sensor.
Popelka teaches a tinnitus therapy device utilizing a sensor configured to produce an electromyography (EMG) signal (Neural activity is evaluated via a noninvasive EMG sensor…..¶ [0165], lines 1-4).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Von Brasch and Kamkar-Parsi to incorporate the teachings of Popelka and provide wherein the physiologic sensor arrangement comprises a sensor configured to produce an electromyography (EMG) signal. This would provide a system which can evaluate the effectiveness of tinnitus therapy (Popelka, ¶ [0164], lines 3-10).
Claims 7 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Von Brasch in view of Kamkar-Parsi in further view of Litvak et al (hereinafter Litvak), US-PG-PUB No. 2023/0173271 (previously cited).
Regarding claim 7, the combination of Von Brasch and Kamkar-Parsi, as explained above, teaches the device of claim 1, but fails to teach wherein the physiologic sensors include an electrodermal activity sensor.
Litvak teaches a tinnitus treatment device wherein a physiologic sensor arrangement comprises a sensor configured to produce an electrodermal activity signal (The sensors may measure skin conductance level (electrodermal activity)…..¶ [0016], lines 6-10).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Von Brasch and Kamkar-Parsi to incorporate the teachings of Litvak and provide wherein the physiologic sensor arrangement comprises a sensor configured to produce an electrodermal activity signal. This would provide the benefit of a system that is able to use wider combinations of sensor readings to evaluate a user’s tinnitus (Litvak, ¶ [0023], lines 20-25).
Regarding claim 22, the combination of Von Brasch and Kamkar-Parsi teaches the device of claim 18, as explained above,
Von Brasch additionally teaches wherein the processor is further configured with instructions to: process first data comprising one or more of the physiologic sensor signals, the non-physiologic sensor signals, and the contextual factor data via a machine learning algorithm to adjust the tinnitus masking sound produced by the sound generator.
This further combination fails to explicitly teach detecting mitigation or non-mitigation of the wearer's tinnitus, via a neural network, in response to second data comprising one or more of the physiologic sensor signals, the non-physiologic sensor signals, and the contextual factor data acquired subsequent to the tinnitus masking sound adjustment.
Litvak teaches a system for mitigating tinnitus wherein a controller comprises a processor (¶ [0036], lines 1-3) configured with instructions to: process first data (The processor is enabled to perform the functions of the device…..¶ [0038], lines 3-5) comprising one or more of the physiologic sensor signals (A sensor for detecting wearer heart rate…..¶ [0016], lines 6-9), the non-physiologic sensor signals (An accelerometer for detecting wearer movement…..¶ [0025], lines 16-21), and the contextual factor data (A stress level of a wearer…..¶ [0015], lines 6-10) via a machine learning algorithm to adjust a tinnitus treatment (Sensor data, the detection of tinnitus severity, and user input are jointly evaluated by a machine learning algorithm to predictively apply changes to the tinnitus treatment…..¶ [0025], lines 1-17, ¶ [0024], lines 1-6); and detect mitigation or non-mitigation of the wearer's tinnitus, in response to second data comprising one or more of the physiologic sensor signals, the non-physiologic sensor signals, and the contextual factor data acquired subsequent to the tinnitus masking sound adjustment (Over time the machine learning aspect builds associations between physiological, non-physiological, and context factor data, and is able to independently determine improvement or worsening (mitigation or non-mitigation) of a user’s tinnitus symptoms…..¶ [0024], lines 1-6, ¶ [0025], lines 10-16).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Von Brasch and Kamkar-Parsi by Litvak to provide the benefit of improving perception of tinnitus by a user without them having to indicate that it has worsened (Litvak, ¶[0024], final 6 lines). Such modification would make obvious the feature(s) wherein the device is enabled to detect mitigation or non-mitigation of the wearer's tinnitus, via a neural network (as the device of Von Brasch operates via deep neural network), in response to second data comprising one or more of the physiologic sensor signals, the non-physiologic sensor signals, and the contextual factor data acquired subsequent to the tinnitus masking sound adjustment.
Claims 8 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Von Brasch and Kamkar-Parsi in view of Dragicevic et al (hereinafter Dragicevic), US-PG-PUB No. 2019/0122125 (previously cited).
Regarding claim 8, the combination of Von Brasch and Kamkar-Parsi, as explained above, teaches the device of claim 1, but fails to explicitly teach wherein the sensors measure glucose or serotonin.
Dragicevic teaches a hearing device comprising a biochemical sensor configured to sense changes in blood glucose levels of the wearer (An earpiece sensor package may contain a glucose sensor (¶ [0036], lines 11-13), used to alert a user with deteriorating (changing) blood sugar levels…..¶ [0071], lines 1-6).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Von Brasch and Kamkar-Parsi to further incorporate the teachings of Dragicevic and provide wherein the physiologic sensor arrangement comprises a biochemical sensor configured to sense changes in the blood glucose levels of the wearer. This would provide a system that may alert a user to deteriorating physical conditions (Dragicevic, ¶ [0071], lines 1-3).
Regarding claim 12, the combination of Von Brasch and Kamkar-Parsi, as explained above, teaches the device of claim 1.
Von Brasch additionally teaches wherein a sensor supported by the housing and coupled to the controller (As previously established, sensors may be provided to the hearing prosthetic and coupled to the controller..... ¶[0219], lines 9-14) may output information regarding local weather, including temperature and humidity conditions ([0091], lines 12-13), and wherein a controller is configured to detect presence of tinnitus of the wearer using the weather information (The controller is able to detect tinnitus using all input signals, including that from weather sensors.....¶[0189], lines 1-3).
This further combination fails to explicitly disclose a barometric pressure sensor configured to generate a pressure sensor signal indicative of ambient barometric pressure.
Dragicevic teaches wherein a sensor may be barometric pressure sensor (Barometer.....¶[0036], line 21) configured to generate a pressure sensor signal indicative of ambient barometric pressure (The function of a barometer).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have Von Brasch and Kamkar-Parsi to incorporate the teachings of Dragicevic, and provide wherein the non-physiological sensor supported by the housing and coupled to the controller is a barometric pressure sensor, configured to generate a pressure sensor signal indicative of ambient barometric pressure; and wherein the controller is configured to detect one or more of absence, presence, and severity of tinnitus of the wearer using the pressure sensor signal. This would provide a system with improved context awareness when making determinations regarding a wearer condition (Dragicevic, ¶[0027], lines 8-13).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kohl el at, US-PG-PUB No. 2022/0141599 teaches wherein a hearing aid neural network is modified by an audio environment.
Durrieu, US-PG-PUB No. 2021/0289299 teaches audio classification and separation via DNN.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/SEAN RINEHART/Examiner, Art Unit 2694
/AHMAD F. MATAR/Supervisory Patent Examiner, Art Unit 2693
