DETAILED ACTION
Applicant’s arguments, filed on 08/25/2025, have been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Applicants have amended their claims, filed on 08/25/2025, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Claims 1, 3-6, 8-12, 14-19, 21-22, and 48-49 are the current claims hereby under examination.
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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 14-19 and 21-22 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 14, the claim recites the limitation “about 0.2 and 30 seconds” in lines 16-17. It is unclear what constitutes as “about” 0.2 and 30 seconds as “about” does not constitute as a definitive value, and it is unclear how close the value can be to 0.2 to be considered as being “about 0.2 seconds”. The broad and indefinite scope of the limitation fails to inform a person of ordinary skill in the art with reasonable certainty of the metes and bounds of the claimed invention, therefore the claim is rendered indefinite. For purposes of examination, it is being interpreted as any value that can be considered close to 0.2 seconds will teach on this limitation.
Claims 15-19 and 21-22 are rejected by virtue of their dependence from claim 14.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1 and 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over Espi (EP 3439327) in further view of Moussavi (WO 2011154791).
Regarding independent claim 1, Espi teaches the ear-wearable device for respiratory monitoring ([0003]: “Various embodiments are directed to method implemented by an ear-worn electronic device configured to be worn by a wearer”) comprising:
a control circuit ([0008]: “Typical components of an ear-worn electronic device can include a digital signal processor (DSP), memory, power management circuitry”);
a microphone ([0008]: “Typical components of an ear-worn electronic device can include … one or more microphones”), wherein the microphone is in electrical communication with the control circuit ([0008]: “Typical components of an ear-worn electronic device can include a digital signal processor (DSP), memory, power management circuitry, one or more communication devices (e.g., a radio, a near-field magnetic induction (NFMI) device), one or more antennas, one or more microphones, and a receiver/speaker, for example.”); and
a sensor package, wherein the sensor package is in electrical communication with the control circuit ([0019]: “The left and right ear devices 302 and 342 include a physiologic sensor module 320 and 360 coupled to the first and second processors 308 and 348.”);
wherein the ear-wearable device for respiratory monitoring is configured to
analyze signals from the microphone and/or the sensor package;
detect a respiratory condition and/or parameter based on analysis of the signals ([0054]: “One or more microphones 802 of the ear-worn electronic device monitor the acoustic environment surrounding the wearer and provide a real-time audio feed to a sound classifier module of the ear-worn electronic device (see, e.g., Figure 3). The sound classifier module performs frequency analysis and feature extraction 804 on the real-time audio feed, which are used by a neural network classifier to classify 806 sounds of interest.”; “one or more microphones of the ear-worn electronic device can be used to detect the wearer's breathing. As shown in Figure 6, audio 606 from microphones (e.g., a microphone array) can be used to detect breathing of the wearer, which can be tracked by the ear-worn electronic device. In some embodiments, a beamforming technique 620 can be used to orient the microphones toward the wearer's mouth and nose region. For example, source separation can be performed to isolate sounds emanating from the direction of the wearer's mouth and nose. High-pass filtering 622 can be used to isolate respiratory specific sounds. The wearer's breath rate (e.g., breaths per minute) can be estimated 626 based on the respiratory specific sounds.”).
However, Espi does not teach wherein the ear-wearable device for respiratory monitoring is configured to operate in an onset detection mode.
Moussavi discloses a system and method for acoustical screening for sleep apnea. Specifically, Moussavi teaches wherein the device is configured to operate in an onset detection mode (Page 18, lines 12-16: “The onset of each inspiratory phase and expiratory phase are then calculated using, for example, the method 100. The onset detection method is automatically calculated, and optionally the detected onsets may be verified manually for each patient by a healthcare professional to ensure the accuracy”). Espi and Moussavi are analogous arts as they are both related to systems that use respiratory sounds to monitor health conditions.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the onset detection from Moussavi into the device from Espi as it allows the device to determine the starting point for the condition being monitored, which can provide more information to the user and determine specific characteristics that can assist in the diagnosis and monitoring of the condition.
The Espi/Moussavi combination teaches operate in an event classification mode when the onset of an event is detected (Espi, [0054]: “the acoustic environment surrounding the wearer of an ear-worn electronic device is monitored to identify events that can be either potentially distracting or are important requiring wearer attention. Figure 8 is a functional block diagram involving processes for classifying sounds of interest by an ear-worn electronic device in accordance with various embodiments”).
Regarding claim 8, the Espi/Moussavi combination teaches the ear-wearable device for respiratory monitoring of claim 1, wherein the ear-wearable device for respiratory monitoring is configured to receive and execute a machine learning classification model that is specific for the detection of one or more respiratory conditions that are selected based on a user input from amongst a set of respiratory conditions (Espi, [0054]: “The sound classifier module performs frequency analysis and feature extraction 804 on the real-time audio feed, which are used by a neural network classifier to classify 806 sounds of interest”; [0054]: “One or more microphones 802 of the ear-worn electronic device monitor the acoustic environment surrounding the wearer and provide a real-time audio feed to a sound classifier module of the ear-worn electronic device (see, e.g., Figure 3). The sound classifier module performs frequency analysis and feature extraction 804 on the real-time audio feed, which are used by a neural network classifier to classify 806 sounds of interest.”; [0043]: “one or more microphones of the ear-worn electronic device can be used to detect the wearer's breathing. As shown in Figure 6, audio 606 from microphones (e.g., a microphone array) can be used to detect breathing of the wearer, which can be tracked by the ear-worn electronic device. In some embodiments, a beamforming technique 620 can be used to orient the microphones toward the wearer's mouth and nose region. For example, source separation can be performed to isolate sounds emanating from the direction of the wearer's mouth and nose. High-pass filtering 622 can be used to isolate respiratory specific sounds. The wearer's breath rate (e.g., breaths per minute) can be estimated 626 based on the respiratory specific sounds.”).
Regarding claim 9, the Espi/Moussavi combination teaches the ear-wearable device for respiratory monitoring of claim 1, wherein the ear-wearable device for respiratory monitoring is configured to send information regarding the detected respiratory conditions and/or parameters to an accessory device for presentation to a device wearer (Espi, [0053]: “In response to acceptance of a recommendation, the wearer's user profile 726 can be updated by the remote server 722. The updated user's profile can be synchronized 728, 730 so that the user's ear-worn electronic device implements the most current user profile 732”; [0048]: “Selecting audio content 704 for rendering can be accomplished directly through wearer interaction with an interface of the ear-worn electronic device (e.g., buttons, switches) or indirectly through a smartphone or other external device communicatively linked to the ear-worn electronic device”).
Regarding claim 10, the Espi/Moussavi combination teaches the ear-wearable device for respiratory monitoring of claim 1, the respiratory condition and/or parameter comprising at least one selected from the group consisting of respiration rate, tidal volume, respiratory minute volume, inspiratory reserve volume, expiratory reserve volume, vital capacity, and inspiratory capacity (Espi, [0043]: “the wearer's breath rate (e.g., breaths per minute) can be estimated 626 based on the respiratory specific sounds”).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over the Espi/Moussavi combination as applied to claim 1 above, and further in view of Schwaibold (US 20190261918).
Regarding claim 3, the Espi/Moussavi combination teaches the ear-wearable device for respiratory monitoring of claim 1.
However, the Espi/Moussavi combination does not teach wherein the ear-wearable device for respiratory monitoring is configured to buffer signals from the microphone and/or the sensor package, execute a feature extraction operation, and classify the event when operating in the event classification mode.
Schwaibold discloses a breathing gas analyzer. Specifically, Schwaibold teaches wherein the ear-wearable device for respiratory monitoring is configured to buffer the signals from the microphone and/or the sensor package ([0022]: “the respiratory gas analyzer comprises at least one buffer memory unit, which is configured to buffer the at least one coefficient and/or the at least one respiratory gas signal and/or the scaled value”). Espi, Moussavi, and Schwaibold are analogous arts as they are all related to systems that use respiratory sounds to monitor health conditions.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the buffering of signals from Schwaibold into the device from the Espi/Moussavi combination as it allows the device to process the signals into a more usable and easier to analyze condition, which can improve analysis and provide more accurate results.
The Espi/Moussavi/Schwaibold combination teaches execute a feature extraction operation (Espi, [0054]: “The sound classifier module performs frequency analysis and feature extraction 804 on the real-time audio feed, which are used by a neural network classifier to classify 806 sounds of interest.”), and classify the event when operating in the event classification mode (Espi, [0054]: “One or more microphones 802 of the ear-worn electronic device monitor the acoustic environment surrounding the wearer and provide a real-time audio feed to a sound classifier module of the ear-worn electronic device”).
Claims 4-6 are rejected under 35 U.S.C. 103 as being unpatentable over the Espi/Moussavi combination as applied to claim 1 above, and further in view of Alshaer (WO 2010054481).
Regarding claim 4, the Espi/Moussavi combination teaches the ear-wearable device for respiratory monitoring of claim 1.
However, the Espi/Moussavi combination does not teach wherein the ear-wearable device for respiratory monitoring is configured to operate in a setup mode prior to operating in the onset detection mode and the event classification mode.
Alshaer discloses a method for monitoring breathing cycles. Specifically, Alshaer teaches wherein the ear-wearable device for respiratory monitoring is configured to operate in a setup mode prior to operating in the onset detection mode and the event classification mode ([0068]-[0070]). Espi, Moussavi, and Alshaer are analogous arts as they are all related to systems that use respiratory sounds to monitor health conditions.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the setup mode from Alshaer into the device from the Espi/Moussavi combination as it allows the device to determine a baseline that the measured sounds will be compared to and allows the device to be calibrated in the correct way, ensuring it provides the most accurate results and determinations.
Regarding claim 5, the Espi/Moussavi/Alshaer combination teaches the ear-wearable device for respiratory monitoring of claim 4.
The Espi/Moussavi/Alshaer combination discloses comparing the monitored respiratory sounds to a threshold (Espi, [0043]: “The wearer's breath rate (e.g., breaths per minute) can be estimated 626 based on the respiratory specific sounds. The wearer's breath rate can be compared to a threshold stored in the user profile 634 to distinguish between a desired and undesired breath rate 628 for the mental exercise”), but does not explicitly disclose how the threshold is determined.
Alshaer teaches wherein the ear-wearable device for respiratory monitoring is configured to query a device wearer to take a respiratory action when operating in the setup mode ([0028]: “The device comprises means for generating a data set representative of an acoustic data stream plot of wave amplitude versus time The data set originating from breathing sounds of an individual”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the user’s prerecorded breathing sounds from Alshaer as the threshold from the Espi/Moussavi/Alshaer combination as the combination is silent on what the threshold is and how it is determined and Alshaer provides a suitable comparison in an analogous device.
Regarding claim 6, the Espi/Moussavi/Alshaer combination teaches the ear-wearable device for respiratory monitoring of claim 4.
The Espi/Moussavi/Alshaer combination discloses comparing the monitored respiratory sounds to a threshold (Espi, [0043]: “The wearer's breath rate (e.g., breaths per minute) can be estimated 626 based on the respiratory specific sounds. The wearer's breath rate can be compared to a threshold stored in the user profile 634 to distinguish between a desired and undesired breath rate 628 for the mental exercise”), but does not explicitly disclose how the threshold is determined.
Alshaer teaches wherein the ear-wearable device for respiratory monitoring is configured to query a device wearer to reproduce a respiratory event when operating in the setup mode ([0028]: “The device comprises means for generating a data set representative of an acoustic data stream plot of wave amplitude versus time The data set originating from breathing sounds of an individual”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the user’s prerecorded breathing sounds from Alshaer as the threshold from the Espi/Moussavi/Alshaer combination as the combination is silent on what the threshold is and how it is determined and Alshaer provides a suitable comparison in an analogous device.
Claims 11 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over the Espi/Moussavi combination as applied to claim 1 above, and further in view of Rahman (US 20200093459).
Regarding claim 11, the Espi/Moussavi combination teaches the ear-wearable device for respiratory monitoring of claim 1.
However, the Espi/Moussavi combination does not teach the respiratory condition and/or parameter comprising at least one selected from the group consisting of bradypnea, tachypnea, hyperpnea, an obstructive respiration condition, Kussmaul respiration, Biot respiration, ataxic respiration, and Cheyne-Stokes respiration.
Rahman discloses a system and method for monitoring pathological breathing patterns. Specifically, Rahman teaches the respiratory condition and/or parameter comprising at least one selected from the group consisting of bradypnea, tachypnea, hyperpnea, an obstructive respiration condition, Kussmaul respiration, Biot respiration, ataxic respiration, and Cheyne-Stokes respiration ([0056]: “FIG. 3 illustrates examples of breathing waveforms for different types of pulmonary conditions according to embodiments of this disclosure. Various pulmonary conditions present distinct and unique waveforms that can aid in identifying a pulmonary condition that a user is experiencing. As illustrated in FIG. 3, breathing patterns are provided for Tachypnea 305, Bradypnea 310, Apnea 315, Cheyne-Stokes 320, Biot's 325, Apneustic 330, Agonal 335, Shallow 340, Hypernea 345, air trapping 350, Kussmaul's 355, and sighing 360. These patterns 305-360 present differently when compared to the breathing pattern of Eupnea 300, which is a normal breathing pattern.”). Espi, Moussavi, and Rahman are analogous arts as they are all related to systems that use respiratory sounds to monitor health conditions.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the conditions being monitored in Rahman into the device from the Espi/Moussavi combination as it allows the device to monitor specific conditions and provide further results to the user about their health and the progression of their condition.
Regarding claim 12, the Espi/Moussavi combination teaches the ear-wearable device for respiratory monitoring of claim 1.
However, the Espi/Moussavi combination does not disclose wherein the ear-wearable device for respiratory monitoring is configured to detect one or more adventitious sounds.
Rahman teaches wherein the ear-wearable device for respiratory monitoring is configured to detect one or more adventitious sounds ([0058]: “An identification of the respiratory phases of a user's breathing pattern can be useful when detecting adventitious sounds to identify a pulmonary condition”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the adventitious sounds being measured from Rahman into the device from the Espi/Moussavi combination as the Espi/Moussavi combination is silent on the types of sounds being measured, and Rahman provides suitable sounds to measure in an analogous device.
Claims 14-16 and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Espi in further view of Moussavi and Schwaibold.
Regarding independent claim 14, Espi teaches an ear-wearable system for respiratory monitoring ([0003]: “Various embodiments are directed to method implemented by an ear-worn electronic device configured to be worn by a wearer”) comprising:
an accessory device, the accessory device comprising a first control circuit; and a display screen ([0053]: “In response to acceptance of a recommendation, the wearer's user profile 726 can be updated by the remote server 722. The updated user's profile can be synchronized 728, 730 so that the user's ear-worn electronic device implements the most current user profile 732”; [0048]: “Selecting audio content 704 for rendering can be accomplished directly through wearer interaction with an interface of the ear-worn electronic device (e.g., buttons, switches) or indirectly through a smartphone or other external device communicatively linked to the ear-worn electronic device”);
an ear-wearable device ([0003]: “Various embodiments are directed to method implemented by an ear-worn electronic device configured to be worn by a wearer”), the ear-wearable device comprising
a second control circuit ([0008]: “Typical components of an ear-worn electronic device can include a digital signal processor (DSP), memory, power management circuitry”);
a microphone ([0008]: “Typical components of an ear-worn electronic device can include … one or more microphones”), wherein the microphone is in electrical communication with the second control circuit ([0008]: “Typical components of an ear-worn electronic device can include a digital signal processor (DSP), memory, power management circuitry, one or more communication devices (e.g., a radio, a near-field magnetic induction (NFMI) device), one or more antennas, one or more microphones, and a receiver/speaker, for example.”); and
a sensor package, wherein the sensor package is in electrical communication with the second control circuit ([0019]: “The left and right ear devices 302 and 342 include a physiologic sensor module 320 and 360 coupled to the first and second processors 308 and 348.”);
wherein the ear-wearable device is configured to analyze the signals from the microphone and/or the sensor package ([0054]: “One or more microphones 802 of the ear-worn electronic device monitor the acoustic environment surrounding the wearer and provide a real-time audio feed to a sound classifier module of the ear-worn electronic device (see, e.g., Figure 3). The sound classifier module performs frequency analysis and feature extraction 804 on the real-time audio feed, which are used by a neural network classifier to classify 806 sounds of interest.”; [0043]: “one or more microphones of the ear-worn electronic device can be used to detect the wearer's breathing. As shown in Figure 6, audio 606 from microphones (e.g., a microphone array) can be used to detect breathing of the wearer, which can be tracked by the ear-worn electronic device. In some embodiments, a beamforming technique 620 can be used to orient the microphones toward the wearer's mouth and nose region. For example, source separation can be performed to isolate sounds emanating from the direction of the wearer's mouth and nose. High-pass filtering 622 can be used to isolate respiratory specific sounds. The wearer's breath rate (e.g., breaths per minute) can be estimated 626 based on the respiratory specific sounds.”).
However, Espi does not disclose to detect the onset of a respiratory event.
Moussavi teaches to detect an onset of a respiratory event (Page 18, lines 12-16:: “The onset of each inspiratory phase and expiratory phase are then calculated using, for example, the method 100. The onset detection method is automatically calculated, and optionally the detected onsets may be verified manually for each patient by a healthcare professional to ensure the accuracy”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the onset detection from Moussavi into the system from Espi as it allows the system to determine the starting point for the condition being monitored, which can provide more information to the user and determine specific characteristics that can assist in the diagnosis and monitoring of the condition.
However, the Espi/Moussavi combination does not teach buffer signals from the microphone and/or the sensor package after a detected onset.
Schwaibold teaches buffer signals from the microphone and/or the sensor package after the detected onset, wherein buffering the signals comprises buffering between about 0.2 and 30 seconds worth of signals ([0022]: “the respiratory gas analyzer comprises at least one buffer memory unit, which is configured to buffer the at least one coefficient and/or the at least one respiratory gas signal and/or the scaled value … The at least one buffered coefficient and/or the at least one buffered respiratory gas signal and/or the at least one scaled value are generally made available to a detector unit of the respiratory gas analyzer for a predetermined time period, preferably between five seconds and five minutes”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the buffering of signals from Schwaibold into the system from the Espi/Moussavi combination as it allows the system to process the signals into a more usable and easier to analyze condition, which can improve analysis and provide more accurate results.
The Espi/Moussavi/Schwaibold combination teaches send buffered signal data to the accessory device (Espi, [0053]: “In response to acceptance of a recommendation, the wearer's user profile 726 can be updated by the remote server 722. The updated user's profile can be synchronized 728, 730 so that the user's ear-worn electronic device implements the most current user profile 732”; [0048]: “Selecting audio content 704 for rendering can be accomplished directly through wearer interaction with an interface of the ear-worn electronic device (e.g., buttons, switches) or indirectly through a smartphone or other external device communicatively linked to the ear-worn electronic device”); and
receive an indication of a respiratory condition from the accessory device; and wherein the accessory device is configured to process signal data from the ear-wearable device to detect the respiratory condition (Espi, [0054]: “One or more microphones 802 of the ear-worn electronic device monitor the acoustic environment surrounding the wearer and provide a real-time audio feed to a sound classifier module of the ear-worn electronic device (see, e.g., Figure 3). The sound classifier module performs frequency analysis and feature extraction 804 on the real-time audio feed, which are used by a neural network classifier to classify 806 sounds of interest.”; [0043]: “one or more microphones of the ear-worn electronic device can be used to detect the wearer's breathing. As shown in Figure 6, audio 606 from microphones (e.g., a microphone array) can be used to detect breathing of the wearer, which can be tracked by the ear-worn electronic device. In some embodiments, a beamforming technique 620 can be used to orient the microphones toward the wearer's mouth and nose region. For example, source separation can be performed to isolate sounds emanating from the direction of the wearer's mouth and nose. High-pass filtering 622 can be used to isolate respiratory specific sounds. The wearer's breath rate (e.g., breaths per minute) can be estimated 626 based on the respiratory specific sounds.”).
Regarding claim 15, the Espi/Moussavi/Schwaibold combination teaches the ear-wearable system for respiratory monitoring of claim 14, wherein the ear-wearable system for respiratory monitoring is configured to operate in an onset detection mode (Moussavi, Page 18, lines 12-16:: “The onset of each inspiratory phase and expiratory phase are then calculated using, for example, the method 100. The onset detection method is automatically calculated, and optionally the detected onsets may be verified manually for each patient by a healthcare professional to ensure the accuracy”); and operate in an event classification mode when the onset of an event is detected (Espi, [0054]: “the acoustic environment surrounding the wearer of an ear-worn electronic device is monitored to identify events that can be either potentially distracting or are important requiring wearer attention. Figure 8 is a functional block diagram involving processes for classifying sounds of interest by an ear-worn electronic device in accordance with various embodiments”).
Regarding claim 16, the Espi/Moussavi/Schwaibold combination teaches the ear-wearable system for respiratory monitoring of claim 15, wherein the ear-wearable device is configured to buffer the signals from the microphone and/or the sensor package when operating in the event classification mode (Schwaibold, [0022]: “the respiratory gas analyzer comprises at least one buffer memory unit, which is configured to buffer the at least one coefficient and/or the at least one respiratory gas signal and/or the scaled value”).
Regarding claim 21, the Espi/Moussavi/Schwaibold combination teaches the ear-wearable system for respiratory monitoring of claim 14, wherein the ear-wearable system for respiratory monitoring is configured to receive and execute a machine learning classification model that is specific for the detection of one or more respiratory conditions that are selected based on a user input from amongst a set of respiratory conditions (Espi, [0054]: “The sound classifier module performs frequency analysis and feature extraction 804 on the real-time audio feed, which are used by a neural network classifier to classify 806 sounds of interest”; [0054]: “One or more microphones 802 of the ear-worn electronic device monitor the acoustic environment surrounding the wearer and provide a real-time audio feed to a sound classifier module of the ear-worn electronic device (see, e.g., Figure 3). The sound classifier module performs frequency analysis and feature extraction 804 on the real-time audio feed, which are used by a neural network classifier to classify 806 sounds of interest.”; [0043]: “one or more microphones of the ear-worn electronic device can be used to detect the wearer's breathing. As shown in Figure 6, audio 606 from microphones (e.g., a microphone array) can be used to detect breathing of the wearer, which can be tracked by the ear-worn electronic device. In some embodiments, a beamforming technique 620 can be used to orient the microphones toward the wearer's mouth and nose region. For example, source separation can be performed to isolate sounds emanating from the direction of the wearer's mouth and nose. High-pass filtering 622 can be used to isolate respiratory specific sounds. The wearer's breath rate (e.g., breaths per minute) can be estimated 626 based on the respiratory specific sounds.”).
Regarding claim 22, the Espi/Moussavi/Schwaibold combination teaches the ear-wearable system for respiratory monitoring of claim 14, wherein the accessory device is configured to present information regarding detected respiratory conditions and/or parameters to the device wearer (Espi, [0053]: “In response to acceptance of a recommendation, the wearer's user profile 726 can be updated by the remote server 722. The updated user's profile can be synchronized 728, 730 so that the user's ear-worn electronic device implements the most current user profile 732”; [0048]: “Selecting audio content 704 for rendering can be accomplished directly through wearer interaction with an interface of the ear-worn electronic device (e.g., buttons, switches) or indirectly through a smartphone or other external device communicatively linked to the ear-worn electronic device”).
Claims 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over the Espi/Moussavi/Schwaibold combination as applied to claim 15 above, and further in view of Alshaer.
Regarding claim 17, the Espi/Moussavi/Schwaibold combination teaches the ear-wearable system for respiratory monitoring of claim 15.
However, the Espi/Moussavi/Schwaibold combination does not teach wherein the ear-wearable system for respiratory monitoring is configured to operate in a setup mode prior to operating in the onset detection mode and the event classification mode.
Alshaer discloses a method for monitoring breathing cycles. Specifically, Alshaer teaches wherein the ear-wearable device for respiratory monitoring is configured to operate in a setup mode prior to operating in the onset detection mode and the event classification mode ([0068]-[0070]).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the setup mode from Alshaer into the system from the Espi/Moussavi/Schwaibold combination as it allows the system to determine a baseline that the measured sounds will be compared to and allows the device to be calibrated in the correct way, ensuring it provides the most accurate results and determinations.
Regarding claim 18, the Espi/Moussavi/Schwaibold/Alshaer combination teaches the ear-wearable system for respiratory monitoring of claim 17.
The Espi/Moussavi/Schwaibold/Alshaer combination discloses comparing the monitored respiratory sounds to a threshold (Espi, [0043]: “The wearer's breath rate (e.g., breaths per minute) can be estimated 626 based on the respiratory specific sounds. The wearer's breath rate can be compared to a threshold stored in the user profile 634 to distinguish between a desired and undesired breath rate 628 for the mental exercise”), but does not explicitly disclose how the threshold is determined.
Alshaer teaches wherein the ear-wearable system for respiratory monitoring is configured to query a device wearer to take a respiratory action when operating in the setup mode ([0028]: “The device comprises means for generating a data set representative of an acoustic data stream plot of wave amplitude versus time The data set originating from breathing sounds of an individual”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the user’s prerecorded breathing sounds from Alshaer as the threshold from the Espi/Moussavi/Schwaibold/Alshaer combination as the combination is silent on what the threshold is and how it is determined and Alshaer provides a suitable comparison in an analogous device.
Regarding claim 19, the Espi/Moussavi/Schwaibold/Alshaer combination teaches the ear-wearable system for respiratory monitoring of claim 17.
The Espi/Moussavi/Schwaibold/Alshaer combination discloses comparing the monitored respiratory sounds to a threshold (Espi, [0043]: “The wearer's breath rate (e.g., breaths per minute) can be estimated 626 based on the respiratory specific sounds. The wearer's breath rate can be compared to a threshold stored in the user profile 634 to distinguish between a desired and undesired breath rate 628 for the mental exercise”), but does not explicitly disclose how the threshold is determined.
Alshaer teaches wherein the ear-wearable system for respiratory monitoring is configured to query a device wearer to reproduce a respiratory event when operating in the setup mode ([0028]: “The device comprises means for generating a data set representative of an acoustic data stream plot of wave amplitude versus time The data set originating from breathing sounds of an individual”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the user’s prerecorded breathing sounds from Alshaer as the threshold from the Espi/Moussavi/Schwaibold/Alshaer combination as the combination is silent on what the threshold is and how it is determined and Alshaer provides a suitable comparison in an analogous device.
Claim 48 is rejected under 35 U.S.C. 103 as being unpatentable over the Espi/Moussavi combination as applied to claim 1 above, and further in view of Maile (US 20170347968).
Regarding claim 48, the Espi/Moussavi combination teaches the ear-wearable device for respiratory monitoring of claim 1.
However, the Espi/Moussavi combination does not teach wherein the ear-wearable device for respiratory monitoring is configured to detect changes in a respiratory parameter over a baseline value for a device wearer when operating in the onset detection mode.
Maile discloses a system and method to detect respiratory diseases using respiratory sounds. Specifically, Maile teaches wherein the device for respiratory monitoring is configured to detect changes in a respiratory parameter over a baseline value for a device wearer when operating in the onset detection mode ([0071]: “The threshold detector 234 may compare the respiratory anomaly indicator generate at the comparator 432B to a specified threshold to detect an onset or worsening of a respiratory condition. For example, an episode of asthma attack or a worsened condition of COPD may be declared if PS1 falls below rPS1 by a specified threshold, if PS2 exceeds rPS2 by a specified threshold, or if PS2/PS1 exceeds rPS2/rPS1 by a specified threshold, among other criteria based on the respiratory anomaly indicator generate at the comparator 432B”). Espi, Moussavi, and Maile are analogous arts as they are all related to systems that use respiratory sounds to monitor health conditions.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to detect changes over a baseline value as it allows the device to determine when the respiratory parameters are showing signs of becoming a health concern or indicating a respiratory condition, which can allow the device to diagnose and provide information about the health condition of the user.
Claim 49 is rejected under 35 U.S.C. 103 as being unpatentable over Espi in further view of Sarlabous (EP 3839972).
Regarding independent claim 49, Espi teaches an ear-wearable device for respiratory monitoring ([0003]: “Various embodiments are directed to method implemented by an ear-worn electronic device configured to be worn by a wearer”) comprising:
a control circuit ([0008]: “Typical components of an ear-worn electronic device can include a digital signal processor (DSP), memory, power management circuitry”);
a microphone ([0008]: “Typical components of an ear-worn electronic device can include … one or more microphones”), wherein the microphone is in electrical communication with the control circuit ([0008]: “Typical components of an ear-worn electronic device can include a digital signal processor (DSP), memory, power management circuitry, one or more communication devices (e.g., a radio, a near-field magnetic induction (NFMI) device), one or more antennas, one or more microphones, and a receiver/speaker, for example.”); and
a sensor package, wherein the sensor package is in electrical communication with the control circuit ([0019]: “The left and right ear devices 302 and 342 include a physiologic sensor module 320 and 360 coupled to the first and second processors 308 and 348.”);
wherein the ear-wearable device for respiratory monitoring is configured to analyze signals from the microphone and/or the sensor package, and detect a respiratory condition and/or parameter ([0054]: “One or more microphones 802 of the ear-worn electronic device monitor the acoustic environment surrounding the wearer and provide a real-time audio feed to a sound classifier module of the ear-worn electronic device (see, e.g., Figure 3). The sound classifier module performs frequency analysis and feature extraction 804 on the real-time audio feed, which are used by a neural network classifier to classify 806 sounds of interest.”; “one or more microphones of the ear-worn electronic device can be used to detect the wearer's breathing. As shown in Figure 6, audio 606 from microphones (e.g., a microphone array) can be used to detect breathing of the wearer, which can be tracked by the ear-worn electronic device. In some embodiments, a beamforming technique 620 can be used to orient the microphones toward the wearer's mouth and nose region. For example, source separation can be performed to isolate sounds emanating from the direction of the wearer's mouth and nose. High-pass filtering 622 can be used to isolate respiratory specific sounds. The wearer's breath rate (e.g., breaths per minute) can be estimated 626 based on the respiratory specific sounds.”).
However, Espi does not teach the steps of: establish a predetermined pattern indicative of an occurrence of respiration; analyze signals from the microphone and/or the sensor package; compare the analyzed signals to the predetermined pattern; if the analyzed signals deviate from the predetermined pattern by a threshold amount, detect a respiratory condition and/or parameter.
Sarlabous discloses a device and method for respiratory monitoring. Specifically, Sarlabous teaches the steps of: establish a predetermined pattern indicative of an occurrence of respiration; analyze signals from the microphone and/or the sensor package; compare the analyzed signals to the predetermined pattern; if the analyzed signals deviate from the predetermined pattern by a threshold amount, detect a respiratory condition and/or parameter ([0010]: “The data processing unit is preferably configured to calculate, for each extracted feature, an increase in the extracted feature relative to a corresponding feature of the baseline condition, and detect a change in the breathing pattern and/or clusters of asynchronies if the increase exceeds a threshold.”). Espi and Sarlabous are analogous arts as they are both related to systems that use respiratory sounds to monitor health conditions.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include comparing the measured values with a predetermined pattern from Sarlabous as it allows the device to determine when the respiratory parameters are showing signs of becoming a health concern or indicating a respiratory condition, which can allow the device to diagnose and provide information about the health condition of the user.
Response to Arguments
All of applicant’s argument regarding the rejections and objections previously set forth have been fully considered and are persuasive unless directly addressed subsequently.
Applicant's arguments filed 08/25/2025 have been fully considered but they are not persuasive. With regards to the argument that the Espi/Moussavi combination does not teach the onset detection mode, Applicant states that the combination does not teach this limitation because the onset detection from the combination does not teach detecting an abnormal respiratory event. However, claim 1 does not state that the onset detection mode is detecting an abnormal respiratory event. An “onset detection mode” is a broad term that can refer to any mode that can detect the onset of anything, which includes the onset detection mode taught in the Espi/Moussavi combination, as detailed in the 103 rejection above. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., the onset detection mode detecting an abnormal respiratory event) are not recited in the rejected claim. 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).
With regards to the 103 rejection of claim 14, Applicant states that the Espi/Moussavi/Schwaibold combination does not teach the newly added limitation of the buffering signals comprising buffering between about 0.2 and 30 seconds worth of signals. However, as stated in the 103 rejection above, Schwaibold does disclose this limitation ([0022]: “the respiratory gas analyzer comprises at least one buffer memory unit, which is configured to buffer the at least one coefficient and/or the at least one respiratory gas signal and/or the scaled value … The at least one buffered coefficient and/or the at least one buffered respiratory gas signal and/or the at least one scaled value are generally made available to a detector unit of the respiratory gas analyzer for a predetermined time period, preferably between five seconds and five minutes”), therefore the argument is unpersuasive.
Conclusion
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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/E.K.M./Examiner, Art Unit 3791
/MATTHEW KREMER/Primary Examiner, Art Unit 3791