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 .
Applicant' s arguments, filed 7/28/2025, 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 7/28/2025, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Claims 1-18 and 20 are the currently pending claims with claims 3 and 14 having been previously withdrawn from further examination. Claims 1, 2, 4-10, 12, and 15-18 have been amended and claim 19 has been canceled.
Claim Objections
Claims 5 and 16 are objected to because of the following informalities:
In claim 5, lines 11-12: “a carotid artery cardiovascular stenosis medical condition of the individual” already has antecedent basis since it was already introduced in claim 1, and should be “the carotid artery cardiovascular stenosis medical condition of the individual”; and
In claim 16, lines 14-15: “a carotid artery cardiovascular stenosis medical condition of the individual” already has antecedent basis since it was already introduced in claim 12, and should be “the carotid artery cardiovascular stenosis medical condition of the individual”;
Appropriate correction is required.
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.
Claims 1-2 are rejected under 35 U.S.C. 103 as being unpatentable over by Bakema et al. (US 20120232427 A1), hereto referred as Bakema, and further in view of Barnacka et al. (US-20190247010-A1), hereto referred as Barnacka.
Regarding claim 1, Bakema teaches that a cardiovascular (CV) stenosis monitoring, diagnosis, analysis and reporting system (CV stenosis system) (Bakema, Abstract: "A sensor, sensor pad and sensor array for detecting infrasonic signals in a living organism is provided... utilized for detecting, determining and/or diagnosing level of stenosis, occlusion, or aneurysm in arteries", describing a system for stenosis diagnosis and analysis), the CV stenosis system comprises: a data analysis system that is configured to detect and characterize a cardiovascular stenosis medical condition of the individual based upon the received biosignals (Bakema, ¶[0011]: "Utilizing certain algorithms... the present components can be utilized in a system to identify the systolic event, calibrate the signal, analyze the signal utilizing low frequency (Spectral) methods and assess the range of stenosis, occlusion or aneurysm within each carotid artery", describing a data analysis system that uses biosignals to characterize stenosis).
Also regarding claim 1, Bakema does not fully teach that the system comprises: an interface configured to receive biosignals including infrasonic cardiac signals from one or more earbuds worn by an individual, wherein the one or more earbuds include sensors that detect the biosignals including infrasonic cardiac signals in ear canals of the individual. Rather, Bakema teaches infrasonic sensors and arrays that can be applied externally to the neck or chest to capture cardiac-related signals and analyze them spectrally for stenosis characterization (Bakema, ¶[0003], ¶[0009], ¶[0011]), but does not teach that such sensors are incorporated into earbuds positioned in the ear canals of an individual.
Barnacka teaches a portable infrasonic biosensor system including earbuds that detect body infrasound in the ear canal (Barnacka, ¶[0006]: "The head-mounted transducer system is equipped with one or more acoustic transducers, e.g., microphones or other sensors... The acoustic transducers detecting acoustic signals in the infrasonic band and/or audible frequency band... The head-mounted transducer system can take the form of a headset, earbuds, earphones and/or headphones… acoustic transducers are installed outside, at the entrance, and/or inside the ear canal of the user"; ¶[0056]: "The biologically-originating sound inside the ear canal is mostly in infrasound range"; ¶[0078]: "The analysis of the cardiac waveform detected using a microphone placed in ear canal can be used to extract precise information related to cardiovascular system such as heart rate, heart rate variability, arrhythmias, blood pressure, etc.").
It would have been prima facie obvious before the effective filing date of the claimed invention to modify Bakema in view of Barnacka to have an interface configured to receive biosignals including infrasonic cardiac signals from one or more earbuds worn by an individual, wherein the one or more earbuds include sensors that detect the biosignals including infrasonic cardiac signals in ear canals of the individual. One skilled in the art would have found it obvious to combine Bakema’s infrasonic stenosis diagnostic algorithms with Barnacka’s earbud-based infrasonic biosignal acquisition system to yield the claimed invention. The references share a focus on non-invasive detection of infrasonic cardiac activity. Barnacka provides a compact and consumer-friendly earbud form factor that enhances portability and enables continuous monitoring, while Bakema provides the diagnostic framework for stenosis analysis. The benefit of this combination would be providing a user-friendly, wearable system for biosignal detection that improves comfort and real-world applicability.
Regarding claim 2, Bakema teaches that the data analysis system includes a processor and a memory and is configured to identify and measure aspects of the cardiac signals using representations of a shape of the cardiac signals (Bakema, ¶[0011]: "Utilizing certain algorithms … the present components can be utilized in a system to identify the systolic event, calibrate the signal, analyze the signal utilizing low-frequency (Spectral) methods and assess the range of stenosis, occlusion, or aneurysm within each carotid artery"; Bakema describes spectral analysis techniques used to represent signal shapes and analyze waveform characteristics to assess stenosis severity, which implicitly uses a processor and memory. Spectral methods encode the signal's frequency composition, which is a mathematical representation of the waveform's shape. By analyzing frequency peaks and distributions, the system visualizes and processes representations of the waveform’s morphological features), and to derive vital signs from the aspects of the cardiac signals (Bakema, ¶[0011]: "Utilizing certain algorithms … the present components can be utilized in a system to identify the systolic event, calibrate the signal, analyze the signal utilizing low-frequency (Spectral) methods and assess the range of stenosis, occlusion, or aneurysm within each carotid artery"; This process aligns with deriving diagnostic indicators such as systolic events from cardiac signals).
Claims 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over by Bakema et al. (US 20120232427 A1), hereto referred as Bakema, and in view of Barnacka et al. (US-20190247010-A1), hereto referred as Barnacka, and further in view of Patangay et al. (US 20080139951 A1), hereto referred as Patangay, and further in view of Shute et al. (US 20190343480 A1), hereto referred as Shute.
The combination of Bakema and Barnacka teaches claim 1 as described above.
Regarding claim 8, Bakema does not fully teach that the data analysis system records values for the detected and characterized cardiovascular stenosis medical condition of the individual to a medical record for the individual, compares the recorded values to reference values for each of the recorded values, and sends notification messages to the individual and to medical professionals when the results of the comparisons exceed threshold levels for each of the reference values. Bakema provides methods for recording and analyzing stenosis-related data. For example, Bakema discusses calibration steps, spectral analysis, and statistical classification of stenosis by using signal features to conduct a statistical analysis against multiple parameters arteries (Bakema, ¶[0011]). Supporting the comparison of recorded data to reference values as it involves evaluating collected data to classify the degree of stenosis, occlusion, or aneurysm within carotid arteries (Bakema, ¶[0011]). Patangay, who investigates a device for detecting stenosis, describes a processing system for analyzing turbulence levels within specific cardiac time windows to detect stenosis based on predefined thresholds (Patangay, ¶[0034]). Specifically, the system evaluates turbulence signals against these thresholds to identify stenosis severity, offering a methodical approach to comparing recorded values to diagnostic criteria (Patangay, ¶[0034]). This explicit discussion of thresholds demonstrates how turbulence analysis can be used to assess stenosis severity effectively. Shute, who is developing head gear for detecting heart sound information, details notification systems that alert medical professionals and patients based on evaluated patient data, which is also recorded to the patient's records (Shute, ¶[0058]). These notifications complement the threshold-based analyses from Patangay and Bakema by providing a mechanism to act upon detected threshold breaches. This ensures that critical diagnostic findings are effectively communicated to relevant parties, facilitating timely responses and effective management of detected conditions (Shute, ¶[0058]). One skilled in the art would have found it obvious to combine Bakema’s data recording and analysis methods with Patangay’s threshold-based turbulence analysis and Shute’s notification functionalities. Bakema’s calibration and statistical analysis provide a robust foundation for processing diagnostic data, while Patangay’s threshold-based turbulence detection adds a precise mechanism for evaluating specific conditions, such as stenosis severity. Shute’s notification system integrates seamlessly with these capabilities, ensuring that critical threshold breaches trigger timely communication to relevant parties. The compatibility of these technologies lies in their shared focus on analyzing cardiovascular data and enhancing diagnostic workflows. Together, they create a system capable of efficient data processing, reliable threshold-based evaluation, and effective communication for improved patient outcomes. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combined Bakema and Barnacka in view of Patangay and Shute to have the system records values for the detected and characterized cardiovascular stenosis to a medical record for the individual while comparing the recorded values to reference values and sends notification messages to the individual and to medical professionals when the results of the comparisons exceed a threshold. This integration would enhance diagnostic accuracy and ensures timely communication, facilitating early intervention and improved patient outcomes.
Regarding claim 9, Bakema does not fully teach that the reference values are previously stored baseline values for the individual. Bakema provides a foundation for recording and analyzing stenosis-related data, which inherently involves comparison against reference parameters(Bakema, ¶[0011]). For example, Bakema discusses calibration steps and the statistical classification of stenosis, indicating the use of stored data to evaluate the degree of stenosis or occlusion (Bakema, ¶[0011]). While it does not explicitly mention individual baseline values, the reliance on calibrated parameters and reference data suggests that the use of patient-specific baselines is an obvious extension of this methodology. Patangay contributes by emphasizing the use of historical data in diagnostics. Specifically, the processing device tracks levels over time and compares them to previously recorded stenosis-related values to observe trends or changes in stenosis severity of a patient. This use of historical data helps distinguish between worsening or improving stenosis and supports the role of patient-specific reference values in the diagnostic process (Patangay, ¶[0034]). One skilled in the art would have found it obvious to combine Bakema’s data recording and analysis methods with Patangay’s use of historical patient data for comparisons. Bakema’s foundational techniques establish the framework for evaluating cardiovascular data, while Patangay’s threshold analysis provides a precise mechanism for detecting deviations. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combined Bakema, Barnacka, Patangay, and Shute in view of Patangay to have the previously stored baseline values be the reference values for the individual. This combination improves diagnostic accuracy by utilizing historical patient data to identify trends or deviations in cardiovascular health over time.
Regarding claim 10, Bakema does not fully teach that the reference values are previously stored baseline values for cohorts of the individual. Bakema provides a foundation for recording and analyzing stenosis-related data, which inherently involves comparison against reference parameters (Bakema, ¶[0011]). For example, Bakema discusses calibration steps and the statistical classification of stenosis, indicating the use of stored data to evaluate the degree of stenosis or occlusion (Bakema, ¶[0011]). While it does not explicitly mention cohort baseline values, the reliance on calibrated parameters and reference data suggests that the use of cohort-based baselines is an obvious extension of this methodology. Patangay contributes by emphasizing the use of cohort-based reference values in diagnostics (Patangay, ¶[0034]). Specifically, the processing device correlates the degree of turbulence to a degree of stenosis. For example, detecting the presence of stenosis involves comparing turbulence levels to values obtained from patient studies that provide correlations between turbulence levels and degrees of stenosis (Patangay, ¶[0034]). This methodology demonstrates the use of cohort-based reference values derived from patient studies to improve diagnostic accuracy (Patangay, ¶[0034]). One skilled in the art would have found it obvious to combine Bakema’s data recording and analysis methods with Patangay’s use of historical cohort data for comparisons. Bakema’s foundational techniques establish the framework for evaluating cardiovascular data, while Patangay’s threshold analysis provides a precise mechanism for detecting deviations. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combined Bakema, Barnacka, Patangay, and Shute in view of Patangay to have the reference values be previously stored baseline values for cohorts of the individual. This combination improves diagnostic accuracy by utilizing historical cohort data to identify trends or deviations in cardiovascular health over time.
Claims 12 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over by Bakema et al. (US 20120232427 A1), hereto referred as Bakema, and further in view of Barnacka et al. (US-20190247010-A1), hereto referred as Barnacka, and further in view of Tran et al. (US-20160287166-A1), hereto referred as Tran.
Regarding claim 12, Bakema teaches that a cardiovascular stenosis monitoring, diagnosis, analysis and reporting method, the method (Bakema, Abstract: "A sensor, sensor pad and sensor array for detecting infrasonic signals in a living organism is provided... utilized for detecting, determining and/or diagnosing level of stenosis, occlusion, or aneurysm in arteries", describing a system/method for stenosis diagnosis and analysis) comprises: detecting and characterizing a cardiovascular stenosis medical condition of the individual based upon the received biosignals (Bakema, ¶[0011]: "Utilizing certain algorithms... the present components can be utilized in a system to identify the systolic event, calibrate the signal, analyze the signal utilizing low frequency (Spectral) methods and assess the range of stenosis, occlusion or aneurysm within each carotid artery", describing a data analysis system/method that uses biosignals to characterize stenosis).
Also regarding claim 12, Bakema does not fully teach that the method comprises: receiving biosignals including infrasonic cardiac signals from one or more earbuds worn by an individual, wherein the one or more earbuds include sensors that detect the biosignals including infrasonic cardiac signals in ear canals of the individual. Rather, Bakema teaches infrasonic sensors and arrays that can be applied externally to the neck or chest to capture cardiac-related signals and analyze them spectrally for stenosis characterization (Bakema, ¶[0003], ¶[0009], ¶[0011]), but does not teach that such sensors are incorporated into earbuds positioned in the ear canals of an individual.
Barnacka teaches a portable infrasonic biosensor system including earbuds that detect body infrasound in the ear canal (Barnacka, ¶[0006]: "The head-mounted transducer system is equipped with one or more acoustic transducers, e.g., microphones or other sensors... The acoustic transducers detecting acoustic signals in the infrasonic band and/or audible frequency band... The head-mounted transducer system can take the form of a headset, earbuds, earphones and/or headphones… acoustic transducers are installed outside, at the entrance, and/or inside the ear canal of the user"; ¶[0056]: "The biologically-originating sound inside the ear canal is mostly in infrasound range"; ¶[0078]: "The analysis of the cardiac waveform detected using a microphone placed in ear canal can be used to extract precise information related to cardiovascular system such as heart rate, heart rate variability, arrhythmias, blood pressure, etc.").
Also regarding claim 12, Bakema does not teach recording values for the detected and characterized cardiovascular stenosis medical condition of the individual to a medical record for the individual, comparing the recorded values to reference values for each of the recorded values, and sending notification messages to the individual and to medical professionals when results of the comparisons exceed threshold levels for each of the reference values.
Tran teaches storing physiologic data into patient medical records, applying threshold comparisons, and transmitting alerts or notifications to both users and clinicians. For example, “The system can show all data elements of a person’s medical record… [which] may be customized by the users” (Tran, ¶[0336]); “the wearable appliance can store patient data… [including] ECG… diabetes test results… X-ray scans… [and] stores the wearer’s medical records and ID information” (¶[0337]); “running average values… are compared to… suggested values… [to] give the patient an idea of how their data compares” (¶[0171]); and alerts are triggered when readings exceed parameters, with destinations that include “the user’s portal, e-mail, pager, [or] Voice-mail” (¶[0175]). Tran also notes that “the user may give permission… for third parties such as family, physicians, or caregivers [to] log in and access information” (¶[0177]).
Barnacka additionally supports notification functionality by disclosing “notify the user” or "notify doctors and emergency services" outputs and cloud connectivity to medical services (Barnacka, ¶[0193]; ¶[0197]-[0199]).
It would have been prima facie obvious before the effective filing date of the claimed invention to modify the combined Bakema and Barnacka in view of Barnacka and Tran to implement the method steps of receiving infrasonic cardiac biosignals with earbuds located in the ear canal, detecting and characterizing stenosis using Bakema’s infrasonic analysis algorithms, recording and comparing the resulting values to reference values, and reporting outcomes to users and medical professionals.
One skilled in the art would have found it obvious to combine Bakema’s infrasonic stenosis diagnostic framework with Barnacka’s earbud-based signal acquisition system and Tran’s medical record and alert functionality. The combination is feasible because Bakema’s algorithms are software-based signal processing steps that can operate on infrasonic cardiac data regardless of the specific sensor location, Barnacka demonstrates that infrasonic signals can be reliably acquired from the ear canal using earbud-mounted microphones and transmitted wirelessly, and Tran discloses infrastructure for storing those values in medical records, comparing them to reference thresholds, and delivering alerts to users and clinicians. Integrating Barnacka’s earbuds as the front-end sensor, feeding Bakema’s diagnostic analysis, and outputting results through Tran’s recordkeeping and alerting system would have been a predictable and straightforward engineering implementation. The benefit would be a practical, non-invasive diagnostic method integrated with electronic medical record systems and communication channels to improve patient care and professional oversight.
Regarding claim 13, Bakema teaches that the method further comprises identifying and measuring aspects of the cardiac signals using representations of a shape of the cardiac signals (Bakema, ¶[0011]: "Utilizing certain algorithms … the present components can be utilized in a system to identify the systolic event, calibrate the signal, analyze the signal utilizing low-frequency (Spectral) methods and assess the range of stenosis, occlusion, or aneurysm within each carotid artery"; Bakema describes spectral analysis techniques used to represent signal shapes and analyze waveform characteristics to assess stenosis severity. Spectral methods encode the signal's frequency composition, which is a mathematical representation of the waveform's shape. By analyzing frequency peaks and distributions, the system visualizes and processes representations of the waveform’s morphological features), and deriving vital signs from the aspects of the cardiac signals (Bakema, ¶[0011]: "Utilizing certain algorithms … the present components can be utilized in a system to identify the systolic event, calibrate the signal, analyze the signal utilizing low-frequency (Spectral) methods and assess the range of stenosis, occlusion, or aneurysm within each carotid artery"; This process aligns with deriving diagnostic indicators such as systolic events from cardiac signals).
Allowable Subject Matter
Claims 4-7, 11, 15-18, and 20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Also, claims 5 and 16 are still objected to.
For example, 4-7, 11, 15-18, and 20 are not rejected under 35 U.S.C. 103 as being unpatentable over by Bakema et al. (US 20120232427 A1), hereto referred as Bakema, and further in view of Barnacka et al. (US-20190247010-A1), hereto referred as Barnacka, and further in view of Tran et al. (US-20160287166-A1) (only for claims 15-18, and 20), hereto referred as Tran, and further in view of Patangay et al. (US 20080139951 A1), hereto referred as Patangay, and further in view of Kline et al. (US 20180184918 A1), hereto referred as Kline.
The combination of Bakema and Barnacka teaches claim 1 as described above.
The combination of Bakema, Barnacka, and Tran teaches claim 12 as described above.
Regarding Claims 4 and 15, Bakema does not teach that the data analysis system (or that the method) derives a left ventricular ejection time (LVET) vital sign and a rapid ejection period (REP) vital sign from the biosignals, divides the REP by the LVET to obtain an Ejection Efficiency Ratio, and compares the Ejection Efficiency Ratio to threshold values to detect aortic cardiovascular stenosis medical condition of the individual and characterize its severity. No rejection is made under 35 U.S.C. § 102 or § 103 for Claim 4 because no prior art is found that anticipates or renders the claim obvious. The specific combination of features in Claim 4 is not anticipated or rendered obvious by the current state of the art. While Bakema provides foundational disclosures, Patangay has a phase-specific analysis, and Barnacka adds context for timing metrics and cardiac signal analysis, none fully disclose or suggest the claimed method. Claim 4 introduces a novel method of calculating an Ejection Efficiency Ratio (REP/LVET) and comparing it to thresholds to detect and characterize aortic stenosis, addressing a gap not covered by the prior art. While ejection fraction (EF) is a common parameter in the prior art, the specific calculation and use of an Ejection Efficiency Ratio as defined in Claim 4 does not appear in the literature. The prior art demonstrates significant progress in cardiac signal analysis, spectral methods, and timing metrics. Bakema’s disclosure of spectral methods for analyzing cardiac signals provides a foundational framework for signal processing and assessing stenosis severity (Bakema, ¶[0011]). Although Bakema focuses on low-frequency analysis, the methodologies it describes are instrumental in developing derived metrics that could be adapted for high-frequency analysis or timing-based calculations like REP/LVET. Patangay emphasizes the importance of analyzing specific phases of the cardiac cycle and highlights how processing devices isolate signals for turbulence measurement, correlating these values to degrees of stenosis (Patangay, ¶[0033]; ¶[0023]). While Patangay does not explicitly address REP or LVET, its methods for phase-specific analysis suggest pathways for implementing timing-based metrics. Barnacka complements these references by describing wearable systems capable of acquiring biosignals for real-time cardiac analysis. Barnacka’s framework supports the derivation of cardiac timing intervals and adds practical context for integrating signal acquisition with diagnostic systems (Barnacka, FIG. 10, 11 13C; ¶[0152]-[0154], ¶[0164]). However, it does not address deriving specific timing intervals or ratios like REP/LVET. Although ejection fraction (EF) is commonly used in the literature to assess cardiac efficiency, the Ejection Efficiency Ratio (REP/LVET) described in Claim 4 is not disclosed in the prior art. The REP/LVET ratio represents a novel approach to evaluating the rapid ejection phase in relation to the left ventricular ejection time, enabling a more specific diagnostic parameter for aortic stenosis detection. The combination of these references demonstrates the feasibility of advanced cardiac diagnostics. Bakema’s spectral methods align with Patangay’s phase-specific evaluations, while Barnacka’s wearable systems enable the practical implementation of such analyses. However, the specific calculation of an Ejection Efficiency Ratio and its use for detecting and characterizing aortic stenosis are not anticipated or rendered obvious by the prior art. Claim 4 represents a novel and non-obvious advancement in cardiovascular diagnostics by introducing a specific method of calculating and comparing an Ejection Efficiency Ratio (REP/LVET) to detect aortic stenosis. Although the prior art provides valuable context, it does not fully address the innovative features of the claim. While no rejection is made under §102 or §103, the claims remain rejected under §101 for failure to claim patent-eligible subject matter.
Regarding Claims 5 and 16, Bakema does not teach that the data analysis system (or that the method) calculates a left high frequency power ratio for a left cardiac signal from a left earbud and calculates a right high frequency power ratio for a right cardiac signal from a right earbud, and wherein the left high frequency power ratio relates a high frequency power calculated for an left ventricular ejection time (LVET) of the left cardiac signal during a cardiac cycle to a high frequency power calculated for the left cardiac signal over the cardiac cycle, and wherein the right high frequency power ratio relates a high frequency power calculated for an LVET of the right cardiac signal during the cardiac cycle to a high frequency power calculated for the right cardiac signal over the cardiac cycle, and wherein the data analysis system subtracts the right high frequency power ratio from the left high frequency power ratio to obtain a difference and compares the difference to a threshold value to detect carotid artery cardiovascular stenosis medical condition of the individual and characterize it as left carotid artery cardiovascular stenosis. No rejection is made under 35 U.S.C. § 102 or § 103 for Claim 5 because no prior art is found that anticipates or renders the claim obvious. The specific combination of features in Claim 5 is not anticipated or rendered obvious by the current state of the art. Bakema provides foundational disclosures, while Barnacka, Patangay, and Kline add relevant context for signal processing, phase-specific analysis, bilateral comparisons, and wearable technology. Kline's bilateral acquisition and spectral analysis methods are particularly relevant to demonstrating feasibility (Kline, ¶[0033]; ¶[0035]). However, none of these references fully disclose or suggest the claimed method. Claim 5 introduces a novel method of calculating high-frequency power ratios (HFRs) during the left ventricular ejection time (LVET), comparing bilateral HFRs from left and right cardiac signals, and using these comparisons to detect and characterize carotid artery stenosis. The prior art demonstrates significant progress in spectral analysis, cardiac timing metrics, and wearable technologies. Bakema’s disclosure of spectral methods for analyzing cardiac signals provides a foundational framework for signal processing and assessing stenosis severity (Bakema, ¶[0011]). Although Bakema focuses on low-frequency analysis, the methodologies it describes are instrumental in developing derived metrics that could be adapted for high-frequency and bilateral signal analysis. Patangay emphasizes the importance of analyzing specific phases of the cardiac cycle and highlights how processing devices isolate signals for turbulence measurement, correlating these values to degrees of stenosis. Patangay’s phase-specific turbulence analysis provides a basis for considering phase-specific metrics like LVET, but it does not address high-frequency power ratios or bilateral comparisons (Patangay, ¶[0033]; ¶[0023]). Kline describes systems using bilateral signal acquisition and spectral analysis for carotid stenosis detection, demonstrating the capacity to compare signals from the left and right carotid arteries (Kline, ¶[0033]; ¶[0035]). Kline’s methods, while not explicitly addressing high-frequency power ratios or LVET, provide foundational insights into bilateral analysis and the use of thresholds for stenosis detection. Barnacka describes wearable systems capable of bilateral signal acquisition, providing a practical framework for real-time monitoring and signal comparison. Barnacka’s emphasis on biosignal acquisition supports the integration of left and right cardiac signals into diagnostic systems (Barnacka, FIG. 10, 11 13C; ¶[0152]-[0154], ¶[0164]). However, Barnacka does not disclose spectral or ratio-based calculations specific to the claimed method. While high-frequency power ratios are not explicitly disclosed in the prior art, the individual components of Claim 5 reflect incremental advancements evident in Bakema’s spectral analysis, Patangay’s phase-specific evaluations, Kline’s bilateral acquisition methods, and Barnacka’s wearable systems. The unique combination of these elements for bilateral comparison of high-frequency power ratios remains novel and non-obvious. As such, Claim 5 represents a novel and non-obvious advancement in cardiovascular diagnostics by introducing a specific method of calculating and comparing HFRs during LVET to detect carotid artery stenosis. Although the prior art provides valuable context, it does not fully address the innovative features of the claim. While no rejection is made under §102 or §103, the claims remain rejected under §101 for failure to claim patent-eligible subject matter.
Regarding Claims 6 and 17, Bakema does not teach that the data analysis system includes a processor and memory and [or that the method] is configured to subtract the left high frequency power ratio from the right high frequency power ratio to obtain a second difference, and compares the second difference to the threshold value to detect carotid artery cardiovascular stenosis and characterize it as right carotid artery cardiovascular stenosis. No rejection is made under 35 U.S.C. § 102 or § 103 for Claim 6 because no prior art is found that anticipates or renders the claim obvious. The specific combination of features in Claim 6 is not anticipated or rendered obvious by the current state of the art. Bakema provides foundational disclosures, while Barnacka, Patangay, and Kline add relevant context for signal processing, phase-specific analysis, bilateral comparisons, and wearable technology. Kline's bilateral analysis and thresholding techniques are particularly relevant to demonstrating feasibility (Kline, ¶[0033]; ¶[0035]). However, none of these references fully disclose or suggest the claimed method. Claim 6 introduces a novel method of using the subtraction of left and right high-frequency power ratios to detect right carotid artery stenosis based on a threshold comparison, addressing a gap not covered by the prior art. The prior art demonstrates significant progress in spectral analysis, cardiac timing metrics, and wearable technologies. Bakema’s disclosure of spectral methods for analyzing cardiac signals provides a foundational framework for signal processing and assessing stenosis severity (Bakema, ¶[0011]). While Bakema focuses on low-frequency analysis, the methodologies it describes are instrumental in developing derived metrics that could be adapted for high-frequency and bilateral signal analysis. Patangay emphasizes the importance of analyzing specific phases of the cardiac cycle and highlights how processing devices isolate signals for turbulence measurement, correlating these values to degrees of stenosis (Patangay, ¶[0033]; ¶[0023]). Patangay’s phase-specific turbulence analysis provides a basis for considering phase-specific metrics like LVET, but it does not address high-frequency power ratios or bilateral comparisons. Kline describes systems using bilateral signal acquisition and spectral analysis for carotid stenosis detection, demonstrating the capacity to compare signals from the left and right carotid arteries (Kline, ¶[0033]; ¶[0035]). Kline’s methods, while not explicitly addressing subtraction-based comparisons, provide foundational insights into bilateral analysis and the use of thresholds for stenosis detection. Barnacka describes wearable systems capable of bilateral signal acquisition, providing a practical framework for real-time monitoring and signal comparison. Barnacka’s emphasis on biosignal acquisition supports the integration of left and right cardiac signals into diagnostic systems (Barnacka, FIG. 10, 11 13C; ¶[0152]-[0154], ¶[0164]). However, Barnacka does not disclose spectral or subtraction-based calculations specific to the claimed method. The prior art collectively demonstrates progress in developing the necessary tools and methodologies for bilateral high-frequency power ratio analysis, yet no reference explicitly discloses or suggests subtracting these ratios and comparing the results to thresholds to detect right carotid artery stenosis. While incremental advancements are evident in Bakema’s spectral analysis, Patangay’s phase-specific evaluations, Kline’s bilateral acquisition methods, and Barnacka’s wearable systems, their unique combination for threshold-based bilateral comparisons remains novel and non-obvious. As such, Claim 6 represents a novel and non-obvious advancement in cardiovascular diagnostics by introducing a specific method of using the subtraction of bilateral high-frequency power ratios for threshold-based right carotid artery stenosis detection. Although the prior art provides valuable context, it does not fully address the innovative features of the claim. While no rejection is made under §102 or §103, the claims remain rejected under §101 for failure to claim patent-eligible subject matter.
Regarding Claims 7 and 18, Bakema does not teach that the data analysis system (or that the method) concludes that the left carotid artery stenosis is present when the left high frequency power ratio exceeds a left threshold value associated with known left carotid artery stenosis, and concludes that right carotid artery stenosis is present when the right high frequency power ratio exceeds right threshold value associated with known right carotid artery stenosis. No rejection is made under 35 U.S.C. § 102 or § 103 for Claim 7 because no prior art is found that anticipates or renders the claim obvious. The specific combination of features in Claim 7 is not anticipated or rendered obvious by the current state of the art. Bakema provides foundational disclosures, while Barnacka, Patangay, and Kline add relevant context for signal processing, phase-specific analysis, bilateral comparisons, and wearable technology (Kline, ¶[0033]; ¶[0035]). However, none of these references fully disclose or suggest the claimed method. Claim 7 introduces a novel method of using threshold values specifically associated with left and right carotid artery stenosis to identify and characterize these conditions based on high-frequency power ratios, addressing a gap not covered by the prior art. The prior art demonstrates significant progress in spectral analysis, threshold comparisons, and wearable technologies. Bakema’s disclosure of spectral methods for analyzing cardiac signals provides a foundational framework for signal processing and assessing stenosis severity (Bakema, ¶[0011]). While Bakema focuses on low-frequency analysis, it uses threshold-based techniques to classify signal characteristics, which are conceptually adaptable to high-frequency power ratio thresholds. Patangay highlights the importance of correlating turbulence measurements to degrees of stenosis and comparing these values to thresholds obtained from patient studies approach (Patangay, ¶[0033]; ¶[0023]). While it does not explicitly address high-frequency power ratios or left/right carotid-specific thresholds, its use of threshold-based methodologies for classification demonstrates a similar diagnostic. Kline describes systems using bilateral signal acquisition and spectral analysis for carotid stenosis detection, demonstrating the capacity to compare signals from the left and right carotid arteries (Kline, ¶[0033]; ¶[0035]). Barnacka describes wearable systems capable of bilateral signal acquisition, which provide a practical framework for comparing signals from the left and right sides of the body. Barnacka’s emphasis on biosignal acquisition supports the broader framework for integrating diagnostic systems based on bilateral data (Barnacka, FIG. 10, 11 13C; ¶[0152]-[0154], ¶[0164]). However, it does not address the specific use of thresholds or high-frequency power ratios for carotid artery stenosis detection. The prior art collectively demonstrates progress in threshold comparisons and bilateral signal analysis. Kline’s bilateral acquisition methods and use of thresholds complement Bakema’s foundational spectral analysis techniques. However, no reference explicitly discloses or suggests using thresholds specific to left and right high-frequency power ratios for stenosis detection. While incremental advancements are evident in Bakema’s spectral analysis, Patangay’s turbulence-based evaluations, and Barnacka’s wearable systems, their unique combination remains novel and non-obvious. As such, Claim 7 represents a novel and non-obvious advancement in cardiovascular diagnostics by introducing a specific method of applying thresholds to bilateral high-frequency power ratios for carotid artery stenosis detection. Although the prior art provides valuable context, it does not fully address the innovative features of the claim. While no rejection is made under §102 or §103, the claims remain rejected under §101 for failure to claim patent-eligible subject matter.
Regarding Claims 11 and 20, Bakema does not teach that the data analysis system (or that the method) calculates a high frequency power ratio for the cardiac signals that relates a high frequency power calculated for a ventricular diastole of the cardiac signals during a cardiac cycle to a high frequency power calculated for the cardiac cycle, and wherein the data analysis system compares the high frequency power ratio to a threshold value to detect whether aortic regurgitation is present. No rejection is made under 35 U.S.C. § 102 or § 103 for Claim 11 because no prior art is found that anticipates or renders the claim obvious. The specific combination of features in Claim 11 is not anticipated or rendered obvious by the current state of the art. Bakema provides foundational disclosures, while Barnacka and Patangay add relevant context for diastolic analysis and spectral evaluation. Kline's bilateral analysis methods align with the diagnostic goals of the claim, but none of these references fully disclose or suggest the claimed method. Claim 11 introduces a novel method of calculating high-frequency power ratios (HFRs) during ventricular diastole and using these comparisons to detect and characterize aortic regurgitation. The prior art demonstrates significant progress in spectral analysis, cardiac timing metrics, and wearable technologies. Bakema’s spectral analysis methods provide a foundation for calculating derived metrics, even though its primary focus is on low-frequency components (Bakema, ¶[0011]). The techniques Bakema outlines for analyzing cardiac signals offer a basis for developing more advanced metrics, including HFRs during diastole. Patangay highlights the diastolic phase of the cardiac cycle, discussing how processing devices can isolate signals for turbulence measurement and correlate these values to stenosis (Patangay, ¶[0033]; ¶[0023]). Patangay further explores the role of phase-specific metrics in diagnostics, enabling the concept of high-frequency analysis during ventricular diastole (Patangay, ¶[0033]; ¶[0023]). However, it does not specifically address HFRs or aortic regurgitation. Barnacka provides practical insights into diagnostic features of murmurs caused by aortic regurgitation. It characterizes these murmurs by their high-frequency nature and specific timing during diastole, aligning closely with Claim 11’s diagnostic focus. Barnacka also describes wearable systems for real-time signal acquisition, supporting the feasibility of implementing phase-specific analysis (Barnacka, ¶[0381]; ¶[0216]). Kline emphasizes bilateral signal acquisition for stenosis detection and describes the potential use of spectral analysis in evaluating cardiac conditions (Kline, ¶[0033]; ¶[0035]). While it does not address high-frequency power ratios or their application to diastole, its bilateral diagnostic framework complements the other references. The combination of these references demonstrates the feasibility of advanced cardiovascular diagnostics. Bakema’s spectral methods align seamlessly with Patangay’s phase-specific evaluations, while Barnacka’s wearable systems enable the practical implementation of such analyses. Together, they provide a logical pathway for high-frequency phase-specific analysis and monitoring of aortic regurgitation, even though no single reference explicitly anticipates the claimed method. As such, Claim 11 represents a novel and non-obvious advancement in cardiovascular diagnostics by introducing a specific method of calculating and comparing HFRs during ventricular diastole to detect aortic regurgitation. Although the prior art provides valuable context, it does not fully address the innovative features of the claim. While no rejection is made under §102 or §103, the claims remain rejected under §101 for failure to claim patent-eligible subject matter.
Response to Arguments
Note: The Remarks filed on 7/28/2025, express that claim 18 has been canceled, however, claim 19 is the claim that officially has been canceled from the claim set, leaving claim 18 as pending.
Objections
Applicant's arguments filed 7/28/2025, pages 7-8, regarding the previous Objections of claims 1, 5, and 16-18 have been fully considered and are persuasive. The previous Objections have been withdrawn. However, there are new objections to claim 5 and 16 as a result of the amendments.
35 U.S.C. §112(b)
Applicant's arguments filed 7/28/2025, pages 8-10, regarding the previous 112(b) Rejections of claims 2, 5-10 and 19 have been fully considered and are persuasive. The previous 112(b) rejections have been withdrawn. (note: claim 19 is referenced as being amended on page 10, but it has been canceled on the official claim set)
35 U.S.C. §101
Applicant's arguments filed 7/28/2025, pages 11-26, regarding the previous 101 Rejections of claims 2, 4-13, and 15-20 have been fully considered and are persuasive. The previous 101 rejections have been withdrawn.
35 U.S.C. §103
Applicant's arguments filed 7/28/2025, pages 26-28, regarding the previous 103 Rejections of claims 1-2, 4-13, and 15-20 have been fully 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. That is, there are new grounds of rejection.
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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/AARON MERRIAM/Examiner, Art Unit 3791
/MATTHEW KREMER/Primary Examiner, Art Unit 3791