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 .
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed applications, CIP PCT/US2023/062068 and Provisional application No. 63560549, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. Specifically, the prior-filed applications fail to provide support for acquiring temporal variations of the speckle pattern over at least one cardiac cycle, producing a speckle plethysmogram from the temporal variations of the speckle pattern and quantifying the degree of aortic stenosis from the speckle plethysmogram. As such, the effective filing date of the instant application is considered to be March 27, 2024, corresponding to the filing date of the instant application.
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.
Claim(s) 2 and 4-5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (“Rolling shutter speckle plethysmography for quantitative cardiovascular monitoring”, Published February 9, 2024) in view of Iannini et al. (US Pub No. 2024/0188835).
With regards to claim 2, Lee et al. disclose a system for quantifying a degree of aortic stenosis in a patient, comprising:
(a) a coherent light source (i.e. laser diode) configured to illuminate a target area of the patient's skin overlying a peripheral sampling location with coherent light to generate a speckle pattern (pg. 1544, Section 3.1, referring to “A volume holographic grating-stabilized laser diode (LD785-SEV300, Thorlabs) illuminates coherent light…Raw speckle image sequences are recorded…”; Figure 1);
(b) an imaging device (i.e. camera) configured to capture temporal variations of the speckle pattern related to a cardiac cycle (pg. 1544, Section 3.1, referring to recording the raw speckle image sequences by a monochrome industrial camera equipped with a rolling shutter CMOS sensor; pg. 1544, Section 3.2, referring to the capture of time-lapse raw speckle images of 1000-by-1000 pixel region of interest (ROI); Figure 1(b), which depict the speckle images acquired over time; Figure 3);
(c) operating the coherent light source and the imaging device during a measurement period comprising at least one cardiac cycle to produce a measurement signal during the systolic and diastolic phases of the at least one cardiac cycle (pg. 1544, Sections 3.1, 3.2, referring to capturing time-lapse raw speckle images with 20 micro-second row time and 20 micro-second exposure time, and a frame rate of 140fps, which requires inherent control/operation of the laser diode and the imaging device/camera; pg. 1547, Section 4.1, referring to quantitative RSSPG signals from the in vivo measurements showing systolic peaks (Figure 4) and dicrotic notches, wherein the dicrotic notch “differentiates between systolic and diastolic flow in a cardiac cycle”; Figures 1,3-6, in particular see Figure 4(c), wherein the RSSPG waveform is depicted as corresponding to a measurement period comprising at least one cardiac cycle and producing a measurement signal (i.e. waveform) during the systolic and diastolic phases (differentiated by the dicrotic notches) of the at least one cardiac cycle);
(d) a data acquisition system configured to acquire temporal variations of the speckle pattern over the at least one cardiac cycle and produce a speckle plethysmogram (i.e. rolling shutter speckle plethysmography (RSSPG)) from the temporal variations of the speckle pattern (Abstract, referring to the rolling shutter speckle plethysmography (RSSPG) technique which measures the velocity and volume fluctuations of blood flow during the cardiac cycle, wherein “Temporal analysis of the speckle field provides rich information regarding the dynamics of the scattering media”; pg. 1541, 3rd paragraph, pg. 1542, Section 2, referring to, from the elongated speckle image, RSSI quantifies the intensity correlation between two distinct rows in the image, each captured at different times in a rolling shutter sensor, thereby acquiring temporal variations of the speckle pattern over time to produce the RSSPG waveform/signal; pgs. 1545-1547, Sections 3.3, 3.4, 3.5, referring to the speckle image processing and RSSPG signal processing, wherein such processing inherently requires a computer/processor to perform the functions; Figures 1, 3-4).
However, though Lee et al. do disclose that the RSSPG technique can be used for quantitative analysis of vascular fluctuations caused by the cardiac cycle and may be used to estimate biomarkers of various circulatory diseases (Abstract, pgs. 1550-1551, Section 5; pg. 1547, Section 4.1, for example, referring to features of RSSPG and PPG signals, such as the dicrotic notch, being indicative of the acute drop of blood flow caused by the aortic valve closure and wherein the dicrotic notch differentiates between systolic and diastolic flow in a cardiac cycle, thus correlated with various cardiovascular risk factors, and serving as a marker for arterial stiffness and compliance; Figure 4), Lee et al. do not specifically disclose that their system further comprises (f) a left ventricular outflow tract assessment system comprising a programmed data processor programmed to quantify the degree of aortic stenosis from the speckle plethysmogram; and (g) an output device configured to provide a quantitative output indicative of the degree of aortic stenosis.
Additionally, Lee et al. do not specifically disclose that the operating of the coherent light source and the imaging device is performed by a sensor control system.
Iannini et al. disclose systems and methods for noninvasive detection, evaluation and monitoring of diverse pathological cardiovascular and pulmonary murmurs, for example, those acquired by the presence of valvular heart diseases, such as aortic stenosis (AS), thus providing a novel non-invasive point-of-care technology that can identify patients correctly, thereby further improving the safety and accuracy of a multifaceted approach to the initial diagnosis of cardiopulmonary murmurs (Abstract; paragraphs [0002], [0004]-[0005]). One or a plurality of signals or data from one or a plurality of noninvasive sensors or transducers that measure one or a plurality of physiological effects that are correlated with cardiopulmonary functions are obtained and a trained algorithm is used to process the data to determine the presence and stage (i.e. degree) of the cardiovascular and pulmonary disease or heart murmurs, e.g., of AS, and generating an output indicative of the state or condition of the analysis (paragraph [0005]). The sensor modalities include ECG, PPG, etc. (paragraph [0011]). To determine a heart murmur level (HML), which provides an alternative method for accurate and absolute estimation of presence and severity of heart murmurs, such as AS, the recorded cardiac data is segmented by a computing device into the corresponding heart beats, based on the R peaks of the simultaneous ECG, for example, from which the average beat is obtained (paragraphs [0049]-[0050]; paragraphs [0011], [0014], [0083], note that though the example for determining the severity/degree of AS is based on ECG, PPG can be used instead). Based on a HML threshold, patients can be categorized into the correct stage of the cardiac murmur tested, e.g, no AS, AS mild, moderate or severe (Figure 9A) (paragraph [0050], note that the degree/stage of aortic stenosis (AS) is quantified based on the quantitative HML threshold value; paragraph [0014]). Patients can also be divided into two or more other subgroups for analysis based on their HML threshold, e.g., the innocent group, which can include the no murmur and mild stage, and the structural group, which can include the moderate and severe stages (Fig. 9B) (paragraph [0050]). The trained AI algorithm may output a disease severity score to a display (paragraphs [0081], [0101]). One or more processors are further operatively coupled to the at least one or more sensors and computer instructions when executed by the one or more processors causes the system to perform the operations of obtaining the one or more signals using the signals sensors that provide the measure of the one or more physiological effects that are correlated with cardiopulmonary functions and recording, by the at least one or more processors and the one or more sensors for turbulence recordings between first and a second cardiac sounds (paragraph [0021], note that the processor corresponds to a sensor control system configured to operate the signal sensors [which in the combined references corresponds to the coherent light source and the imaging device]).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have the system of Lee et al. further comprise (f) a left ventricular outflow tract assessment system comprising a programmed data processor programmed to quantify the degree of aortic stenosis from the speckle plethysmogram and (g) an output device configured to provide a quantitative output indicative of the degree of aortic stenosis and further have the operating of the coherent light source and the imaging device of Lee et al. be performed by a sensor control system, as taught by Iannini et al., in order to identify patients correctly, thereby further improving the safety and accuracy of a multifaceted approach to the initial diagnosis of aortic stenosis (AS) (Abstract; paragraphs [0002], [0004]-[0005]).
With regards to claim 4, Iannini et al. disclose that the programmed data processor is programmed to use a set of parameters defining a mapping function between the measured speckle plethysmogram and the area of the aortic valve and to determine the severity of the aortic stenosis (paragraphs [0048]-[0050], referring to mapping/associating of narrower heart valves or heart valves that are not properly closing (i.e. descriptors of the area of the aortic valve) to the sensor signal (i.e. PPG signal) of patients with cardiac murmurs, such as Aortic stenosis (AS), further referring to the frequency of the recorded murmur increasing as the severity of the disease increases, wherein the HML threshold can be used to categorize the patients into the correct stage of the cardiac murmur tested, e.g., no AS, AS mild, moderate or severe; paragraphs [0053]-[0064], referring to associating/mapping the narrowing/thickening of the aortic valve to AS patients, wherein in a patient with severe AS, an S4 can be audible, etc., paragraph [0015], referring to the measure of one or more physiological effects from the signals sensors being correlated/mapped with cardiopulmonary functions; Figures 1, 5, 7, 9).
With regards to claim 5, Iannini et al. disclose that the programmed data processor is programmed to a prediction model where the prediction model comprises multiple hierarchical layers (paragraph [0005], referring to using a trained algorithm to process the data to determine a presence and stage of the cardiovascular and pulmonary disease or heart murmurs, e.g., of AS; paragraphs [0079], [0100], [0104], referring to applying machine learning techniques, such as neural network of multiple layers (i.e. “multiple hierarchical layers”) to provide the output from the raw recording) and wherein the trained AI algorithm may be implemented by a deep learning model, wherein deep learning is based on deep neural networks with hierarchical intermediate layers of artificial neurons).
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. in view of Iannini et al. as applied to claim 2 above, and further in view of Warren et al. (US Pub No. 2021/0000385).
With regards to claim 3, as discussed above, the above combined references meet the limitations of claim 2. Further, Lee et al. disclose that their system comprises (h) a physiological assessment system configured to determine from the temporal variations of the speckle pattern (h1) a heart rate from each of two or more cardiac cycles and (h2) a variability between two or more heart rates (pg. 1541, last paragraph, referring to RSSPG successfully measuring variations of the heart rate in various physiological conditions; Figure 4).
However, the above combined references do not specifically disclose that the heart rate is determined as an interbeat time interval between successive openings of the patient’s aortic valve from each of the two or more cardiac cycles and the physiological assessment system is configured to determine the presence of a cardiac vagal control based on the interbeat time interval and the variability between the two or more interbeat time intervals and wherein the output device is further configured to provide an indication of the presence of cardiac vagal control.
Warren et al. disclose a device configured to analyze the heart rate signal of the user to determine at least one of an interbeat interval (IVI) or a heart rate variability of the heart rate signal determined from the IBI (Abstract; paragraph [0004]). A PPG sensor is used to detect the heart rate and a processing unit (106) may be adapted to detect heart rate variability (HRV) in the user during sleep, such as by converting R-R data sets into frequency domain using Fast Fourier Transform algorithms (paragraph [0047], note that an R-R interbeat interval is representative of an interbeat interval between successive openings of the patient’s aortic valve from two or more cardiac cycles). The PPG sensor can further detect inter-beat intervals, wherein the heart rate data can be in the form of interbeat intervals (IBI’s) which can be used to derive heart rate variability (paragraphs [0048], [0052]). In a healthy person, heart rate is known to vary synchronously with breathing rate, wherein the greater this coherence, the more efficient is the pulmonary function and the more controlled is the autonomous breathing system (i.e. vagal control) (paragraph [0052]; note that variability between two or more interbeat time intervals (i.e. heart rate/interbeat interval variability) is used to determine the presence of cardiac vagal control (i.e. control of autonomous breathing)). The various quantities, including interbeat interval (IBI) and heart rate variability (HRV), may be output to the user via the software application and/or to a healthcare provider for a professional opinion (paragraph [0079]). An intensity of the occurrence of the hypoxia may be determined based on a change in the IBI or the amplitude or the HRV over a time period (Abstract).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have the heart rate of the above combined references be determined as an interbeat time interval between successive openings of the patient’s aortic valve from each of the two or more cardiac cycles and the physiological assessment system be configured to determine the presence of a cardiac vagal control based on the interbeat time interval and the variability between the two or more interbeat time intervals and wherein the output device is further configured to provide an indication of the presence of cardiac vagal control, as taught by Warren et al., in order to determine an occurrence of hypoxia in a user over time (Abstract).
Response to Arguments
Applicant's arguments filed July 21, 2026 have been fully considered but they are not persuasive.
With regards to Lee, Applicant asserts that Applicant’s prior-filed PCT/US2023/062068 was filed more than a year before Lee published and provides written description support under 35 USC 112(a) for each limitation of claim 2. As such, Applicant asserts that the claims are entitled to the PCT’s Feb. 6, 2023 filing date, which predates the Feb. 9, 2024 publication date of Lee by more than a year and thus Lee is not available as prior art.
Examiner respectfully disagrees and notes that Applicant points to paragraphs [29], [0214], [0222] and [0126] for providing support for “a left ventricular outflow tract assessment system quantifying the degree of aortic stenosis and an output device providing a quantitative output”. Applicant indicates that paragraph [29] sets forth “a set of parameters defining a mapping function between the measured signals and the area of the aortic valve and to determine the quantitative severity of aortic stenosis”. However, paragraph [29] of the PCT is completely silent with regards to the term “quantitative” and makes no mention of a “quantitative” determination of the severity of aortic stenosis. Paragraph [0029] of the PCT does set forth determining the severity of aortic stenosis and determining the presence or absence of a left ventricular tract anomaly; however, paragraph [29] is completely silent with regards to the determined severity being specifically a “quantitative” severity and there is no description in the PCT of “quantifying” the degree of aortic stenosis from the speckle plethysmogram. Examiner maintains that the PCT does not provide support for a programmed data processor being programmed to “quantify the degree of aortic stenosis from the speckle plethysmogram [which is produced from the temporal variations of the speckle pattern as claimed]”, and as such, also does not provide support for the output device configured to provide “a quantitative output” indicative of the degree of aortic stenosis. The effective filing date is thus considered to be March 27, 2024, which corresponds to the filing date of the instant application. The rejection over Lee, published on February 9, 2024, is therefore maintained as Lee does qualify as prior art.
With regards to Iannini, Applicant argues that Iannini determines a “Heart Murmur Level” from acoustic and vibrational signals and not from a speckle plethsymogram. Because neither reference, alone or in combination, discloses or suggests quantifying the degree of aortic stenosis from a speckle plethysmogram, Applicant asserts there is no prima facie case of obviousness of claim 2 or claims 3-5 depending therefrom.
Examiner respectfully disagrees and emphasizes that claim 1 is rejected under the combination of Lee in view of Iannini, wherein Lee is relied upon for producing a speckle plethysmogram and further, as noted in the above rejection, Lee further discloses that the RSSPG technique can be used for quantitative analysis of vascular fluctuations caused by the cardiac cycle and may be used to estimate biomarkers of various circulatory diseases. See specifically Abstract, pgs. 1550-1551, Section 5; pg. 1547, Section 4.1 of Lee, which refers to features of RSSPG and PPG signals, such as the dicrotic notch, being indicative of the acute drop of blood flow caused by the aortic valve closure and wherein the dicrotic notch differentiates between systolic and diastolic flow in a cardiac cycle, thus correlated with various cardiovascular risk factors, and serving as a marker for arterial stiffness and compliance; Figure 4). Note that Lee discloses that features associated with systolic and diastolic flow in a cardiac cycle, such as peaks and dicrotic notch, determined from PPG signals can also be determined from speckle plethsymogram (i.e. RSSPG) signals. Iannini discloses PPG (or ECG) signals can be used (paragraph [00111) to determine the heart murmur level, which provides an alternative method for accurate and absolute estimation of the presence and severity of heart murmurs, such as aortic stenosis, wherein the recorded cardiac data (i.e. PPG data) is segmented into corresponding heart beats (i.e. cardiac cycle features), based on the R peaks of the signals (i.e. PPG signal), from which the average beat can be obtained, etc (see Iannini, paragraphs [0049]-[0050]; paragraphs [0011], [0014], [0083], note that though the example for determining the severity/degree of AS is based on ECG, PPG can be used instead). As recognized by Lee, the same features (i.e. R peaks, heart beats, etc.) determined from the PPG can also be determined from the speckle plethysmogram signal (i.e RSSPG signals), and therefore it would follow that the speckle plethysmogram signal of Lee can be analyzed in the same manner that the PPG signal of Iannini is analzyed in order to determine features which can be used to quantify the degree of aortic stenosis as taught by Iannini.
With regards to claim 3, Applicant argues that Warren discloses deriving interbeat interval and heart rate variability from “a PPG signal” to detect hypoxia during sleep, which Applicant asserts is a completely different signal and a completely different purpose.
Examiner respectfully disagrees and refers Applicant to the above discussion with regards to Iannini, wherein it is set forth that the same features that can be derived from a PPG signal can also be derived from a speckle plethysmogram signal, and therefore it would be reasonable to one of ordinary skill in the art to further use Warren’s teachings directed to a PPG signal to reject claim 3.
The claims therefore remain rejected under the previously applied prior art.
Conclusion
THIS ACTION IS MADE FINAL. 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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/KATHERINE L FERNANDEZ/Primary Examiner, Art Unit 3798
