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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. A streamlined analysis of claim 1 follows.
STEP 1
Regarding claim 1, the claim recites a series of steps or acts, including indicating determining, by the computing device using the prediction model, the cardiac events for one or more valves of the heart, wherein the cardiac events include timing of opening and closing of the one or more valves of the heart. Thus, the claim is directed to a process, which is one of the statutory categories of invention.
STEP 2A, PRONG ONE
The claim is then analyzed to determine whether it is directed to any judicial exception. The step of determining, by the computing device using the prediction model, the cardiac events for one or more valves of the heart, using the features of the physiological data signal alone and independent of features of another physiological or modality measurement of the heart wherein the cardiac events include timing of opening and closing of the one or more valves of the heart sets forth a judicial exception. This step describes a concept performed in the human mind (including an observation, evaluation, judgment, opinion). Thus, the claim is drawn to a Mental Process, which is an Abstract Idea.
STEP 2A, PRONG TWO
Next, the claim as a whole is analyzed to determine whether the claim recites additional elements that integrate the judicial exception into a practical application. The claim fails to recite an additional element or a combination of additional elements to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limitation on the judicial exception. Claim 11 recites outputting, by the computing device, the cardiac events determined using the prediction model, which is merely adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)). The output of the cardiac events does not provide an improvement to the technological field, the method does not effect a particular treatment or effect a particular change based on the output cardiac events, nor does the method use a particular machine to perform the Abstract Idea.
STEP 2B
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, is sufficient to ensure that the claim amounts to significantly more than the exception. Claim 1 describes the step of identifying, by the computing device, features of the physiological data signal and applying the features as inputs to a prediction model which is an abstract idea in the form of a mathematical concept. Besides the Abstract Ideas, the claim recites additional step of obtaining, by a computing device, a physiological data signal of a heart of an individual. Obtaining data physiological data is well-understood, routine and conventional activity for those in the field of medical diagnostics. Further, the obtaining step is recited at a high level of generality such that it amounts to insignificant presolution activity, e.g., mere data gathering step necessary to perform the Abstract Idea. When recited at this high level of generality, there is no meaningful limitation, such as a particular or unconventional step that distinguishes it from well-understood, routine, and conventional data gathering and comparing activity engaged in by medical professionals prior to Applicant's invention. Furthermore, it is well established that the mere physical or tangible nature of additional elements such as the obtaining and comparing steps do not automatically confer eligibility on a claim directed to an abstract idea (see, e.g., Alice Corp. v. CLS Bank Int'l, 134 S.Ct. 2347, 2358-59 (2014)).
Consideration of the additional elements as a combination also adds no other meaningful limitations to the exception not already present when the elements are considered separately. Unlike the eligible claim in Diehr in which the elements limiting the exception are individually conventional, but taken together act in concert to improve a technical field, the claim here does not provide an improvement to the technical field. Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claim as a whole does not amount to significantly more than the exception itself. The claim is therefore drawn to non-statutory subject matter.
The same rationale applies to claim 11.
Regarding claim 11, the device recited in the claim is a generic device comprising generic components configured to perform the abstract idea. The processor and memory are configured to perform pre-solutional data gathering activity, outputting the cardiac events, and perform the Abstract Ideas. According to section 2106.05(f) of the MPEP, merely using a computer as a tool to perform an abstract idea does not integrate the Abstract Idea into a practical application.
The dependent claims also fail to add something more to the abstract independent claims as they generally recite method steps pertaining to abstract ideas in the form of mathematical concepts/mental processes (claims 2, 4-6, 9-10, 12, 14-16, and 19-20) and pre-solutional data gathering (claims 3,7,13, and 17). The feature extracting and synchronizing steps recited in the independent claims maintain a high level of generality even when considered in combination with the dependent claims.
Claim Rejections - 35 USC § 103
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) 1-3, 5-8, 11-13, and 15-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Houlton (US 20150038856 A1 – previously cited) in view of Laurin (Accurate and consistent automatic seismocardiogram annotation without concurrent ECG) in view of Lau (US 20220370140 A1 – previously cited).
In regards to claim 1 Houlton teaches a method for determining cardiac events comprising:
obtaining, by a computing device, a physiological data signal of a heart of an individual ([0045] As used herein, the term "magnitude data" refers to data comprising features obtained from direct reading of values from SCG or those obtained from subtracting certain ones from each other);
identifying, by the computing device, features of the physiological data signal and applying the features as inputs to a prediction model ([0019] “annotating cardiac events on the processed SCG data using deterministic rule set approach or the probabilistic machine learning approach or both”);
determining, by the computing device using the prediction model, the cardiac events for one or more valves of the heart, wherein the cardiac events include timing of opening and closing of the one or more valves of the heart ([0019] annotating cardiac events on the processed SCG data using deterministic rule set approach or the probabilistic machine learning approach or both [0056] The automatic annotation can be done using either deterministic or probabilistic algorithms.);
Houlton fails to teach determining, by the computing device using the prediction model, the cardiac events for one or more valves of the heart using the features of the physiological data signal alone and independent of features of another physiological or modality measurement of the heart. Laurin teaches an algorithm capable of annotating SCG without the use any other concurrent measurement (Abstract “So far, SCG annotation has relied on concurrent ECG measurements. An algorithm capable of annotating SCG without the use any other concurrent measurement was designed”, Discussion, peaks used to find IM fiducial point that is used to determine valve openings and closings). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the method of Houlton to utilize the algorithm of Laurin to annotate the cardiac timings using only SCG data. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of only taking one measurement from the user making the method more convenient.
Modified Houlton fails to teach outputting, by the computing device, the cardiac events determined using the prediction model. Lau teaches outputting, by the computing device, the cardiac events determined using a prediction model ([0067] “wherein the numerical model is adapted to receive physiological data as an input and output a simulated function of the cardiac system in real-time, wherein the simulated function of the cardiac system comprises a simulated function of a valve within the cardiac system”, [0121] “Put another way, the real-time simulation of the cardiac system generates an output that is, or is almost, concurrent with the current state of the actual cardiac system of the subject. In this way, the system may provide an accurate real-time simulation of the functioning of the cardiac system that may be used during an interventional procedure, such as an edge-to-edge repair, in order to assess the progress of the procedure”). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the method of modified Houlton to include the model of Lau to output the cardiac events in the form of a simulated function of the cardiac system in order to provide an accurate real-time simulation of the functioning of the cardiac system that may be used during an interventional procedure, such as an edge-to-edge repair, in order to assess the progress of a procedure.
In regards to claim 2 modified Houlton teaches the method of claim 1, wherein the one or more valves of the heart comprise the aortic valve and the mitral valve (Laurin Table 1).
In regards to claim 5 modified Houlton teaches the method of claim 1, further comprising synchronizing timing of the physiological data signal with another modality measurement of the heart using the timing of the opening and closing of the one or more valves of the heart ([0063]-[0064] FIG. 5 illustrates an example of a synchronized electrocardiogram waveform 510, an x-axis seismocardiogram waveform 525, a y-axis seismocardiogram waveform 530, a z-axis seismocardiogram waveform 535, an aortic blood pressure waveform 540 (upper, thin), a left ventricular pressure waveform 545 (lower, thick), and a derivative waveform 550 of the left ventricular pressure waveform 545. MVC and AVO events are annotated).
In regards to claim 11 Houlton teaches a system for determining cardiac events comprising: a processor of a computing device; and a memory in communication with the processor, the memory storing program instructions, the processor operative with the program instructions to perform the operations of ([0083] “A computing device may comprise one or more microprocessors operatively coupled to memory and configured to perform numerical processing operations as would be readily understood by a worker skilled in the art”):
obtaining, by a computing device, a physiological data signal of a heart of an individual ([0045] As used herein, the term "magnitude data" refers to data comprising features obtained from direct reading of values from SCG or those obtained from subtracting certain ones from each other);
identifying, by the computing device, features of the physiological data signal and applying the features as inputs to a prediction model ([0019] “annotating cardiac events on the processed SCG data using deterministic rule set approach or the probabilistic machine learning approach or both”);
determining, by the computing device using the prediction model, the cardiac events for one or more valves of the heart, wherein the cardiac events include timing of opening and closing of the one or more valves of the heart ([0019] annotating cardiac events on the processed SCG data using deterministic rule set approach or the probabilistic machine learning approach or both [0056] The automatic annotation can be done using either deterministic or probabilistic algorithms.);
Houlton fails to teach determining, by the computing device using the prediction model, the cardiac events for one or more valves of the heart using the features of the physiological data signal alone and independent of features of another physiological or modality measurement of the heart. Kale teaches an algorithm capable of annotating SCG without the use any other concurrent measurement (Abstract “So far, SCG annotation has relied on concurrent ECG measurements. An algorithm capable of annotating SCG without the use any other concurrent measurement was designed”, Discussion, peaks used to find IM fiducial point that is used to determine valve openings and closings). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the method of Houlton to utilize the algorithm of Laurin to annotate the cardiac timings using only SCG data. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of only taking one measurement from the user making the method more convenient.
Modified Houlton fails to teach outputting, by the computing device, the cardiac events determined using the prediction model. Lau teaches outputting, by the computing device, the cardiac events determined using a prediction model ([0067] “wherein the numerical model is adapted to receive physiological data as an input and output a simulated function of the cardiac system in real-time, wherein the simulated function of the cardiac system comprises a simulated function of a valve within the cardiac system”, [0121] “Put another way, the real-time simulation of the cardiac system generates an output that is, or is almost, concurrent with the current state of the actual cardiac system of the subject. In this way, the system may provide an accurate real-time simulation of the functioning of the cardiac system that may be used during an interventional procedure, such as an edge-to-edge repair, in order to assess the progress of the procedure”). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the method of modified Houlton to include the model of Lau to output the cardiac events in the form of a simulated function of the cardiac system in order to provide an accurate real-time simulation of the functioning of the cardiac system that may be used during an interventional procedure, such as an edge-to-edge repair, in order to assess the progress of a procedure.
In regards to claim 12 modified Houlton teaches the system of claim 111, wherein the one or more valves of the heart comprise the aortic valve and the mitral valve (Laurin Table 1).
In regards to claim 15 modified Houlton teaches the system of claim 11, further comprising synchronizing timing of the physiological data signal with another modality measurement of the heart using the timing of the opening and closing of the one or more valves of the heart ([0063]-[0064] FIG. 5 illustrates an example of a synchronized electrocardiogram waveform 510, an x-axis seismocardiogram waveform 525, a y-axis seismocardiogram waveform 530, a z-axis seismocardiogram waveform 535, an aortic blood pressure waveform 540 (upper, thin), a left ventricular pressure waveform 545 (lower, thick), and a derivative waveform 550 of the left ventricular pressure waveform 545. MVC and AVO events are annotated).
Claim(s) 1-3, 5-7,11-13 and 15-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hettrick (US 20190059839 A1) in view of Sharma (US 20150348260 A1) in view of Lau (US 20220370140 A1– previously cited).
In regards to claim 1 Hettrick teaches a method for determining cardiac events comprising:
obtaining, by a computing device, a physiological data signal of a heart of an individual ([0066] Left Ventricular pressure is the data signal);
identifying, by the computing device, features of the physiological data signal and applying the features as inputs ([0066] As noted above, "isovolumic contraction" occurs during the transition between ventricular filling and ejection, and is characterized by a sharp increase in pressure within the ventricle due to the contraction of the ventricle following closure of the mitral valve and prior to opening of the aortic valve);
and determining, by the computing device, the cardiac events for one or more valves of the heart using the features of the physiological data signal alone and independent of features of another physiological or modality measurement of the heart, wherein the cardiac events include timing of opening and closing of the one or more valves of the heart ([0066] As noted above, "isovolumic contraction" occurs during the transition between ventricular filling and ejection, and is characterized by a sharp increase in pressure within the ventricle due to the contraction of the ventricle following closure of the mitral valve and prior to opening of the aortic valve).
Hettrick does not explicitly teach a prediction model to determine the cardiac events. Sharma teaches a machine learning model that maps inputs with a corresponding output state ([0006] “The trained mapping function may be a machine-learning based mapping function trained based on training data comprising quantities of interest of the set of patient at the first physiological state and corresponding quantities of interest of the set of patients at the second physiological state”). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the method of Schaefer to carry out the ECG wave to Aortic cardiac event correlation using a machine learning model that maps inputs and outputs like the method of Sharma. Doing so would merely be choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success.
Modified Hettrick fails to teach outputting, by the computing device, the cardiac events determined using the prediction model. Lau teaches outputting, by the computing device, the cardiac events determined using a prediction model ([0067] “wherein the numerical model is adapted to receive physiological data as an input and output a simulated function of the cardiac system in real-time, wherein the simulated function of the cardiac system comprises a simulated function of a valve within the cardiac system”, [0121] “Put another way, the real-time simulation of the cardiac system generates an output that is, or is almost, concurrent with the current state of the actual cardiac system of the subject. In this way, the system may provide an accurate real-time simulation of the functioning of the cardiac system that may be used during an interventional procedure, such as an edge-to-edge repair, in order to assess the progress of the procedure”). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the method of modified Hettrick to include the model of Lau to output the cardiac events in the form of a simulated function of the cardiac system in order to provide an accurate real-time simulation of the functioning of the cardiac system that may be used during an interventional procedure, such as an edge-to-edge repair, in order to assess the progress of a procedure.
In regards to claim 2 modified Hettrick teaches the method of claim 1, wherein the one or more valves of the heart comprise the aortic valve and the mitral valve (Hettrick [0066]).
In regards to claim 3 modified Hettrick teaches the method of claim 2, wherein the physiological data signal comprises a left ventricular pressure data signal (Hettrick [0066]).
In regards to claim 5 modified Hettrick teaches the method of claim 1. Modified Hettrick fails to teach synchronizing timing of the physiological data signal with another modality measurement of the heart using the timing of the opening and closing of the one or more valves of the heart. Lau teaches generating, by the computing device, a synchronized pressure-volume loop display by aligning the non-simultaneously acquired left ventricular volume data with the non-simultaneously acquired left ventricular pressure data ([0119-0120] the numerical model may also produce the corresponding pressure-volume loops of the left heart (atrium and ventricle)). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the method of modified Hettrick to include a step of generating a pressure-volume loop of the left heart like the method of Lau in order to provide further physiological information for clinical assessment.
In regards to claim 6 modified Hettrick teaches the method of claim 5 wherein the physiological data signal comprises a left ventricular pressure data signal and the another modality measurement comprises a left ventricular volume data recording of the heart that is acquired non-simultaneously with the left ventricular pressure data signal (Lau [0119-0120] the numerical model may also produce the corresponding pressure-volume loops of the left heart (atrium and ventricle)).
In regards to claim 7 modified Hettrick teaches the method of claim 6, further comprising generating, by the computing device, a synchronized pressure-volume loop display by aligning the non-simultaneously acquired left ventricular volume data with the non-simultaneously acquired left ventricular pressure data (Lau [0119-0120] the numerical model may also produce the corresponding pressure-volume loops of the left heart (atrium and ventricle)).
In regards to claim 11 modified Hettrick teaches a system for determining cardiac events comprising: a processor of a computing device; and a memory in communication with the processor, the memory storing program instructions, the processor operative with the program instructions to perform the operations of ([0080] Programming device inherently has processor and memory):
obtaining, by a computing device, a physiological data signal of a heart of an individual ([0066] Left Ventricular pressure is the data signal);
identifying, by the computing device, features of the physiological data signal and applying the features as inputs ([0066] As noted above, "isovolumic contraction" occurs during the transition between ventricular filling and ejection, and is characterized by a sharp increase in pressure within the ventricle due to the contraction of the ventricle following closure of the mitral valve and prior to opening of the aortic valve);
and determining, by the computing device, the cardiac events for one or more valves of the heart using the features of the physiological data signal alone and independent of features of another physiological or modality measurement of the heart, wherein the cardiac events include timing of opening and closing of the one or more valves of the heart ([0066] As noted above, "isovolumic contraction" occurs during the transition between ventricular filling and ejection, and is characterized by a sharp increase in pressure within the ventricle due to the contraction of the ventricle following closure of the mitral valve and prior to opening of the aortic valve).
Hettrick does not explicitly teach a prediction model to determine the cardiac events. Sharma teaches a machine learning model that maps inputs with a corresponding output state ([0006] “The trained mapping function may be a machine-learning based mapping function trained based on training data comprising quantities of interest of the set of patient at the first physiological state and corresponding quantities of interest of the set of patients at the second physiological state”). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the method of Schaefer to carry out the ECG wave to Aortic cardiac event correlation using a machine learning model that maps inputs and outputs like the method of Sharma. Doing so would merely be choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success.
Modified Hettrick fails to teach outputting, by the computing device, the cardiac events determined using the prediction model. Lau teaches outputting, by the computing device, the cardiac events determined using a prediction model ([0067] “wherein the numerical model is adapted to receive physiological data as an input and output a simulated function of the cardiac system in real-time, wherein the simulated function of the cardiac system comprises a simulated function of a valve within the cardiac system”, [0121] “Put another way, the real-time simulation of the cardiac system generates an output that is, or is almost, concurrent with the current state of the actual cardiac system of the subject. In this way, the system may provide an accurate real-time simulation of the functioning of the cardiac system that may be used during an interventional procedure, such as an edge-to-edge repair, in order to assess the progress of the procedure”). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the method of modified Hettrick to include the model of Lau to output the cardiac events in the form of a simulated function of the cardiac system in order to provide an accurate real-time simulation of the functioning of the cardiac system that may be used during an interventional procedure, such as an edge-to-edge repair, in order to assess the progress of a procedure.
In regards to claim 12 modified Hettrick teaches the system of claim 11, wherein the one or more valves of the heart comprise the aortic valve and the mitral valve (Hettrick [0066]).
In regards to claim 13 modified Hettrick teaches the system of claim 12, wherein the physiological data signal comprises a left ventricular pressure data signal (Hettrick [0066]).
In regards to claim 15 modified Hettrick teaches the system of claim 11. Modified Hettrick fails to teach synchronizing timing of the physiological data signal with another modality measurement of the heart using the timing of the opening and closing of the one or more valves of the heart. Lau teaches generating, by the computing device, a synchronized pressure-volume loop display by aligning the non-simultaneously acquired left ventricular volume data with the non-simultaneously acquired left ventricular pressure data ([0119-0120] the numerical model may also produce the corresponding pressure-volume loops of the left heart (atrium and ventricle)). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the system of modified Hettrick to include a step of generating a pressure-volume loop of the left heart like the method of Lau in order to provide further physiological information for clinical assessment.
In regards to claim 16 modified Hettrick teaches the system of claim 5 wherein the physiological data signal comprises a left ventricular pressure data signal and the another modality measurement comprises a left ventricular volume data recording of the heart that is acquired non-simultaneously with the left ventricular pressure data signal (Lau [0119-0120] the numerical model may also produce the corresponding pressure-volume loops of the left heart (atrium and ventricle)).
In regards to claim 17 modified Hettrick teaches the system of claim 6, further comprising generating, by the computing device, a synchronized pressure-volume loop display by aligning the non-simultaneously acquired left ventricular volume data with the non-simultaneously acquired left ventricular pressure data (Lau [0119-0120] the numerical model may also produce the corresponding pressure-volume loops of the left heart (atrium and ventricle)).
Claim(s) 1-2, 8-9, 11-12, and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schaefer (US 20190059839 A1) in view of Sharma (US 20150348260 A1).
In regards to claim 1 Schaefer teaches a method for determining cardiac events comprising:
obtaining, by a computing device, a physiological data signal of a heart of an individual ([0124] According to a further example, the body-surface signal b is an ECG-signal indicating an electric activity of the heart.);
identifying, by the computing device, features of the physiological data signal and applying the features as inputs ([0135] “On the basis of the detected characteristic points q, r, s, t, u the start 48 of the open-state 30 of the aortic valve and/or the end 52 of the open-state 30 of the aortic valve may be determined” waves are characteristics);
determining, by the computing device, the cardiac events for one or more valves of the heart using the features of the physiological data signal alone and independent of features of another physiological or modality measurement of the heart, wherein the cardiac events include timing of opening and closing of the one or more valves of the heart ([0130] Thus, a start 48 and an end 52 of the open-state 30 of the aortic valve may be recognized from the ECG-signal. The time from the start 48 of the open-state 30 to the end 52 of the open-state 30 may define an open-state time period of the aortic valve.);
and outputting, by the computing device, the cardiac events determined using the prediction model ([0137] According to a further example, the processing unit 14 is configured to determine a closed-state 56 of the aortic valve on the basis of the body-surface signal b, wherein the processing unit 14 is configured to calculate, on the basis of the determined closed-state 56 of the aortic valve, a further display signal component (hereinafter also “third display component”) for signaling the aortic valve as closed during the closed-state 56 of the aortic valve).
Schaefer does not explicitly teach a prediction model to determine the cardiac events. Sharma teaches a machine learning model that maps inputs with a corresponding output state ([0006] “The trained mapping function may be a machine-learning based mapping function trained based on training data comprising quantities of interest of the set of patient at the first physiological state and corresponding quantities of interest of the set of patients at the second physiological state”). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the method of Schaefer to carry out the ECG wave to Aortic cardiac event correlation using a machine learning model that maps inputs and outputs like the method of Sharma. Doing so would merely be choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success.
In regards to claim 2 Schaefer teaches the method of claim 1, wherein the one or more valves of the heart comprise the aortic valve and the mitral valve (Schaefer [0130] Thus, a start 48 and an end 52 of the open-state 30 of the aortic valve may be recognized from the ECG-signal. The time from the start 48 of the open-state 30 to the end 52 of the open-state 30 may define an open-state time period of the aortic valve.).
In regards to claim 8 Schaefer teaches the method of claim 1, wherein the physiological data signal comprises an electrocardiogram signal (Schaefer [0124] According to a further example, the body-surface signal b is an ECG-signal indicating an electric activity of the heart).
In regards to claim 9 Schaefer teaches the method of claim 8, wherein the features comprise R wave, S wave, and end of T wave features of the electrocardiogram signal (Schaefer [0135] “On the basis of the detected characteristic points q, r, s, t, u the start 48 of the open-state 30 of the aortic valve and/or the end 52 of the open-state 30 of the aortic valve may be determined”).
In regards to claim 11 Schaefer teaches a system for determining cardiac events comprising: a processor of a computing device; and a memory in communication with the processor, the memory storing program instructions, the processor operative with the program instructions to perform the operations of ([0010] It should be noted that the following described aspects of the invention apply also for the apparatus, the system, the method, the computer program element and the computer-readable medium):
obtaining, by a computing device, a physiological data signal of a heart of an individual ([0124] According to a further example, the body-surface signal b is an ECG-signal indicating an electric activity of the heart.);
identifying, by the computing device, features of the physiological data signal and applying the features as inputs ([0135] “On the basis of the detected characteristic points q, r, s, t, u the start 48 of the open-state 30 of the aortic valve and/or the end 52 of the open-state 30 of the aortic valve may be determined” waves are characteristics);
determining, by the computing device, the cardiac events for one or more valves of the heart using the features of the physiological data signal alone and independent of features of another physiological or modality measurement of the heart, wherein the cardiac events include timing of opening and closing of the one or more valves of the heart ([0130] Thus, a start 48 and an end 52 of the open-state 30 of the aortic valve may be recognized from the ECG-signal. The time from the start 48 of the open-state 30 to the end 52 of the open-state 30 may define an open-state time period of the aortic valve.);
and outputting, by the computing device, the cardiac events determined using the prediction model ([0137] According to a further example, the processing unit 14 is configured to determine a closed-state 56 of the aortic valve on the basis of the body-surface signal b, wherein the processing unit 14 is configured to calculate, on the basis of the determined closed-state 56 of the aortic valve, a further display signal component (hereinafter also “third display component”) for signaling the aortic valve as closed during the closed-state 56 of the aortic valve).
Schaefer does not explicitly teach a prediction model to determine the cardiac events. Sharma teaches a machine learning model that maps inputs with a corresponding output state ([0006] “The trained mapping function may be a machine-learning based mapping function trained based on training data comprising quantities of interest of the set of patient at the first physiological state and corresponding quantities of interest of the set of patients at the second physiological state”). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the method of Schaefer to carry out the ECG wave to Aortic cardiac event correlation using a machine learning model that maps inputs and outputs like the method of Sharma. Doing so would merely be choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success.
In regards to claim 12 modified Schaefer teaches the system of claim 11, wherein the one or more valves of the heart comprise the aortic valve and the mitral valve (Schaefer [0130] Thus, a start 48 and an end 52 of the open-state 30 of the aortic valve may be recognized from the ECG-signal. The time from the start 48 of the open-state 30 to the end 52 of the open-state 30 may define an open-state time period of the aortic valve.).
In regards to claim 18 modified Schaefer t teaches the system of claim 11, wherein the physiological data signal comprises an electrocardiogram signal (Schaefer [0124] According to a further example, the body-surface signal b is an ECG-signal indicating an electric activity of the heart).
In regards to claim 19 modified Schaefer teaches the system of claim 18, wherein the features comprise R wave, S wave, and end of T wave features of the electrocardiogram signal (Schaefer [0135] “On the basis of the detected characteristic points q, r, s, t, u the start 48 of the open-state 30 of the aortic valve and/or the end 52 of the open-state 30 of the aortic valve may be determined”).
Claim(s) 10 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schaefer (US 20190059839 A1) in view of Sharma (US 20150348260 A1) as applied to claim 8, in view of Burton-Krahn (US 5758654 A).
In regard to claim 10 modified Schaefer teaches the method of claim 9. Modified Schaefer fails to teach how the waves are identified. Burton-Krahn teaches a method of identifying R wave, S wave, and T waves by analyzing maxima and minima of the electrocardiogram signal, wherein the end of T wave is identified using a second order derivative of the electrocardiogram signal (Col 3 Lines 44-58 “As hereinafter described in greater detail, QRS detector 22 applies the algorithm depicted in FIGS. 6A and 6B to the ECG samples stored in buffer 21, to the first derivative representations stored in buffer 24 and to the second derivative representations stored in buffer 27 to detect the onsets, peaks, and offsets of QRS waves in each ECG sample waveform”). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the method of modified Schaefer to carry out the ECG wave determination method of Burton-Krahn. Doing so would merely be choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success.
In regard to claim 20 modified Schaefer teaches the system of claim 19. Modified Schaefer fails to teach how the waves are identified. Burton-Krahn teaches a method of identifying R wave, S wave, and T waves by analyzing maxima and minima of the electrocardiogram signal, wherein the end of T wave is identified using a second order derivative of the electrocardiogram signal (Col 3 Lines 44-58 “As hereinafter described in greater detail, QRS detector 22 applies the algorithm depicted in FIGS. 6A and 6B to the ECG samples stored in buffer 21, to the first derivative representations stored in buffer 24 and to the second derivative representations stored in buffer 27 to detect the onsets, peaks, and offsets of QRS waves in each ECG sample waveform”). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the system of modified Schaefer to carry out the ECG wave determination method of Burton-Krahn. Doing so would merely be choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success.
Examiner’s Note
In regards to claims 4 and 14, none of the prior art teaches or suggests, either alone or in combination, a device or method comprising comprise using first and second derivatives of the left ventricular pressure data signal in order to determine the timing of opening and closing of the one or more valves of the heart, in combination with the other claimed elements/steps.
Claims 4 and 14 contain no prior art rejections, however they are not in condition for
allowance due to their rejections under 35 U.S.C. 101 and their dependencies on rejected claims 1 and 11.
Response to Arguments
Applicant's arguments filed 06/24/2026 in regards to the 35 U.S.C 101 rejection of claims 1-20 have been fully considered but they are not persuasive. Applicant is reminded that abstract ideas cannot provide a practical application or significantly more (e.g., an improvement). Both Step 2A Prong 2 and Step 2B require an additional element, not an abstract idea, to provide a practical application or significantly more (e.g., an improvement). See Genetic Technologies Limited v. Merial LLC (Fed Cir 2016). See MPEP 2106.05(a), wherein “[i]t is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. See MPEP § 2106.04(d) (discussing Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303-04, 125 USPQ2d 1282, 1285-87 (Fed. Cir. 2018)). Thus, it is important for examiners to analyze the claim as a whole when determining whether the claim provides an improvement to the functioning of computers or an improvement to other technology or technical field.”
Applicant’s arguments, see remarks, filed 06/24/2026, with respect to the rejection(s) of claim(s) 1-20 under 35 U.S.C 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Houlton (US 20150038856 A1 – previously cited) in view of Laurin (Accurate and consistent automatic seismocardiogram annotation without concurrent ECG) in view of Lau (US 20220370140 A1 – previously cited), Hettrick (US 20190059839 A1) in view of Sharma (US 20150348260 A1) in view of Lau (US 20220370140 A1– previously cited), and Schaefer (US 20190059839 A1) in view of Sharma (US 20150348260 A1).
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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/LUCY EPPERT/ Examiner, Art Unit 3791
/ADAM J EISEMAN/ Primary Examiner, Art Unit 3791
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