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 .
DETAILED ACTION
Status of the Application
Claims 1-3, 7, 9, 10-15, and 17-25 are currently pending in this case and have been examined and addressed below. This communication is a Non-Final Rejection in response to the Claims filed on 05/23/2024.
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 1-3, 7, 9, 10-15, and 17-25 are rejected because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-3, 7, 9, 10-15, and 19-25 fall within the statutory category of an apparatus or system. Claim 17 falls within the statutory category of a process. Claim 18 falls within the statutory category of an article of manufacture as a computer-readable medium.
Step 2A, Prong One
As per Claims 1, 17, and 18, the limitation of obtaining an indication as to whether the subject has the cardiovascular abnormality, under its broadest reasonable interpretation, covers managing personal behavior or personal interactions. The steps of obtaining an electrocardiogram of the subject, obtaining a corresponding plurality of sub-waveforms from the electrocardiogram, and obtaining an indication as to whether the subject has the cardiovascular abnormality are concepts performed by a physician or healthcare provider in the normal activity of evaluating and assessing a patient to determine whether a patient has a cardiovascular abnormality. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or personal interactions, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application because the additional elements and combination of additional elements do not impose meaningful limits on the judicial exception. In particular, the claims recite the additional element – a computer system comprising processors and a memory storing a program comprising instructions executable by the processors. The computer system in these steps is recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also recites the additional element of inputting the plurality of sub-waveforms for each lead in the plurality of leads into a neural network which amounts to the use of a neural network for obtaining an indication of the subject having a cardiovascular abnormality. The use of a neural network recited at a high-level of generality to execute the abstract idea amounts to mere instructions to apply the exception. As per MPEP 2106.05(f), a claim that recites use of computers as a tool to perform existing processes such as a commonplace mathematical algorithm applied on a general purpose computer, has been found by the courts to amount to mere instructions to apply the exception and does not integrate the abstract idea into a practical application or provide significantly more. The claims also include obtaining an electrocardiogram of the subject, wherein the electrocardiogram represents a total time interval, the electrocardiogram comprising electronic measurements for a plurality of leads and obtaining, for each lead in the plurality of leads, a corresponding plurality of sub-waveforms from the electrocardiogram, the plurality of sub-waveforms having a common duration that is less than the total time interval, wherein the common duration represents a fixed multiple of a reference heartbeat duration, and wherein each of the sub-waveforms in the plurality of sub-waveform being offset from a beginning of the electrocardiogram by a unique respective multiple of a sliding parameter. Obtaining an electrocardiogram of a subject and obtaining a corresponding plurality of sub-waveforms from the electrocardiogram amounts to mere data gathering similar to performing clinical tests on individuals to obtain input for an equation, which is found to be activities the courts have found to be insignificant extra-solution activity, as per MPEP 2106.05(g)(3). The electrocardiogram description of representing a total time, comprising electronic measurements and the plurality of sub-waveforms having a common duration is merely descriptive and does not provide functional elements to the claims. Because the additional elements do not impose meaningful limitations on the judicial exception, the claim is directed to an abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with the respect to integration of the abstract idea into a practical application, the additional element of a computer system comprising processors and a memory storing a program comprising instructions executable by the processors to perform the method of the invention amounts to no more than mere instructions to apply the exception using a generic computing component. The computer system including processor and memory storing a program comprising instructions executable by the processors are recited at a high level of generality and are recited as generic computer components by reciting the computer system comprises one or more computers which can be a single computer or any number of network computers (Specification, [0080]). The memory is specifically described as random access memory, magnetic disk storage (Specification, [0083]). These are described as general purpose computing components, which do not add meaningful limitations to the abstract idea beyond mere instructions to apply an exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims also include the additional elements of using a neural network to obtain whether the subject has the cardiovascular abnormality by inputting sub-waveforms. The neural network is described as a convolutional neural network and/or residual neural network which include convolutional and/or residual neural network algorithms (specification [0018], [0059]), which describe known mathematical algorithms. As per MPEP 2106.05(f), a claim that recites use of computers as a tool to perform existing processes such as a commonplace mathematical algorithm applied on a general purpose computer, has been found by the courts to amount to mere instructions to apply the exception and does not integrate the abstract idea into a practical application or provide significantly more. The claims also include obtaining an electrocardiogram of the subject, wherein the electrocardiogram represents a total time interval, the electrocardiogram comprising electronic measurements for a plurality of leads; obtaining, for each lead in the plurality of leads, a corresponding plurality of sub-waveforms from the electrocardiogram, the plurality of sub-waveforms having a common duration that is less than the total time interval, wherein the common duration represents a fixed multiple of a reference heartbeat duration, and wherein each of the sub-waveforms in the plurality of sub-waveform being offset from a beginning of the electrocardiogram by a unique respective multiple of a sliding parameter, which is mere data gathering that amounts to insignificant extra-solution activity. Obtaining an electrocardiogram of the subject and obtaining a corresponding plurality of sub-waveforms from the electrocardiogram are described in the specification as being obtained from a 12-lead ECG device ([0029]). The specification further discloses that the current state-of-the art includes obtaining waveforms directly from ECG devices (0045]). Therefore, obtaining an electrocardiogram and sub-waveforms from an ECG device is a well-understood, routine, and conventional in the art. Therefore, this does not provide significantly more than the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of the computer or improves another technology. The claims do not amount to significantly more than the underlying abstract idea.
Dependent Claims
Dependent Claims 2-3, 7, 9, 10-15, and 19-25 add further limitations which are also directed to an abstract idea. For example, Claims 2-3, 7, 9-11, 19-25 include limitations which further specify or limit the elements of the independent claims, and hence are nonetheless directed towards fundamentally the same abstract idea as independent Claims 1, 17 and 18. Claim 12 includes standardizing the plurality of data points of each respective lead to have zero-mean and unit-variance, which recites a mathematical relationship and therefore falls into the mathematical concepts abstract idea. Claim 13 includes computing a saliency map of the subset of parameters which also recites a mathematical relationship and falls into the mathematical concepts abstract idea. Claim 14 includes determining a weighting of effect on the indication for each data point using an occlusion approach which also recites a mathematical relationship and algorithm and falls into the mathematical concepts abstract idea. Claim 15 further specifies the algorithm for the occlusion approach and therefore also falls into the abstract grouping of mathematical concepts. Because the additional elements do not impose meaningful limitations on the judicial exception, the claims are directed to an abstract idea and are not patent eligible.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 24-25 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
As per Claim 24, the claim recites the common duration is four reference heartbeat durations and the sliding parameter is two reference heartbeat durations. However, there is not sufficient support for this in the specification or original claims. The specification describes the common duration as 1.48 seconds and sliding parameter as being a minimum of the length of one reference heartbeat of 0.74 seconds, which is a common duration of two reference heartbeat durations and a sliding parameter of one reference heartbeat duration ([00137]). However, there is no description for the common duration being four reference heartbeat durations and the sliding parameter being two reference heartbeat durations. The specification provides exemplary description of the common duration as 3 heartbeats with a sliding parameter of 2 heartbeats ([00150]). This also does not teach a common duration of four heartbeats and a sliding parameter of two reference heartbeats.
As per Claim 25, the claim recites the common duration represents an optimal fixed multiple of the reference heartbeat, optimal fixed multiple being determined among multiple values of the fixed multiple, based on a measured prediction performance. There is not sufficient support in the specification or original claims for the concept of the common duration representing an optimal fixed multiple of the reference heartbeat. The specification does disclose that the common duration represents a fixed multiple of a reference heartbeat duration ([0023], [0026], [0046], [00133]) and describes the fixed multiple can be a scalar value of many options ([00141]), and gives examples of the fixed multiple ([00153]). However, none of these descriptions from the specification include that the common duration is an optimal fixed multiple. Additionally, there is no description in the specification of the optimal fixed multiple being determined based on a measured prediction performance. The specification includes optimal performance of predictions ([00252]), but this is not linked to using this measured prediction performance to determine the fixed multiple. Therefore, there is not sufficient support for this concept in the disclosure.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 9-10, 12-14, 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Attia et al. (US 2020/0397313 A1), hereinafter Attia, in view of Khosousi et al. (US 2020/0205745 A1), hereinafter Khosousi.
As per Claims 1, 17, and 18, Attia discloses computer system for determining whether a subject has a cardiovascular abnormality, the computer system comprising:
one or more processors ([0008]); and memory addressable by the one or more processors, the memory storing at least one program for execution by the one or more processors ([0008]),
non-transitory computer-readable storage medium, wherein the non-transitory computer readable storage medium stores instructions, which when executed by a computer system, cause the computer system to perform a method ([0008], see Claim 16) comprising:
(A) obtaining an electrocardiogram of the subject ([0009] receive ECG over a period of time, [0058]), wherein the electrocardiogram represents a total time interval, the electrocardiogram comprising electronic measurements for a plurality of leads ([0006] ECG is a measurement of electrical activity, [0009-0011] ECG data includes data from a lead of the ECG over a period of time, [0047] recording of signals of cardiac electrical activity during the ECG over a period of time, the signal is recorded to capture information, i.e. measurement for each lead);
(B) obtaining, for each lead in the plurality of leads, a corresponding plurality of sub-waveforms from the electrocardiogram ([0011-0012] ECG data includes a plurality of channels where each channel is the subset of ECG data for a respective lead over the period of time), and
(C) inputting the corresponding plurality of sub-waveforms for each lead in the plurality of leads into a neural network ([0011-0012] provide input to the predictive model, the input being the subset of ECG data for each respective lead, [0016] predictive model can be a neural network, [0058]), thereby obtaining an indication as to whether the subject has the cardiovascular abnormality ([0058] output of predictive model is estimated ejection-fraction characteristic such as range/values).
However, Attia may not explicitly disclose the following which is taught by Khosousi: the plurality of sub-waveforms having a common duration that is less than the total time interval ([0026] pre-processing the signal data by segmenting the signal into segmented data sets, i.e. sub-waveforms, for each channel where each has a time window which corresponds to each other, [0109] the same time window is applied to each channel), wherein the common duration represents a fixed multiple of a reference heartbeat duration ([0026] the signal data is for a cardiac cycle, see Fig. 4 which is a multiple of heartbeat duration/cardiac cycle), and wherein each of the sub-waveforms in the plurality of sub-waveform being offset from a beginning of the electrocardiogram by a unique respective multiple of a sliding parameter ([0106] each signal data set begins at a data set offset value, [0109] data segments of a complete cardiac cycle are from a phase-aligned time window from each channel).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present application to combine the known concept of segmenting the total waveform into sub-waveforms having a common duration representing a multiple of a heartbeat cycle, where the sub-waveforms are offset by a sliding parameter from Khosousi with the system of using a neural network to analyze subsegments of ECG data from Attia in order to diagnose the presence and localization of conditions such as heart failure including whether this is left or right side heart failure (Khosousi [0012]).
As per Claim 2, Attia and Khosousi discloses the limitations of Claim 1. Attia also teaches the cardiovascular abnormality is left ventricular systolic dysfunction (LVSD) or asymptomatic left ventricular dysfunction (ALVD) ([0095] detecting asymptomatic low left ventricular dysfunction, [0096] identifying left ventricular systolic dysfunction, see Claim 32).
As per Claim 3, Attia and Khosousi discloses the limitations of Claim 1. Attia also teaches the cardiovascular abnormality is right ventricular dysfunction ([0075] the predictive model can be used to detect presence of right ventricular enlargement which is a dysfunction).
As per Claim 9, Attia and Khosousi discloses the limitations of Claim 1. Attia also teaches the indication is in a binary discrete classification form ([0058] the output indication is one of two possible categories from a binary classification model); in which a first possible value outputted by the neural network indicates the subject has the cardiovascular abnormality; and a second possible value outputted by the neural network indicates the subject does not have the cardiovascular abnormality ([0058] the model results in a classification into two categories which could be low ejection fraction and normal ejection fraction, [0004] where low ejection fraction is an asymptomatic ventricular dysfunction, i.e. cardiac abnormality).
As per Claim 10, Attia and Khosousi discloses the limitations of Claim 1. Attia also teaches the indication is in a scalar form indicating a likelihood or probability that the subject has the cardiovascular abnormality or a likelihood or probability that the subject does not have the cardiovascular abnormality ([0062] estimated ejection-fraction characteristic, i.e. output of the model, is an absolute value that indicates predicted ejection fraction of the patient, if the value is below the threshold, it is low which indicates cardiac abnormality, [0070-0072] output of neural network is a number value which determines if the patient is at risk for dysfunction).
As per Claim 12, Attia and Khosousi discloses the limitations of Claim 1. Khosousi also teaches the electronic measurements for each of the plurality of leads comprise a plurality of data points representing electronic measurements at different respective time intervals of a plurality of time intervals summing to the total time interval ([0070] signals with measurements received for plurality of data sets from multiple channels, [0106] each data set is a portion of time, [0152] use of all segment data sets of extracted sets of data from the channels), the method further comprising standardizing the plurality of data points of each respective lead in the plurality of leads to have zero-mean and unit-variance ([0110-0112] normalizing the signal data set to be bounded by the range of -1 and +1).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present application to combine the known concept of ECG data measurements for plurality of leads at different time intervals that sum to the total time interval from Khosousi with the system of using a neural network to analyze subsegments of ECG data from Attia in order to diagnose the presence and localization of conditions such as heart failure including whether this is left or right side heart failure (Khosousi [0012]).
As per Claim 13, Attia and Khosousi discloses the limitations of Claim 1. Attia also teaches the neural network comprises a plurality of parameters ([0056] features are parameters that characterize the shape of the waveform and a plurality of parameters are used in the model, a feature extractor determines which parameters to include in the neural network), the method further comprising:
(D) using the plurality of parameters to compute a saliency map of at least a subset of the plurality of parameters ([0056] the ECG data is processed to determine morphological features which characterize the shape of the waveform, these values/features generate a graphical image of the ECG waveform).
As per Claim 14, Attia and Khosousi discloses the limitations of Claim 1. Attia also teaches (D) using an occlusion approach to determine a weighting of effect on the indication for each data point in the plurality of data points for at least one lead in the plurality of leads ([0064] prediction model including parameters with associated weights for each perceptron).
Khosousi also teaches the electronic measurements for each of the plurality of leads comprise a plurality of data points representing electronic measurements at different respective time intervals of a plurality of time intervals summing to the total time interval ([0070] signals with measurements received for plurality of data sets from multiple channels, [0106] each data set is a portion of time, [0152] use of all segment data sets of extracted sets of data from the channels).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present application to combine the known concept of ECG data measurements for plurality of leads at different time intervals that sum to the total time interval from Khosousi with the system of using a neural network to analyze subsegments of ECG data from Attia in order to diagnose the presence and localization of conditions such as heart failure including whether this is left or right side heart failure (Khosousi [0012]).
As per Claim 19, Attia and Khosousi discloses the limitations of Claim 1. Khosousi also teaches the common duration represents at least two reference heartbeat durations (see Fig. 4 where common duration includes more than two heartbeat cycles).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present application to combine the known concept of segmenting the total waveform into sub-waveforms having a common duration representing a multiple of a heartbeat cycle from Khosousi with the system of using a neural network to analyze subsegments of ECG data from Attia in order to diagnose the presence and localization of conditions such as heart failure including whether this is left or right side heart failure (Khosousi [0012]).
Claims 11 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Attia (US 2020/0397313 A1), in view of Khosousi (US 2020/0205745 A1), in view of Yang et al. (US 2019/0090774 A1), hereinafter Yang.
As per Claim 11, Attia and Khosousi discloses the limitations of Claim 3. Attia also teaches the neural network is a convolutional neural network ([0016] predictive model can be a convolutional neural network) having a plurality of convolutional layers followed by a plurality of residual blocks, wherein each residual block in the plurality of residual blocks comprises a pair of convolutional layers, followed by a fully connected layer ([0068] predictive model is a neural network with input layer, output layer, and hidden layers in between, data provided to a fully connected layer of the network, see Fig. 8).
However, Attia and Khosousi may not explicitly disclose the following which is taught by Yang: neural network is a convolutional neural network using a sigmoid function ([0037] use of a sigmoid function as the activation function for feed forward propagation in a neural network to analyze ECG waveform data).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present application to combine the known concept of using a sigmoid function in a convolutional neural network following layers of the network from Yang with the system of using a neural network to analyze subsegments of ECG data from Attia and Khosousi in order to localize where a heart abnormality is originating in a patient (Yang [0008]).
As per Claim 15, Attia and Khosousi discloses the limitations of Claim 14. However, Attia and Khosousi may not explicitly disclose the following which is taught by Yang: the occlusion approach is forward pass attribution ([0037] neural network using feed forward propagation for ECG analysis).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present application to combine the known concept of using feed forward propagation in a convolutional neural network from Yang with the system of using a neural network to analyze subsegments of ECG data from Attia and Khosousi in order to localize where a heart abnormality is originating in a patient (Yang [0008]).
Claims 7, 20-23 are rejected under 35 U.S.C. 103 as being unpatentable over Attia (US 2020/0397313 A1), in view of Khosousi (US 2020/0205745 A1), in view of Tarassenko et al. (US 2010/0056939 A1), hereinafter Tarassenko.
As per Claim 7, Attia and Khosousi discloses the limitations of Claim 1. Attia also teaches the total time interval is X seconds, wherein X is a positive integer of 5 or greater ([0047] signal can be recorded for a period of time which is 5, 10, or 15 seconds in length to capture electrical activity information).
However, Attia may not explicitly disclose the following which is taught by Khosousi: the reference heartbeat duration is between 0.70 seconds and 0.78 seconds ([0108],[0152] heart beat segment of a cardiac cycle/duration is a fixed window of 0.75 seconds).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present application to combine the known concept of a reference heartbeat duration between 0.7 and 0.78 seconds from Khosousi with the system of using a neural network to analyze subsegments of ECG data from Attia in order to diagnose the presence and localization of conditions such as heart failure including whether this is left or right side heart failure (Khosousi [0012]).
However, Attia and Khosousi may not explicitly disclose the following which is taught by Tarassenko: and the fixed multiple is a scalar value between 2 and 30 ([0029] the subsection of the ECG selected can be three pairs of beats, see Fig. 3 which shows the offset of the time windows of the ECG, [0058] separating ECG into windows which are separated by each 30 second segments of the ECG which overlap by 10 seconds which results in a sliding window of every 20 seconds resulting in a continuous stream of 20 second segments).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present application to combine the known concept of setting the fixed multiple between 2 and 30 from Tarassenko with the system of using a neural network to analyze subsegments of ECG data from Attia in order to save time and money in analyzing biomedical signals such as ECG waveforms (Tarassenko [0002]).
As per Claim 20, Attia and Khosousi discloses the limitations of Claim 1. However, Attia and Khosousi may not explicitly disclose the following which is taught by Tarassenko: the sliding parameter represents a multiple of a reference heartbeat duration ([0029] the subsection of the ECG selected can be three pairs of beats).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present application to combine the known concept of using a sliding window which is the duration of multiple heartbeats from Tarassenko with the system of using a neural network to analyze subsegments of ECG data from Attia in order to save time and money in analyzing biomedical signals such as ECG waveforms (Tarassenko [0002]).
As per Claim 21, Attia and Khosousi discloses the limitations of Claim 1. However, Attia and Khosousi may not explicitly disclose the following which is taught by Tarassenko: the sliding parameter is a no greater than the common duration ([0058] the ECG is separated into a plurality of 20 second windows which together make up the continuous stream of ECG data to be analyzed, the first 5 seconds of the ECG are not analyzed which means they are not included in the common duration total time).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present application to combine the known concept of using a sliding window which is a smaller duration than the common duration from Tarassenko with the system of using a neural network to analyze subsegments of ECG data from Attia in order to save time and money in analyzing biomedical signals such as ECG waveforms (Tarassenko [0002]).
As per Claim 22, Attia, Khosousi, and Tarassenko discloses the limitations of Claim 21. Tarassenko also teaches the sliding parameter is smaller than the common duration ([0058] the ECG is separated into a plurality of 20 second windows which together make up the continuous stream of ECG data to be analyzed, the first 5 seconds of the ECG are not analyzed which means they are not included in the common duration total time).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present application to combine the known concept of using a sliding window which is a smaller duration than the common duration from Tarassenko with the system of using a neural network to analyze subsegments of ECG data from Attia in order to save time and money in analyzing biomedical signals such as ECG waveforms (Tarassenko [0002]).
As per Claim 23, Attia, Khosousi, and Tarassenko discloses the limitations of Claim 22. Tarassenko also teaches the common duration is two reference heartbeat durations and the sliding parameter is one reference heartbeat duration ([0021-0022] the sliding window spans one beat and moves forward one beat which would indicate a two beat total duration).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present application to combine the known concept of using a sliding window which is the duration of one beat which slides to a duration of two beats from Tarassenko with the system of using a neural network to analyze subsegments of ECG data from Attia in order to save time and money in analyzing biomedical signals such as ECG waveforms (Tarassenko [0002]).
Subject Matter Free of the Prior Art
The following is an examiner’s statement of subject matter free of the prior art: The limitations in Claim 24 stating the common duration is four reference heartbeat durations and the sliding parameter is two reference heartbeat durations is free of the prior art. The limitations in Claim 25 stating the common duration represents an optimal fixed multiple of the reference heartbeat, optimal fixed multiple being determined, among multiple values of the fixed multiple, based on a measured prediction performance. The most remarkable prior arts of record are as follows: Tarassenko which teaches a sliding time window of one beat duration which slides one beat forward to make a total time of two beats duration. Shafiq et al. which teaches a sliding window to segment the actual waveform signal and determining the sliding template based on number of beats. The number of beats is given as an example to be 60 beats, 40 beats, etc. However, Tarassenko and Shafiq do not teach a window of two heartbeat durations with a total duration of four beats or determining an optimal fixed multiple based on prediction performance. Therefore, claims 24-15 are free of the prior art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Shafiq et al. (Ghufran Shafiq, Sivanagaraja Tatinati, Wei Tech Ang, and Kalyana C. Veluvolu; Automatic Identification of Systolic Time Intervals in Seismocardiogram; 22 November 2016; Scientific Reports; P. 1-11) teaches identifying a cardiovascular disease through analysis of electrocardiogram signal data using sliding time windows which are the duration of a heartbeat.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Evangeline Barr whose telephone number is (571)272-0369. The examiner can normally be reached Monday to Friday 8:00 am to 4:00 pm.
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/EVANGELINE BARR/Primary Examiner, Art Unit 3682