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 .
Response to Arguments
Applicant’s arguments with respect to claims 2-21 have been considered but are not persuasive.
Claims 2-21 have been amendment to include additional limitations in attempts to overcome the rejection under 35 U.S.C. 101. Specifically, the Applicant argues “that this is not directed to an abstract idea” with steps that cannot be performed in the human mind or with the aid of pencil and paper” (Remarks on page 8.) This argument is not persuasive. These additional recitation of electrodes and stethoscope are mere insignificant extra solution activity as it does not add additional elements demonstrating that the claim as a whole integrates the exception info a practical application to effect a particular treatment of prophylaxis for disease or medical condition. The computing device and processor are recited at a high-level of generality (i.e., as a generic computing device and processor performing a generic computer function of receiving information and generating information based on a determined use) such that it amounts no more than mere instructions to apply the exception using a generic computer component as a tool for performing a mental process. Therefore, for the reasons stated above and previously made of record, the claims remain rejected as detailed below.
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.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
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 of carrying out his invention.
Claims 2-21 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.
While there is support for “machine-learning techniques in order to identify main-rhythm features in new ECG data. This training may include, for example, the use of linear classifiers (e.g., Fisher's linear discriminates, logistic regression, Naive Bayes classifiers, perceptrons), support vector machines (e.g., least square support vector machines), quadratic classifiers, kernel estimation (e.g., k-nearest neighbor engines), decision trees (e.g., random forests), neural networks, learning vector quantization, or any other appropriate machine-learning process” ([0042]). There is no support for “training a machine learning model as a function of the training data”.
Additionally, claims 2, 7 and 13 recites “submitting the ECG data to a plurality of machine-learning cardiac classifiers trained on training data, wherein the plurality of machine-learning cardiac classifiers are generated by using machine learning comprising a confidence level”.
While there is support for ““ECG data 110 is submitted to the feature classifiers 112, the feature classifiers 112 identify one or more identified features 114 within the ECG data 110. For example, a feature classifier may be configured to identify if an ECG contains sinus rhythm (or not), what kind of sinus rhythm, AV-conduction abnormalities (or not), arrhythmia (or not), etc. In some cases, these feature classifiers may be generated via machine learning. That is, using a very large dataset of ECG data known to have various features, machine learning techniques can examine this dataset and construct feature classifiers that are able to determine if a new ECG data 110 is likely to or not likely to have some feature. This identification may take the form of, for example, a confidence level (e.g., 80% confidence that Normal Sinus Rhythm is present)” ([0023]). There is no support for “machine learning cardiac classifiers” “comprising a confidence level”. Since the “confidence level” as disclosed in paragraph 23, is related to “feature classifiers”. Additionally, as disclosed in paragraph 4, ECG data is submitted “to a plurality of cardiac classifiers, each cardiac classifier configured to identify, in the ECG, at least some of a plurality of cardiac features that are within a particular feature-class; receive, from each of the plurality of cardiac classifiers, a classification message containing data of the cardiac classifiers identifying of cardiac features in the ECG”([0004]). Thus, there is no support for “machine learning cardiac classifiers” “comprising a confidence level”.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2-21 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 2, 7 and 13 recites “submitting the ECG data to a plurality of machine-learning cardiac classifiers trained on training data, wherein the plurality of machine-learning cardiac classifiers are generated by using machine learning comprising a confidence level”. Based on the recitation, it is unclear if this limitation is: the “machine-learning” comprises “a confidence level” or the “machine-learning” generates “a confidence level”. Further clarification is requested.
Claims 2, 7 and 13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential structural cooperative relationships of elements, such omission amounting to a gap between the necessary structural connections. See MPEP § 2172.01. The omitted structural cooperative relationships are: a remote cardiac sensor, commuting device and stethoscope.
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 2-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Following is an analysis of subject matter eligibility according to MPEP 2106:
Step 1:
Independent claims 1, 7 and 13 recite an apparatus and method, and are thus directed towards statutory categories of invention.
Step 2A Prong 1:
Claims 1, 13 and 17 recite the following limitations: receiving ECG data that was generated to reflect cardiac activity of a particular mammal; submitting the ECG data to a plurality of cardiac classifiers, each cardiac classifier configured to identify, in the ECG, at least some of a plurality of cardiac features that are within a particular feature-class; receiving, from each of the plurality of cardiac classifiers, a classification message containing data of the cardiac classifiers identifying of cardiac features in the ECG; and assembling, from the received classification messages, ECG features for the ECG, the ECG features identifying at least some features of different feature-classes. In addition, there is identifying features for further investigation.
The limitation of receiving data, analyzing data to determine features of the data and providing an output based on the analysis is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “electrodes”, “processors”, “computing device” and “stethoscope”, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “processor” and “computing device” language in the context of this claim encompasses the user manually analyzing the sensed data (via the electrodes). Similarly, the limitation of receiving data, analyzing data to determine features of the data and providing an output based on the analysis, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. In particular, the claim only recites generic computing device and processors to perform both the receiving, generating and comparing data steps. The further adding the limitation of “wherein the ECG data is automatically analyzed to generate identified features in an ECG reading to create a queue based on a risk metric as a function of the identified features” is considered insignificant extra solution activity as it does not add additional elements demonstrating that the claim as a whole integrates the exception info a practical application to effect a particular treatment of prophylaxis for disease or medical condition. The computing device and processor are recited at a high-level of generality (i.e., as a generic computing device and processor performing a generic computer function of receiving information and generating information based on a determined use) such that it amounts no more than mere instructions to apply the exception using a generic computer component as a tool for performing a mental process.
Step 2B:
The additional elements such as electrodes and stethoscope, do not integrate the judicial exception into a practical application. There is no integration of the abstract idea. Accordingly, this additional elements of electrodes, stethoscope and a computing device does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the receiving data, analyzing data to determine features of the data and providing an output based on the analysis amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept and does not impose any meaningful limits of practicing the abstract idea. The claim is not patent eligible.
Dependent claims:
Claims 3-6, 8-12 and 14-21 are dependent claims that further limit the method into steps relating to monitoring and revising the data through a mental process. Accordingly, these claims do not integrate the abstract idea into a practical application for similar reason to claims 2, 7 and 13. These limitations comprise well-understood, routine and conventional activity in the medical diagnostic and treatment devices, where it is common to have computer devices evaluate and “recommend or implement” changes to treatment based on the sensed data. Thus by MPEP 2106.05(d) these steps do not comprises integration of the mental process into practical 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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 2-211 is rejected under 35 U.S.C. 103 as being unpatentable over Tamil et al. (US Patent Publication 20120179055 A1) in view of Hussain (US Patent Publication 20150100817 A1).
As to claims 2 and 21, as best understood in light of the rejection under 35 U.S.C. above, Tamil et al. discloses generating data that describes features of an electrocardiogram (ECG) of a subject comprising: a remote cardiac sensor (ECG sensor; Figure 3; [0020]) configured to receive a signal and detect ECG data as a function of the signal ([0020]); one or more electrodes ([0064]; Figure 3); a computing device (microcontroller in Figure 3) comprising an electrode-interface for communicably coupling to the one or more electrodes (Figure 3), the computing device capable of detecting electrical changes due to cardiac activity of a particular mammal when the electrodes are communicably coupled and placed on skin of the particular mammal (Figure 3), the computing device configured to generate ECG data to reflect cardiac activity of a particular mammal (Figure 3; [0020-0021]); one or more hardware processors (ECG module in Figure 3; [0020, 0053]) and non-transitory computer-readable memory containing instructions that, when processed by the one or more hardware processors, cause the system to perform operations comprising: receiving the ECG data from the computing device (Figure 3), wherein the ECG data is automatically analyzed ([0047]) to generate identified features in an ECG reading ([0047-0050]) to create a queue based on a risk metric as a function of the identified features ([0047-0054]); submitting the ECG data to a plurality of machine-learning cardiac classifiers trained on training data (Figure 8, [0040, 0047]), each machine-learning cardiac classifier configured to identify, in the ECG data, at least some of a plurality of cardiac features that are within a particular feature-class ([0025-0033]), wherein the plurality of cardiac classifiers comprises i) a main-rhythm-classifier configured to identify, in the ECG, at least some main-rhythm features ([0032]), and ii) a secondary-rhythm-classifier configured to identify, in the ECG, at least some secondary-rhythm features ([0033]); training a machine learning model ([0040, 0047]); receiving, from each of the plurality of machine-learning cardiac classifiers (Figures 8-9), a classification message containing data of the machine-learning cardiac classifiers identifying of cardiac features in the ECG (Figures 8-9); determining from the received ECG data, if the ECG data contains normal sinus rhythm ([0028]); assembling, from the received classification messages, ECG features for the ECG, the ECG features identifying at least some features of different feature-classes (Figure 9; [0047, 0050]); and generating an updated classification message containing one or more tags (Figure 9).
Tamil et al. discloses the invention substantially as claimed, but does not explicitly disclose the inclusion of a stethoscope. Hussain disclose ECG sensors carried by a stethoscope. It would have been obvious to one having ordinary skill in the art at the time the invention was made to modify the sensing and computing device of Tamil et al. to include a stethoscope as disclosed by Hussain in order to provide the predictable results of observing and verifying patient data for validating, refining and optimizing treatment to meet specific patient therapeutic needs and requirements.
Additionally, as best understood in light of the rejection under 35 U.S.C. above, the modified Tamil et al. discloses the invention substantially as claimed with signal processing ([0031-0032]) but does not explicitly disclose “anti-aliasing”. It would have been an obvious matter of design choice to a person of ordinary skill in the art to modify the data processing as taught by Tamil et al., and thus the modified Tamil et al., with “anti-aliasing”, because Applicant has not disclosed the anti-aliasing provides an advantage, is used for a particular purpose, or solve a stated problem. One of ordinary skill in the art, furthermore, would have expected the Applicant’s invention to perform equally well with data processing as taught by Tamil et al., because both prepare ECG data for further processing (as disclosed by Applicant in paragraph 22). Therefore, it would have been an obvious matter of design choice to modify the data processing to obtain the invention as specified in the claim(s).
Furthermore, as to claims 2 and 21, as best understood in light of the rejection under 35 U.S.C. above, the modified Tamil et al. discloses the invention substantially as claimed but does not explicitly disclose determining a confidence value for each identified cardiac features. It would have been obvious to one having ordinary skill in the art at the time the invention was made to modify determine cardiac data and features of the modified Tamil et al. to include a confidence value in order to provide the predictable results of substantiating and verifying the cardiac data for optimal patient diagnosis and treatment.
As to claim 3, as best understood in light of the rejection under 35 U.S.C. above, Tamil et al. , and thus the modified Tamil et al., disclose classifiers to identify features in the ECG for arrhythmia diagnosis [0047, 0049, 0071]. Therefore, Tamil et al. discloses wherein the plurality of machine-learning cardiac classifiers further comprises: an atrial-enlargement-classifier configured to identify, in the ECG, at least some atrial-enlargement features ([0047, 0049, 0071]; the classifiers identify features, which are correlated to “at least some atrial-enlargement features”); an atrioventricular (AV)-conduction main-rhythm-classifier configured to identify, in the ECG, at least some AV-conduction features ([0047, 0049, 0071]; the classifiers identify features, which are correlated to “at least some AV-conduction features”) ; a QRS-classifier configured to identify, in the ECG, at least some QRS-features ([0047, 0049, 0070-0071]); a ST\T-wave-classifier configured to identify, in the ECG, at least some ST\T- wave features ([0047, 0049, 0070-0071]); and a noisy-classifier configured to identify, in the ECG, at least some noisy features ([0047, 0049, 0064, 0071]; the classifiers identify features, which are correlated to “at least some noisy features” [0064]).
As to claim 4, as best understood in light of the rejection under 35 U.S.C. above, Tamil et al. , and thus the modified Tamil et al., discloses wherein at least some of the cardiac classifiers are configured to access physiological-constraint data that defines constraints on possible feature identification such that identified features are constrained to only features that are physiologically possible in a single given ECG ([0043-0045]; Figure 9).
As to claim 5, as best understood in light of the rejection under 35 U.S.C. above, Tamil et al. , and thus the modified Tamil et al., discloses at least one of the cardiac classifiers are configured to identify three or more cardiac features that are within a particular feature-class ([0043-0045]; Figure 9).
As to claim 6, as best understood in light of the rejection under 35 U.S.C. above, Tamil et al. , and thus the modified Tamil et al., discloses a second plurality of cardiac classifiers are at least some of the cardiac classifiers that are each configured to identify cardiac features within a particular feature-class ([0041-0051, 0086]); and wherein the second plurality of cardiac classifiers are arranged in a decision tree such that identification of some cardiac features by a first cardiac classifier of the second plurality of cardiac classifiers causes a second cardiac classifier to identify at least one cardiac feature within the particular feature-class ([0041-0051, 0086]; Figures 8-9).
As to claim 7, as best understood in light of the rejection under 35 U.S.C. above, Tamil et al. , and thus the modified Tamil et al., discloses instructions that, when executed by one or more processors, cause the processors to perform operations for generating data that describes features of an electrocardiogram (ECG) of a subject comprising: receiving ECG data that was generated by a computing device (Figure 3) comprising an electrode- interface for communicably coupling to one or more electrodes ([0064]; Figure 3), the computing device capable of detecting electrical changes due to-cardiac activity of a particular mammal when the electrodes are communicably coupled and placed on skin of the particular mammal (Figure 3), the computing device configured to generate ECG data to reflect cardiac activity of a particular mammal (Figure 3; [0020-0021]), wherein the ECG data is automatically analyzed ([0047]) to generate identified features in an ECG reading ([0047-0050]) to create a queue based on a risk metric as a function of the identified features ([0047-0054]); submitting the ECG data to a plurality of machine-learning cardiac classifiers trained on training data (Figure 8, [0047]), each machine-learning cardiac classifier configured to identify, in the ECG data, at least some of a plurality of cardiac features that are within a particular feature-class ([0025-0033]), wherein the plurality of cardiac classifiers comprises i) a main-rhythm-classifier configured to identify, in the ECG, at least some main-rhythm features ([0032]), and ii) a secondary-rhythm-classifier configured to identify, in the ECG, at least some secondary-rhythm features ([0033]); training a machine learning model ([0040, 0047]); receiving, from each of the plurality of machine-learning cardiac classifiers (Figures 8-9), a classification message containing data of the machine-learning cardiac classifiers identifying of cardiac features in the ECG (Figures 8-9); determining from the received ECG data, if the ECG data contains normal sinus rhythm ([0028]); assembling, from the received classification messages, ECG features for the ECG, the ECG features identifying at least some features of different feature-classes (Figure 9; [0047, 0050]); employing a stethoscope for further investigation (Tamil et al. in view of Hussain as presented above).
Additionally, as best understood in light of the rejection under 35 U.S.C. above, the modified Tamil et al. discloses the invention substantially as claimed with signal processing ([0031-0032]) but does not explicitly disclose “anti-aliasing”. It would have been an obvious matter of design choice to a person of ordinary skill in the art to modify the data processing as taught by Tamil et al., and thus the modified Tamil et al., with “anti-aliasing”, because Applicant has not disclosed the anti-aliasing provides an advantage, is used for a particular purpose, or solve a stated problem. One of ordinary skill in the art, furthermore, would have expected the Applicant’s invention to perform equally well with data processing as taught by Tamil et al., because both prepare ECG data for further processing (as disclosed by Applicant in paragraph 22). Therefore, it would have been an obvious matter of design choice to modify the data processing to obtain the invention as specified in the claim(s).
Furthermore, as to claim 7, as best understood in light of the rejection under 35 U.S.C. above, the modified Tamil et al. discloses the invention substantially as claimed but does not explicitly disclose determining a confidence value for each identified cardiac features. It would have been obvious to one having ordinary skill in the art at the time the invention was made to modify determine cardiac data and features of the modified Tamil et al. to include a confidence value in order to provide the predictable results of substantiating and verifying the cardiac data for optimal patient diagnosis and treatment.
As to claim 8, as best understood in light of the rejection under 35 U.S.C. above, Tamil et al. , and thus the modified Tamil et al., discloses classifiers to identify features in the ECG for arrhythmia diagnosis [0047, 0049, 0071]. Therefore, Tamil et al. discloses wherein the plurality of machine-learning cardiac classifiers further comprises: an atrial-enlargement-classifier configured to identify, in the ECG, at least some atrial-enlargement features ([0047, 0049, 0071]; the classifiers identify features, which are correlated to “at least some atrial-enlargement features”); an atrioventricular (AV)-conduction main-rhythm-classifier configured to identify, in the ECG, at least some AV-conduction features ([0047, 0049, 0071]; the classifiers identify features, which are correlated to “at least some AV-conduction features”) ; a QRS-classifier configured to identify, in the ECG, at least some QRS-features ([0047, 0049, 0070-0071]); a ST\T-wave-classifier configured to identify, in the ECG, at least some ST\T- wave features ([0047, 0049, 0070-0071]); and a noisy-classifier configured to identify, in the ECG, at least some noisy features ([0047, 0049, 0064, 0071]; the classifiers identify features, which are correlated to “at least some noisy features” [0064]).
As to claim 9, as best understood in light of the rejection under 35 U.S.C. above, Tamil et al. , and thus the modified Tamil et al., discloses at least some of the cardiac classifiers are configured to access physiological-constraint data that defines constraints on possible feature identification such that identified features are constrained to only features that are physiologically possible in a single given ECG ([0043-0045]).
As to claim 10, as best understood in light of the rejection under 35 U.S.C. above, Tamil et al. , and thus the modified Tamil et al., discloses at least one of the cardiac classifiers are configured to identify three or more cardiac features that are within a particular feature-class ([0043-0045]; Figure 9).
As to claims 11-12, as best understood in light of the rejection under 35 U.S.C. above, Tamil et al. , and thus the modified Tamil et al., discloses a second plurality of cardiac classifiers are at least some of the cardiac classifiers that are each configured to identify cardiac features within a particular feature-class ([0041-0051, 0086]); and wherein the second plurality of cardiac classifiers are arranged in a decision tree such that identification of some cardiac features by a first cardiac classifier of the second plurality of cardiac classifiers causes a second cardiac classifier to identify at least one cardiac feature within the particular feature-class ([0041-0051, 0086]; Figures 8-9).
As to claim 13, as best understood in light of the rejection under 35 U.S.C. above, Tamil et al. , and thus the modified Tamil et al., discloses generating data that describes features of an electrocardiogram (ECG) of a subject comprising: receiving ECG data that was generated by a computing device comprising an electrode- interface for communicably coupling to one or more electrodes ([0064]; Figure 3), the computing device capable of detecting electrical changes due to-cardiac activity of a particular mammal when the electrodes are communicably coupled and placed on skin of the particular mammal (Figure 3), the computing device configured to generate ECG data to reflect cardiac activity of a particular mammal (Figure 3; [0020-0021]), wherein the ECG data is automatically analyzed ([0047]) to generate identified features in an ECG reading ([0047-0050]) to create a queue based on a risk metric as a function of the identified features ([0047-0054]); submitting the ECG data to a plurality of machine-learning cardiac classifiers trained on training data (Figure 8, [0047]), each machine-learning cardiac classifier configured to identify, in the ECG data, at least some of a plurality of cardiac features that are within a particular feature-class ([0025-0033]), wherein the plurality of cardiac classifiers comprises i) a main-rhythm-classifier configured to identify, in the ECG, at least some main-rhythm features ([0032]), and ii) a secondary-rhythm-classifier configured to identify, in the ECG, at least some secondary-rhythm features ([0033]); training a machine learning model ([0040, 0047]); receiving, from each of the plurality of machine-learning cardiac classifiers (Figures 8-9), a classification message containing data of the machine-learning cardiac classifiers identifying of cardiac features in the ECG (Figures 8-9); determining from the received ECG data, if the ECG data contains normal sinus rhythm ([0028]); assembling, from the received classification messages, ECG features for the ECG, the ECG features identifying at least some features of different feature-classes (Figure 9; [0047]); employing a stethoscope for further investigation (Tamil et al. in view of Hussain as presented above).
Additionally, as best understood in light of the rejection under 35 U.S.C. above, the modified Tamil et al. discloses the invention substantially as claimed with signal processing ([0031-0032]) but does not explicitly disclose “anti-aliasing”. It would have been an obvious matter of design choice to a person of ordinary skill in the art to modify the data processing as taught by Tamil et al., and thus the modified Tamil et al., with “anti-aliasing”, because Applicant has not disclosed the anti-aliasing provides an advantage, is used for a particular purpose, or solve a stated problem. One of ordinary skill in the art, furthermore, would have expected the Applicant’s invention to perform equally well with data processing as taught by Tamil et al., because both prepare ECG data for further processing (as disclosed by Applicant in paragraph 22). Therefore, it would have been an obvious matter of design choice to modify the data processing to obtain the invention as specified in the claim(s).
Furthermore, as to claim 13, as best understood in light of the rejection under 35 U.S.C. above, the modified Tamil et al. discloses the invention substantially as claimed but does not explicitly disclose determining a confidence value for each identified cardiac features. It would have been obvious to one having ordinary skill in the art at the time the invention was made to modify determine cardiac data and features of the modified Tamil et al. to include a confidence value in order to provide the predictable results of substantiating and verifying the cardiac data for optimal patient diagnosis and treatment.
As to claim 14, as best understood in light of the rejection under 35 U.S.C. above, Tamil et al. , and thus the modified Tamil et al., discloses classifiers to identify features in the ECG for arrhythmia diagnosis [0047, 0049, 0071]. Therefore, Tamil et al. discloses wherein the plurality of machine-learning cardiac classifiers further comprises: an atrial-enlargement-classifier configured to identify, in the ECG, at least some atrial-enlargement features ([0047, 0049, 0071]; the classifiers identify features, which are correlated to “at least some atrial-enlargement features”); an atrioventricular (AV)-conduction main-rhythm-classifier configured to identify, in the ECG, at least some AV-conduction features ([0047, 0049, 0071]; the classifiers identify features, which are correlated to “at least some AV-conduction features”) ; a QRS-classifier configured to identify, in the ECG, at least some QRS-features ([0047, 0049, 0070-0071]); a ST\T-wave-classifier configured to identify, in the ECG, at least some ST\T- wave features ([0047, 0049, 0070-0071]); and a noisy-classifier configured to identify, in the ECG, at least some noisy features ([0047, 0049, 0064, 0071]; the classifiers identify features, which are correlated to “at least some noisy features” [0064]).
As to claim 15, as best understood in light of the rejection under 35 U.S.C. above, Tamil et al. , and thus the modified Tamil et al., discloses at least some of the cardiac classifiers are configured to access physiological-constraint data that defines constraints on possible feature identification such that identified features are constrained to only features that are physiologically possible in a single given ECG ([0043-0045]).
As to claim 16, as best understood in light of the rejection under 35 U.S.C. above, Tamil et al. , and thus the modified Tamil et al., discloses at least one of the cardiac classifiers are configured to identify three or more cardiac features that are within a particular feature-class ([0043-0045]; Figure 9).
As to claim 17, as best understood in light of the rejection under 35 U.S.C. above, Tamil et al. , and thus the modified Tamil et al., discloses a second plurality of cardiac classifiers are at least some of the cardiac classifiers that are each configured to identify cardiac features within a particular feature-class ([0041-0051, 0086]); and wherein the second plurality of cardiac classifiers are arranged in a decision tree such that identification of some cardiac features by a first cardiac classifier of the second plurality of cardiac classifiers causes a second cardiac classifier to identify at least one cardiac feature within the particular feature-class ([0041-0051, 0086]; Figures 8-9).
As to claim 18, as best understood in light of the rejection under 35 U.S.C. above, Tamil et al. , and thus the modified Tamil et al., discloses the training data comprises a corpus of stored ECG data and corresponding tags that identify features in the stored ECG data (Figure 8; [0040, 0074-0077]).
As to claim 19, as best understood in light of the rejection under 35 U.S.C. above, Tamil et al. , and thus the modified Tamil et al., discloses the main-rhythm-classifier is trained on the training data using at least one first training technique of the group consisting of vi) support vector machine ([0040]).
As to claim 20, as best understood in light of the rejection under 35 U.S.C. above, the modified Tamil et al. discloses the main-rhythm-classifier is trained on the training data using at least one first training technique of the group consisting of vi) support vector machine ([0040]). Tamil et al. discloses the invention substantially as claimed but does not explicitly disclose the secondary-rhythm-classifier is trained on the training data using at least one second training technique that is different than the first training technique. There are a plurality of learning algorithms (Tamil et al. [0040]) well known in machine learning. It would have been obvious to one having ordinary skill in the art at the time the invention was made to incorporate a classifier is trained on the training data using a technique other than support vector machine, such as kernel or neural networks, in order to provide the predictable results of modifying the algorithm and training technique in order to optimize performance.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALYSSA M ALTER whose telephone number is (571)272-4939. The examiner can normally be reached M-F 8am-4pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David E Hamaoui can be reached on (571) 270-5625. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALYSSA M ALTER/Primary Examiner, Art Unit 3792