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 .
This office action is in response to communication filed on 4/2/2025.
Claims 1-20 are presented for examination.
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, 9, 16 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. The specification as filed teaches on paragraph 0003 “ generating one or more cohorts as a function of the voltage-time data ……. using a different cohort of the one or more cohorts, generating an indication of a presence or absence of pulmonary hypertension (PH).” The specification lacks disclosure for “generating cohorts based on tricuspid regurgitation velocity”.
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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Determining that a claim falls within one of the four enumerated categories of patentable subject matter recited in 35 U.S.C. 101 (i.e., process, machine, manufacture, or composition of matter). (MPEP 2106.03)
Claims 1-7 recite a series of steps, thus falling within one of the four statutory classes; i.e., a process. Claims 8-14 describe tangible system components, thus falling within one of the four statutory classes; i.e., machine. Claims 15-20 recite a non-transitory storage medium and fall under one of the four statutory classes; i.e. manufacture.
Step 2A, Prong One: Evaluating whether the claim(s) recite(s) a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. (MPEP 2106.04).
Representative claim 1 recites:
Receiving voltage-time data of a subject, the voltage-time data comprising an electrocardiogram (ECG) waveform;
generating a plurality of feature vectors from the voltage-time data;
providing the plurality of the feature vectors; wherein providing the plurality of the feature vectors comprises:
generating one or more cohorts as a function of the voltage-time data; and selecting, using a different cohort of the one or more cohorts;
generating an indication of a presence or absence of pulmonary hypertension (PH) in the subject and providing for display to a user.
The claims as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting “pretrained learning system“, “machine learning models”, “computing node”, can be performed in the mind. For example e.g., a human can determine a presence or absence of pulmonary hypertension of a user. If a claim limitation, 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, under step 2A, prong one.
In addition, the limitations mentioned above (i.e., “receiving”, “generating”, “providing” in the context of this claim) as drafted, are processes that, under their broadest reasonable interpretations, include managing personal behavior but for the recitation of generic computer components.
If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, then it falls within the “Certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Independent claims 8 and 15 recite the same abstract idea as identified above and dependent claims 2-7, 9-14 and 16-20 further narrow it.
Step 2A, Prong Two: Identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and then evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application. Prong Two distinguishes claims that are "directed to" the recited judicial exception from claims that are not "directed to" the recited judicial exception. (MPEP 2106.04).
This judicial exception is not integrated into a practical application. In particular, the claims recite the following additional elements:
• pretrained learning system; machine learning model (claims 1, 8 and 15)
• computing node (claims 8 and 15);
• operating on a processor, a non-transitory computer readable storage medium (claims 8 and 15)
The “a processor” and the “one or more non-transitory computer-readable media” are recited at a high-level of generality (i.e., as generic processors) such that they amount no more than mere instructions to apply the exception using generic computer components. They are no more than a tool to perform the “receiving”, “generating” and “providing” steps.
The additional elements of pretrained learning system, machine learning model and computing node are considered as “apply it” as the claim invokes the computer as a tool to perform the abstract idea. See MPEP 2106.05(f)(2) (similar to Apple, Inc. v Ameranth and Intellectual Ventures I LLC v Capital One Bank (USA).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. (MPEP 2106.05(f) Mere Instructions To Apply An Exception).
Regarding the limitations processor, non-transitory compute readable medium, machine learning systems, computing node, as seen above, this limitation has been interpreted as “apply it”. However, these limitations can be additionally interpreted as insignificant extra-solution activity. As such, these limitations alone and in combination, does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. (MPEP 2106.05(g) Insignificant Extra-Solution Activity).
Therefore, under Step 2A, Prong Two, the claims are directed to an abstract idea.
Step 2B: Identifying whether there are any additional elements (features/limitations/steps) recited in the claim beyond the judicial exception(s), and then evaluating those additional elements individually and in combination to determine whether they contribute an inventive concept (i.e., amount to significantly more than the judicial exception(s)). (MPEP 2106.05)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of processor, non-transitory compute readable medium, machine learning systems, computing node, alone and in combination amount to no more than mere instructions to apply the exception using generic computer components.
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Regarding the limitations processor, non-transitory compute readable medium, machine learning systems, computing node; it is noted that sending information over a network has been recognized in the courts as being Well Understood Routine and Conventional (see MPEP 2106.05(d)(II) - i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Therefore, this additional elements do not amount to significantly more than a judicial exception and cannot provide an inventive concept. (MPEP 2106.05(d) Well-Understood, Routine, Conventional Activity).
Therefore, claims 1-20 are not patent eligible.
Double Patenting
A rejection based on double patenting of the “same invention” type finds its support in the language of 35 U.S.C. 101 which states that “whoever invents or discovers any new and useful process... may obtain a patent therefor...” (Emphasis added). Thus, the term “same invention,” in this context, means an invention drawn to identical subject matter. See Miller v. Eagle Mfg. Co., 151 U.S. 186 (1894); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Ockert, 245 F.2d 467, 114 USPQ 330 (CCPA 1957).
A statutory type (35 U.S.C. 101) double patenting rejection can be overcome by canceling or amending the claims that are directed to the same invention so they are no longer coextensive in scope. The filing of a terminal disclaimer cannot overcome a double patenting rejection based upon 35 U.S.C. 101.
Claims 1-20 are rejected on the ground of nonstatutory anticipated double patenting as being unpatentable over claims 1-18 of U.S. Patent No. 12,340,906.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-5, 7-12 and 14-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Schulhauser et al. (2020/0038671 Schulhauser hereinafter).
With respect to claims 1, 8 and 15, Schulhauser teaches methods, systems and product for:
receiving voltage-time data of a subject, the voltage-time data comprising voltage data of a plurality of leads of an electrocardiograph (see Figures 1-2 and 7 apparatus 310 receives data (700). The data includes a cardiac signal sensed by apparatus 310. The data may also include data from sensing devices, such as another cardiac or other physiological signal, or data derived therefrom);
generating a feature vector from the voltage-time data; providing the feature vector to a pretrained learning system (see paragraph 0080 for the data may comprise amplitude and temporal information, e.g., an electrocardiogram (ECG) with a measured voltage over time. In general, the machine learning algorithm may consider features of a signal formed by changing values over time, or changes in such features over time. For example, in the case of a cardiac signal, the variables may relate to features of the cardiac signal, such as the P-wave, R-wave, QRS-complex, S-T segment, Q-T interval, and T-wave, as well as heart rate and heart rate variability);
and receiving from the pretrained learning system an indication of the presence or absence of pulmonary hypertension in the subject (see paragraph 0016 for applying a machine learning algorithm of a defibrillation apparatus 310 to data to determine whether to provide an indication of chronic obstructive pulmonary disease).
With respect to claims 2-3, 9-10 and 16-17, Schulhauser further teaches generating cohorts based on tricuspid regurgitation velocity; mean pulmonary velocity (i.e. The characteristics that may distinguish patient populations include gender, age, BMI, weight, and blood pressure. Further, the machine learning algorithm may use the expected difference in data illustrated by regions 1428 and 1428 to determine the state of a comorbidity related to blood pressure for a patient)(see paragraph 0130).
With respect to claims 4, 11 and 18, Schulhauser further teaches wherein generating the one or more cohorts comprises: extracting a diagnosis from clinical notes; and generating the one or more cohorts using the diagnosis and echocardiogram measurements. (see paragraph 0068 for memory 314 may be configured to store data about the environment that the patient was in at a particular time, e.g., as indicated by location data, fluid state information (e.g., hydration level or edema), and patient health record information).
With respect to claims 5, 12 and 19, Schulhauser further teaches wherein the one or more cohorts comprises a positive PH cohort and a negative PH cohort, wherein the positive PH cohort and the negative PH cohort are generated based on sentiment of the diagnosis ( FIG. 14 is a flowchart illustrating an example technique for applying a machine learning algorithm of a defibrillation apparatus to data to determine whether to provide an indication of chronic obstructive pulmonary disease).
With respect to claims 7, 14 and 20, Schulhauser further teaches pretrained learning systems comprising a classifier (see paragraph 0075 for the machine learning algorithm may be configured to employ any one or more of Bayesian, random forest, decision tree, linear regression, deep learning, neural network and/or dimensionality reduction techniques, as examples. In some examples, a result of the application of the machine learning algorithm to the data, e.g., one or more physiological signals from sensing devices 130 or 230 and/or the cardiac signal from sensing circuitry 330 includes classification of the data, e.g., the cardiac signal and physiological signal (individually or collectively) as one of normal or not normal, or indicating one or more other states of the patient, such as whether a treatable tachyarrhythmia is indicated or predicted, or whether one or more comorbidities are indicated or predicted).
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.
Claims 6, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Schulhauser in view of Official Notice.
With respect to claims 6 and 13, Schulhauser teaches identifying Pulmonary hypertension/HP and an electrocardiogram (ECG) with a measured voltage over time. Schulhauser is silent as to identifying genetic mutations associated with the PH and determining datum from the genetic mutations modulate the ECG waveform. Official Notice is taken that it is old and well known to use ECG waveform to determine gene mutations such as BMPR2, that causes pulmonary arterial hypertension. It would have been obvious to a person of ordinary skill in the art at the time of Applicant’s invention to have included in the Pulmonary hypertension/HP and an electrocardiogram (ECG) of Schulhauser, to have included identifying generic mutations, in order to find early detection of the disease.
References of record but not applied in the current rejection:
Proquest article by Aaron Michael Wenger “A Dissertation submitted to the Department of Computer Science and the Committee on graduate studies of Standford University in Partial Fulfillment of the Requirements For the Degree of Doctor of Philosophy” teaches a novel approach that combines computational analysis of regulatory elements with high-throughput sequencing techniques to uncover gene interaction networks in a rapid, non-targeted manner that is not possible with classical genetics methods.
WO 2019/122919 teaches a medical intervention control system for providing a risk analysis and influencing intervention action on a patient, the system comprising: a database with a data set containing data from at least one data source comprising: a) study data; and b) sensed data; a waveform detector operable to identify a waveform from a data source, extract the waveform, categorise the waveform, normalise the waveform to a predetermined format and determine waveform characteristics and parameters of the waveform, the waveform detector populating part of the sensed data; a measurement module to derive subject data from the patient; an analyser operable to analyse the subject data with respect to the data set from the at least one data source and output an associated probability for each of one or more outcomes, wherein the associated probability is affected by an intervention.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAQUEL ALVAREZ whose telephone number is (571)272-6715. The examiner can normally be reached Mondays thru Thursdays 8:30-6:30.
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/RAQUEL ALVAREZ/ Primary Examiner, Art Unit 3622
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