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 Amendment
The amendment filed June 29, 2026 has been entered. No claims have been amended. Currently, claims 1-20 are pending for examination.
Response to Arguments
Applicant's arguments filed June 29, 2026 with respect to claims 1-11 and 18-20 have been fully considered but they are not persuasive after further consideration of the inherited rejection from the previous examiner.
Under Step 2A, applicant argues the modular machine learning architecture, identified in the previous Office action as an abstract idea in the form of mathematical algorithms, that is, the machine learning architecture is a series of mathematical steps performed by a processor, should not be regarded as such. Applicant also notes the most recent interview (06/25/2026) with the current examiner where the modular machine learning architecture was discussed as an abstract idea directed to a mental process, and states that regardless of the alleged abstract idea, the claims are still patentable. Applicant directs attention to Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016), presenting the argument based on the reasoning that the Federal Circuit indicates that claims directed to improvements to technology can be non-abstract (p. 9). This argument is not persuasive since each case has its own set of facts and the facts of the instant application are different than that of Enfish v. Microsoft. While the referenced case states software can make non-abstract improvements to computer technology, it does not state that all software is non-abstract. In the instant application, processing circuitry is configured to detect a cardiac event type for the patient based on a classification of patient physiological data in accordance with a modular machine learning architecture. The modular machine learning architecture classifies EGM data into one of a plurality of cardiac event types using respective component models and further comprises masked component models that are for at least one cardiac event type of a plurality of cardiac event types other than the detected cardiac event type. Applicant argues that the claims are directed to “improvement in cardiac event type classification”. Cardiac event type classification is not a technology or technical field. At most, the claimed invention is an improvement to a method of classifying, but classifying in it of itself is not a technology, technical computer, or functioning of a computer. Furthermore, the claimed invention uses machine learning models to classify, it is not improving the machine learning models themselves. Applicant’s assertion that the claimed invention is an improvement on modular updates is not relevant since the claimed invention is directed to detecting cardiac event types using machine learning models, not how the models are being updated.
Applicant argues the rejection has not considered the claims as an ordered combination, without ignoring the requirements of the individual steps (p. 10). This argument is unpersuasive as the previous Office action has considered the claims as an ordered combination in Step 2b of the rejection.
Applicant argues the 2016 Memo emphasizes citing to an appropriate court decision that supports the identification of the subject matter recited in the claim language as an abstract idea is a best practice that will advance prosecution, and the previous Office action has not cited any court decision supporting the identification of a modular machine learning architecture as an abstract idea. This argument is unpersuasive as the 2016 Memo does not require citation of an appropriate court decision.
Under Step 2B, applicant argues contradictory arguments are made in the Office action (p. 11). Stating the identified insignificant extra-solution activity (sensors, sensing circuitry, processor, display, communication device) do not apply the abstract idea to affect a particular treatment or improve the functioning of a technology is not contradictory to the statement that these identified insignificant extra-solution activity are merely used to apply or practice the abstract idea. Applicant misunderstands that all application or practices of the abstract idea must inherently affect a particular treatment or improve the functioning of a technology. Applicant also argues the previous Office action did not provide evidence as to why these examples were not considered as integrating the allegedly abstract idea into practical applications. MPEP 2106.05(b)-(c) readily sets forth guidelines for determining whether a claim integrates a judicial exception into a practical application. As such, the rejection properly evaluated the particularity or generality of the elements of the machine or apparatus and identifying its involvement as extra-solution activity or a field-of-use. Additionally the rejection indicated these elements do not integrate the abstract idea into a practical application since they do not apply the abstract idea to affect a particular treatment or improve the functioning of a technology.
Applicant improperly asserts that all claims directed to “applying” or “practicing” a modular machine learning model architecture, are evidence that it is integrated into a practical application (p. 12). This is unpersuasive for the reasons provided above.
Applicant further directs attention to Ex Parte Desjardins, listing examples xiii and xiv that show improvement in computer functionality (p. 12). These examples are not relevant to the facts of the case as they are directed to an improved way of training a machine learning model and improvements to computer component or system performance based on adjustments to parameters of a machine learning model. The claims at hand are neither directed to an improved way of training a machine learning model or adjustments to parameters of a machine learning model. Applicant’s arguments that the specification supports these improvements (p. 13) are unpersuasive as the claims do not recite these limitations. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
To further emphasize the claims as written are directed to an abstract idea that does not integrate the abstract idea into a practical application and the claim does not recite significantly more than the judicial exception, clarifying remarks are provide below.
The 35 U.S.C. 101 rejection of claims 12-17 has been withdrawn.
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 1-11, 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1-11 and 18-20 do not include additional elements that integrate the exception into a practical application of the exception or that are sufficient to amount to significantly more than the judicial exception for the reasons provided below which are in line with the 2014 Interim Guidance on Patent Subject Matter Eligibility (Federal Register, Vol. 79, No. 241, p. 74618, December 16, 2014), the July 2015 Update on Subject Matter Eligibility (Federal Register, Vol. 80, No. 146, p. 45429, July 30, 2015), the May 2016 Subject Matter Eligibility Update (Federal Register, Vol. 81, No. 88, p. 27381, May 6, 2016), and the 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register, Vol. 84, No. 4, p. 50, January 7, 2019).
Regarding claim 1:
Step 1: claim 1 is directed to an apparatus.
one or more sensors configured to sense at least one physiological parameter corresponding to a cardiac physiology of a patient
sensing circuitry configured to generate patient physiological data based on the sensed physiological parameter, the patient physiological data comprising cardiac EGM data for the patient
processing circuitry configured to:
detect a cardiac event type for the patient based on a classification of the patient physiological data in accordance with a modular machine learning architecture, wherein the modular machine learning architecture comprises, for each of a plurality of cardiac event types, a respective component model for classifying the cardiac EGM data as evidence of that respective one of the plurality of cardiac event types, wherein the modular machine learning architecture comprises one or more masked component models for at least one cardiac event type of the plurality of cardiac event types other than the detected cardiac event type
generate for display output data indicative of a positive detection of the cardiac event type
Step 2A – Prong 1: The underlined emphasized limitations are directed to an abstract idea in the form of an apparatus that, under its broadest reasonable interpretation, covers mathematical steps performed by a processor, or alternatively, mental processes of comparing detected EGM data to a various models of cardiac event types to classify the EGM data into these cardiac event types.
Step 2A – Prong 2: The bolded emphasized limitations do not integrate the exception into a practical application of the exception because the elements are directed to insignificant extra-solution activity. The sensors, sensing circuitry, processor and generation of display output data are considered to be either for data gathering, for use with the abstract idea, or extra-solution activity. They are recited at a high level of generality to perform the abstract idea and are merely regarded as including instructions to implement the abstract idea on a computer, or merely using a computer as a tool to perform the abstract idea. See MPEP 2106.04(d) and 2106.05(f). Obtaining data and outputting data for display does not integrate the exception into a practical application of the exception because it does not amount to more than generally linking the use of the exception to a particular technological environment or field of use. See MPEP 2106.05(h). The judicial exception does not integrate the claim as a whole into a practical application because the claimed invention does not improve another technology or technical field.
Step 2B: Claim 1 does not recite additional elements that amount to significantly more than the judicial exception itself. The sensors, sensing circuitry and processor are recited at a high level of generality to perform the abstract idea. See MPEP 2106.04(d) and 2106.05(f). Furthermore these elements are well-understood, routine, and conventional elements as evidenced by and not limited to Cheng et al. (US 2022/0398470), submitted by applicant, see at least Figures 2 and 4 for sensing circuitry, processor, sensors and display; and Musgrove et al. (US 2020/0357518), submitted by applicant, see Figures 3 and 4 for communication circuitry, sensors, sensing circuitry, processor and user interface display. Collecting data, analyzing it and displaying results of the collection is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016). The bolded emphasized elements do not amount to significantly more than the judicial exception because these limitations are simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)).
In view of the above, the bolded additional elements individually do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) 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 a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)).
Dependent claims 2-11 depend on and recite the same abstract idea as claim 1. Claim 2 recites various IMDS but they are merely used to apply the abstract idea. Claim 3 further recites sensors and processing circuitry which are merely used to practice the abstract idea. Claims 4-7 further limit the abstract idea of the modular machine learning architecture and do not add structure that integrate the abstract idea into a practical application. Claims 8-11 merely give further details regarding the processing circuitry, and as such do not add a limitation that integrates the abstract idea into a practical application.
Regarding claim 18:
Claim 18 is directed to non-transitory computer-readable storage medium that recites the same abstract idea in Step 2A – Prong 1 and similar additional elements in Step 2A – Prong 2/Step 2B as identified above for claim 1 and are therefore rejected to for the same reasons as identified above. Claim 18 does not recite sensors and sensing circuitry but still recites processing circuitry and the generation of output data for display.
Dependent claims 19-20 depend on and recite the same abstract idea as claim 18. They merely provide further details of the workings of the abstract idea, and as such do not add limitations that integrate the abstract idea into a practical application.
Allowable Subject Matter
Claims 12-17 are allowed.
The following is a statement of reasons for the indication of allowable subject matter: The prior art does not disclose or teach processing circuitry operative to execute logic for a remote monitoring service, wherein the logic is configured to: maintain a most recent version of a modular machine learning architecture comprising a plurality of modules, and in each of the plurality of modules, a respective neural network for predicting a likelihood that any given cardiac EGM sample indicates a respective cardiac arrhythmia type, and in response to an update to the modular machine learning architecture, deploy, to medical devices corresponding to the remote monitoring service via communication circuitry, a new module that corresponds to the update, wherein the most recent version of the modular machine learning architecture further comprises, in each module of the plurality of modules, a neural network ensemble comprising the current neural network for predicting a likelihood that any given cardiac EGM sample indicates the respective cardiac arrhythmia type and a masked neural network for at least one other cardiac arrhythmia type.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERICA S LEE whose telephone number is (571)270-1480. The examiner can normally be reached M-F 8-7pm, flex.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Hamaoui can be reached at (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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/ERICA S LEE/Primary Examiner, Art Unit 3796
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