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
In the amendment filed 24 March 2026:
Claims 20, 30 are cancelled
Claims 36-37 are new
Claims 16-17, 19, 21, 25-27, 29, 31, 34-35 are amended
Claims 16-19, 21-29, 31-37 are pending
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 16-19, 21-29, 31-37 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 16, 26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The claims recites a computer-implemented method, and system, which are within a statutory category ([if 112(f) or other 101 issues] or are interpreted to be within a statutory category for subject matter eligibility analysis purposes).
Step 2A1
The limitations of:
extracting a first plurality of features from electrocardiogram (ECG) signal data;
applying to the first plurality of features to generate a disease classification corresponding to the ECG data, trained using first training data, and wherein the disease classification comprises a positive diagnosis or a negative diagnosis of a disease;
accessing second training data;
and training on a second plurality of features extracted from the second training data, the second plurality of features configured for input, such that:
an estimated disease classification output corresponding to the second plurality of features is negative when a reference disease classification corresponding to the second training data is positive, and the estimated disease classification output corresponding to the second plurality of features is positive when the reference disease classification corresponding to the second training data is negative, wherein training comprises:
applying to the first plurality of features to transform the first plurality of features into comparison ECG signal data;
inputting features of the comparison ECG signal data to generate a comparison disease classification:
comparing the comparison disease classification with the disease classification to calculate a loss value;
updating one or more parameters based on the loss value;
and generating commands, wherein the graph is generated based on one or more of outputs,
as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for recitation of generic computer components. The claims encompass a series of rules or instructions for a person or persons to follow, with or without the aid of a computer, to classify disease in the manner described in the identified abstract idea, supra. The rules or instructions are the claimed steps of “extracting, assessing, applying, inputting, comparing, generating” as indicated supra.
Other than reciting generic computer components (discussed infra), i.e., a system implemented by a data processor (computer), the claimed invention amounts to managing personal behavior or interaction between people. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The Examiner notes that the training of a neural network models is recited in the claim. The type of training utilized by the claimed invention is not described by the Applicant. As such the Examiner is required to analyze the training step given the broadest reasonable interpretation. The step(s) performed to train step(s) of the model/algorithm is/are considered to be part of the abstract idea because it/they fall(s) under data manipulations that humans perform (i.e., fitting a model to data) and thus are interpreted to be part of the abstraction--the rules or instructions that fall under Certain Methods of Organizing Human Activity. See, e.g., Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 12 (Fed. Cir. April 18, 2025) (finding that “[i]terative training using selected training material…are incident to the very nature of machine learning.”). As such, the training of the machine learning model represents a mathematical concept that is interpreted to be part of the identified abstract idea, supra. The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
Step 2A2
The claim further recites the additional element of using a trained neural network models to classify disease. This represents mere instructions to implement the abstract idea on a generic computer. Implementing an abstract idea using a generic computer or components thereof does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. See, e.g., Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 10 (Fed. Cir. April 18, 2025) (finding that claims that do no more than apply established methods of machine learning to a new data environment are ineligible). Alternatively, or in addition, the implementation of the trained machine learning model to classify disease merely confines the use of the abstract idea (i.e., the trained model) to a particular technological environment or field of use ([neural network]) and thus fails to add an inventive concept to the claims.
The claims further recite the additional elements of a graphical display, user interface. The graphical display, user interface merely generally links the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide a practical application. Accordingly, even in combination, this additional elements do not integrate the abstract idea into a practical application.
Step 2B
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the trained neural network models to classify disease was found to represent mere instructions to implement the abstract idea on a generic computer and/or confine the use of the abstract idea (i.e., the trained model) to a particular technological environment or field of use (neural network). This has been re-evaluated under the “significantly more” analysis and determined to be insufficient to provide significantly more. MPEP 2106.05(I) indicates that mere instructions to implement the abstract idea on a generic computer and/or confining the use of the abstract idea to a particular technological environment or field of use cannot provide significantly more. See also Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 17 (Fed. Cir. April 18, 2025) (finding that applying machine learning to an abstract idea does not transform a claim into something significantly more).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a graphical display, user interface were determined to generally link the abstract idea to a particular technological environment or field of use. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP 2106.05(A) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide significantly more. As such the claims are not patent eligible.
Claims 17-25, 26-37 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination.
Claim(s) 17, 27 merely describe(s) training the first neural network (i.e., the mathematics behind the training), which further defines the abstract idea.
Claim(s) 18, 28 merely describe(s) what the parameters contains, which further defines the abstract idea.
Claim(s) 19, 29 merely describe(s) using morphological interpretation, which further defines the abstract idea.
Claim(s) 20, 30 merely describe(s) using biosignal data, which further defines the abstract idea.
Claim(s) 21,31 merely describe(s) diagnosing hyperkalemia, which further defines the abstract idea.
Claim(s) 22, 32 merely describe(s) what the first neural network comprises, which further defines the abstract idea.
Claim(s) 23,33 merely describe(s) what the first neural network comprises, which further defines the abstract idea.
Claim(s) 24,34 merely describe(s) what the first neural network comprises, which further defines the abstract idea.
Claim(s) 25,35 merely describe(s) training of the first neural network comprises, which further defines the abstract idea.
Claim(s) 36, 37 merely describe(s) configuring the comparison ecg data, which further defines the abstract idea.
Subject Matter Free of Prior Art
Claim(s) 16-19, 21-29, 31-37 recite subject matter that is free of prior art. In particular, the cited prior art of record fails to teach or suggest the combination of:
Claims 16, 26 describe(s) applying the second neural network model to the first plurality of features to transform the first plurality of features into comparison ECG signal data; inputting features of the comparison ECG signal data into the first neural network model to generate a comparison disease classification: comparing the comparison disease classification with the disease classification to calculate a loss value;
updating one or more parameters of the second neural network model based on the loss value;
Claims 17-19, 21-25,27-29, 31-37 depend on Claim 16, 26.
Response to Arguments
Rejection under 35 U.S.C. § 101
Regarding the rejection of Claims 16-19, 21-29, and 31- 37, the Examiner has considered the Applicant’s arguments; however, the arguments are not persuasive. Applicant argues:
Claims are an improvement to the technical field of automated disease detection using machine learning models.
Regarding (a), the Examiner respectfully disagrees. This is an improvement to the abstract idea, not an improvement to the machine learning model (discussed infra) or any technical field. As indicated in MPEP 2106.05(a)(II), improvements to technology are evidenced by providing a practical application or significantly more using the tests provided for these. Applicant’s claims to not provide a practical application or significantly more and thus the cannot provide an improvement to a technical field. Furthermore, outputting data from a machine learning model does not provide an improvement to the computer, machine learning model, or technical field. It is just outputting data. Examiner respectfully again points to MPEP 2106.05(a)(II), which cites examples that may not be sufficient to show an improvement to technology. Example (iii) recites gathering, and analyzing information using conventional techniques and displaying the result from TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48 which the application claims closely resemble. If the data is only being displayed, there is no improvement to the technical field being provided, only an improvement to the abstract idea.
Claims are a technical improvement by improving the functioning of a machine learning model, as per Desjardins. Applicant submits that, as amended, the claim language reflects the technical improvements analogous to those in Ex parte Desjardins, Appeal No. 2024-000567, Application No. 16/319,040, at *7 (P.T.A.B. Sept. 26, 2025) (Appeals Review Panel). Specifically, in the Desjardins decision, the Panel determined that improvements "that constitute[] an improvement to how the machine learning model itself operates" were found to be technical improvements that are patent-eligible under Step 2A, prong 2 (p. 8-9). Similarly, as described in the specification, the invention improves the functioning of the machine learning model by improving training of a model.
Regarding (b), the Examiner respectfully disagrees. The Examiner respectfully submits that there is no improvement to the claim machine learning as there is in Desjardins. As found by the Panel, the claimed “training strategy allows the model to preserve performance on earlier tasks even as it learns new ones, directly addressing the technical problem of 'catastrophic forgetting' in continual learning systems" represents “technical improvements over conventional systems by addressing challenges in continual learning and model efficiency by reducing storage requirements and preserving task performance across sequential training.” This analysis represents implementation of the practical application-“improvement” analysis of MPEP 2106.04(d)(I) to the facts before the Panel.
Applicant’s claims do not provide such an improvement. There is no indication in the cited portion of the Specification that the claimed invention provides an improvement as to how model is trained. Improving the transparency of a machine learning model by supplying it with specific data is not an improvement to how the model is trained within the meaning of Desjardins (see quotations from Recentive, infra). This is how all ensemble machine learning models are optimized (i.e., select training data, train the model, compare the output to validation data, receive feedback, adjust the parameters of the training data according to the comparison/feedback, and repeat until an accuracy threshold is met). Put another way, the particular way the machine learning models of applicant’s invention use the data to perform training is not improved, which is the holding of Desjardins. Applicant is merely improving the accuracy of the model by optimizing the data selected/used by the model. Improving the accuracy of a model is not an improvement by any measure in MPEP 2106.
Examiner’s position is also supported by the decision in Recentive Analytics, Inc. v. Fox Corp. Recentive held that non-specifically claimed training of an ML algorithm is insufficient to provide a practical application or significantly more because it does not result in “improving the mathematical algorithm or making machine learning better.” Recentive at 12. The decision further instructed that “[i]terative training using selected training material…are incident to the very nature of machine learning” and thus does not provide for an improvement. Recentive at 12.
Claims improve transparency in models. As a non-limiting example, the current application describes training a second neural network model to combat the lack of transparency at a first neural network model, e.g., a disease classification model.
Regarding (c), the Examiner respectfully disagrees. Initially, it is unclear what transparency the Applicant is referring to as the claims only output an unspecified graph of one of the model outputs; generating commands and a graph based on one output from one model does not provide an improve the transparency in models. The models are performing their normal functions and there is no indication in the claims that the models are more transparent than generic machine learning models. Even assuming “transparency” was provided, this is, at best, an improvement to the abstract idea. The Examiner also further reiterates the response to Ex part Desjardins of (b) in regards to training of a machine learning model
In particular, the second neural network model may be trained to take in ECG data/features and transform the input ECG data/features into those that will cause an opposite diagnosis at a disease classification model. By doing so, the system can improve transparency into the disease classification model's disease detection process. For example, doing so shows a user which features of the ECG data, when flipped, cause an opposite diagnosis and therefore were heavily weighted or considered in causing a determination of disease.
Regarding (d), the Examiner respectfully disagrees. There is no support in the Specification to suggest causing an opposite diagnosis at a disease classification model improves the model’s transparency. Nor is the claim providing such transparency as described above.
Applicant submits that, similarly, improving the training of a machine learning model (i.e., the second neural network model) constitutes "an improvement to how the machine learning model works" as it improves the accuracy of the model over time. Ex parte Desjardins, Appeal 2024-000567 (PTAB Sept. 26, 2025). Thus, the amendments to the claims recite limitations that reflect technical improvements to the usage of a machine learning model, which the Panel in Desjardins found to be patent-eligible under Step 2A, prong 2
Regarding (e), the Examiner respectfully disagrees. There is no support in the Specification to suggest improving the accuracy of the model nor is there is technical problem tied to the accuracy of the model. Furthermore, the Examiner respectfully points to response to (b) in regards to Ex parte Desjardins.
Furthermore, as described in the specification, generating such ECG data/features also improves representation in data used during training. For example, "[t]he processor 110 may train a model by repeatedly performing the forward propagation and backpropagation of the deep learning model so that the features opposite to those of the input represented by the medical data transformed by the deep learning model are clearly represented" ([0041]).
Regarding (f), the Examiner respectfully disagrees. Changing how the data is represented does not alter or improve the functioning of the machine learning model, nor improve the transparency of the model. The machine learning model is trained the same way no matter how the data is represented. Again, any improvement present in the claims is an improvement to the abstraction.
Conclusion
The prior art made of record and not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
OH et al (US Publication No. 20200337580) discloses a method for learning and analyzing time series data using artificial intelligence.
MAVRIEUDUS et al (Foreign Publication CN-111656373-A) discloses a system and method for training a neural network model.
ZHU et al (Foreign Publication CN-112603324-A) discloses a method of training a neural network based on an improved loss function.
THIS ACTION IS MADE FINAL, necessitated by amendment. 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 extension fee 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 JONATHAN C EDOUARD whose telephone number is (571)270-0107. The examiner can normally be reached M-F 730 - 430.
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/JONATHAN C EDOUARD/Examiner, Art Unit 3683
/JASON S TIEDEMAN/Primary Examiner, Art Unit 3683
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