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 .
The present Office Action is in response to the Request for Continued Examination dated 10 September 2026.
Request for Continued Examination
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10 September 2026 has been entered.
DETAILED ACTION
In the RCE filed 10 September 2026:
Claims 16, 23, 26, 33-34 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 recite a computer-implemented method, and system, which are within a statutory category.
Step 2A1
The limitations of:
Claims 16, 26 (Claim 16 being representative)
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, wherein the first neural network model is 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 the second neural network model 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 for causing graphical display, of a graph overlaying the ECG signal data and the comparison ECG signal data exhibiting the second plurality of features for presenting morphological features contributing to the disease classification, 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.
Response to Arguments
Rejection under 35 U.S.C. § 101
Regarding the rejection of Claims 16-19, 21-29, 31-37, the Examiner has considered the Applicant’s arguments; however the arguments are not persuasive. Any arguments inadvertently not addressed are unpersuasive for at least the following reasons. Applicant argues:
Applicant respectfully submits that the Office has failed to identify any personal behavior that claims 16 or 26 manage, or any interpersonal interaction that either claim organizes. The Office characterizes the claims as covering "certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions)" and asserts that 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." Office Action at 3-4. However, the Office does not identify whose behavior is managed, what relationship is organized, or what interaction between people is directed. That omission is material under MPEP § 2106.04(a)(2)(II), which explains that "the determination should be based on whether the activity itself falls within one of the sub-groupings" and that "this grouping is limited to activity that falls within the enumerated sub-groupings of fundamental economic principles or practices, commercial or legal interactions, and managing personal behavior and relationships or interactions between people." MPEP § 2106.04(a)(2)(II). Because the Office has not identified any claimed activity that actually falls within one of these enumerated sub-groupings, the rejection under the grouping is unsupported.
Furthermore, the Office's treatment of the training limitations is internally inconsistent. The Office describes training as "fitting a model to data" and characterizes it as a "mathematical concept," while simultaneously placing the same limitations in certain methods of organizing human activity, collapsing both groupings into "a single abstract idea." Office Action at 4. When the Office relies on multiple abstract idea groupings, each grouping must be independently established, i.e. the claim must actually recite limitations that fall within each asserted grouping. Here, the Office has not independently established either grouping.
Applicant further submits that the Office's reliance on Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 (Fed. Cir. Apr. 18, 2025), does not address the claims as written. Recentive involved generic, unspecified machine-learning training applied to a new data environment; its discussion concerned training that did not specify how the model itself was improved. Claims 16 and 26 are materially different. Claim 16 recites "training the second neural network model on a second plurality of features extracted from the second training data, the second plurality of features configured for input to the first neural network model, such that: an estimated disease classification output by the first neural network model 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 by the first neural network model corresponding to the second plurality of features is positive when the reference disease classification corresponding to the second training data is negative." Claim 16 further requires "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; [and] updating one or more parameters of the second neural network model based on the loss value." There is a defined structural and functional relationship between two models unlike the generic model fitting or the mere selection of training material in Recentive. This relationship is also subject matter that the Office found absent from the prior art. See Office Action at 7.
Regarding (a), the Examiner respectfully disagrees. The claim represents a list of steps or instructions for a human to follow, which therefore, recites the abstract idea of certain methods of organizing human activity. Furthermore, the Examiner has been consistent with its analysis, as per the MPEP, when it comes to the analysis of the training limitations. Furthermore, the first and second neural network models recite generic neural network models because there is nothing structurally unique recited in the claims to perceive otherwise. Whether or not the claims are subject matter free of prior art has no relevance as to subject matter eligibility.
Applicant submits that, as amended, the claims recite features similar to features in Example 37 Claim 1 (Subject Matter Eligibility Examples: Abstract Ideas p. 18-19), which was found to be patent eligible under Step 2A prong 2, and that those features integrate the claims into a practical application as "a specific manner of automatically displaying ...to the user based on usage which provides a specific improvement over prior systems, resulting in an improved user interface for electronic devices," as in Claim 1 of Example 37.
As discussed in the Subject Matter Eligibility Examples: Abstract Ideas, Claim 1 of Example 37 is found patent eligible under Step 2A prong 2 because "[t]he claim as a whole integrates the [judicial exception] into a practical application. Specifically, the additional elements recite a specific manner of automatically displaying ... resulting in an improved user interface ... [t]hus, the claim is eligible because it is not directed to the recited judicial exception."
Similarly, the current claims recite a specific manner of displaying ECG signal data that is an improvement to existing interfaces. That is, by displaying a graph overlaying ECG signal data with comparison ECG data, the graph may serve as a "grounds for explaining the features of the medical data that the first neural network model 200 has interpreted to predict the medical data to be positive or negative for the disease." Specifically, the graph aims to show comparison ECG data that identifies the specific features that cause a specific disease diagnosis, which may vary in every signal. This results in an improved user interface as existing research may only statically deem certain regions of an ECG signal to be related to disease but does not detect what features in particular caused a specific disease classification as the current application aims to do (Specification at Background).
Just as the additional features in the USPTO-provided example recite a specific manner of displaying on a user interface, thus resulting in an improved user interface, the current claims recite a specific manner of displaying, for example, embodiments of the application relate to displaying a counter factor signal (e.g., comparison ECG data) overlaid with ECG signal of a user on a user interface, e.g., so that a user can see the specific morphological features that impact or cause the disease classification on a user interface.
Regarding (b), the Examiner respectfully disagrees. There is nothing recited in the claims that would even remotely suggest an improvement to the graphical user interface, as compared to Example 37. Displaying multiple pieces of data on a graph does not provide an improvement to the graphical user interface.
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:
WANG et al (Foreign Publication WO-2019200746-A1) discloses a method, an apparatus, a computer device, and a storage medium for detecting an ECG signal.
CHU et al (Foreign Publication CN-111568410-A) discloses a method for ECG data classification.
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.
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, Robert Morgan can be reached on (571) 272 - 6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JONATHAN C EDOUARD/Examiner, Art Unit 3683
/JASON S TIEDEMAN/Primary Examiner, Art Unit 3683
