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 .
Status of Claims
This action is in response to the reply received 01/09/2026.
Claims 12, 22 and 30 were amended 01/09/2026.
Claims 26-27 were canceled 01/09/2026.
Claims 33-36 were added 01/09/2026.
Claims 12-17, 21-25, 28, 30-36 are currently pending and have been examined.
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 12-17, 21-25, 28, 30-36 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 12-17, 21-25, 28, 30-36 are drawn to a system which is a statutory category of invention (Step 1: YES).
Independent claim 12 recites classify and associate beats within electrocardiogram (ECG) data as at least one of a first beat classification, a second beat classification, or a third beat classification; trained to generate first latent space representations of beats associated with the first beat classification, trained to generate second latent space representations of beats associated with the second beat classification, trained to generate third latent space representations of beats associated with the third beat classification, and configured to apply to the first, second, and third latent space representations to associate associating similar shaped beats with each other wherein configured to input ECG data of the beats associated with the first beat classification, the beats associated with the second beat classification, and the beats associated with the third beat classification.
The recited limitations, as drafted, under their broadest reasonable interpretation, cover certain methods of organizing human activity between patients and care providers, as reflected in the specification, which states that “Classifying individual heartbeats can help accurately identify and classify cardiac events such as abnormal cardiac rhythms so that critical alerts can be provided to patients, physicians, or other care providers and patients can be treated.” (see: specification paragraph 3). 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. The present claims cover certain methods of organizing human activity because they address “The server 106 includes multiple models, platforms, layers, or modules that work together to process and analyze the ECG data such that cardiac events can be detected, filtered, prioritized, and ultimately reported to a patient's physician for analysis and treatment.” (see: specification paragraph 50). Accordingly, the claims recite an abstract idea(s) (Step 2A Prong One: YES).”
The judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including “system”, “server”, “initial machine learning model” “first trained machine learning model”, “second trained machine learning model”, and “third trained machine learning model”, “one or more processors”, “clustering algorithm” are recited at a high level of generality (e.g., that the calculating and analyzing of data is performed using generic computer components with instructions to use generic machine learning algorithms (i.e., generic clustering algorithms and machine learning models) are executed to perform the claimed limitations). Such that they amount to no more than mere instructions to apply the exception using generic computer components. See: MPEP 2106.05(f).
Hence, the 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. Accordingly, the claims are directed to an abstract idea (Step 2A Prong Two: NO).
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, using the additional elements to perform the abstract idea amounts to no more than mere instructions to apply the exception using generic components. Mere instructions to apply an exception using a generic component cannot provide an inventive concept. See MPEP 2106.05(f).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are configured to perform well-understood, routine, and conventional activities previously known to the industry. See MPEP 2106.05(d). Said additional elements are recited at a high level of generality and provide conventional functions that do not add meaningful limits to practicing the abstract idea. The originally filed specification supports this conclusion at Figure 1, Figure 3, Figure 10 and
Paragraph 49, “FIG. 1 illustrates a patient 10 and an example system 100. The system 100 includes a monitor 102 attached to the patient 10 or implanted in the patient 10 (e.g., pacemaker, ICD, CRT, or ICM) to detect cardiac activity of the patient 10. The monitor 102 may produce electric signals that represent the cardiac activity in the patient 10. For example, the monitor 102 may detect the patient's heart beating (e.g., using infrared sensors, electrodes, heart sounds) and convert the detected heartbeat into electric signals representing ECG data. In certain instances, the monitor 102 stores the ECG data of a patient study (e.g., one or more days of ECG data), after which the ECG data is transmitted to another device or system such as a server. Additionally or alternatively, the monitor 102 transmits the ECG data to a mobile device 104 (e.g., a mobile phone). In such instances, the mobile device 104 can include a program (e.g., mobile phone application) that receives, processes, and analyzes the ECG data. For example, the program may analyze the ECG data and detect or flag cardiac events (e.g., periods of irregular cardiac activity) contained within the ECG data.”
Paragraph 50, “The ECG data (and associated metadata, if any) is transmitted to and stored by a cardiac event server 106 (hereinafter "the server 106'' for brevity). The server 106 includes multiple models, platforms, layers, or modules that work together to process and analyze the ECG data such that cardiac events can be detected, filtered, prioritized, and ultimately reported to a patient's physician for analysis and treatment. In the example of FIG. 1, the server 106 includes one or more machine learning models 108A, 108B, and 108C, a clustering algorithm module 109, a cardiac event router 110, a report platform 112, and a notification platform 114. Although only one server 106 is shown in FIG. 1, the server 106 can include multiple separate physical servers, and the various models/platforms/modules/layers can be distributed among the multiple servers. Each of the models/platforms/modules/layers can represent separate programs, applications, and/or blocks of code where the output of one of the models/platforms/modules/layers is an input to another of the models/platforms/modules/layers. Each of the models/ platforms/modules/layers can use application programming interfaces to communicate between or among the other models/platforms/modules/layers as well as systems and devices external to the server 106.”
Paragraph 32, where “In Example 29, the system of Example 27, wherein the first, second, and third trained machine learning models are deep learning neural networks.”
Paragraph 86, where “FIG. 10 shows an outline of steps of a method 400 for training the machine learning model 108C (including models 108C-N, 108C-V, 108C-S). In certain instances, the machine learning model 108 is an autoencoder neural network (e.g., an autoencoder DNN). Autoencoder neural networks can be trained to take a large set of input data (e.g., hundreds of thousands or millions of beats worth of ECG data) and extract features into much smaller latent space representations which can be used as an approximation of the original input data.”
Paragraph 95, “The bus 510 represents what may be one or more busses (such as, for example, an address bus, data bus, or combination thereof). Similarly, in instances, the computing device 500 may include a number of processors 520, a number of memory components 530, a number of 1/0 ports 540, a number of 1/0 components 550, and/or a number of power supplies 560. Additionally, any number of these components, or combinations thereof, may be distributed and/or duplicated across a number of computing devices.”
Paragraph 66, “In certain instances, the clustering algorithm module 109 is programmed to apply a clustering algorithm such as the k-means clustering algorithm or a derivation or variation thereof to the latent space representations. In certain instances, the same clustering algorithm module 109 and the same algorithm is used to process the latent space representations of each of the third machine learning models (108C-N, 108C-V, 108C-S). In certain instances, the output of the clustering algorithm module 109 includes assigning a value (e.g., an identifier such as a number) to each beat that is indicative of the group selected by the clustering algorithm module 109”
Viewing the limitations as an ordered combination, the claims simply instruct the additional elements to implement the concept described above in the identification of abstract idea with route, conventional activity specified at a high level of generality in a particular technological environment.
Hence, the claims as a whole, considering the additional elements individually and as an ordered combination, do not amount to significantly more than the abstract idea (Step 2B: NO).
Dependent claims 13-17 and 21-25, 28, 30-36 when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are directed to an abstract idea without significantly more. Claim 13-17 and 21-28, 30-33 recite classifying, clustering, and generating data using the generic machine learning models and algorithms as shown in the parent claims above.
Claim 14 further recites “a k-means clustering algorithm” which is recited at a high level of generality (e.g., that the generic clustering algorithm that uses machine learning is performed using generic computer components with instructions are executed to perform the claimed limitations) as shown in the specification paragraph 66. Such that they amount to no more than mere instructions to apply the exception using generic computer components. See: MPEP 2106.05(f).
Claim 16 further recites “neural networks” which is recited at a high level of generality (e.g., that neural networks that use machine learning is performed using generic computer components with instructions are executed to perform the claimed limitations) as shown in the specification paragraph 57. Such that they amount to no more than mere instructions to apply the exception using generic computer components. See: MPEP 2106.05(f).
Claim 17 further recites “deep learning neural networks” which is recited at a high level of generality (e.g., that deep learning neural networks that use machine learning is performed using generic computer components with instructions are executed to perform the claimed limitations) as shown in the specification paragraph 57. Such that they amount to no more than mere instructions to apply the exception using generic computer components. See: MPEP 2106.05(f).
Claim 21 further recites “layers of nodes” which are nominal or tangential addition to the abstract idea and amount to insignificant post-solution activity concerning an insignificant application. The addition of an insignificant extra-solution activity limitation does not impose meaningful limits on the claim such that is it not nominally or tangentially related to the invention. In the claimed context, these claimed additional elements are incidental to the performance of correlating patient data using generic neural networks as outlined in the recitations above. See: MPEP 2106.05(g).
Claim 32 further recites “a remote computing system” which is recited at a high level of generality (e.g., that the receiving and modifying of data performed using generic computer components with instructions are executed to perform the claimed limitations) as shown in the specification paragraph 51. Such that they amount to no more than mere instructions to apply the exception using generic computer components. See: MPEP 2106.05(f).
Claim 34 further recites “a deep convolutional neural network” which is recited at a high level of generality (e.g., that neural networks that use machine learning is performed using generic computer components with instructions are executed to perform the claimed limitations) as shown in the specification paragraph 57. Such that they amount to no more than mere instructions to apply the exception using generic computer components. See: MPEP 2106.05(f).
Claim 35 further recites “a fully connected neural network” which is recited at a high level of generality (e.g., that neural networks that use machine learning is performed using generic computer components with instructions are executed to perform the claimed limitations) as shown in the specification paragraph 57. Such that they amount to no more than mere instructions to apply the exception using generic computer components. See: MPEP 2106.05(f).
Claim 36 further recites “the deep convolutional neural network” which is recited at a high level of generality (e.g., that neural networks that use machine learning is performed using generic computer components with instructions are executed to perform the claimed limitations) as shown in the specification paragraph 57. Such that they amount to no more than mere instructions to apply the exception using generic computer components. See: MPEP 2106.05(f).
These claims fail to remedy the deficiencies of their parent claims above, and therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
Allowable Subject Matter
Claims 12-17, 21-25, 28, 30-36 are allowable over the prior art of Ravishankar (US 2021/0204884 A2), Fontanarava (US 2022/0039729 A1), Clifton (US 2022/0249031 A1), and Teplitzky (WO 2020112739 A3). The newly amended independent claim limitation of the server being configured to input ECG data of the beats associated with the different beat classifications of the different trained machine learning model in combination with the other claim limitations overcomes the prior art of record. A new prior art search was conducted and found the prior art of Brue (US 20210313067 A1) that teaches using multiple machine learning models to process medical data, however it did not explicitly teach training the models based on beat classification of ECGs.
Response to Arguments
The arguments filed 01/09/2026 have been fully considered.
Regarding the arguments pertaining to the Claim Objections, these arguments are persuasive. The amendments overcome the objections and they have been withdrawn.
Regarding the arguments pertaining to the 103 rejection, these arguments are persuasive. The amendments to independent claim 12 are not taught by the prior art of record and the rejections to claim 12 and therefore its dependent claims are withdrawn.
Regarding the arguments pertaining to the 101 rejection, these arguments are not persuasive. The 101 rejection is properly made under current USPTO guidelines.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Brue (US 20210313067 A1) teaches using multiple machine learning models to process medical data, however it did not explicitly teach training the models based on beat classification of ECGs.
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.
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/K.A.S./Examiner, Art Unit 3686
/JASON B DUNHAM/Supervisory Patent Examiner, Art Unit 3686
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