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 .
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 May 27, 2026 has been entered.
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 1-7, 10-17, 20 and 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claim(s) recite(s) the mentally performable steps of supplying unlabeled waveform training data to a transformer model to pre-train the transformer model by masking a portion of an input to the transformer model; supplying labeled waveform training data to the transformer model without masking a portion of the input to the transformer model, wherein each waveform in the labeled waveform training data includes at least one label identifying a feature of the waveform; and classifying at least one feature of the target waveform, wherein the at least one classified feature corresponds to the least one label of the labeled waveform training data. Such steps can be performed within the mind of a cardiologist (or with the aid of pen and paper) as they involve observation, analysis, judgement and opinion. The applicant, in fact, states that a cardiologist may label the ECG waveform in order to pre-train the system (par. 0075).
This judicial exception is not integrated into a practical application because there are no improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a), because the memory hardware and processor hardware function in their usual capacity of storing, obtaining and processing data; there is no application or use of a judicial exception to effect a particular treatment or prophylaxis for disease or medical condition, but only classification of data – see Vanda Memo; there is no application of the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b), but only generic hardware in conventional arrangements; there is no transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05(c), but only data manipulation; and there is no application or use of the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to the particular technological environment of ECG signal processing using machine learning, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP 2106.05(e) and the Vanda Memo issued in June 2018.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the steps of obtaining labeled and unlabeled waveform training data, and capturing, by multiple electrodes positioned at different locations on a patient, electrical signals of the patient to obtain ECG waveform measurements to generate and supply a target waveform to the transformer model, wherein at least one processor is configured to receive signals from the multiple electrodes to generate the target waveform as an ECG waveform (claim 1), constitute insignificant data gathering/processing that would be required in any implementation of the abstract idea. In order for an ECG waveform to be analyzed, it must first be captured, which requires multiple electrodes positioned at different locations on a patient (i.e., a standard ECG monitor). Signals from the individual ECG electrodes would have to be processed and combined by a processor in order to form the standard leads and provide a waveform recognizable as an ECG. All ECG monitoring devices require such capturing and processing steps. The presence of these steps therefore fails to provide any practical application to the abstract idea. Reference to a transformer model is insufficient because it only nominally relates the invention to the particular environment of machine learning, where machine learning is a tool to perform the exception. In effect, such limitations are akin to saying, “apply it using a machine.”
The collection of data necessary to implement an algorithm is further well-understood, routine and conventional (WURC) in the art. Clearly input data is required if one desires to process the data. The applicant states that the system described is merely exemplary and the method may be implemented in other computing devices and/or systems (pars. 0069 and 0098). A standard 12-lead ECG, for example, requires multiple electrodes located at standard positions, and a processor configured to receive the raw electrical signals from each electrode, to generate a target ECG waveform comprising the various leads (e.g., Leads I, II, III, AVR, AVL, AVF). The applicant discloses that more than 300 million ECGs of this type are recorded annually (pars. 0003, 0034).
Claims 2-7, 12-15, 17 and 20 contain no new additional elements. It should be noted that the act of compressing the target waveform (claim 20) is disclosed as involving the use of FFT or other type of compression technique (par. 0035). FFT is a technique subject to mental performance via mathematical operations.
Regarding claim 10, the reference to a processing server and a local storage device separate from the processing server represent insignificant extra-solution activity. The processing server is generic and operates in its usual capacity of processing data. The local storage device is also generic and it too operates in its usual capacity of storing data. The fact that the storage device is separate from the processing server is only nominally related to the invention and thus fails to integrate the abstract idea into a practical application. The combination of a generic processing server and a generic local storage device separate from the processing server is also insignificant as this combination would be required in any computerized system. Again, the fact that the storage device is separate from the processing server is only nominally related to the invention. Also as stated above, the combination of a generic processing server and a generic local storage device separate from the processing server is WURC in the art. The applicant assesses no criticality to the structure and states that the invention is not limited to any one arrangement, but may include different components and/or arrangements of components in other computing devices (par. 0098).
Substantially related comments apply to patentably indistinct apparatus claim 11 and non-transitory computer-readable medium claim 16.
Regarding new claim 21, as discussed above, the obtainment of health data is considered insignificant data gathering that is WURC in the cardiac diagnostic art. The applicant states that such information is widely gathered on an annual basis, where it is widely known that factors such as activity levels, weight of the patient, diet, family history, etc., affect one’s heart health and may assist the cardiologist in rendering an accurate diagnosis. Supplying the health data to the transformer model represents insignificant data input to a computer, where the computer functions as a tool to gather the data and run the abstract idea. It is only nominally related to the invention as a method of supplying data to a machine learning model.
Response to Arguments
Applicant's arguments filed May 27, 2026 have been fully considered but they are not persuasive.
Regarding the rejection of claims under §101, the applicant argues that the newly amended claim features cannot be performed within the human mind.
The examiner agrees that data gathering using electrodes cannot be performed within the human mind, and as such, has not considered such data gathering and the formation of ECG signals therefrom to constitute mentally performable actions. As discussed in the rejection, however, the examiner considers the newly added material to represent insignificant data gathering and data processing that would be required in any method or system attempting to analyze a patient’s ECG. The placement of multiple electrodes on the body and the generation of an ECG waveform from the raw sensor signals is a basic requirement for any electrocardiographic monitoring system. It does not integrate the abstract idea into a practical application. The use of multiple electrodes and the generation of an ECG does not solve any known problem in the art. It is merely the means by which the data is gathered and only nominally related to the invention (see MPEP 2106.05(g)). The existence of elements outside of the abstract idea in a claim does not eliminate the claim from the realm of abstract ideas, as the mere inclusion of a judicial exception in a claim means that the claim recites a judicial exception under Step 2A, Prong One (MPEP 2106.04, II, A, 2).
Nor has the examiner argued that a cardiologist can mentally perform all of the steps recited by the pending claims on file (e.g., steps related to the additional element of data gathering is not considered mentally performable), but only those involving observation, evaluation, judgement and opinion. Labeling is a step that a cardiologist can mentally perform, such as discussed in par. 0075 of the present invention. The act of masking data is also considered mentally performable. The applicant does not define the term “masking” in any particular manner to eliminate such interpretation. Masking in its broadest reasonable terms merely involves the act of hiding or ignoring data –which a human is capable of doing when presented with data. A transformer model is simply a name given to define a particular mathematical construct in machine learning. The human mind is capable of performing mathematical operations. The human mind can further classify features of a target waveform to detect heart arrhythmias, such as a cardiologist would do when reviewing the ECG waveform.
The applicant then refers to the USPTO’s August 4, 2025 Memorandum addressing claims directed to AI, where it is stated that claim limitations that encompass AI in a way that cannot be practically performed in the human mind do not fall within the grouping of abstract ideas.
As argued above, the masking of a portion of data can be performed within the human mind because it involves judgement as to which portions of the data to hide/ignore/mask. Similarly, the labeling of data to identify a feature of the waveform and classifying the at least one feature can be done by a cardiologist. A transformer model is based on mathematical constructs involving the calculation of the relationship between elements in a sequence. A human mind is capable of such determinations. There are no limitations regarding the complexity of the model, the accuracy of the model, or any other specifics associated with the model so as to make performance of the invention within the mind impractical. The examiner considers the current invention to be analogous to Example 47, Claim 2 contained in the July 2024 Subject Matter Eligibility Examples relating to artificial intelligence. Like in Example 47, the reference to machine learning (i.e., use of a transformer model) involves a series of mathematical calculations (e.g., vector and matrix operations using linear algebra; note also the various mathematical equations associated with the training of the transformer model as discussed in pars. 0047+). The fact that such operations may be performed on a generic computer (see par. 0098) is insufficient to convey eligibility as the generic computer is merely a tool upon which the abstract idea is performed. The alleged improvement does not lie within the computer itself, but within the mathematical calculations and mentally performable operations.
The applicant refers to Example 39 in the Memorandum, and states that no judicial exception is recited because, like in Example 39, there are no mathematical relationships, calculations, formulas or equations set forth.
The examiner, however, considers the judicial exception to relate to a mental process which is distinct from a mathematical concept (see MPEP 2106.04(a)(2)). As already argued, the limitations associated with the abstract idea are considered to be mentally performable.
Similar to arguments already made above, the applicant asserts that under Step 2A, Prong Two that the claims are patent eligible because they recite a computerized method including specific ways to pre-train and fine-tune a transformer model, obtain ECG waveforms measurements from multiple sensors/electrodes, and classify a detected heart arrhythmia.
Again, the pre-training and fine-tuning of the transformer model involve mathematical operations that can be performed within the human mind or with pen and paper. The collection of ECG measurements via multiple sensors, while not being within the capabilities of a human mind, represents insignificant data gathering that would be required in any system attempting to analyze ECG data in order to detect cardiac arrhythmias. The collection of ECG data using multiple generic sensors is WURC in the art (e.g., use of a standard 12-lead sensor device). The classification of the detected heart arrhythmia is performable within the human mind as cardiologists have been performing such diagnosis using ECG waveforms since the invention of the ECG. The reference to a transformer model merely limits the field of use to machine learning, and as stated above, involves mathematical operations capable of being performed within the mind. The claimed invention therefore fails to provide practical integration of the judicial exception.
Regarding new claim 21, the applicant argues that a practical application is provided by improving diagnostic accuracy of the output of the transformer model, with the use of additional health data. This is not convincing, however, because if an improvement is asserted, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing the improvement. The applicant does not provide any discussion in the specification that would identify such a feature as an improvement in technology other than bare assertions. There is no discussion of any identified technical problem or explanation including details of an unconventional technical solution (see MPEP 2106.05(a)). Cardiologists have conventionally analyzed ECG data along with other patient health information (e.g., family history, weight, exercise/activity levels, blood sugar levels, etc.) when rendering their diagnosis. The machine of the present invention acts simply as a tool upon which the abstract idea is run. The mere presence of the machine learning model acting on standard/conventional data and performing steps that a human can perform mentally, thus fails to provide any practical application.
Conclusion
All claims are identical to or patentably indistinct from, or have unity of invention with claims in the application prior to the entry of the submission under 37 CFR 1.114 (that is, restriction (including a lack of unity of invention) would not be proper) and all claims could have been finally rejected on the grounds and art of record in the next Office action if they had been entered in the application prior to entry under 37 CFR 1.114. Accordingly, THIS ACTION IS MADE FINAL even though it is a first action after the filing of a request for continued examination and the submission under 37 CFR 1.114. See MPEP § 706.07(b). 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 KENNEDY SCHAETZLE whose telephone number is (571)272-4954. The examiner can normally be reached on the 2nd Monday of the biweek and W-F.
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 E. Hamaoui can be reached on 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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/KENNEDY SCHAETZLE/Primary Examiner, Art Unit 3796
KJS
September 5, 2026
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