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 Restriction
Applicant’s election without traverse of Group I: Claims 1-16 in the reply filed on 05/26/2026 is acknowledged.
Claims 1-16 are examined.
Claims 17-20 are withdrawn.
Claim Objections
Claim 6 is objected to because of the following informalities: a space should be added between the words “to” and “learn” in line 2.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“Prediction information provider” in claims 1 and 8: interpreted as software/hardware specifically programmed to performed the claimed functions (see [0059], [0101] of specification).
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-8 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, the limitation “an artificial intelligence model configured to be trained to predict” (line 6) renders the claim indefinite because the claim does not require training or predicting with the model. This limitation suggests that the model is not trained yet and therefore does not predict anything; thus, the scope is unclear. The Examiner has interpreted the limitation to read “trained to predict”.
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 1-16 are rejected under 35 U.S.C 101 because the claimed invention is directed to non-statutory subject matter of abstract ideas under the mental processes grouping, without significantly more.
The framework for establishing a prima facie case of lack of subject matter eligibility requires that the Examiner determine: (1) Does the claim fall within the four categories of patent eligible subject matter; (2a) Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon and (2a) Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application; and (2b) Does the claim recite additional elements that amount of significantly more than the judicial exception.
Step (1)
The claimed invention in claims 1-16 are directed to a device and method, and thus, the claims all fall under one of the four patent eligible categories.
Step (2a) Prong 1 (Judicial Exception)
Regarding claims 1-16, the recited steps are directed towards mental processes of performing concepts in a human mind or by a human using a pen and paper (See MPEP 2106.04(a)(2) subsection (III)).
Independent claim 1 recites:
acquire individual ECG pairs measured at a certain period of time and generate a difference between the ECG pairs…;
predict an onset-AF possibility from an ECG difference.
Independent claim 9 recites:
acquiring individual electrocardiogram (ECG) pair measured at a certain period of time, and predicting a probability of onset-AF for an ECG difference between the ECG pairs
Under the broadest reasonable interpretation, these limitations require finding a difference between two ECG measurements and predicting the possibility/probability of onset atrial fibrillation based on the difference. These limitations are processes that, as drafted, cover that which can be wholly performed in a person’s mind via a series of mental observations and judgements. In particular, a person can analyze the differences between a patient’s ECG measurements to predict their chances of onset AF. These are data gathering and processing steps (acquire, generate, predict) that reflect mental processes.
Accordingly, claims 1 and 9 are directed to a judicial exception including one or more abstract ideas, specifically mental processes.
Independent claims 1 and 9 recite the corresponding apparatus associated with the system/method, including a processor and ECG preprocessor. Under the broadest reasonable interpretation, these claims also recite a judicial exception including one or more abstract ideas under the mental processes bucket.
The additional limitations in dependent claims 2-8, 10-16, including:
Claims 2 and 11- extract features from ECG signals/generate feature difference
Claims 3 and 12- ECG features are waveform features
Claims 4 and 13- ECG features include beat similarity, fibrillation wave energy, P-wave features
Claims 5 and 14- input data includes gender/age
Claims 6 and 10- AI model training based on patient visit data
Claims 7 and 16- performance indicator
Claims 8 and 15- provide decision-making assistance
These limitations comprise additional abstract ideas and/or further limit the abstract ideas of claims 1 and 9.
Step (2a) Prong 2 (Integration into a Practical Application)
This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. MPEP 2106.04(d).
For claims 1-16, the judicial exception is not integrated into a practical application.
Regarding claim 1, the additional element of providing the AF prediction to a designated device amounts to recitation of a generic results communication mechanism. Under the broadest reasonable interpretation, these elements are nothing more than the post-solution activity of providing results using generic components.
Regarding claims 1-16, the additional elements of a processor, ECG preprocessor, artificial intelligence model, and prediction information provider amount to recitation of a generic computer/processor. This additional element merely defines the field of use of the current claim. This additional element does not practically integrate the judicial exception because this element does not provide improvements to the functioning of a computer or to any the technical field under MPEP 2106.05(a). Furthermore, when the claims, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it is still in the mental processes grouping unless the claim limitation cannot practically be performed in the mind. Likewise, performance of a claim limitation using generic computer components does not preclude the claim limitation from being in the mental processes grouping.
Step (2b) (Inventive Concept)
The claims also 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 judicial exception into a practical application, the additional elements of a processor and ECG pre-processor in the field of cardiac monitoring are well-understood, routine and conventional activities previously known in the industry as indicated in the following references:
Scheinowitz et al. (US Pre-Grant Publication 2015/0343233) teaches that an ECG monitor/analyzer (embodied as a processor) has commercially available programs to detect cardiac conditions of interest [0046].
Cox et al. (US Pre-Grant Publication 2022/0071570) teaches a commercially available processor [0113].
Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claims 1-16 are thus rejected under 35 USC 101 for reciting patent-ineligible subject matter- abstract ideas.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-5, 7-9, 11-16 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Attia et al. (US Pre-Grant Publication 2022/0047201), hereinafter ‘Attia’.
Regarding claim 1, Attia teaches an apparatus (system 100, Fig. 1) for predicting atrial fibrillation (AF) (abstract, generate an atrial fibrillation prediction) operated by at least one processor ([0069], processor), comprising:
an electrocardiogram (ECG) preprocessor (interface 106, Fig. 1) configured to acquire individual ECG pairs (first/second NN inputs 118, 124, Fig. 1) measured at a certain period of time ([0042], interface receives ECG recording 116 from ECG recorder 104) and generate a difference between the ECG pairs (Fig. 4, ECG NN Input 402, 404, [0052], differences between ECG recordings) as input data for an artificial intelligence model (atrial fibrillation detection neural network 108, Fig. 1);
an artificial intelligence model (atrial fibrillation detection neural network 108, Fig. 1) configured to be trained (Fig. 2, training of neural network) to predict an onset-AF possibility (A-Fib prediction 120, Fig. 4) from an ECG difference ([0052], process inputs to generate prediction) and output a probability of onset-AF predicted from the input data (Fig. 10, [0038], generate output representing likelihood of patient developing AF); and
a prediction information provider configured to provide AF prediction including the probability of onset-AF to a designated device ([0075], feedback provided to user, display device, [0071], software).
Regarding claim 2, Attia teaches the device of claim 1, further comprising:
wherein the ECG preprocessor is configured to extract ECG features (morphological feature extractor 110, Fig. 1) from ECG signals of each ECG included in the ECG pair ([0044], measure various morphological features from ECG recording), and generate a feature difference between the two ECGs as the input data ([0052], features representing differences between ECG recordings).
Regarding claim 3, Attia teaches the device of claim 2, further comprising:
wherein the ECG features include P-QRS-T waveform features ([0044], ECG morphological features include P-wave, QRS-complex, T-wave, Fig. 5).
Regarding claim 4, Attia teaches the device of claim 3, further comprising:
wherein the ECG features further include at least one of ECG beat similarity, fibrillation wave energy, and P-wave features ([0044], area of the P-wave).
Regarding claim 5, Attia teaches the device of claim 1, further comprising:
wherein the input data further includes at least one of an individual gender and age (third NN input 126, Fig. 1, [0045], third NN input can be patient profile data and can include age, sex of patient).
Regarding claim 7, Attia teaches the device of claim 1, further comprising:
wherein the AF prediction further includes a performance indicator for the probability of onset-AF (Fig. 9, model performance study, [0054], accuracy, sensitivity, specificity).
Regarding claim 8, Attia teaches the device of claim 1, further comprising:
wherein the prediction information provider is configured to provide decision-making assistance information related to the AF prediction ([0040], recommend monitoring/treatment for patient based on likelihood of patient developing AF).
Regarding claim 9, Attia teaches an operating method of an apparatus for predicting atrial fibrillation (AF) ([0040], generate an atrial fibrillation prediction 120, Fig. 1) operated by at least one processor ([0069], processor), comprising:
acquiring individual electrocardiogram (ECG) pair (first/second NN inputs 118, 124, Fig. 1) measured at a certain period of time ([0042], interface receives ECG recording 116 from ECG recorder 104), and
predicting a probability of onset-AF for an ECG difference between the ECG pairs using an artificial intelligence model (atrial fibrillation detection neural network 108, Fig. 4) trained to predict an onset-AF possibility from the ECG difference ([0052], generate AF prediction based on features representing differences between ECG recordings).
Regarding claim 11, Attia teaches the method of claim 9, further comprising:
extracting ECG features (morphological feature extractor 110, Fig. 1) from ECG signals of each ECG included in the ECG pair ([0044], measure various morphological features from ECG recording), and generating a feature difference between the two ECGs as input data of the artificial intelligence model ([0052], features representing differences between ECG recordings, Fig. 4).
Regarding claim 12, Attia teaches the method of claim 11, further comprising:
wherein the ECG features include P-QRS-T waveform features ([0044], ECG morphological features include P-wave, QRS-complex, T-wave, Fig. 5).
Regarding claim 13, Attia teaches the method of claim 12, further comprising:
wherein the ECG features further include at least one of ECG beat similarity, fibrillation wave energy, and P-wave features ([0044], area of the P-wave).
Regarding claim 14, Attia teaches the method of claim 11, further comprising:
wherein the input data further includes at least one of an individual gender and age (third NN input 126, Fig. 1, [0045], third NN input can be patient profile data and can include age, sex of patient).
Regarding claim 15, Attia teaches the method of claim 9, further comprising:
predicting the AF including the probability of onset-AF ([0038], generate output representing likelihood of patient developing AF); and
providing decision-making assistance information related to the AF prediction to a designated device ([0040], recommend monitoring/treatment for patient based on likelihood of patient developing AF).
Regarding claim 16, Attia teaches the method of claim 15, further comprising:
wherein the AF prediction further includes a performance indicator for the probability of onset-AF (Fig. 9, model performance study, [0054], accuracy, sensitivity, specificity).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 6 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Attia et al. (US Pre-Grant Publication 2022/0047201).
Regarding claims 6 and 10, Attia teaches the device/method of claims 1/9, further comprising:
wherein the artificial intelligence model is trained to learn a relationship between an input and a label tagged in the input using training data (Fig. 2, model training, [0049], compares predictions 208 to labels in training examples 204 that indicate target predictions) to output a value between 0 and 1 predicted for the input data ([0040], prediction is a probability), and the training data is generated using ECGs of patients whose first ECG is normal ([0049], ECG of a patient under normal sinus rhythm), and whose ECG measured at another visit is normal or has AF, and the label is given according to the ECG measured at another visit ([0049], label indicates whether the patient is known to have actually experienced AF at another time).
Attia additionally teaches an atrial fibrillation detection neural network 108 that processes ECG recordings at a first time 402 and a second time 404, both under normal sinus rhythm (see [0052], Fig. 4), then processes the inputs to generate an AF prediction. Attia also teaches the training neural network subsystem 206 has the same architecture of the neural network 108 (see [0047], Fig. 2)
Attia does not specifically teach that the training data is generated using patient ECGs from three visits, the first two being normal and the third being normal or AF.
It is the Examiner’s position that the atrial fibrillation detection neural network 108 of Fig. 4 would require a training dataset. Based on the system of Fig. 4, this training dataset would contain two normal ECGs and a label indicating if that patient experienced AF at another time.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Edelberg et al. (US Pre-Grant Publication 2024/0350095) teaches a system/method for detecting a cardiac health status for a subject using ECG data (abstract). See Figs. 6 and 12, [0124] (identifying changes in P wave from past measurements, patient follow up visit ECGs).
Zimmerman (US Pre-Grant Publication 2022/0384045) teaches a system/method for predicting the likelihood that a patient will suffer from a cardiac event (abstract). See Fig. 5B, [0151] (multiple ECG measurements).
Lange (US Pre-Grant Publication 2017/0258406) teaches a system/method for monitoring cardiac function and providing a clinical indication associated with a disease (abstract). See Fig. 2, [0036] (recording ECG data periodically).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELIZABETH L OKONAK whose telephone number is (571)272-1594. The examiner can normally be reached Monday-Friday 8-5.
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, Benjamin Klein can be reached at (571) 270-5213. 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.
/E.L.O./Examiner, Art Unit 3792
/SHIRLEY X JIAN/Primary Examiner, Art Unit 3792
July 23, 2026
Prosecution Insights