CTNF 19/228,075 CTNF 92386 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 the Claims The office action is in response to the claims filed on June 4, 2025 for the application filed June 4, 2025 which claims priority to a foreign application filed on June 5, 2024. Claims 1-20 are currently pending and have been examined. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-20 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. Eligibility Step 1: Under step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, claims 1-7 are directed towards a method (i.e. a process), which is a statutory category. Claims 8-14 are directed towards a device (i.e. a machine), which is a statutory category. Claims 15-20 are directed towards a non-transitory computer-readable medium (i.e. a manufacture), which is a statutory category. Since the claims are directed toward statutory categories, it must be determined if the claims are directed towards a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea). In the instant application, the claims are directed towards an abstract idea. Eligibility Step 2A, Prong One: Under step 2A, prong one of the 2019 Revised Patent Subject Matter Eligibility Guidance, independent claims 1, 8 and 15 are determined to be directed to an judicial exception because an abstract idea is recited in the claims which fall within the subject matter groupings of abstract ideas. The abstract idea (identified in bold) recited in the representative claim 8 is identified as: A device comprising: a memory configured to store instructions; and one or more processors configured to: receive, by a rule-based electrocardiogram (ECG) model of the device, an ECG; determine , by the rule-based ECG model of the device, features of the ECG ; determine , by the rule-based ECG model of the device, a diagnosis using the features of the ECG and criteria extracted by an artificial intelligence (AI) model; and transmit the diagnosis. The identified limitations of the abstract idea fall within the subject matter grouping of mental processes. If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea. A human mind is capable of determining features of a ECG, extracting criteria for diagnosis based on ECG features and determining a diagnosis using the features of the ECG and criteria using observations, evaluations, judgements and opinions. Accordingly, claims 1, 8 and 15 recite an abstract idea under step 2A, prong one. Eligibility Step 2A, Prong Two: Under step 2A, prong two of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether the identified abstract ideas are integrated into a practical application. After evaluation, there is no indication that any additional elements or combination of elements integrate the abstract idea into a practical application, such as through: an additional element that reflects an improvement to the functioning of a computer, or an improvements to any other technology or technical field; an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; an additional element that implements the judicial exception with, or uses the judicial exception in connection with, a particular machine or manufacture that is integral to the claim; an additional element that effects a transformation or reduction of a particular article to a different state or thing; or an additional element that applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. As shown below, the additional elements, other than the abstract idea per se, when considered both individually and as an ordered combination, amount to no more than a recitation of: generally linking the abstract idea to a particular technological environment or field of use; insignificant extra-solution activity to the judicial exception; and/or adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea as evidenced below. The additional elements recited in representative claim 8 are identified in italics as: A device comprising: a memory configured to store instructions; and one or more processors configured to: receive, by a rule-based electrocardiogram (ECG) model of the device, an ECG; determine , by the rule-based ECG model of the device, features of the ECG ; determine , by the rule-based ECG model of the device, a diagnosis using the features of the ECG and criteria extracted by an artificial intelligence (AI) model ; and transmit the diagnosis. The additional limitations of “A device comprising: a memory configured to store instructions; and one or more processors configured to:” and “by a rule-based electrocardiogram (ECG) model of the device” and “by an artificial intelligence (AI) model” are determined to be mere instructions to apply an abstract idea under MPEP §2106.05(f). The device, memory and processor are recited at a high level of generality and used in their ordinary capacity to perform the abstract idea. The rule-based model and AI model generally apply the abstract idea without placing any limits on how the models. Rather, these limitations only recite the outcome of “determining” features and a diagnosis and “extracting” criteria and do not include any details about how the “determining” and “extracting” are accomplished . Therefore, these additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or no more than mere instructions to implement an abstract idea or other exception on a computer or no more than merely using a computer as a tool to perform an abstract idea. The additional limitations of “receive, by a rule-based electrocardiogram (ECG) model of the device, an ECG” and “transmit the diagnosis” are determined to be no more than the insignificant extra-solution activity to the judicial exception of mere necessary data gathering and data outputting under MPEP §2106.05(g). Accordingly, claims 1, 8 and 15 do not recite additional elements which integrate the abstract idea into a practical application. Eligibility Step 2B: Under step 2B of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether provide an inventive concept by determining if the claims include additional elements or a combination of elements that are sufficient to amount to significantly more than the judicial exception. After evaluation, there is no indication that an additional element or combination of elements 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, the additional limitations are determined to be mere instructions to apply an abstract idea under MPEP §2106.05(f) and the insignificant extra-solution activity to the judicial exception of mere necessary data gathering and data outputting under MPEP §2106.05(g), which do not amount to significantly more than the abstract idea. Evidence that receiving necessary data for the abstract idea and transmitting a result of the abstract idea are well-understood, routine and conventional activities is provided by MPEP §2106.05(d), subsection II. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements amounts to an inventive concept. Dependent Claims: The dependent claims merely present additional abstract information in tandem with further details regarding the elements from the independent claims and are, therefore, directed to an abstract idea for similar reasons as given above. None of these limitations are deemed to integrate the claims into a practical application or to amount to significantly more than the abstract idea as detailed below. Claims 2, 9 and 16 merely define the type of AI model used without any details as to how the decision tree functions to accomplish the determining and extracting and is therefore also determined to be mere instructions to apply an abstract idea under MPEP §2106.05(f). Regarding claims 3, 10 and 17, the extracting criteria based a decision branch is determined to be encompassed by the abstract idea as this can be performed mentally. The “by the AU model and “branch of the AI model” is determined to be mere instructions to apply an abstract idea under MPEP §2106.05(f). Claims 4-7, 11-14 and 18-20 are determined to be encompassed by the abstract idea as the steps may be performed in the human mind. Therefore, whether taken individually or as an ordered combination, 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 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 – 07-08-aia AIA (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. 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15 AIA Claim s 1-3, 6 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by Yu et al. (U.S. Pub. No. 2021/0100468) . Regarding claim 1, Yu further discloses method comprising: receiving, by a rule-based electrocardiogram (ECG) model, an ECG ( Paragraph [0089], Method 600 begins at 602, where acquired ECG data is received by a hybrid ECG diagnostic system, wherein the trained hybrid ECG diagnostic system comprises a rule-based system, such as rule-based system 204 described above, and a deep neural network, such as deep neural network 210 described above. The received ECG data includes one or more ECG waveforms/signals, indicating a heart's electrical activity through time. In some embodiments, the received ECG data may comprise data from a twelve lead ECG, as described in more detail above, with reference to FIG. 1. Also see paragraph [0082]. ); determining, by the rule-based ECG model, features of the ECG ( Paragraph [0090], At operation 604, the received ECG data is input into an input layer of a convolutional neural network of the deep neural network, and mapped to a plurality of features. Said another way, at operation 604, a convolutional neural network extracts one or more features from ECG data using one or more convolutional layers. Also see paragraph [0053]. ); determining, by the rule-based ECG model, a diagnosis using the features of the ECG and criteria extracted by an artificial intelligence (AI) model ( Paragraph [0096], At operation 608, the rule-based system, receives the preliminary diagnosis produced by the deep neural network, and the ECG data received at operation 602, and determines a diagnosis based thereon. In some embodiments, the preliminary diagnosis produced by the deep neural network may be used to adjust one or more criteria of the rule-based system. In some embodiments, the preliminary diagnosis may be used to select one or more types of pre-determined features for a feature extractor of the rule-based system to extract. In some embodiments, the preliminary diagnosis may be fed into a decision tree of the rule-based system along with the features extracted by the feature extractor, and used to determine a final diagnosis for the received ECG data. Also see paragraph [0084]. ); and transmitting the diagnosis ( Paragraph [0085], At operation 506, the diagnosis is displayed via a display device. Paragraph [0086], At operation 508, the ECG system may transmit the diagnosis to one or more client systems. Also see paragraph [0064]. ). Regarding claim 2, Yu further discloses wherein the Al model is a decision tree ( Paragraph [0062] ). Regarding claim 3, Yu further discloses wherein the criteria were extracted by the Al model based on a decision branch of the Al model ( Paragraph [0062], Features extracted by feature extractor 206 may be input into one or more root nodes, wherein the features may be evaluated based on one or more pre-determined, expert criteria/heuristics. Each node, other than the leaf nodes, may comprise one or more sub-nodes. The evaluation at each node may determine to which sub node the features are propagated to. The sub nodes branching from a given node may in turn comprise one or more sub nodes, and this pattern may continue until one or more leaf nodes is reached. Each node which branches to two or more sub nodes may be referred to as a decision node, as a “decision” between which sub-node is to be selected is made therein. At each decision node, the features of ECG data 202 determined by feature extractor 206 may be evaluated based on expert criteria, and the result of the evaluation may determine to which sub node the extracted features are propagated. ). Regarding claim 6, Yu further discloses determining a score associated with the diagnosis based on respective criterion, of the criteria, being satisfied ( Paragraph [0062], Each node which branches to two or more sub nodes may be referred to as a decision node, as a “decision” between which sub-node is to be selected is made therein. At each decision node, the features of ECG data 202 determined by feature extractor 206 may be evaluated based on expert criteria, and the result of the evaluation may determine to which sub node the extracted features are propagated. A terminal node, also referred to as a leaf node, may comprise one or more diagnoses. Decision tree 208 may comprise a plurality of leaf nodes, each uniquely corresponding to a distinct diagnosis. The extracted features of ECG data 202 propagate through the branching structure of decision tree 208, until one or more leaf nodes are reached, and a diagnosis, such as diagnosis 216, is determined based on the one or more leaf nodes. Paragraph [0063], In some embodiments, diagnosis 216 may include a confidence rating, indicating a degree of agreement/disagreement between a preliminary diagnosis produce by deep neural network 210, and rule-based system 204. ). Regarding claims 8-10, 13, 15-17 and 19: all limitations as recited have been analyzed and rejected with respect to claims 1-3 and 6. Claims 8-10 and 13 pertain to a device, corresponding to the method of claims 1-3 and 6. Claims 15-17 and 19 pertain to a non-transitory computer-readable medium, corresponding to the method of claims 1-3 and 6. Claims 8-10, 13, 15-17 and 19 do not teach or define any new limitations beyond claims 1-3 and 6 apart from the processors, memory and non-transitory computer-readable medium disclosed by Yu in paragraph [0034]; therefore claims 8-10, 13, 15-17 and 19 are rejected under the same rationale . Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-23-aia AIA The factual inquiries set forth in Graham v. John Deere Co. , 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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. 07-20-02-aia AIA This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 07-21-aia AIA Claim s 4-5, 11-12 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Yu et al. (U.S. Pub. No. 2021/0100468) in view of Neumman et al. (U.S. Pub. No. 2016/0210435) . Regarding claim 4, Yu further discloses wherein the criteria were extracted by the Al model based on the decision branch including an attribute selection measure that satisfies a threshold ( Paragraph [0062], Each node which branches to two or more sub nodes may be referred to as a decision node, as a “decision” between which sub-node is to be selected is made therein. At each decision node, the features of ECG data 202 determined by feature extractor 206 may be evaluated based on expert criteria, and the result of the evaluation may determine to which sub node the extracted features are propagated. ). Yu does not appear to explicitly disclose wherein the criteria include respective thresholds for the features wherein the criteria were extracted by the Al model based on an attribute selection measure that satisfies a threshold. Neumman teaches that it was old and well known in the art of clinical decision trees at the time of the filing wherein the criteria include respective thresholds for the features wherein the criteria were extracted by the Al model based on an attribute selection measure that satisfies a threshold ( Neumman, paragraph [00072], Within each tree, starting from its root node, the best split (based on a feature chosen from the set of all features φ and a threshold) is chosen in order to maximize the information gain by splitting the training samples passed to the tree. This process is recursively repeated until the tree is grown to a user-specified depth. Each leaf will then contain a set of predictions, where for instance the mean of the predictions will be used as regression value later. ) Therefore, it would have been obvious to one of ordinary skill in the art of clinical decision trees at the time of the filing to modify the method of Yu such that the criteria include respective thresholds for the features, as taught by Neumman, in order to estimats the uncertainty associated with a prediction by a randomized tree regression model ( Neumman, paragraph [0072] ). Regarding claim 5, Yu as modified by Neumman further discloses wherein the attribute selection measure includes an information gain, a gain ratio, or a Gini index ( Neumman, paragraph [00072], Within each tree, starting from its root node, the best split (based on a feature chosen from the set of all features φ and a threshold) is chosen in order to maximize the information gain by splitting the training samples passed to the tree. This process is recursively repeated until the tree is grown to a user-specified depth. Each leaf will then contain a set of predictions, where for instance the mean of the predictions will be used as regression value later. ). Claims 11-12 and 18 are rejected based on the same rationale as claims 4-5 . 07-21-aia AIA Claim s 7, 14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Yu et al. (U.S. Pub. No. 2021/0100468) in view of Vogelstein et al. (WO 2025/240004 A1) Regarding claim 7, Yu further discloses wherein the criteria include respective features of the ECG and respective thresholds for the features ( Paragraph [0062], Each node which branches to two or more sub nodes may be referred to as a decision node, as a “decision” between which sub-node is to be selected is made therein. At each decision node, the features of ECG data 202 determined by feature extractor 206 may be evaluated based on expert criteria, and the result of the evaluation may determine to which sub node the extracted features are propagated. ). Yu does not appear to explicitly disclose wherein the criteria include respective thresholds for the features Vlgelstein teaches that it was old and well known in the art of clinical decision trees at the time of the filing wherein the criteria include respective thresholds for the features ( Vogelstein, paragraph [0033], When a decision tree evaluates a dataset, the dataset can follow a decision path from the root, passing through intermediate child nodes based on feature conditions until the dataset reaches a leaf node, which provides a classification or prediction. At each node of the decision tree, the dataset can be evaluated based on a rule associated with that node. Such rules can include, for example, whether a certain feature of the dataset exceeds a threshold. Paragraph [0038], decision tree model 124 can determine whether the current data sample is associated with a patient who is positive or negative for a particular medical condition…. the current data sample can include medical data corresponding to the patient such as biometric data (e.g., electrocardiogram (ECG) data…). ) to improve an ability of the decision tree to determine whether the data sample is associated with the first characteristic or the second characteristic ( Vogelstein, paragraph [0013] ). Therefore, it would have been obvious to one of ordinary skill in the art of clinical decision trees at the time of the filing to modify the criteria of Yu such that the criteria include respective thresholds for the features, as taught by Vogelstein, in order to improve an ability of the decision tree to determine whether the data sample is associated with the first characteristic or the second characteristic. Claims 14 and 20 are rejected based on the same rationale as claim 7. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to Devin C. Hein whose telephone number is (303)297-4305. The examiner can normally be reached 9:00 AM - 5:00 PM M-F MDT. 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, Jason B. Dunham can be reached at (571) 272-8109. 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. /DEVIN C HEIN/Examiner, Art Unit 3686 Application/Control Number: 19/228,075 Page 2 Art Unit: 3686 Application/Control Number: 19/228,075 Page 3 Art Unit: 3686 Application/Control Number: 19/228,075 Page 4 Art Unit: 3686 Application/Control Number: 19/228,075 Page 5 Art Unit: 3686 Application/Control Number: 19/228,075 Page 6 Art Unit: 3686 Application/Control Number: 19/228,075 Page 7 Art Unit: 3686 Application/Control Number: 19/228,075 Page 8 Art Unit: 3686 Application/Control Number: 19/228,075 Page 9 Art Unit: 3686 Application/Control Number: 19/228,075 Page 10 Art Unit: 3686 Application/Control Number: 19/228,075 Page 11 Art Unit: 3686 Application/Control Number: 19/228,075 Page 12 Art Unit: 3686 Application/Control Number: 19/228,075 Page 14 Art Unit: 3686 Application/Control Number: 19/228,075 Page 15 Art Unit: 3686 Application/Control Number: 19/228,075 Page 16 Art Unit: 3686 Application/Control Number: 19/228,075 Page 17 Art Unit: 3686
