CTNF 18/915,543 CTNF 97721 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. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 2. 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. 3. Claims 1-15 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea (i.e. mental process) without significantly more. Step 1: The claimed invention in claims 1-15 are directed to statutory subject matter as the claims recite a method (i.e. a process) and a system (i.e. a machine). Thus, they are directed to statutory categories of invention (See MPEP 2106.03). Step 2A – (Prong 1): Independent claim 1 (and similarly independent claim 10) recites a judicial exception by reciting the limitations of “receiving an ECG input for the subject, wherein the ECG input does not comprise or is not accompanied with an age for the subject; determining… based on the received ECG input, that the subject is a pediatric or non-pediatric subject; upon determining that the subject is a pediatric subject, determining…on the received ECG input, that the subject belongs in one of a plurality of different pediatric age groups; upon determining that the subject is a non-pediatric subject, determining…based on the received ECG input, that the subject belongs in one of a plurality of different non-pediatric age groups; performing, using an automated ECG analysis tool, an automated analysis of the received ECG input for the subject, wherein the automated analysis is based in part on the one of a plurality of different pediatric age groups or the one of a plurality of different non-pediatric age groups in which the subject belongs” . These limitations, as drafted, is a process that, under its broadest reasonable interpretation covers performance of the limitation that can be performed by a human mind (including an observation, evaluation, judgment, opinion) or by a person using a pen and paper. For example, these limitations are nothing more than a clinician receiving a print-out of ECG data of a subject. The clinician can then use the print-out of ECG data to determine whether the ECG data belongs to a pediatric subject or a non-pediatric subject. If it is a pediatric subject, the clinicians determines one of a plurality of different pediatric age groups in which the subject belongs in. If it is a non-pediatric subject, the clinicians determines one of a plurality of different non-pediatric age groups in which the subject belongs in. Finally, the clinician performs further analysis of the ECG data based on the age groups previously determined. Therefore, the claims are directed to a judicial exception (see MPEP 2106.04(a)(2)). Step 2A – (Prong 2): Regarding claim 1 (and similarly 10), the judicial exception is not integrated into a practical application. Applicant includes a trained algorithm which is nothing more than the computer implementation/automation of an abstract mental process of screening a patient, which is what a physician typically does with a patient in a diagnostic setting. In addition, claim 10 recites a processor at a high-level of generality and amounts to nothing more than parts of a generic computer. Merely including instructions to implement an abstract idea on a computer does not integrate a judicial exception into practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. 4. Regarding dependent claims 2-9 and 11-15, the limitations of claims 2-9 and 11-15 further define the limitations already indicated as being directed to the abstract idea. While the dependent claims further define the abstract idea, it does not set forth any additional elements that integrate the claims into a practical application or add any additional elements that amount to significantly more than the abstract idea. Thus, when considered as a whole and in combination, claims 1-15 are directed to an abstracted idea and are therefore rejected. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 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. 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. 07-15 AIA Claim s 1-5 and 7-15 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by Gregg et al. (US Pub.: 2017/0000366 A1) . Regarding claim 1, Gregg discloses a method for analysis of an ECG input for a subject to determine whether the subject is a pediatric subject or non-pediatric subject (e.g. paragraphs 0015, 0051) , and to classify the subject in one of a plurality of different age groups based on an estimated age of the subject (e.g. paragraph 0015) , comprising: receiving an ECG input for the subject, wherein the ECG input does not comprise or is not accompanied with an age for the subject (e.g. paragraphs 0049, 0051) ; determining, by a trained algorithm based on the received ECG input, that the subject is a pediatric or non-pediatric subject (e.g. paragraphs 0011, 0061, – Examiner notes that this is a conditional limitation and those limitations related to a pediatric patient are addressed below) ; upon determining that the subject is a pediatric subject, determining, by the trained algorithm based on the received ECG input, that the subject belongs in one of a plurality of different pediatric age groups (e.g. paragraph 0067) ; upon determining that the subject is a non-pediatric subject, determining by the trained algorithm based on the received ECG input, that the subject belongs in one of a plurality of different non-pediatric age groups (e.g. This limitation is in the alternate. See Examiner note above.) ; performing, using an automated ECG analysis tool, an automated analysis of the received ECG input for the subject, wherein the automated analysis is based in part on the one of a plurality of different pediatric age groups or the one of a plurality of different non-pediatric age groups in which the subject belongs (e.g. Fig. 2 – controller 20; paragraphs 0039, 0071, – pediatric age groups) . Regarding claim 2, Gregg discloses the method of claim 1 as discussed above, and Gregg further teaches further comprising the step of extracting one or more features from the received ECG input, wherein the extracted one or more features are provided as input to the trained algorithm (e.g. paragraphs 0033, 0051) . Regarding claim 3, Gregg discloses the method of claim 1 as discussed above, and Gregg further teaches wherein the trained algorithm is a trained machine learning algorithm or an end-to-end deep learning algorithm (e.g. paragraphs 0007, 0033, – trained machine learning model) . Regarding claim 4, Gregg discloses the method of claim 3 as discussed above, and Gregg further teaches wherein the algorithm is trained using multi-lead ECG data for a plurality of subjects at a plurality of different ages (e.g. Fig. 1B; paragraphs 0008, 0031, 0033) . Regarding claim 5, Gregg discloses the method of claim 4 as discussed above, and Gregg further teaches wherein a plurality of features are extracted from the multi-lead ECG data for each of the plurality of subjects, and wherein the plurality of extracted features are used to train the algorithm (e.g. paragraphs 0033, 0062) . Regarding claim 7, Gregg discloses the method of claim 1 as discussed above, and Gregg further teaches further comprising the step of displaying, via a user interface, the determined one of a plurality of different non-pediatric age groups or determined one of a plurality of different non-pediatric age groups in which the subject belongs (e.g. paragraphs 0040, 0073) . Regarding claim 8, Gregg discloses the method of claim 7 as discussed above, and Gregg further teaches further comprising the step of displaying, results of the automated analysis (e.g. paragraphs 0040, 0073) . Regarding claim 9, Gregg discloses the method of claim 7 as discussed above, and Gregg further teaches further comprising the step of receiving, from a clinician via a user interface, a selection of a preferred age range, wherein the determined one of a plurality of different non-pediatric age groups or determined one of a plurality of different non- pediatric age groups in which the subject belongs comprises the preferred age range (e.g. paragraphs 0046-0047) . Regarding claim 10, Gregg discloses a system for analysis of an ECG input for a subject to determine whether the subject is a pediatric subject or non-pediatric subject (e.g. paragraphs 0015, 0051) , and to classify the subject in one of a plurality of different age groups based on an estimated age of the subject (e.g. paragraphs 0015) , comprising: ECG input for the subject, wherein the ECG input does not comprise or is not accompanied with an age for the subject (e.g. paragraphs 0049, 0051) ; a trained algorithm, wherein the trained algorithm is trained to determine whether the subject is a pediatric subject or non-pediatric subject (e.g. paragraphs 0011, 0061, – Examiner notes that this is a conditional limitation and those limitations related to a pediatric patient are addressed below) , and to classify the subject in one of a plurality of different age groups based on an estimated age of the subject (e.g. paragraphs 0015, 0067) ; a processor (e.g. paragraphs 0016, 0076) configured to: (i) determine, by the trained algorithm based on the ECG input, that the subject is a pediatric or non-pediatric subject (e.g. paragraphs 0011, 0061) ; (ii) upon determining that the subject is a pediatric subject, determine, by the trained algorithm based on the ECG input, that the subject belongs in one of a plurality of different pediatric age groups (e.g. paragraph 0067) or upon determining that the subject is a non-pediatric subject, determine by the trained algorithm based on the ECG input, that the subject belongs in one of a plurality of different non-pediatric age groups (e.g. This limitation is in the alternate. See Examiner note above.) ; and (iii) perform, using an automated ECG analysis tool, an automated analysis of the received ECG input for the subject, wherein the automated analysis is based in part on the one of a plurality of different pediatric age groups or the one of a plurality of different non-pediatric age groups in which the subject belongs (e.g. Fig. 2 – controller 20; paragraphs 0039, 0071, – pediatric age groups) . Regarding claim 11, Gregg discloses the system of claim 10 as discussed above, and Gregg further teaches wherein the processor is further configured to extract one or more features from the ECG input, wherein the extracted one or more features are provided as input to the trained algorithm (e.g. paragraphs 0033, 0051) . Regarding claim 12, Gregg discloses the system of claim 10 as discussed above, and Gregg further teaches wherein the trained algorithm is a trained machine learning algorithm or an end-to-end deep learning algorithm (e.g. paragraphs 0007, 0033) . Regarding claim 13, Gregg discloses the system of claim 10 as discussed above, and Gregg further teaches wherein the processor is further configured to cause the display, via a user interface, of the determined one of a plurality of different non-pediatric age groups or determined one of a plurality of different non-pediatric age groups in which the subject belongs (e.g. paragraphs 0040, 0073) . Regarding claim 14, Gregg discloses the system of claim 10 as discussed above, and Gregg further teaches wherein the processor is further configured to cause the display, via a user interface, of results of the automated analysis (e.g. paragraphs 0040, 0073) . Regarding claim 15, Gregg discloses the system of claim 10 as discussed above, and Gregg further teaches wherein the processor is further configured to receive, from a clinician via a user interface, a selection of a preferred age range, wherein the determined one of a plurality of different non-pediatric age groups or determined one of a plurality of different non-pediatric age groups in which the subject belongs comprises the preferred age range (e.g. paragraphs 0046-0047) . 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. 07-21-aia AIA Claim 6 is rejected under 35 U.S.C 103 as being unpatentable over Gregg and further in view of Feng et al. (US Pub.: 2016/0232340 A1) . Regarding claim 6, Gregg discloses the method of claim 5 as discussed above. However, Gregg does not explicitly teach wherein the plurality of extracted features are reduced via dimensionality reduction prior to training the algorithm. Feng, in a same field of endeavor of electrocardiogram (ECG) analysis methods, discloses wherein the plurality of extracted features are reduced via dimensionality reduction prior to training the algorithm (e.g. paragraphs 0034, 0079) . Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Gregg to incorporate wherein the plurality of extracted features are reduced via dimensionality reduction prior to training the algorithm, as taught and suggested by Feng, in order to save calculation time and increase calculation accuracy (Feng, paragraph 0079). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL TEHRANI whose telephone number is (571)270-0697. The examiner can normally be reached 9:00am-5:00pm. 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. /D.T./Examiner, Art Unit 3792 /Benjamin J Klein/Supervisory Patent Examiner, Art Unit 3792 Application/Control Number: 18/915,543 Page 2 Art Unit: 3792 Application/Control Number: 18/915,543 Page 3 Art Unit: 3792 Application/Control Number: 18/915,543 Page 4 Art Unit: 3792 Application/Control Number: 18/915,543 Page 6 Art Unit: 3792 Application/Control Number: 18/915,543 Page 7 Art Unit: 3792 Application/Control Number: 18/915,543 Page 8 Art Unit: 3792 Application/Control Number: 18/915,543 Page 9 Art Unit: 3792 Application/Control Number: 18/915,543 Page 10 Art Unit: 3792
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