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. The information disclosure statement filed on 12/11/2023 has been entered. Claims 1-20 are presented for examination. 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. (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. Claims 1- 4, 8-11, 14-17, and 20 are rejected under 35 U.S.C. 102(a) (1) as being anticipated by Jelliffe et al. (2019/0072564 – Jelliffe et al.; herein after referred to as “ Jelliffe ”) . Regarding claim s 1, 8 , and 15, Jelliffe discloses a computer-implemented method comprising: receiving, by one or more processors, one or more data objects associated with an entity, each of the one or more data objects including one or more input indicators and one or more output indicators based on the one or more input indicators; determining, by the one or more processors and using a deterministic rules graph that maps each of the one or more input indicators to a corresponding one of the one or more output indicators, at least one of a first missing value for an input indicator of the one or more input indicators or a second missing value for an output indicator of the one or more output indicators; generating, by the one or more processors and using a trained machine learning model, a risk score associated with each of the one or more output indicators based on the at least one of the first missing value or the second missing value; comparing, by the one or more processors, the risk score associated with each of the one or more output indicators to a predetermined threshold value ( Jelliffe ; Figs 1, 2A-2B ; 0035 - 0038, 0041 - 0046, 0052-0056, 0068, 0069 - inputting risk indicators to a predictive model which predicts the r isk of PTB in the subject and administering a PTB intervention to the subject if elevated PTB risk is assessed , risk scores, missing values from data objects , generate risk scores, compare with selected cutoffs values >55 % , >60%, >70%, >75%, >85%, >90%, >95% or higher, machine generated models, supervised and unsupervised models, generate, train and validate the model ) ; and by the one or more processors, the at least one of the first missing value or the second missing value to be displayed on a user device as an alert generated based on the comparing ( Jelliffe ; Figs 1, 2A-2B; 0035-0038, 0041-0046, 0052-0056, 0068, 0069 - inputting risk indicators to a predictive model which predicts the risk of PTB in the subject and administering a PTB intervention to the subject if elevated PTB risk is assessed,risk scores, missing values from data objects, generate risk scores, compare with selected cutoffs values >55%, >60%, >70%, >75%, >85%, >90%, >95% or higher, machine generated models, supervised and unsupervised models, generate, train and validate the model) . Regarding claim s 2 , 9 and 16 , Jelliffe discloses the computer-implemented method of claim 1, wherein determining the missing values that exist in connection with the one or more output indicators further comprises: identifying one or more data clusters within the deterministic rules graph, each data cluster comprising an output indicator and one or more input indicators that the output indicator is based on, and determining that the one or more data clusters include the one or more missing values ( Jelliffe ; Figs 1, 2A-2B; 0035-0038, 0041-0046, 0052-0056, 0068, 0069 - inputting risk indicators to a predictive model which predicts the risk of PTB in the subject and administering a PTB intervention to the subject if elevated PTB risk is assessed,risk scores, missing values from data objects, generate risk scores, compare with selected cutoffs values >55%, >60%, >70%, >75%, >85%, >90%, >95% or higher, machine generated models, supervised and unsupervised models, generate, train and validate the model) . Regarding claim s 3 and 10, Jelliffe discloses t he computer-implemented method of claim 1, wherein generating the risk score comprises:providing the missing values and the one or more input indicators to the trained machine learning model to generate the risk score for each of the one or more output indicators ( Jelliffe ; Figs 1, 2A-2B; 0035-0038, 0041-0046, 0052-0056, 0068, 0069 - inputting risk indicators to a predictive model which predicts the risk of PTB in the subject and administering a PTB intervention to the subject if elevated PTB risk is assessed,risk scores, missing values from data objects, generate risk scores, compare with selected cutoffs values >55%, >60%, >70%, >75%, >85%, >90%, >95% or higher, machine generated models, supervised and unsupervised models, generate, train and validate the model) . Regarding claim s 4 , 11 and 17 , Jelliffe discloses the computer-implemented method of claim 1, further comprising:generating , by the one or more processors, a list of input indicators with missing values associated with the output indicators with missing values, the input indicators within the list being ordered based on the risk scores associated with the corresponding output indicators ( Jelliffe ; Figs 1, 2A-2B; 0035-0038, 0041-0046, 0052-0056, 0068, 0069 - inputting risk indicators to a predictive model which predicts the risk of PTB in the subject and administering a PTB intervention to the subject if elevated PTB risk is assessed,risk scores, missing values from data objects, generate risk scores, compare with selected cutoffs values >55%, >60%, >70%, >75%, >85%, >90%, >95% or higher, machine generated models, supervised and unsupervised models, generate, train and validate the model) . Regarding claim s 7 , 14 and 20 , Jelillffe discloses t he computer-implemented method of claim 1, wherein the trained machine learning model is trained using a training data set comprising a plurality of input indicators and a plurality of output indicators, to identify one or more associations between the input indicators and the output indicators ( Jelliffe ; Figs 1, 2A-2B; 0035-0038, 0041-0046, 0052-0056, 0068, 0069 - inputting risk indicators to a predictive model which predicts the risk of PTB in the subject and administering a PTB intervention to the subject if elevated PTB risk is assessed,risk scores, missing values from data objects, generate risk scores, compare with selected cutoffs values >55%, >60%, >70%, >75%, >85%, >90%, >95% or higher, machine generated models, supervised and unsupervised models, generate, train and validate the model) . Allowable Subject Matter Claims 5-6 , 12-13, and 18-19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. The following is a statement of reasons for the indication of allowable subject matter: the prior art fails to disclose: i . The computer-implemented method of claim 1, wherein the risk score associated with output indicators with a known value is zero (claim 5 ; claim s 12 and 18 recite similar limitations to claim 5 ) . ii . The computer-implemented method of claim 1, further comprising:calculating , by the one or more processors, a cumulative risk score by summing the risk score(s) associated with the one or more output indicators (claim 6 ; claim s 13 and 19 recite similar limitations to claim 6) . Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT THIEN MINH LE whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-2396 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT 6:30-5:00 PM M-Th. . 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, FILLIN "SPE Name?" \* MERGEFORMAT Steven Paik can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT 571-272-2404 . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. 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