CTNF 19/249,177 CTNF 84065 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. DETAILED ACTION Claim Rejections- 35 U.S.C § 101 of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. 6. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims are either directed to a method, device and computer readable medium which are one of the statutory categories of invention. (Step 1: YES). Claim 1 recites the limitations of: A method for processing data records, comprising: partitioning, by a device, historical information associated with a plurality of data records into a training set and a validation set, the training set including data used to train a machine learning model, and the validation set including data used to assess performance of the machine learning model; performing, by the device, dimensionality reduction on the training set, reducing the training set to a minimum feature set, wherein the minimum feature set includes a subset of features selected based on predefined criteria; training, by the device, the machine learning model using the minimum feature set t o predict outcomes associated with the plurality of data records; determining, by the device , a predictive score for a data record of the plurality of data records based on input processed by the machine learning model trained with the minimum feature set; and modifying , by the device, the data record based on the predictive score, wherein the modification includes updating parameters associated with the data record. The claim recites elements that are in bold above, (e.g., partitioning, historical information associated with a plurality of data records into a training set and a validation set ; to predict outcomes associated with the plurality of data records; determining, a predictive score for a data record of the plurality of data records based on input and modifying , the data record based on the predictive score, wherein the modification includes updating parameters associated with the data record), under its broadest reasonable interpretation, covers performance of the limitation(s) as a mental process, more specifically a concept performed mentally by a human with a pen and paper (steps for generating and modifying scores associated with data records). If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a certain method of a concept performed in the human mind, then it falls within the “mental process” grouping of abstract ideas. Accordingly, claim 1 recites an abstract idea. Claims 10,15 recite substantially the same subject matter as claim 1 and are abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract) This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application include: (1) 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 uses a computer as a tool to perform an abstract idea (MPEP 2106.05.f), (2) Adding insignificant extra solution activity to the judicial exception (MPEP 2106.05.g), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05.h). Claims 1,10, 15 includes the following additional elements: -A device comprising one or more memories and one or more processors -A non-transitory computer readable medium -A machine learning model - Training a machine learning model using dimensionality reduction techniques and a minimum feature set The device comprising one or more memories and one or more processors, a non-transitory computer readable medium, machine learning model, the training a machine learning model using dimensionality reduction techniques and a minimum feature set are recited at a high level of generality and being used in its ordinary capacity and are being used as a tool for implementing the steps of the identified abstract idea, see MPEP 2106.05(f) , where applying a computer or using a computer as a tool to perform the abstract idea is not indicative of a practical application. Therefore, there are no additional elements in the claim that amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore claims 1, 10, 15 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application) The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered individually and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using computer hardware amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use. Generally linking the use of the judicial exception to a particular technological environment or field of use, with the use of generic computer components, cannot provide an inventive concept - rendering the claim patent ineligible. Thus claims 1, 10, 15 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more) Dependent claims 2-9, 11-14, 16-20 further define the abstract idea that is present in their respective independent claims 1, 10, 15 and thus correspond to a Mental process and hence are abstract for the reasons presented above. Claims 6,19 recites the additional element of a “secure data repository”, the “secure data repository” is recited at a high level of generality, which is operating in its ordinary capacity and is being used as a tool to implement the steps the identified abstract idea. Claims 8, 20 recite the additional element of a “generating user interface”, the “user interface” is recited at a high level of generality, which is operating in its ordinary capacity and is being used as a tool to implement the steps the identified abstract idea. Claim 9 recites, “the machine learning model is retrained periodically using updated historical information”. The “retraining of the machine learning model” is recited a high level of generality and are operating in their ordinary capacity and is being used as a tool to implement the steps of the identified abstract idea. Therefore, the dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the dependent claims 2-9, 11-14, 16-20 are directed to an abstract idea. Thus, claims 1-20 are not patent-eligible. No Prior Art Based on prior art search results, the prior art of record neither anticipates nor renders obvious the claimed subject matter of the instant application as a whole either taken alone or in combination, in particular, the prior art does not teach, “ determining, by the device, a predictive score for a data record of the plurality of data records based on input processed by the machine learning model trained with the minimum feature set; and modifying, by the device, the data record based on the predictive score, wherein the modification includes updating parameters associated with the data record.” The closest prior art of record: US 2014/0279794 to Aliferis et al, teaches, “ Predictive modeling is an important class of data analytics with applications in numerous fields. Once a predictive model is built, validated, and applied on a set of objects, by a data analytics system (or even by manual modeling), consumers of the model information need assistance to navigate through the results. This is because both regression and classification models that output continuous values (eg, probability of belonging to a class) are often used to rank objects and then a thresholding of the ranked scores needs to be used to separate objects into a "positive" and a "negative" class. The choice of threshold greatly affects the true positive, false positive, true negative, and false negative results of the model's application. An ideal data analytics system should allow the user to understand the tradeoffs of different threshold values for different thresholds. The user interface should convey this information in an intuitive manner and provide the ability to vary the threshold interactively while simultaneously presenting the effects of thresholding on predictivity. This is precisely the function of the present invention. In addition to manual thresholding, the invention also allows for the thresholding to be performed by fully automated means (via standard statistical optimization methods) once a user has identified the desired balance of false positives and false negatives (or other predictivity metrics of interest). The invention can be applied to any application field of predictive modeling.” US 2003/0212851 to Drescher et al, teaches, “A system, method, and computer program product provides a useful measure of the accuracy of a Nave Bayes predictive model and reduced computational expense relative to conventional techniques. A method for measuring accuracy of a Naive Bayes predictive model comprises the steps of receiving a training dataset comprising a plurality of rows of data, building a Nave Bayes predictive model using the training dataset, for each of at least a portion of the plurality of rows of data in the training dataset incrementally untraining the Nave Bayes predictive model using the row of data and determining an accuracy of the incrementally untrained Nave Bayes predictive model, and determining an aggregate accuracy of the Nave Bayes predictive model.” US Patent 7,788,195 to Subramanian et al, teaches, “Systems and methods for performing fraud detection. As an example, a system and method can be configured to build a set of predictive models to predict credit card or debit card fraud. A first predictive model is trained using a set of training data. A partitioning criterion is used to determine how to partition the training data into partitions. Another predictive model is trained using at least one of the partitions of training data in order to generate a second predictive model. The predictive models are combined for use in predicting credit card or debit card fraud.” CONCLUSION Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMAD Z SHAIKH whose telephone number is (571)270-3444. The examiner can normally be reached M-T, 9-600; Fri, 8-11, 3-5. 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, BENNETT SIGMOND can be reached at 303-297-4411. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /MOHAMMAD Z SHAIKH/Primary Examiner, Art Unit 3694 6/6/2026 Application/Control Number: 19/249,177 Page 2 Art Unit: 3694 Application/Control Number: 19/249,177 Page 3 Art Unit: 3694 Application/Control Number: 19/249,177 Page 4 Art Unit: 3694 Application/Control Number: 19/249,177 Page 5 Art Unit: 3694 Application/Control Number: 19/249,177 Page 6 Art Unit: 3694 Application/Control Number: 19/249,177 Page 7 Art Unit: 3694 Application/Control Number: 19/249,177 Page 8 Art Unit: 3694 Application/Control Number: 19/249,177 Page 9 Art Unit: 3694
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