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. 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)(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. Claim s 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Yarabolu et al. (hereinafter “Yarabolu”), US Pub. No. 2024/0273184. Regarding claim 1, Yarabolu teaches a method for managing evolving artificial intelligence (AI) models (data poisoning and model drift prevention system, fig. 1A,1B) , comprising: making an identification that a downstream consumer will perform a process (fig. 1A, user computing device 110) , the process comprising consuming inferences from an AI model of the evolving AI models, the AI model being subject to an update process, and the update process being used to generate different instances of the AI model (fig. 1A 2; model 220, training data set 230) ; and while the process is being performed, providing a set of consistent inferences to the downstream consumer using an instance of the different instances of the AI model to facilitate completion of the process (fig. 5, merging trusted data with actual data and performing verification). Regarding claim 2, Yarabolu teaches wherein providing the set of consistent inferences comprises: identifying the instance of the AI model usable to generate the set of consistent inferences for the downstream consumer (fig. 2, trusted data 230) ; and obtaining the set of consistent inferences using the instance of the AI model (fig. 5, analyzing trusted data). Regarding claim 3, Yarabolu teaches wherein identifying the instance of the AI model comprises: suspending the update process for the instance of the AI model; and using a most up to date instance of the AI model as the instance of the AI model ([0040], data poisoning system triggers the ml engine to continue with data analysis once the cluster characteristics are determined to be unchanged). Regarding claim 4, Yarabolu teaches wherein identifying the instance of the AI model further comprises: performing an action set to initiate resumption of the update process for the instance of the AI model to obtain an updated instance of the AI model ([0040]). Regarding claim 5, Yarabolu teaches wherein the action set comprises: identifying a period of time for the process to complete; and waiting at least the period of time before performing an action of the action set to resume performance of the update process ([0040], the period of time is the time it takes for the data poisoning prevention system to analyze the data before moving forward with continued analysis). Regarding claim 6, Yarabolu teaches wherein identifying the instance of the AI model comprises: identifying a first instance of the AI model used to service a first inference request for inferences used in the process; and using the first instance of the AI model as the instance of the AI model (fig. 4, stream data 410, batch data 412; certify data 432). Regarding claim 7, Yarabolu teaches after identifying the first instance of the AI model, identifying that a second instance of the AI model has been generated by the update process, the second instance of the AI model being an updated version of the first instance of the AI model (fig. 5, verifying resultant data) ; and after identifying that the second instance of the AI model has been generated, servicing all subsequent requests for inferences used in the process with the first instance of the Al model rather than the second instance of the AI model and any subsequent instance of the AI model generated by the update process to provide the set of consistent inferences (fig. 4, stream data 10, batch data 412; fig. 5, mixed data analysis). Regarding claim 8, Yarabolu teaches wherein at a time of identification of the first instance of the AI model, the first instance of the AI model is a most up to date version of the AI model (fig. 4, stream data 410, batch data 412). Regarding claim 9, Yarabolu teaches wherein facilitating completion of the process comprises providing a computer-implemented service using the set of consistent inferences ( fig. 1B, data poisoning and model drift prevention computing system 104). Regarding claim 10, it is a non-transitory machine-readable medium of claim 1 and is rejected on the same grounds presented above. Regarding claims 11-15, they have similar limitations to those of claims 2-6 and are rejected on the same grounds presented above. Regarding claim 16, it is a data processing system of claim 1 and is rejected on the same grounds presented above. Regarding claims 17-20, they have similar limitation to those of claims 2-5 and are rejected on the same grounds presented above. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Boue et al. (US Pub. No. 2025/0013913) teaches a system and method for machine learning model re-formulation. Lancioni et al. (US Pub. No. 2023/0030136) teaches a false positive correction apparatus. Kursun (US Patent No. 11,354,602) teaches systems and methods to mitigate poisoning attacks within machine learning systems. Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT KENNETH B LEE JR whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)270-3147 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT Mon - Fri 9am-5pm . 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. 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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. /KENNETH B LEE JR/ Primary Examiner, Art Unit 2625