CTNF 19/176,303 CTNF 76474 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. Information Disclosure Statement The information disclosure statement (IDS) submitted on April 11, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Double Patenting 08-33 AIA The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg , 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman , 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi , 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum , 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel , 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington , 418 F.2d 528, 163 USPQ 644 (CCPA 1969). A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b). The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13. The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA/25, or PTO/AIA/26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer. 08-34 AIA Claim s 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 1-18 of U.S. Patent No. 12,299,155 . Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the instant application are anticipated by the ‘155 patented claims in that the claims of the ‘155 patent contains all of the limitations of the instant application . claim 1 of the instant corresponds to claim 1 of the ‘155 patent; claim 2 of the instant corresponds to claim 2 of the ‘155 patent; claim 3 of the instant corresponds to claim 3 of the ‘155 patent; claim 4 of the instant corresponds to claim 4 of the ‘155 patent; claim 5 of the instant corresponds to claim 5 of the ‘155 patent; claim 6 of the instant corresponds to claim 6 of the ‘155 patent; claim 7 of the instant corresponds to claim 7 of the ‘155 patent; claim 8 of the instant corresponds to claim 1 of the ‘155 patent; claim 9 of the instant corresponds to claim 8 of the ‘155 patent; claim 10 of the instant corresponds to claim 9 of the ‘155 patent; claim 11 of the instant corresponds to claim 10 of the ‘155 patent; claim 12 of the instant corresponds to claim 11 of the ‘155 patent; claim 13 of the instant corresponds to claim 12 of the ‘155 patent; claim 14 of the instant corresponds to claim 13 of the ‘155 patent; claim 15 of the instant corresponds to claim 14 of the ‘155 patent; claim 16 of the instant corresponds to claim 8 of the ‘155 patent; claim 17 of the instant corresponds to claim 15 of the ‘155 patent; claim 18 of the instant corresponds to claim 16 of the ‘155 patent; claim 19 of the instant corresponds to claim 17 of the ‘155 patent; and claim 20 of the instant corresponds to claim 18 of the ‘155 patent. Claims 1-20 therefore are not patentably distinct from the earlier filed ‘155 patented claims, and as such, are unpatentable for obvious-type double patenting . Allowable Subject Matter Claims 1-20 would be allowable upon the submission of a terminal disclaimer to overcome the obvious-type double patenting rejection. 13-03-01 AIA The following is a statement of reasons for the indication of allowable subject matter: The closed prior art teachings of Wolff et al, US 2020/0259852 (cited in the IDS filed on 4/11/2025) disclose of insider threats with disgruntled employees who were recently terminated. Machine learning is used to compare users’ activities with other users having the same or similar roles with an organization, and to identify if a user deviates from the norm within a group. Deviations can be indicative of insider threats, and can trigger an alert and/or cause a system to take action to address the threat, see paragraph 0043. In another relevant teaching, Reddy et al, US 2023/0177934 (cited in the IDS filed on 4/11/2025) discloses of monitoring the behavior of each worker entering a secure area. An Artificial Intelligence (AI) model is developed for each worker with a specific representation of their behavior in the secure area. Each worker’s behavior is then monitored. An alarm is raised when the current behavior of one or more workers diverges from the behavior represented by the AI model, see paragraph 0003. Both the teachings of Wolff et al and Reddy et al disclose of using machine learning/artificial intelligence to detect deviations from expected employee/worker which are indicative of threats, but fail to meet the claimed limitations of the instant application. Bettaiah et al, U.S. Patent 10,505,825 (cited in the IDS filed on 4/11/2025) teaches of a related aspect with regards to defining a testing environment that provides sandbox service for isolating and tested untested program code changes, see column 28, lines 37-40. The combination of Wolff et al, Reddy et al, and Bettaiah et al fail to disclose of the claimed limitations when taught alone, or in combination. As per claim 1, it was not found to be taught in the prior art of: generating data of employee activity associated with an employee of a plurality of employees of an enterprise organization that is used to train a machine learning model, wherein the training data comprises, employee control thresholds associated with the employee, and modified employee control thresholds associated with the employee; training the machine learning model, wherein training the machine learning model configures the machine learning model to perform anomaly detection in analysis of employee activity information; generating test data, wherein the test data comprises a subset of the employee activity associated with the employee; analyze, within a sandbox environment, the test data; determining, based on the analysis, whether to: transmit a notification to an enterprise computing device indicating the test data complies with the employee control thresholds associated with the employee; or transmit a notification to the enterprise computing device indicating the test data does not comply with the employee control thresholds associated with the employee; and refine, using a dynamic feedback loop and based on the modified employee control thresholds, the machine learning model, wherein the refining continuously improves accuracy of the machine learning model. Independent claims 8 and 15 are similar in scope to independent claim 1, and would be allowable upon submission of a terminal disclaimer to overcome the obvious-type double patenting rejection with respect to claims 1-20 of the instant application . Conclusion 07-96 The relevant art made of record and not relied upon is considered pertinent to applicant's disclosure. Patel et al, US 2015/0309493 is relied upon for disclosing of tasks and/or operations can be dynamically adjusted based on intelligence refinement or otherwise development of an extant service based at least on (1) information collected in response to implementation of an automation control service, and/or (2) collection, identification, and/or analysis of information indicative or otherwise representative of events that may occur at a device within or in proximity to a controlled environment; during execution of an application in a computing device; during implementation of a control sequence; and/or due to activity of an agent (human or otherwise) within or in proximity of a controlled environment, see paragraph 0057. Buzek et al, U.S. Patent 11,361,323 is relied upon for disclosing of a central database system receives information associated with an employee from an employer. Using this information, the central database system can provision one or more user accounts for the employee, for instance via an API of an account provider. The central database system can use a machine learned model to identify fields of the API and to translate the information associated with the employee based on information requirements associated with the API. When a characteristic of the employee, such as the employee's title, subsequently changes within the central database system, one or more features associated with the user account can be automatically updated in response to the change, see abstract. Barsoum et al, EP 4708257 A1 is relied upon for machine learning may be used to identify operator behavior data that represents certain operator behaviors and to determine whether the identified operator behaviors are characteristic or anomalous. For instance, machine learning may be used to distinguish between normal and unusual operator behaviors , particularly in relation to the context or environment in which the driving is occurring, which can be useful for identifying patterns of operator behavior, see paragraph 0078. Astakhova et al, “Scanning the Resilience of an Organization Employees to Social Engineering Attacks using Machine Learning Techniques” is relied upon for disclosing of using machine learning to protect organizations from social engineering attacks, see abstract. Pratibha et al, “Learning Correlation Graph and Anomalous Employee Behavior for Insider Threat Detection” is relied upon for disclosing of detecting insider threat using machine learning to infer from a correlation graph for an organization’s employees, see abstract. Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER REVAK whose telephone number is (571)272-3794. The examiner can normally be reached 5:30am - 3:00pm. 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, Catherine Thiaw can be reached at 571-270-1138. 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. /CHRISTOPHER A REVAK/Primary Examiner, Art Unit 2407 Application/Control Number: 19/176,303 Page 2 Art Unit: 2407 Application/Control Number: 19/176,303 Page 3 Art Unit: 2407 Application/Control Number: 19/176,303 Page 4 Art Unit: 2407 Application/Control Number: 19/176,303 Page 5 Art Unit: 2407 Application/Control Number: 19/176,303 Page 6 Art Unit: 2407 Application/Control Number: 19/176,303 Page 7 Art Unit: 2407 Application/Control Number: 19/176,303 Page 8 Art Unit: 2407 Application/Control Number: 19/176,303 Page 9 Art Unit: 2407
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