CTFR 18/774,139 CTFR 82214 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 1. The office acknowledges the receipt of the following and placed of record in the file: Amendment dated 4/8/2026. 2. Claims 1-20 are presented for examination. 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 3. Claim (s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sundaresan (“Sunda”), U.S. Patent publication No. 2015/0045049, Fields et al. (“Fields”), U. S. Patent Publication No. 2025/0265624 and Prasad et al. (“Prasad”), U.S. Pub No. 2025/0307696 . Regarding Claims 1, 11 and 20, Sunda teaches a computer-implemented method for reconfiguring a process model to complete a transaction during runtime based on urgency [Fig-4], comprising: initializing, by one or more processors, the process model to complete the transaction for runtime reconfiguration, the initializing comprising: extracting, by the one or more processors, dependencies for components comprising the process model [Para: 0029(in order to “indicate dependencies” extracting dependencies graph 120)]; and generating or updating, by the one or more processors, a dependency graph representing the dependencies, based on the extracting0046(“generate a dependency graph 120 … block 402”); Fig-4(generating a dependency graph at 402)]; and initializing, by the one or more processors, a protocol across a stack for executing the process based on the dependencies [Para: 0048-0051(dependencies are used to understand how the different parts of code and node interact with each other and to “extract the files containing the changes”, see 0054)]; initiating, by the one or more processors, the transaction [Fig-4]; during runtime, receiving, by the one or more processors, an input related to the transaction performed by the process model [Para: 0054(pull request)]; cognitively analyzing, by the one or more processors, the input utilizing a large language model (LLM) to determine an urgency level for the transaction [Para: 0058, 0059(“analyze the extracted method and classes …”), 0059(“generate score …”) – 0064 and Fig-6(602-610)]; determining, by the one or more processors, that the urgency level is determined based on score [Para: 0064(“code diff hunk having the k highest score are selected for input into the prompt”)]; and reconfiguring, by the one or more processors, the process model to comport with the urgency level [Para: 0071(code changes are merge back to repository at 424)]. Sunda does not disclose expressly determining the urgency level based on meet or exceed a threshold level; and wherein the cognitively analyzing comprises determining sentiment at a point in the in the transaction to determine urgency based on the determining the sentiment. In the same field of endeavor (e.g., updating and training LLM models based on urgency), Field teaches a training model update by LLM for an urgency level based on meet or exceed a threshold level [Para: 0029(“system determines the need for a model change based upon the analysis exceeding one or more thresholds, … by the code LLM”) and 0035]. Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Sunda’s teachings of cognitively analyzing, by the one or more processors, the input utilizing a large language model (LLM) to determine an urgency level based on score for the transaction with Fields’s teachings of determining the urgency level based on meet or exceed a threshold level for the purpose of efficiently and reliably building testing and validating various computer model for AI tools. Also in the same field of endeavor (e.g., reconfiguring large language model using domain date in a system), Prasad teaches initializing by a processor a process model to complete a transaction for a runtime configuration [Para: 0025,0034 (process of 200 as it trains large language model (LLM) with input data (112), Figg-2)]; and cognitively analyzing an input utilizing the LLM [Para: 0025(as “second large language model 106 … try to predict next sentences on the input data 112”)], wherein the cognitively analyzing comprises determining sentiment (when computing a threshold at step 206) at a point in the in the transaction to determine urgency level based on the determining the sentiment [Para: 0026 (as utilizing the threshold to determine whether “to iteratively adjust a set of attention heads of the second large language model 106”) and 0036; Fig-2(206 – 208)] and complete the transaction [Para: 0027]. Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Sunda’s teachings of cognitively analyzing, by the one or more processors, the input utilizing a large language model (LLM) to determine an urgency level based on score for the transaction with prasad’s teachings of cognitively analyzing comprises determining sentiment at a point in the in the transaction to determine urgency based on the determining the sentiment for the purpose of “maintaining a high accuracy of the large language model uses a large amount of computing resources” [Parasad, Para: 0002]. Regarding Claims 2 and 12, Sunda teaches wherein the reconfiguring can comprise implementing changes to one or more components selected from the group consisting of: omitting a component [Para: 0032(changed source code lines, including deleted lines)], replacing the component, and reordering the component. Regarding Claims 3 and 13, Sunda teaches wherein the reconfiguring comprises implementing a new pattern to complete the transaction [Para: 0071(code change are merged) and 0111; Fig-4(424)]; and executing, by the one or more processors, the new transaction to complete the transaction [Para: 0112(computer device perform action based on top-k code changes)]. Regarding Claims 4 and 14, Sunda teaches wherein the new pattern is selected from the group consisting of: a sequential pattern [Para: 0032] and a centralized pattern [Para: 0021(source code)]. Regarding Claims 5 and 15, Sunda teaches wherein the initializing further comprises: utilizing, by the one or more processors, the dependency graph, to identify component types in the process model [Para: 0048(“dependency graph represents …the type of entity modeled” such as connected components and node type, see 0060)]; and determining, by the one or more processors, a depth of control for each component in the process model [Para: 0049-0051]. Regarding Claims 6 and 16, Sunda teaches wherein the component types are selected from the group consisting of LLMs [Para: 0037(large language model)], traditional artificial intelligence (AI) [Abstract and para: 0005], and rules-based tasks [Para: 0034(“statistical methods to learn from data and to predict future”)]. Regarding Claims 7 and 17, Sunda teaches wherein determining the urgency level for the transaction further comprises: augmenting, by the one or more processors, the transaction with parameters, wherein the reconfiguring comprises utilizing the parameters and the urgency level to reconfigure the process model [Para: 0022(model containing billions of parameters) and 0079(There is a change in the input parameter type)]. Regarding Claims 8 and 18, Sunda teaches wherein the parameters are selected from the group consisting of: transaction identifier [Para: 0022(language model type)], node identifier [Para: 0048(node identifier)], an in and out parameter [Para: 0036, 0074], direction [Para: 0096(by bi-directional encoder), channel identifier [Para: 0035(deep learning of neural network)], and one of ordinary skill in the art would utilize participation flag based on circuit requirement in order to achieve target result. Regarding Claims 9 and 19, Sunda teaches initializing further comprising: identifying, by the one or more processors, the components comprising the process model [Para: 0005(as “large language model predicts the pull request classification and summary given a prompt that includes the top-k code changes in the pull request of a repository”)]; implementing, by the one or more processors, an agent at each component of the components [Para: 0022-0026], wherein the agent initializes protocol across a stack executing the process [Para: 0036(by stacked encoder and decoder)] and provides an extensible metadata-based protocol template for communication across the components [Para: 0022], Regarding Claim 10, Sunda teaches wherein the process model comprises a business process model [Para: 0120(machine learning model for code review process is for business related usage)]. Response to Arguments 4. Applicant’s arguments with respect to claim(s) 1, 11 and 20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Conclusion 07-40 AIA Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMED H REHMAN whose telephone number is (571)272-1412. The examiner can normally be reached 8.00 - 5.00. 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, Jaweed Abbaszadeh can be reached at 571-270-1640. 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. /MOHAMMED H REHMAN/Primary Examiner, Art Unit 2176 Application/Control Number: 18/774,139 Page 2 Art Unit: 2176 Application/Control Number: 18/774,139 Page 3 Art Unit: 2176 Application/Control Number: 18/774,139 Page 4 Art Unit: 2176 Application/Control Number: 18/774,139 Page 5 Art Unit: 2176 Application/Control Number: 18/774,139 Page 6 Art Unit: 2176 Application/Control Number: 18/774,139 Page 7 Art Unit: 2176 Application/Control Number: 18/774,139 Page 8 Art Unit: 2176
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