DETAILED ACTION Claims 1-20 are pending in this action. Notice of Pre-AIA or AIA Statu s 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 8/29/2023 and 11/27/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements has been considered by the examiner. Claim Rejections - 35 USC § 103 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. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness . Claim s 1-3, 5- 6, 8-13, and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Nemirovsky et al. (WO-2022055964-A1) [hereinafter “ Nemirovsky ”] in view of Ramamurthy et al. (WO-2022046086-A1) [hereinafter “Ramamurthy”] . As per claim 1, Nemirovsky teaches a computer-implemented method for improving entity matching, comprising: training a neural network (NNs) model on an output of a probabilistic matching engine to perform entity matching (Abstract, processing an input to obtain an outcome and comparing to a set of one or more target values) ; determining counterfactual explanations for non-matches of entities (Abstract, for non-matching outcomes, generating an counterfactual input with corresponding counterfactual feedback) ; and identifying a list of data transformations by actionable recourse using the NN model (Abstract, corresponding output is generated that comprises changes to the input, i.e. data transformation) . Nemirovsky does not explicitly teach a graph neural network model. Ramamurthy teaches a graph neural network model (Abstract, using a graph neural network to track costs to change a graph to another class). At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Nemirovsky with the teachings of Ramamurthy, a graph neural network model, to explicitly track relationships and dependencies, along with data inputs. As per claim 2, the combination of Nemirovsky and Ramamurthy teaches the computer-implemented method of claim 1, further comprising ranking the list of data transformations based on a computational overhead and an estimated improvement in entity matching (Ramamurthy; [0021] and [0029], determining top formulas for the purposes of computation savings) . As per claim 3, the combination of Nemirovsky and Ramamurthy teaches the computer-implemented method of claim 2, wherein the ranking of the list of data transformations is performed using the GNN model (Ramamurthy; [0005] and [0031], ranking performed by a GNN) . As per claim 5, the combination of Nemirovsky and Ramamurthy teaches the computer-implemented method of claim 2, further comprising deploying one or more data transformations from the ranked list of data transformations on the probabilistic matching engine to improve entity matching (Ramamurthy; [0005] and [0031], using formulas to improve matching of a query class) . As per claim 6, the combination of Nemirovsky and Ramamurthy teaches the computer-implemented method of claim 2, further comprising setting an upper bound on a number of data transformations in the ranked list of data transformations (Ramamurthy; [0029] -[ 030], the formulas including the top formulas can have a maximum limit on the length to cap computation time) . As per claim 8, the combination of Nemirovsky and Ramamurthy teaches the computer-implemented method of claim 1, further comprising generating rules based on the list of data transformations (Ramamurthy; [0031], new logic rules are generated and tested based on changes to graphs) . As per claim 9, the substance of the claimed invention is identical or substantially similar to that of claim 1. Accordingly, this claim is rejected under the same rationale. As per claim 10, the substance of the claimed invention is identical or substantially similar to that of claim 2. Accordingly, this claim is rejected under the same rationale. As per claim 11, the substance of the claimed invention is identical or substantially similar to that of claim 3. Accordingly, this claim is rejected under the same rationale. As per claim 12, the substance of the claimed invention is identical or substantially similar to that of claim 5. Accordingly, this claim is rejected under the same rationale. As per claim 13, the substance of the claimed invention is identical or substantially similar to that of claim 6. Accordingly, this claim is rejected under the same rationale. As per claim 15, the substance of the claimed invention is identical or substantially similar to that of claim 8. Accordingly, this claim is rejected under the same rationale. As per claim 16, the substance of the claimed invention is identical or substantially similar to that of claim 1. Accordingly, this claim is rejected under the same rationale. As per claim 17, the substance of the claimed invention is identical or substantially similar to that of claim 2. Accordingly, this claim is rejected under the same rationale. As per claim 18, the substance of the claimed invention is identical or substantially similar to that of claim 3. Accordingly, this claim is rejected under the same rationale. As per claim 19, the substance of the claimed invention is identical or substantially similar to that of claim 5. Accordingly, this claim is rejected under the same rationale. Claim s 4 are rejected under 35 U.S.C. 103 as being unpatentable over Nemirovsky and Ramamurthy in further view of Park et al. (WO-202 3224428 -A1) [hereinafter “ Park ”] . As per claim 4, the combination of Nemirovsky and Ramamurthy teaches the computer-implemented method of claim 2 . The combination of Nemirovsky and Ramamurthy does not explicitly teach determining a feature o verhead (R) value to calculate the computational overhead. Park determining a feature o verhead (R) value to calculate the computational overhead ([242]-[260], calculating path cost for a particular feature to calculate total cost of an object). At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Nemirovsky and Ramamurthy with the teachings of Park, determining a feature o verhead (R) value to calculate the computational overhead , along with data inputs. Claim s 7 , 14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Nemirovsky and Ramamurthy in further view of Dalli et al. ( US PGPUB No. 2021/0256377 ) [hereinafter “ Dalli ”] . As per claim 7 , the combination of Nemirovsky and Ramamurthy teaches the computer-implemented method of claim 1 . The combination of Nemirovsky and Ramamurthy does not explicitly teach receiving user input to approve or reject one or more data transformations in the list of data transformations. Dalli teaches receiving user input to approve or reject one or more data transformations in the list of data transformations ([0091], including a human input in the decision making of an AI model). At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Nemirovsky and Ramamurthy with the teachings of Dalli , receiving user input to approve or reject one or more data transformations in the list of data transformations , along with data inputs. As per claim 14, the substance of the claimed invention is identical or substantially similar to that of claim 7. Accordingly, this claim is rejected under the same rationale. As per claim 20 , the substance of the claimed invention is identical or substantially similar to that of claim 7 . Accordingly, this claim is rejected under the same rationale. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Tay et al. (US PGPUB No. 2024/0256964), Rodriguez Ramirez et al. (US PGPUB No. 2023/0280701), Aggarwal et al. (US PGPUB No. 2023/0012650), Verma et al. (US Patent No. 11,403,538), Narayanaswami et al. (US PGPUB No. 2022/0215243), Agarwal et al. ( “Counterfactual Learning-to-Rank for Additive Metrics and Deep Models”, arXiv preprint arXiv:1805.00065 (2018) ), Chen et al ( "View-based Explanations for Graph Neural Networks", arXiv:2401.02086 (2024) ), Abrate et al. ( "Counterfactual Explanations for Graph Classification Through the Lenses of Density", arXiv:2307.14849 (2023) ) and Zhang et al. ( "Trustworthy Graph Neural Networks: Aspects, Methods, and Trends," in Proceedings of the IEEE, vol. 112, no. 2, pp. 97-139, Feb. 2024, doi : 10.1109/JPROC.2024.3369017 ) all disclose various aspects of the claimed invention including using GNN and counterfactual to determine costs to match entities . Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT PETER C SHAW whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)270-7179 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT Max Flex . 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 Carl Colin can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT 571-272-3862 . 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. /PETER C SHAW/ Primary Examiner, Art Unit 2493 March 10, 2026