DETAILED ACTION
Claims 1, 3, 5-9, 11-16 ad 18-21 are pending in this 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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 3/17/2026, 5/27/2026 and 6/11/2026 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.
Claims 1, 3, 5-6, 8-9, 11-13, 15-16, 18-19 and 21 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”] in further view of Park et al. (WO-2023224428-A1) [hereinafter “Park”] in further view of Holmes et al. (US PGPUB No. 2021/0124834) [hereinafter “Holmes”].
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), wherein the output of the probabilistic matching engine comprises the non-matches of entities (Abstract, output does not contain one or more of the target values); 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); and ranking the list of data transformations based on a computational overhead of a respective data transformation of the list of the data transformations and an estimated improvement in entity matching ([0021] and [0029], determining top formulas for the purposes of computation savings).
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.
The combination of Nemirovsky and Ramamurthy does not explicitly teach determining a feature overhead (R) value corresponding to a sum of costs associated with features transformed by the respective data transformations. Park determining a feature overhead (R) value corresponding to a sum of costs associated with features transformed by the respective data transformations ([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 overhead (R) value corresponding to a sum of costs associated with features transformed by the respective data transformations, to present options to a user that allow the user to make efficient and quality decisions that are in line with required policy.
The combination of Nemirovsky, Ramamurthy and Park does not explicitly teach the costs are learned using pairwise feature comparisons indicating that a first feature of the features is harder to change than a second feature of the features. Holmes teaches the costs are learned using pairwise feature comparisons indicating that a first feature of the features is harder to change than a second feature of the features ([0064], ranking and list top-n-opportunities would require pairwise comparisons based on their “probability of success” – this probability is interpreted to be substantially similar to “difficulty of change”).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Nemirovsky, Ramamurthy and Park with the teachings of Holmes, the costs are learned using pairwise feature comparisons indicating that a first feature of the features is harder to change than a second feature of the features, to present options to a user that allow the user to make efficient and quality decisions that are in line with required policy.
As per claim 3, the combination of Nemirovsky, Ramamurthy, Park and Holmes teaches the computer-implemented method of claim 1, 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, Ramamurthy, Park and Holmes teaches the computer-implemented method of claim 1, 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, Ramamurthy, Park and Holmes teaches the computer-implemented method of claim 1, 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, Ramamurthy, Park and Holmes 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 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 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.
As per claim 21, the combination of Nemirovsky, Ramamurthy, Park and Holmes teaches the computer-implemented method of claim 1, further comprising: performing, by the GNN model, the entity matching on a pair of records to determine whether each data transformation of the list of data transformations causes a match between the pair of records, wherein the pair of records is associated with the non- matches of entities (Nemirovsky; [0024], changes made to an output to match a generated counterfactual input for a candidate); and estimating, by the GNN model, an improvement in the entity matching by each data transformation of the list of data transformations, wherein the improvement in the entity matching is estimated based on the performing of the entity matching (Nemirovsky; [0098] and [00147], estimating matching of profile to another profile, i.e. diabetes risk or membership into hiring marketplace, will change based on data transformations like changing BMI or changing salary).
Claims 7, 14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Nemirovsky, Ramamurthy, Park and Holmes in further view of Dalli et al. (US PGPUB No. 2021/0256377) [hereinafter “Dalli”].
As per claim 7, the combination of Nemirovsky, Ramamurthy, Park and Holmes 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, Ramamurthy, Park and Holmes 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.
Response to Arguments
Applicant's arguments with respect to the rejection of claims 1-20 under 35 U.S.C. 103 have been fully considered and in light of the latest amendments a new prior art reference, Holmes, has been introduced and cited to.
To expedite prosecution, Examiner is open to an after-final interview to discuss claim amendments to overcome the current rejection and/or place the application in condition for allowance.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Penner (US PGPUB No. 2021/0279809), Kappor et al. (US PGPUB No. 2021/0216945), Niininen et al. (US PGPUB No. 2018/0150758), Murray et al ("Probabilistic Neural Programs," arXiv:1612.00712, December 2, 2016), Biswas et al. ("Statistical Perspective on Functional and Causal Neural Connectomics: A Comparative Study," arXiv:2111.01961, November 3, 2021) and Liu et al. ("Research on Requirement Vulnerability Detection Method Based on Graph Neural Network and Counterfactual Explanation," 2025 IEEE 33rd (REW), Valencia, Spain, 2025, pp. 200-207, doi: 10.1109/REW66121.2025.00031) all disclose various aspects of the claimed invention including using GNN and counterfactual to determine costs to match entities.
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 PETER C SHAW whose telephone number is (571)270-7179. The examiner can normally be reached 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, Carl Colin can be reached at 571-272-3862. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PETER C SHAW/Primary Examiner, Art Unit 2493 July 19, 2026
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