CTNF 18/372,594 CTNF 93457 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. Priority This application claims priority to U.S. Application No. 63/410,560, filed on Sep. 27, 2022, and titled “CAUSAL INFERENCE ON CATEGORY AND GRAPH DATA STORES,” the entire disclosure of which is incorporated herein by reference. Information Disclosure Statement The information disclosure statement (IDS) submitted on 04/22/2024, 06/26/2024, 07/17/2024, 0925/2024, 04/22/2025, and 09/23/2025 were filed after and along with the mailing date of the Non-Provisional Patent Application on 09/25/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. DETAILED ACTION This Office Action is in response to a Non-Provisional Patent Application received on 09/25/2023. In the application, claims 1-20 have been received for consideration and have been examined. Specification Applicant’s submitted specification has been reviewed and found to be in compliance. Drawings Applicant’s submitted drawings have been reviewed and found to be in compliance. Claim Rejections - 35 USC § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 Claims 1-7 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 1 recites the limitation " a data model generator software component, configured to query and receive data in the form of normalized attribute vectors ”. There is insufficient antecedent basis for underlined phrase in this limitation in the claim. Dependent claims inherit this deficiency. Subject Matter Free of Prior Art Claim(s) 1-20 are allowable over prior art because the prior art of record fail to expressly teach or suggest, either alone or in combination, the features found within the independent claims, in particular: " a data model generator software component, configured to query and receive data in the form of normalized attribute vectors, each vector comprised of mathematical model attributes, experiment attributes, and experimental data attributes, from one or more databases, to generate data models from the received normalized attribute vectors, and to store the generated data models in a data model database ” ( claim 1 ), “ receiving, at a data model generator software component, a query; retrieving data from at least one database storing one or more normalized attribute vectors, each normalized attribute vector comprised of a plurality of attributes; for each normalized attribute vector in the at least one database, calculating a similarity score between the respective normalized attribute vector attributes and the query; and at the data model generator software component, generating a data model from the normalized attribute vectors whose calculated similarity scores meet a predetermined similarity score threshold ( claim 8 ), and “ at a machine learning algorithm software component, receiving a data model comprised of a plurality of normalized attribute vectors, each normalized attribute vector including a plurality of mathematical model attributes; at the machine learning algorithm software component, generating candidate correlations between two more normalized attribute vectors in the data model based on patterns between attributes of normalized attribute vectors, and a machine learning model based on the attributes; at the machine learning algorithm software component for each generated candidate correlation, generating a confidence score; and validating each generated candidate correlation based at least on the corresponding generated confidence score, and storing the validated correlations as causal relationships in a causal inference data store ( claim 13 )”. Because the prior art does not teach or disclose the above features in the specific manner and combinations recited in independent claims 1, 8, and 13 . Therefore, Claims 1, 8, and 13 are hereby deemed to be allowable over prior art. Originally numbered dependent claims 2-7, 9-12, and 14-20 incorporate the allowable features of originally numbered independent claims through dependency, respectively. However , the claims are still rejected under 101 reciting Abstract Idea. Claim Rejections - 35 USC § 101 (Abstract Idea) 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more analyzed according to MPEP 2106. Step 1: The independent claims 1 , 8, and 13 do fall into one of the four statutory categories of “ a system ” and “ a method ” claims. Nevertheless, the claim still is considered as abstract idea ( i.e., combination of Methods Of Organizing Human Activity, Mental process and Mathematical Concepts ) for the following prongs and reasons. Step 2A: Prong 1: The limitations of the independent claims 1, 8, and 13 recite the abstract idea of: Claim 1. A system to generate and manage data models for causal inference, comprising: [[ a computer processor; a memory configured to store computer executable instructions and computer readable data ]] ; and a data model generator [[ software component ]] , configured to query and receive data in the form of normalized attribute vectors, each vector comprised of mathematical model attributes, experiment attributes, and experimental data attributes, from one or more databases, to generate data models from the received normalized attribute vectors, and to store the generated data models in a data model database ( Mental process, Organizing human activity & Mathematical concepts: a trained human tasked to receive data in the form of normalized attribute vectors wherein each vector comprised of mathematical model attributes, experiment attributes, and experimental data attributes, from one or more databases, to generate data models from the received normalized attribute vectors, and to store the generated data models in a data model database ). Claim 8 . A method to perform causal inference, comprising: receiving, at a data model generator [[ software component ]] , a query ( Mental process, Organizing human activity & Mathematical concepts: a trained human receives a query to generate data model ); retrieving data from at least one database storing one or more normalized attribute vectors, each normalized attribute vector comprised of a plurality of attributes ( Mental process, Organizing human activity & Mathematical concepts: the trained human retrieves data from at least one database storing one or more normalized attribute vectors, each normalized attribute vector comprised of a plurality of attributes ); for each normalized attribute vector in the at least one database, calculating a similarity score between the respective normalized attribute vector attributes and the query ( Mental process, Organizing human activity & Mathematical concepts: the trained human using each normalized attribute vector in the at least one database, calculate a similarity score between the respective normalized attribute vector attributes and the query ); and at the data model generator [[ software component ]] , generating a data model from the normalized attribute vectors whose calculated similarity scores meet a predetermined similarity score threshold ( Mental process, Organizing human activity & Mathematical concepts: the trained human generates a data model from the normalized attribute vectors whose calculated similarity scores meet a predetermined similarity score threshold ). Claim 13 . A method to validate correlations in a causal inference [[ engine ]] , comprising: [[ at a machine learning algorithm software component ]] , receiving a data model comprised of a plurality of normalized attribute vectors, each normalized attribute vector including a plurality of mathematical model attributes ( Mental process, Organizing human activity & Mathematical concepts: a trained human receives a data model comprised of a plurality of normalized attribute vectors, each normalized attribute vector including a plurality of mathematical model attributes ); [[ at the machine learning algorithm software component ]] , generating candidate correlations between two more normalized attribute vectors in the data model based on patterns between attributes of normalized attribute vectors, and a [[ machine ]] learning model based on the attributes ( Mental process, Organizing human activity & Mathematical concepts: the trained human generates candidate correlations between two more normalized attribute vectors in the data model based on patterns between attributes of normalized attribute vectors, and a learning model based on the attributes ); [[ at the machine learning algorithm software component ]] for each generated candidate correlation, generating a confidence score ( Mental process, Organizing human activity & Mathematical concepts: the trained human generates a confidence score for each generated candidate correlation ); and validating each generated candidate correlation based at least on the corresponding generated confidence score, and storing the validated correlations as causal relationships in a causal inference data store ( Mental process, Organizing human activity & Mathematical concepts: the trained human validates each generated candidate correlation based at least on the corresponding generated confidence score, and storing the validated correlations as causal relationships in a causal inference data store ). Overall, a data model is an abstract model that organizes elements of data and standardizes how they relate to one another and to the properties of real-world entities. For instance, a data model may specify that the data element representing a car be composed of a number of other elements which, in turn, represent the color and size of the car and define its owner. The corresponding professional activity is called generally data modeling or, more specifically, database design. Data models are typically specified by a data expert, data specialist, data scientist, data librarian, or a data scholar. A data modeling language and notation are often represented in graphical form as diagrams. A data model can be referred to as a data structure, especially in the context of programming languages. Data models are often complemented by function models, especially in the context of enterprise models. A data model explicitly determines the structure of data; conversely, structured data is data organized according to an explicit data model or data structure. Step 2A: Prong 2: The judicial exception (i.e., a data model generator software component, a machine leaning algorithm) is not integrated into a practical application. In particular, the claims do not recite any additional element to perform beyond routine steps. To show that the involvement of a computer assists in improving the technology, the claims must recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology (MPEP 2106.5(a) II). In this particular case, the additional elements of the claim are: “ the system comprising: a computer processor; a memory configured to store computer executable instructions and computer readable data ” ( claim 1 ), “ a data model generator software component ” ( claim 8 ), and “ a machine learning algorithm software component ” ( claim 13 ). The additional elements are recited at a high-level of generality (i.e., as generic terms performing generic computer functions ( see instant spec. PreGrant-Pub [0065], and [0068-0069] ) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea. Step 2B : The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the claims do not reflect improvement in the technology. Further, mere automated instructions to apply an exception using a generic computer component cannot provide an inventive concept. Thus, the claims are not patent eligible. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements identified above amount to no more than mere instructions to apply the exception using general purpose computer. To support this factual conclusion, the examiner takes Official Notice that one of the ordinary skill in the art, before the effective filing date of the claimed invention, would have found processors and/or software well-known and routine in technology that involves computers ( instant spec. PreGrant-Pub [0065], and [0068-0069] discloses that the functions of the disclosed claims can be implemented using generic computer(s) ) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, the examiner asserts that the above noted elements, when considered individually or in combination, do not constitute as “ significantly more ” than the abstract idea. The dependent claims 2-7, 9-12, and 14-20 of respective independent claims 1, 8, and 13 have been analyzed and fall into one of the statutory categories and therefore passes step 1 analysis. However, under step 2, 2A & 2B analysis, the dependent claims recite Mental process, Organizing human activity & Mathematical concepts which can be implemented by one or more human users using pen and paper. Thus, dependent claims also recite abstract idea and considered ineligible. Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. a. Wick et al., US-11790268-B1, teaches causal inference machine learning with statistical background subtraction. b. Florez et al., US-10977580-B1, teaches techniques for integrating common sense into a machine learning (ML) system. c. Chen et al., US-20180107763-A1, teaches methods and apparatus for predicting unknown values given a data set of known values. d. Puranic et al., US-12208521-B1, teaches using a directed acyclic graph as an exemplary causal model within the use of robotics and surgeries. e. Iliev et al., US-20220365926-A1, teaches a method of identifying causal relationships includes receiving data comprising a set of values corresponding to one or more variables, and generating a list of candidate causal models of relationships between or within the variables. Any inquiry concerning this communication or earlier communications from the examiner should be directed to SYED M AHSAN whose telephone number is (571)272-5018. The examiner can normally be reached 8:30 AM - 6:00 PM. 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, William Korzuch can be reached at 571-272-7589. 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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. /SYED M AHSAN/Primary Examiner, Art Unit 2491 Application/Control Number: 18/372,594 Page 2 Art Unit: 2491 Application/Control Number: 18/372,594 Page 3 Art Unit: 2491 Application/Control Number: 18/372,594 Page 4 Art Unit: 2491 Application/Control Number: 18/372,594 Page 5 Art Unit: 2491 Application/Control Number: 18/372,594 Page 6 Art Unit: 2491 Application/Control Number: 18/372,594 Page 7 Art Unit: 2491 Application/Control Number: 18/372,594 Page 8 Art Unit: 2491 Application/Control Number: 18/372,594 Page 9 Art Unit: 2491 Application/Control Number: 18/372,594 Page 10 Art Unit: 2491 Application/Control Number: 18/372,594 Page 11 Art Unit: 2491 Application/Control Number: 18/372,594 Page 12 Art Unit: 2491 Application/Control Number: 18/372,594 Page 13 Art Unit: 2491