DETAILED 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. Status of Claims Claims 1-22 are currently pending in application 18/539,942. Claim Rejections - 35 USC § 112 (b) 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. Claims 1-20 rejected under 35 U.S.C. 112(b) , as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Claims independent Claims 1, 10, and 20-22 recite “ generating or updating decision boundary rules based on the evaluations ” . However, the Examiner is unclear what the “rules” determine in relation to a decision boundary. It is also unclear to the Examiner what the purpose of the evaluations establishing the rules is. The expression “generating or updating decision boundary rules based on evaluations” is so vague and broad that is cannot be considered that all functions generating rules having evaluations values as input are supported by the application. Claims 2- 9 and 11-19 are also rejected as being dependent from claims 1 and 10 , under the same rationale and reasoning as identified above. Claim Rejections – 35 USC §101 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 1-2 2 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter, specifically an abstract idea. Claims 1-2 2 are directed to a judicial exception (i.e., abstract idea), without providing a practical application, and without providing significantly more. Under the 35 U.S.C. §101 subject matter eligibility two-part analysis, Step 1 addresses whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. See MPEP §2106.03. If the claim does fall within one of the statutory categories, it must then be determined in Step 2A [prong 1] whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea). See MPEP §2106.04. If the claim is directed toward a judicial exception, it must then be determined in Step 2A [prong 2] whether the judicial exception is integrated into a practical application. See MPEP §2106.04(d). Finally, if the judicial exception is not integrated into a practical application, it must additionally be determined in Step 2B whether the claim recites "significantly more" than the abstract idea. See MPEP §2106.05. Examiner note : The Office’s 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG) is currently found in the Ninth Edition, Revision 10.2019 (revised June 2020) of the Manual of Patent Examination Procedure (MPEP), specifically incorporated in MPEP §2106.03 through MPEP §2106.07(c). Regarding Step 1, Claims 1- 9 and 22 are directed toward a process (method). Claims 10-19 and 21 are directed toward an apparatus (system). Claims 20 are directed toward a computer program product having computer-readable tangible storage media (article of manufacture). Thus, all claims fall within one of the four statutory categories as required by Step 1. Regarding Step 2A [prong 1], Claims 1-2 2 are directed toward the judicial exception of an abstract idea. Independent claims 1, 1 0, and 20-22 are directed specifically to the abstract idea of d ata analysis/ modeling (molecular design/ drug discovery) . Regarding independent claims 1, 10 and 20 -2 2 , the underlined limitations emphasized below correspond to the abstract ideas of the claimed invention: A computer-implemented method for generative modelling of molecular structures for chemical applications, the method comprising: providing labelled training data for training a generative model over a defined feature space, wherein the labelled training data includes representations of molecular structures and property values for each molecular structure, and wherein the generative model outputs generated candidate molecular structures with target properties ( Mathematical Concept - G eneric machine learning applied to chemical data / Conventional data preparation ) ; receiving evaluations of generated candidate molecular structure outputs from the generative model, wherein the evaluations provide feature representations of candidates with evaluation labels ( Organizing human activity (evaluation/review)/ Mathematical process of evaluating model output against a standard ) ; generating or updating decision boundary rules based on the evaluations ( Mathematical concepts (optimizing a classifier, determining boundaries ) ; and applying the decision boundary rules to update the labelled training data ( Mathematical concepts - re-training the model, incident to the very nature of machine learning) . As the underlined claim limitations above demonstrate, independent claims 1, 10, and 20 -2 2 are directed to the abstract idea of Mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations); Mental process; and Certain methods of organizing human activity (commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations)). Dependent claims 2-9 and 11-19 provide further details to the abstract idea of claims 1, 10, and 20-22 regarding the received data, therefore, these claims include mathematical concepts and certain methods of organizing human activities for similar reasons provided above for claims 1, 10, and 20-22 . After considering all claim elements, both individually and in combination and in ordered combination, it has been determined that the claims do not amount to significantly more than the abstract idea itself. Regarding Step 2A [prong 2], Claims 1-2 2 f ail to integrate the recited judicial exception into any practical application. The claims recite additional limitations which are hardware or software elements or particular technological environment, such as a “ computer ”, a “system”, a “computer program product”, a, “non-transitory tangible storage device”, a “processor”, computer “memory”, a “ generation algorithm” , and a “ generative model ”. However, the se limitations are not enough to qualify as “practical application” being recited in the claims along with the abstract idea since these limitations are merely invoked as a tool to perform instruction of an abstract idea in a particular technological environment and/or are generally linking the use of the abstract idea to a particular technological environment or field of use, and merely applying and abstract idea in a particular technological environment and merely limiting use of an abstract idea to a particular field or a technological environment do not provide practical application for an abstract idea (MPEP 2106.05 (f) & (h)). The claims do not amount to "practical application" for the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment. The presence of a n artificial intelligence (AI) generative model or computer implementations do not necessarily restrict the claim from reciting an abstract idea. The generative model and computer limitations claimed herein are simply used as a tool to apply the abstract idea without transforming the underlying abstract idea into patent eligible subject matter. Examiner notes that the additional limitations of generative model and computer processing do not result in computer functionality or technical/technology improvement and hence do not result in a practical application. The generative model and the computer limitation simply process the data through inputting and outputting data. Processing data is mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 Fed.Cir. 2017) or speeding up a loan application process by enabling borrowers to avoid physically going to or calling each lender and filling out a loan application, Lending Tree, LLLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2019)(non-precedential). Thus, the additional limitations of a generative model and computer limitations do not transform the abstract idea into a practical application. The relevant question under Step 2A [prong 2] is not whether the claimed invention itself is a practical application, instead, the question is whether the claimed invention includes additional elements beyond the judicial exception that integrate the judicial exception into a practical application by imposing a meaningful limit on the judicial exception. This is not the case with Applicant’s claimed invention. Automating the recited claimed features as a combination of computer instructions implemented by computer hardware and/or software elements as recited above does not qualify an otherwise unpatentable abstract idea as patent eligible. Examples where the Courts have found selecting a particular data source or type of data to be manipulated to be insignificant extra-solution activity include selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016); Applicant’s limitations as recited above do nothing more than supplement the abstract idea using additional hardware/software computer components as a tool to perform the abstract idea and generally link the use of the abstract idea to a technological environment, which is not sufficient to integrate the judicial exception into a practical application since they do not impose any meaningful limits. Dependent claims 2-9 and 11-19 mere ly incorporate the additional elements recited above, along with further embellishments of the abstract idea of independent claims respectively, but these features only serve to further limit the abstract idea of independent claims. Therefore, the additional elements recited in the claimed invention individually, and in combination fail to integrate the recited judicial exception into any practical application. Regarding Step 2B, Claims 1-2 2 fail to amount to “significantly more” than an abstract idea. The claims recite additional limitations which are hardware or software elements or particular technological environment, such as a “computer”, a “system”, a “computer program product”, a, “non-transitory tangible storage device”, a “processor”, computer “memory”, a “ generation algorithm” , and a “generative model”. However, the se limitations are not enough to qualify as “significantly more” being recited in the claims along with the abstract idea since these limitations are merely invoked as a tool to perform instruction of Abstract idea in a particular technological environment and/or are generally linking the use of the abstract idea to a particular technological environment or field of use, and merely applying and abstract idea in a particular technological environment and merely limiting use of an abstract idea to a particular field or a technological environment do not provide significantly more to an abstract idea (MPEP 2106.05(f) & (h)). The claims do not amount to "significantly more" than the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) add a specific limitation other than what is well-understood, routine and conventional in the field; (6) add unconventional steps that confine the claim to a particular useful application; nor (7) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment. Dependent claims 2-9 and 11-19 merely recite further additional embellishments of the abstract idea of independent claims 1, 10, and 20-22 respectively, but these features only serve to further limit the abstract idea of independent claims 1, 10, and 20-22 ; however, none of the dependent claims recite an improvement to a technology or technical field or provide any meaningful limits. The addition of another abstract concept to the limitations of the claims does not render the claim other than abstract. Under the Interim Guidance on Patent Subject Matter Eligibility (PEG 2019), it specifically states that narrowing an abstract idea of claims do not resolve the claims of being "significantly more" than the abstract idea. Thus, the additional elements in the dependent claims only serve to further limit the abstract idea utilizing the computer components as a tool and/or generally link the use of the abstract idea to a particular technological environment. Therefore, since there are no limitations in the claims 1-2 2 that transform the exception into a patent eligible application such that the claims amount to significantly more than the exception itself, and looking at the limitations as a combination and as an ordered combination adds nothing that is not already present when looking at the elements taken individually, claims 1-2 2 are rejected under 35 USC § 101 as being directed to non-statutory subject matter under 35 U.S.C. § 101 . In order to overcome the 101 rejection above, the Examiner suggests that the Applicant argue how t he specification provides a technical explanation of how the "decision boundary rules" are calculated, how they differ from conventional techniques, and how they specifically improve the accuracy, speed, or quality of the "generated candidate molecular structures"; and amend the independent claims to include specific technical limitations (e.g., particular algorithmic steps for defining the decision boundary), avoiding purely functional language. The independent claims should not simply say "do this on a computer" or use standard, generic ML techniques. They should detail the specific "decision boundary rules" and how they structurally alter the training set to improve the generative model's output. Claim Rejections - 35 USC § 102 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. Claims 1-22 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zeng et al. (Zeng X, Wang F, Luo Y, Kang SG, Tang J, Lightstone FC, Fang EF, Cornell W, Nussinov R, Cheng F.; "Deep generative molecular design reshapes drug discovery"; Cell Reports Medicine; December 20, 2022; Pgs. 1-13.) . As per independent Claim s 1 , 10, and 20 , Zeng discloses a computer-implemented method (system, computer program product) for generative modelling of molecular structures for chemical applications ( See at least Fig.1; Pgs. 1-3) , the method comprising: providing labelled training data for training a generative model over a defined feature space, wherein the labelled training data includes representations of molecular structures and property values for each molecular structure, and wherein the generative model outputs generated candidate molecular structures with target properties ( See at least Fig.1; Pgs. 2-3, “… commonly used chemical and bioinformatics databases, which provide both labeled and unlabeled data to train, validate, and test deep generative models for the drug discovery community. … They can be used for training models to generate molecules with certain properties.”) ; receiving evaluations of generated candidate molecular structure outputs from the generative model, wherein the evaluations provide feature representations of candidates with evaluation labels (See at least Fig.1; Pg.6 , “Generative tensorial reinforcement learning (GENTRL) was designed to generate novel molecules that can inhibit DDR1(discoidin domain receptor 1) by designing a reward function. The generated molecules were evaluated using in vitro and in vivo mouse assays to verify the binding affinity on DDR1 and the pre-clinical and pharmacokinetic properties. With a time frame of 46 days from target selection to partially validated molecule, GENTRL validated a promising outlook for accelerating drug discovery (Figure 1D). Notably, GENTRL leveraged a set of relevant information which is frequently available, such as crystal structure data and information related to active compounds. This model is not generalizable to cases where target-specific activity data are unavailable, and a model requiring less information could be more practical in such cases.”) ; generating or updating decision boundary rules based on the evaluations; and applying the decision boundary rules to update the labelled training data. (See at least Fig s .1 and 3 ; Pg.4, “As depicted in Figure 3E,RL—consisting of an agent, a reward function, and environment—aims to optimize toward a user-directed target. The agent chooses the next action, and the reward function evaluates the quality of the actions according to the environment (domain-specific rules) and provides feedback to the agent. After the generative model is trained on a large and general set of molecules to learn the SMILES grammar, RL can be applied as a technique for fine-tuning of target properties, such as synthetic accessibility42 and quantitative estimate of druglikeness,43 which assesses physical properties.” ; Pg.6 , “PGFS44 was designed to generate molecules that can be feasibly synthesized. PGFS treats the molecular generation problem as a sequential decision process of selecting reactant molecules and reaction transformation in a linear synthetic sequence, where the choice of reactants is considered an action and synthetic accessibility a reward. PGFS has been validated in an in-silico proof-of-concept associated with three HIV targets.”) . As per Claims 2 and 11, Zeng discloses modifying a generation algorithm of the generative model with structural constraints representing the decision boundary rules (See at least Pg s . 2- 4 , Deep RL - PGFS ) . As per Claims 3 and 12, Zeng discloses passing features of feature representations of candidates with evaluation labels to generative model as user-specified features to update the generative model (See at least Pgs. 2-4) . As per Claims 4 and 13, Zeng discloses modifying training data using chemical similarity measures in the feature space to represent learned decision boundaries (See at least Pgs. 2-4) . As per Claims 5 and 14, Zeng discloses wherein generating decision boundary rules includes preparing feature values for constructing conditions for the decision boundary rules including: preparing a class function for each class in an ontology diagram, where the class function checks whether a molecule in question belongs to that class or not; and preparing a list of elements where each element consists of a molecule, a label, and a set of the values calculated by the class functions (See at least Pgs. 2-4) . As per Claims 6 and 15, Zeng discloses wherein receiving evaluations of the generated molecular structure outputs receives evaluations from a subject matter expert using ontological feature representations to provide evaluation labels of candidate representations (See at least Pgs. 2-4) . As per Claims 7 and 16, Zeng discloses wherein receiving evaluations of the generated molecular structure outputs comprises: measuring predicted property values against tested property values to provide evaluation labels of candidate representations in the form of predicted property drift labels (See at least Pgs. 2-4) . As per Claim 8 (7) , Zeng discloses wherein tested property values are obtained by real or simulated experimental data (See at least Pgs. 2-4) . As per Claim 9, Zeng discloses wherein receiving evaluations of the generated molecular structure outputs receives evaluations using previously generated decision boundary rules (See at least Pgs. 2-4) . As per Claim 17, Zeng discloses wherein the evaluation component receives evaluations of the generated molecular structure outputs using previously generated decision boundary rules when available (See at least Pg.4; Under Broadest Reasonable Interpretation (BRI) of the claim, the “previously generated decision boundary rules” are not available, and thus the claim is not necessary ) . As per Claim 18, Zeng discloses a user interface for interaction between the user and the modelling system for providing evaluation labels (See at least Pgs. 8-10, human-computer interaction discusses throughout) . As per Claim 19, Zeng discloses wherein the system is incorporated into a molecular discovery accelerator platform including a generative model (See at least Pgs. 8-10) . As per independent Claims 21-22 , Zeng discloses a computer-implemented method (system) for molecular structure generative model augmenting, the method comprising: providing labelled training data for training a generative model over a defined feature space, wherein the labelled training data includes representations of molecular structures and property values for each molecular structure, and wherein the generative model outputs generated candidate molecular structures with target properties (See at least Fig.1; Pgs. 2-3) ; receiving evaluations of generated candidate molecular structure outputs from the generative model, wherein the evaluations provide feature representations of candidates with evaluation labels (See at least Fig.1; Pg.6 ) ; generating or updating decision boundary rules based on the evaluations (See at least Figs.1 and 3; Pg.4) ; applying the decision boundary rules to update the labelled training data, wherein the training data is updated using chemical similarity measures in the feature space to represent learned decision boundaries (See at least Figs.1 and 3, Pgs. 2-4) ; modifying, by a model constraint input component, a generation algorithm of the generative model with structural constraints representing the decision boundary rules (See at least Figs.1 and 3, Pgs. 2-4) ; and passing, by a model feature update component, features of feature representations of candidates with evaluation labels to generative model as user-specified features to update the generative model (See at least Figs.1 and 3, Pgs. 2-4) . Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found in the PTO-892 Notice of References Cited. The Examiner suggests the applicant review all of these documents before submitting any amendments , especially the following: Plumbley et al. (US 2021/0090690 A1) – Plumbley discloses Method(s), apparatus and system(s) for designing a compound exhibiting one or more desired property(ies) using a machine learning (ML) technique ( See at least Para 006 2-0064 , “ [0062] For example, reinforcement learning (RL) techniques may be applied that use one or more ML technique(s), by way of example only but are not limited to, neural networks to design and generate new molecules/compounds exhibiting one or more desired property(ies). The RL technique uses an ML technique to iteratively generate a sequence of actions for modifying an initial compound molecule/compound into another molecule/compound that may exhibit the desired properties. The ML technique may be configured to apply all known or possible actions it can take (e.g. add atom(s), break bonds, take away atoms etc.) to the initial molecule/compound or fragment(s) thereof and desired properties to output one or more possible candidate compounds . [0063] Each candidate compound may be scored based on, by way of example only but it not limited to, atomistic computer simulations (e.g. molecular dynamics®) and/or knowledge based experts , or one or more ML techniques trained for scoring a compound against one or more desired properties, to determine whether the candidate compound is already known and how close it exhibits the desired property(ies) . The RL technique updates or adapts the ML technique based on the scoring. The update of a ML technique may include, by way of example only but is not limited to, updating or adapting the parameters, coefficient(s) and/or weight(s) of the ML technique . During the update, the RL technique may penalise the ML technique if the desired properties are further away from the starting molecule/compound or if the modified molecule/compound is too big/small, and/or any other undesirable quality or difference. The RL technique may reward the ML technique if the modified molecule exhibits properties closer to the desired properties that are required. The RL technique then re-iterates the design process, which may include the ML technique starting again with the initial compound and/or starting with one of the output candidate compounds, and applying another sequence of actions to get to another modified molecule/compound. The RL technique's iterative process may complete, by way of example only but not limited to, when either a maximum number of iterations has occurred, there are no further significant improvements in candidate compounds (e.g. seen when the scoring plateaus compared with previous iterations), when the scoring indicates one or more candidate compounds exhibit the desired properties and/or there are no further significant improvements to the candidate compounds. [0064] A compound (also referred to as one or more molecules) may comprise or represent a chemical or biological substance composed of one or more molecules (or molecular entities), which are composed of atoms from one or more chemical element(s) (or more than one chemical element) held together by chemical bonds . Example compounds as used herein may include, by way of example only but are not limited to, molecules held together by covalent bonds, ionic compounds held together by ionic bonds, intermetallic compounds held together by metallic bonds, certain complexes held together by coordinate covalent bonds, drug compounds, biological compounds, biomolecules, biochemistry compounds, one or more proteins or protein compounds, one or more amino acids, lipids or lipid compounds, carbohydrates or complex carbohydrates, nucleic acids, deoxyribonucleic acid (DNA), DNA molecules, ribonucleic acid (RNA), RNA molecules, and/or any other organisation or structure of molecules or molecular entities composed of atoms from one or more chemical element(s) and combinations thereof. ”; See also Para 0088-0090 ) . 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Status information for unpublished applications is available through Patent Center for authorized users only. Should you have questions about access to Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). 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) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form. DATE \@ "MMMM d, yyyy" March 2, 2026 /JONATHAN P OUELLETTE/ Primary Examiner, Art Unit 3629