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 § 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 Plumbley et al. (US2021/0090690 A1).
As per independent Claims 1, 10, and 20, Plumbley discloses a computer-implemented method (system, computer program product) for generative modelling of molecular structures for chemical applications (See at least Figs 1a-2b; Para 0006), the method comprising:
training a generative model (Para 0090, “… Some examples of semi-supervised ML techniques may include or be based on, by way of example only but is not limited to, one or more of active learning, generative models …”) over a defined feature space using labelled training data, 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 Figs 1a-2b; Para 0088, Para 0094-0095; See also Para 0089, “… generating a model from labelled and/or unlabelled training data and the like.”; and Para 0090);
receiving evaluations of the generated candidate molecular structure outputs from the generative model, wherein the evaluations provide feature representations of the generated candidate molecular structures with evaluation labels (See at least Para 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.”; See also Para 0096);
generating or updating decision boundary rules based on the evaluations, wherein the decision boundary rules represent one or more decision boundaries within the defined feature space as a plurality of Boolean rules; applying the decision boundary rules to update the labelled training data; and training the generative model using the updated labelled training data (See at least Figs.1a-1b; Para 0069, and Para 0097-0100).
As per Claims 2 and 11, Plumbley discloses modifying a generation algorithm of the generative model with structural constraints representing the decision boundary rules (See at least Para 0069-0084, Para 0097-0098; and Claim 10).
As per Claims 3 and 12, Plumbley discloses passing features of the feature representations of candidates with the evaluation labels to the generative model as user-specified features to update the generative model (See at least Figs.1a-1b; Para 0088-0090; and Para 0094-0095).
As per Claims 4 and 13, Plumbley discloses modifying training data using chemical similarity measures in the feature space to represent learned decision boundaries (See at least Para 0065-0070, Para 0096).
As per Claims 5 and 14, Plumbley discloses wherein generating the 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 the molecule, a label, and a set of the values calculated by the class functions (See at least Para 0068-0084; See also Para 0088).
As per Claims 6 and 15, Plumbley discloses wherein receiving the evaluations of the generated molecular structure outputs comprises receiving the evaluations from a subject matter expert using ontological feature representations to provide the evaluation labels of candidate representations (See at least Para 0063, Para 0107; See also Para 0101).
As per Claims 7 and 16, Plumbley discloses wherein receiving the evaluations of the generated molecular structure outputs comprises: measuring predicted property values against tested property values to provide the evaluation labels of candidate representations as predicted property drift labels (See at least Para 0063).
As per Claim 8 (7), Plumbley discloses wherein the tested property values are obtained by real or simulated experimental data (See at least Para 0066-0067, Para 0096).
As per Claim 9, Plumbley discloses wherein receiving the evaluations of the generated molecular structure outputs comprises receiving the evaluations using previously generated decision boundary rules (See at least Para 0096-0099).
As per Claim 17, Plumbley discloses wherein the evaluation component receives the evaluations of the generated molecular structure outputs using previously generated decision boundary rules when available (See at least Para 0096-0099; 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, Plumbley discloses a user interface for interaction between the user and the modelling system for providing the evaluation labels (See at least Fig.6a; Para 0034, Para 0193).
As per Claim 19, Plumbley discloses wherein the system is incorporated into a molecular discovery accelerator platform including the generative model (See at least Para 0202).
As per independent Claims 21-22, Plumbley discloses a computer-implemented method (system) for molecular structure generative model augmenting (See at least Figs 1a-2b; Para 0006), the method comprising:
training a generative model (Para 0090, “… Some examples of semi-supervised ML techniques may include or be based on, by way of example only but is not limited to, one or more of active learning, generative models …”) over a defined feature space using labelled training data, 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 Figs 1a-2b; Para 0088, Para 0094-0095; See also Para 0089, “… generating a model from labelled and/or unlabelled training data and the like.”; and Para 0090);
receiving evaluations of the generated candidate molecular structure outputs from the generative model, wherein the evaluations provide feature representations of the generated candidate molecular structures with evaluation labels (See at least Para 0063; See also Para 0096);
generating or updating decision boundary rules based on the evaluations, wherein the decision boundary rules represent one or more decision boundaries within the defined feature space as a plurality of Boolean rules (See at least Figs.1a-1b; Para 0069, and Para 0097-0100);
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 Para 0065-0070, Para 0096);
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 Para 0065-0070, Para 0096-0100);
passing, by a model feature update component, features of feature representations of candidates with the evaluation labels to generative model as user-specified features to update the generative model; and training the generative model using the updated labelled training data (See at least Figs.1a-1b; Para 0069, and Para 0097-0100).
Response to Arguments
Applicant’s arguments filed on 6/2/2026, with respect to the prior art rejection of Claims 1-22, have been considered but are moot based on the new grounds of rejection.
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.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN P OUELLETTE whose telephone number is (571)272-6807. The examiner can normally be reached on M-F 8am-6pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lynda C Jasmin, can be reached at telephone number (571) 272-6782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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July 7, 2026
/JONATHAN P OUELLETTE/Primary Examiner, Art Unit 3629
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