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 .
Applicant's response filed 4/2/2026 has been fully considered. The following rejections
and/or objections are either reiterated or newly applied.
Status of Claims
Claims 1-4, 6-7, 10, 12, 13, 15, 17, 19-22, 24, 27, 29, and 31 pending and examined on the merits.
Claims 5, 8, 9, 11, 14, 16, 18, 23, 25, 26, 28, 30 and 32-35 cancelled.
Priority
The instant application filed on 3/23/2022 is a 371 national stage entry of PCT/CA2020/051285 having an international filing date of 9/25/2020, and claims the benefit of priority to U.S. Provisional Patent Application No. 62/987,751 filed on 3/10/2020 and to U.S. Provisional Patent Application No. 62/987,751 filed on 9/26/2019. Thus, the effective filing date of the claims is 9/26/2019.
The applicant is reminded that amendments to the claims and specification must comply with 35 U.S.C. § 120 and 37 C.F.R. § 1.121 to maintain priority to an earlier-filed application. Claim amendments may impact the effective filing date if new subject matter is introduced that lacks support in the originally filed disclosure. If an amendment adds limitations that were not adequately described in the parent application, the claim may no longer be entitled to the priority date of the earlier filing.
Claim Objections
The objection to claim 22 withdrawn in view of Applicant's claim amendments filed on 4/2/2026.
Withdrawn Rejections
35 USC § 112(b)
The rejection of claims 2-4 and 15 under 35 USC 112(b) withdrawn in view of Applicant's claim amendments filed on 4/2/2026.
35 USC § 112(d)
The rejection of claim 5 under 35 USC 112(d) withdrawn in view of Applicant's claim amendments filed on 4/2/2026.
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 1-4, 6-7, 10, 12, 13, 15, 17, 19-22, 24, 27, 29, and 31 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of a mental process, a mathematical concept, organizing human activity, or a law of nature or natural phenomenon without significantly more. In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claims 1, 27, 29, and 31: “the trained parameters of the classifier having been trained over at least one synergistic chemical interaction between compounds of at least one composition against at least one training pest” provides a mathematical calculation (training a model involves many mathematical calculations, but specifically, clustering data and calculating a graph similarity metric as described in para.0023 of the instant specification: "selecting one or more high-importance compositions comprises selecting the one or more high-importance compositions based on a representativeness criterion. In some embodiments, selecting the one or more high-importance compositions based on a representativeness criterion comprises determining a plurality of clusters of the plurality of training compositions and selecting at least one high-importance compositions from each of at least two of the plurality of clusters. In some embodiments, determining the plurality of clusters of the plurality of training compositions comprises determining a graph similarity metric between at least one graph representing at least one compound of a first one of the training compositions and at least one graph representing at least one compound of a second one of the training compositions") that is considered a mathematical concept, which is an abstract idea.
Claim 2: “combining the plurality of synergy predictions into a combined prediction” provides an evaluation (analyzing one or more predictions) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea. Additionally, given the interpretation above, the claim may also provide a mathematical calculation (summing synergy predictions) that is considered a mathematical concept, which is an abstract idea.
Claim 3: “determining at least one of: a confidence interval, a standard deviation, and a variance based on the plurality of predictions” provides a mathematical calculation (determining statistical metrics requires mathematical calculations) that is considered a mathematical concept, which is an abstract idea.
Claim 10: “selecting the classifier from a plurality of classifiers based on the one or more pests, the method optionally further comprising receiving a representation of the one or more pests and selecting the classifier comprises selecting the classifier based on the representation of the one or more pests” provides an evaluation (making a selection requires evaluating available options) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 12: “selecting one of the first and second classifiers based on the one or more pests” provides an evaluation (making a selection requires evaluating available options) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 13: “the plurality of constituent classifiers comprising at least a first constituent classifier and a second constituent classifier, respective trained parameters of the first and second constituent classifiers each having been trained over at least one synergistic interaction between compounds of at least one composition against at least one of the one or more pests” provides a mathematical calculation (similar to claim 1, training a model involves many mathematical calculations, but specifically, clustering data and calculating a graph similarity metric as described in para.0023 of the instant specification) that is considered a mathematical concept, which is an abstract idea.
Claim 17: “excluding an excluded composition comprising the third compound from prediction based on determining at least one of: a chemical feature of the third compound matches an exclusion rule, an availability value corresponding to the third compound being less than a threshold, a similarity metric between the third compound and a fourth compound being greater than a threshold, and a toxicity indication of the third compound matches a toxicity criterion” provides an evaluation (determining if exclusion criteria have been met via thresholds) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea. The claim also provides a mathematical calculation (calculating a similarity metric) that is considered a mathematical concept, which is an abstract idea.
Claim 22: “determining an importance metric for each of a plurality of training compositions” or “determining the importance metric for a given composition comprises determining the importance metric for the given training composition based on a variance” provides an evaluation (determining an importance metric involves evaluating the metrics) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
“selecting one or more high-importance compositions from the plurality of training compositions based on the importance metric for each of the one or more high-importance compositions” provides an evaluation (making a selection requires evaluating available options) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 24: “determining a plurality of clusters of the plurality of training compositions and selecting at least one high-importance composition from each of at least two of the plurality of clusters” or “determining the plurality of clusters of the plurality of training compositions comprises determining a graph similarity metric between at least one graph representing at least one compound of a first one of the training compositions and at least one graph representing at least one compound of a second one of the training compositions” provides a mathematical calculation (clustering data and calculating a graph similarity metric involves mathematical calculations) that is considered a mathematical concept, which is an abstract idea.
These recitations are similar to the concepts of collecting information, analyzing it, and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind or are mathematical relationships. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. Additionally, while claims 27 and 29 recite performing some aspects of the analysis on “A computer system comprising: one or more processors; and a memory storing instructions which cause the one or more processors to perform operations” and “A non-transitory machine-readable medium storing instructions which cause one or more processors to perform operations”, there are no additional limitations that indicate that this requires anything other than carrying out the recited mental processes or mathematical concepts in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental processes” grouping of abstract ideas. As such, claims 1-4, 6-7, 10, 12, 13, 15, 17, 19-22, 24, 27, 29, and 31 recite an abstract idea (Step 2A, Prong 1: YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). The judicial exceptions listed above are not integrated into a practical application because the claims do not recite an additional element or elements that reflects an improvement to technology. Specifically, the claims recite the following additional elements:
Claim 1: “receiving a first representation of a pesticidal compound corresponding to a chemical structure or chemical properties of the pesticidal compound” and “receiving a second representation of a synergistic compound corresponding to a chemical structure or chemical properties of the pesticidal compound” provides insignificant extra-solution activities (receiving data is a pre-solution activity involving data gathering steps) that do not serve to integrate the judicial exceptions into a practical application.
“generating an encoded representation of a composition comprising the pesticidal and synergistic compounds based on the respective first and second representations by encoding a first chemical feature of the pesticidal compound to generate a first encoded feature and a second chemical feature of the synergistic compound to generate a second encoded feature” provides insignificant extra-solution activities (encoding/transforming data, as interpreted in para.0110-129 of the instant specification for what "generating an encoded representation" entails: specifically, para.0110 "The transformation effected by encoder 210 may comprise one or more of: compression, feature selection, and/or transcoding to generate encoded representations of candidate pesticidal compositions which are amenable to classification by classifier", is a pre-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
“generating one or more predictions of a synergistic interaction between the pesticidal compound and the synergistic compound against one or more pests” and “generating the one or more predictions based on the first and second encoded compound representations” provides insignificant extra-solution activities (using a model is a pre- and post-solution activity involving data gathering and manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 4: “generating the one or more predictions comprises transforming the encoded representation based on the trained parameters of the classifier over a plurality of iterations and generating a prediction for each iteration” provides insignificant extra-solution activities (using a trained model is a pre- and post-solution activity involving data gathering and manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 6: “generating the encoded representation to be lower-dimensional than at least one of the first and second representations” provides insignificant extra-solution activities (encoding/transforming data is a pre-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 7: “transforming the first and second chemical features of the respective pesticidal and synergistic compounds into the encoded representation based on trained parameters of an encoder model” provides insignificant extra-solution activities (using a trained model and encoding/transforming data are a pre-solution activities involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
“the encoder portion operable to transform the first and second chemical features from an input space to a latent space of the variational autoencoder” provides insignificant extra-solution activities (encoding/transforming data is a pre-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 13: “generating one or more predictions comprises generating a first prediction based on the first constituent classifier and generating a second prediction based on the second constituent classifier” provides insignificant extra-solution activities (using a trained model is a pre- and post-solution activity involving data gathering and manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 15: “generating an enhanced representation of at least one of the pesticidal and synergistic compounds” provides insignificant extra-solution activities (using a trained model is a pre- and post-solution activity involving data gathering and manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 17: “receiving a third representation of a third compound” provides insignificant extra-solution activities (receiving data is a pre-solution activity involving data gathering steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 22: “updating the trained parameters” provides insignificant extra-solution activities (updating parameters is a pre-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 27 and 29: “A computer system comprising: one or more processors; and a memory storing instructions which cause the one or more processors to perform operations” (claim 27) and “A non-transitory machine-readable medium storing instructions which cause one or more processors to perform operations” (claim 29) provides insignificant extra-solution activities (running instructions on generic computer components) that do not serve to integrate the judicial exceptions into a practical application.
Claim 31: “determining a prediction of a synergistic interaction between a pesticidal compound and a synergistic compound” provides insignificant extra-solution activities (using a model is a pre- and post-solution activity involving data gathering and manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
“combining the pesticidal compound and the synergistic compound to yield a composition; exposing the one or more pests to the composition in a test environment; and evaluating an efficacy of the composition as a pesticide” provides insignificant extra-solution activities (compounding and testing compositions are post-solution activities involving sample gathering and manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
The steps for obtaining, inputting, encoding/transforming, and outputting data; and compounding and testing samples are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application because they are pre- and post-solution activities involving data and sample gathering and manipulation steps (see MPEP 2106.04(d)(2)). Furthermore, the limitations regarding implementing program instructions do not indicate that they require anything other than mere instructions to implement the abstract idea in a generic way or in a generic computing environment. As such, this limitation equates to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. Therefore, claims 1-4, 6-7, 10, 12, 13, 15, 17, 19-22, 24, 27, 29, and 31 are directed to an abstract idea (Step 2A, Prong 2: NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application, or equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment.
As discussed above, there are no additional elements to indicate that the claimed “A computer system comprising: one or more processors; and a memory storing instructions which cause the one or more processors to perform operations” and “A non-transitory machine-readable medium storing instructions which cause one or more processors to perform operations” requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims. Additionally, the limitations for obtaining, inputting, encoding/transforming, and outputting data; and compounding and testing samples are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application. Furthermore, no inventive concept is claimed by these limitations as they are well-understood, routine, and conventional: As evidenced by Hernández et al., mixture effects models have been studied and interpreted since the 1930s using statistical principles which all would require obtaining, inputting, encoding/transforming, and outputting data, page 3 col 1 paragraph 2, "Currently, three basic types of action for the combined effects of chemicals in a mixture are defined: (a) dose or concentration addition (CA), which implies a similar MoA of the substances; (b) independent action (IA) or response addition, which implies a dissimilar MoA; and (c) interactions between substances in the mixture. The distinction between similar and dissimilar action was first introduced by Bliss in 1939 and by Hewlett and Plackett in 1952 on the basis of statistical principles, and then was widely accepted for the interpretation of mixture effects" (Hernández et al. "Toxicological interactions of pesticide mixtures: an update." Archives of toxicology 91.10 (2017): 3211-3223). These models continue to be built upon using method that also required the same obtaining, inputting, encoding/transforming, and outputting of data: Hernández et al., page 10 col 1 paragraph 3 "In a systematic literature review, Quignot et al. (2015) gathered quantitative data on interaction of chemical mixtures, including pesticides. Authors collected in vitro and in vivo TK data for metabolic interactions, and in vivo TD data for toxicological effects (synergistic effects) in humans and test species to support evidence-based risk assessment". Likewise, for test and evaluation of the predicted mixtures, these are also well-understood, routine, and conventional in the art: Hernández et al., page 8 col 2 paragraph 1 "Another study evaluated the cytotoxic effects of a number of pesticide mixtures found in the French diet using two human cell lines, and observed greater than the additive toxicity for the equimolar mixture of DDT and dieldrin irrespective of using the CA or IA approaches to predict effects (Takakura et al. 2013). These results, however, need to be confirmed in other cellular and animal models", and Hernández et al., page 8 col 2 last paragraph "When synergism was defined as a minimum twofold deviation from CA predictions, 7% of the 194 binary pesticide mixture toxicity experiments conducted with aquatic organisms by Belden et al. (2007) and then re-evaluated by Cedergreen (2014) showed synergism, although the difference between observed and predicted effect concentrations rarely exceeded tenfold". Finally, Kar et al. are clear that this step of testing and evaluation should be routine practice in the industry: page 1 last paragraph "Exploration of chemicals’ toxicity towards living things as well as ecosystems should be one of the primary steps before introducing into industry or commerce any new chemicals and/or drugs" (Kar et al. "Exploration of computational approaches to predict the toxicity of chemical mixtures." Toxics 7.1 (2019): 15).
The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-4, 6-7, 10, 12, 13, 15, 17, 19-22, 24, 27, 29, and 31 are not patent eligible.
Response to Arguments under 35 USC § 101
Applicant’s arguments filed 4/2/2026 are fully considered but they are not persuasive.
Applicant asserts that claim 31 "is clearly directed to statutory subject matter" because "the steps of preparing a pesticidal composition and exposing a pest to the composition are physical steps that directly implement the provided solution" and "is analogous to the step of treating a patient with julitis in claims 5 and 6 in Example 29 in the Subject Matter Eligibility Examples and cannot be dismissed as mere instructions to implement the abstract idea in a generic way (Remarks 4/2/2026 page 4). Examiner notes above in the section "Claim Rejections - 35 USC 101" that these "physical steps" limitations do not transform the recited judicial expectations into a patent-eligible application because they are additional elements that do not comprise an inventive concept when considered individually or as an ordered combination.
Applicant also asserts that "the ordered combination of steps set forth in the claims as a whole does integrate any judicial exceptions into a practical application and provides an inventive concept", and that "the claims, as amended, specifically set forth how representations of both a pesticidal compound and a synergistic compound that correspond to a chemical structure or chemical properties of the compound can be used to generate predictions of a synergistic interaction between the two compounds based on trained parameters of a classifier that has been trained over at least one synergistic chemical interaction between compounds of at least one compositions against at least one training pest (Remarks 4/2/2026 pages 4-6). Finally, applicant asserts that "in the context of claim 1, the entire method clearly relates to the technical field of developing improved pesticidal compositions, and all of the inputs and outputs of the recited method are explicitly tied to that field (Remarks 4/2/2026 page 7). Examiner notes that while claim 1 is related to the technical field of developing improved pesticidal compositions, this does not imply that what is claimed is in fact an improvement to the technical field per se. As noted above in the section "Claim Rejections - 35 USC 101" the amendments to the independent claims do not transform the recited judicial expectations into a patent-eligible application. The Examiner also notes that MPEP 2106(I) states that if the claims are directed to a judicial exception, the second part of the Mayo test is to determine whether the claim recites additional elements that amount to significantly more than the judicial exception. Id. citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966). In the “search for an ‘inventive concept’” (the second part of the Alice/Mayo test), the additional elements identified do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception because obtaining, inputting, encoding/transforming, and outputting data, and compounding and testing samples (data and sample gathering and manipulation steps) are all well-understood, routine, and conventional techniques that are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application. Therefore, combining insignificant extra-solution activities with any of the identified judicial exceptions would not result in patent eligible subject matter because integrating well-understood, routine, and conventional techniques does not yield “significantly more” to a mental process, a mathematical concept, organizing human activity, or a law of nature or natural phenomenon.
Therefore, the rejection of claims 1, 27, 29, and 31 under 35 USC 101 is maintained. All other claims depend from these independent claims; therefore, their rejection is likewise maintained.
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 –
Claims 1-4, 6-7, 10, 12, 13, 15, 20, 22, 24, 27, 29, and 31 rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wildenhain et al. (Wildenhain et al. "Prediction of synergism from chemical-genetic interactions by machine learning." Cell Systems 1.6 (2015): 383-395).
Regarding claims 1, 20, 27, and 29, Wildenhain teaches a method for generating a prediction of a synergistic interaction between two or more compounds against one or more pests; and the one or more pests comprise the at least one training pest (claim 20) (Page 4 Figure 1 Legend (A) "Predicted synergistic combinations were tested in S. cerevisiae, fungal pathogens, and human cell lines" and page 3 col 1 paragraph 3 "By analogy to the genetic networks that underpin biology, it should be possible to identify combinations of chemicals that mimic genetic interactions, such that compounds that cause minimal phenotypes alone exhibit strong synergies when combined").
Wildenhain also teaches receiving a first representation of a pesticidal compound and receiving a second representation of a synergistic compound corresponding to a chemical structure or chemical properties of the pesticidal compound (Page 5 col 1 paragraph 2 "In order to develop computational approaches for synergy prediction, we required a large unbiased dataset of pairwise chemical combinations and growth phenotypes. We therefore used the CGM data to seed an experimental matrix of chemical-chemical interaction data for training and evaluation of synergy prediction algorithms" and Page 8 col 1 first paragraph "Despite this complexity, the integration of chemical structural features with genetic interactions via the combined SONARGNR algorithm was able to effectively predict many synergistic chemical Interactions").
Wildenhain also teaches the limitation of transforming the input data by compression, feature selection, and/or transcoding to generate encoded representations of candidate pesticidal compositions which are amenable to classification by classifier (as interpreted above); and generating one or more predictions of a synergistic interaction between the pesticidal compound and the synergistic compound against one or more pests (Page 4 Figure 1 Legend (A) "Data generation and analysis workflow. 4,915 unique molecules from four different chemical libraries were screened against a panel of 195 S. cerevisiae deletion strains (termed sentinels) to identify compounds that inhibit the growth of specific deletion strains (termed cryptagens). Pairwise combinations of 128 structurally diverse cryptagens from the chemical genetic matrix (CGM) were screened in a 128 x 128 cryptagen matrix (CM) to identify synergistic compound pairs. The CGM dataset and chemical structural features were used to build a NBL to predict compound activity likelihoods for each sentinel strain", specifically demonstrating feature selection of said encoding limitation).
Wildenhain also teaches transforming the encoded representation based on trained parameters of a classifier, the trained parameters of the classifier having been trained over at least one synergistic interaction between compounds of at least one composition against at least one training pest (Page 7 col 1 paragraph 1 "For each compound combination cx- cy, we calculated parameters based on the sum of the highest ranked target candidates hsVx and hsVy, the corresponding p values pvalVx and pvalVy, and the intercompound edge list hsExy, where hs indicates high sum").
Furthermore, regarding claims 27 and 29, in In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958), the court held that broadly providing an automatic or mechanical means to replace a manual activity which accomplish the same result is not sufficient to distinguish over the prior art (see also Manual of Patent Examining Procedure, U.S. Trademark and Patent Office, section 2144.04, III).
In the instant case, the claimed invention merely makes the process of Wildenhain et al. as computer-implemented or automatic and indeed accomplishes the same result. It is thus not sufficient to distinguish over Wildenhain et al. Therefore, the claimed invention, i.e. “A computer system comprising: one or more processors; and a memory storing instructions which cause the one or more processors to perform operations” (claim 27) and “A non-transitory machine-readable medium storing instructions which cause one or more processors to perform operations” (claim 29) would have been obvious to a person of ordinary skill in the art at the time the invention was made over the process disclosed by Wildenhain et al. There would have been a reasonable expectation of success because the court held regarding software that “writing code for such software is within the skill of the art, not requiring undue experimentation, once its functions have been disclosed.” Fonar Corp., 107 F.3d at 1549, 41 USPQ2d at 1805.
Regarding claim 2, Wildenhain teaches the methods of Claim 1 on which this claim depends/these claims depend, respectively. Wildenhain also teaches the one or more predictions of synergistic interaction comprise a plurality of predictions and the method further comprises: combining the plurality of synergy predictions into a combined prediction (Page 6 Figure 3 Legend (E) "The integrated probability for compound activity across all features and classes is represented as a likelihood score").
Regarding claim 3, Wildenhain teaches the methods of Claim 1 on which this claim depends/these claims depend, respectively. Wildenhain also teaches the method further comprises determining at least one of: a confidence interval, a standard deviation, and a variance based on the plurality of predictions (Page 5 col 2 paragraph 1 "Based on the distribution of all Bliss independence values, we defined synergistic effects with Bliss >0.25 and antagonistic effects with Bliss <0.18, corresponding to a 90% confidence interval of 3 MAD [median absolute deviation]").
Regarding claim 4, Wildenhain teaches the methods of Claim 1 on which this claim depends/these claims depend, respectively. Wildenhain also teaches the classifier comprises a stochastic classifier, generating the one or more predictions comprises transforming the encoded representation based on the trained parameters of the classifier over a plurality of iterations and generating a prediction for each iteration (Page 7 col 2 last paragraph "The random forest model was trained on one-third of the CM data with 5-fold cross-validation, and optimized for tree size and variable split points" and Page 8 col 2 last paragraph "To understand the importance of each sentinel strain for synergy prediction, we investigated whether specific strains or biological processes dominated the prediction of synergy. A balanced subset of 700 compound pairs each for synergistic and non-synergistic classes was selected at random from the CM (see Experimental Procedures). The contribution of each deletion strain model was assessed by Gini impurity, a measure of the importance of input variables provided to a random forest learner. A wide variety of biological processes contributed to synergy prediction, including membrane transport, DNA repair, chromatin assembly, and transcription and mRNA processing (Figure 4C)", as random forest classifiers using bootstrapping or feature subsampling will have predictions that vary across runs).
Regarding claims 6 and 7, Wildenhain teaches the methods of Claim 1 on which this claim depends/these claims depend, respectively. Wildenhain also teaches generating the encoded representation comprises generating the encoded representation to be lower-dimensional than at least one of the first and second representations; optionally wherein the trained parameters of the encoder model have been trained over a different training set than the trained parameters of the classifier (claim 6); and the input space being transformed by the autoencoder to a lower-dimensional representation of data that captures its essential features (claim 7, as interpreted above) (Page 7 col 1 last paragraph "To extract more granular information from the CGM dataset, we built a Naive Bayes learner (NBL) to identify characteristic structural features of active versus non-active molecules for every sentinel strain (Figure 3E). The NBL constructs a Bayesian probabilistic model wherein each deletion strain represents a different class to which a likelihood score is assigned for the inhibitory effect of different compound substructures" demonstrates that a separate model (the NBL) was built to extract "more granular" information which is a different training (and lower-dimensional data) set than that of the classifier).
Regarding claims 10, 12, and 13, Wildenhain teaches the methods of Claim 1 on which this claim depends/these claims depend, respectively. Wildenhain also teaches selecting the classifier from a plurality of classifiers based on the one or more pests, the method optionally further comprising receiving a representation of the one or more pests and selecting the classifier comprises selecting the classifier based on the representation of the one or more pests (claim 10); the classifier is a first one of a plurality of classifiers, at least a second classifier of the plurality having been trained against different pests than the one or more pests, and selecting the classifier from the plurality of classifiers comprises selecting one of the first and second classifiers based on the one or more pests (claim 12); and the classifier comprises an ensemble classifier comprising a plurality of constituent classifiers, the plurality of constituent classifiers comprising at least a first constituent classifier and a second constituent classifier, respective trained parameters of the first and second constituent classifiers each having been trained over at least one synergistic interaction between compounds of at least one composition against at least one of the one or more pests (claim 13) (Page 4 Figure 1 Legend (A) describes using multiple classifiers for syngergistic interaction prediction against over 195 "sentinels" (i.e. pests), demonstrating the manifold nature of their work which also covers the limitations of these claims).
Regarding claim 15, Wildenhain teaches the methods of Claim 1 on which this claim depends/these claims depend, respectively. Wildenhain also teaches adding an additional chemical property of the at least one of the pesticidal and synergistic compounds to the first or second representation (Page 8 col 2 last paragraph " A wide variety of biological processes contributed to synergy prediction, including membrane transport, DNA repair, chromatin assembly, and transcription and mRNA processing (Figure 4C)" suggests multiple properties used for modeling).
Wildenhain also teaches wherein generating the enhanced representation comprises determining the enhanced chemical feature based on trained parameters of a quantitative structure-activity relationship model (as interpreted above) (Page 4 Figure 1 Legend (A) "The CGM [chemical genetic matrix] dataset and chemical structural features were used to build a NBL to predict compound activity likelihoods for each sentinel strain" and also shows structural features as parameters to the Naïve Bayes Learner, demonstrating an enhancement of chemical representation used for modeling).
Regarding claim 22, Wildenhain teaches the methods of Claim 1 on which this claim depends/these claims depend, respectively. Wildenhain also teaches the trained parameters of the classifier have been trained by: determining an importance metric for each of a plurality of training compositions; selecting one or more high-importance compositions from the plurality of training compositions based on the importance metric for each of the one or more high-importance compositions; and updating the trained parameters of the classifier based on the one or more high- importance composition (Page 6 col 2 paragraph 2 "Nodes in target space t represent known gene/protein targets of the cryptagen compounds, and each node was ranked according to the number of connected neighbors in the genetic network, such that nodes with more shared neighbors received a higher score", page 7 col 1 paragraph 1 "Once each compound was assigned a candidate list of gene/protein targets, the genetic interactions between these targets could then be used to predict potential compound-compound Interactions [] For computational tractability, we restricted the target space represented by hsExy to the 35 top ranked genes by interaction count (the procedure was robust to the actual cut-off value chosen for counts, see Experimental Procedures)").
Regarding claim 24, Wildenhain teaches the methods of Claim 1 on which this claim depends/these claims depend, respectively. Wildenhain also teaches selecting the one or more high-importance compositions based on a representativeness criterion comprises determining a plurality of clusters of the plurality of training compositions and selecting at least one high-importance composition from each of at least two of the plurality of clusters (Page 6 Figure 3D and 3G show PCA plots of parameters used, and page 7 col 1 paragraph 1 "We applied principal component analysis (PCA) to these seven parameters derived from the CGM (pvalVx, pvalVy, hsVx, hsVy, hsExy, Pxy, sgi) to identify those that might explain the observed Bliss independence. However, none of the parameters contributed strongly to the Bliss vector (Figure 3D) nor did any parameters perform better than random as measured by the area under the curve (AUC) for receiver operating characteristic (ROC) plots (Figure S3D)" demonstrates that the idea of selecting parameters based on contribution to the model is extant).
Wildenhain also teaches determining the plurality of clusters of the plurality of training compositions comprises determining a graph similarity metric between at least one graph representing at least one compound of a first one of the training compositions and at least one graph representing at least one compound of a second one of the training compositions (Page 4 Figure 1 Legend (A) "A graph-based algorithm was used to integrate chemical-genetic and genetic interactions to predict compound targets, based on either CGM interaction data (SONAR[G]) or NBL likelihood scores (SONAR[GN])").
Regarding claim 31, Wildenhain teaches the methods of Claim 1 on which this claim depends/these claims depend, respectively. Wildenhain also teaches a method of evaluating a prediction of a synergistic interaction between two or more compounds against one or more pests, the method comprising: determining a prediction of a synergistic interaction between a pesticidal compound and a synergistic compound by the method of claim 1; combining the pesticidal compound and the synergistic compound to yield a composition; exposing the one or more pests to the composition in a test environment; and evaluating an efficacy of the composition as a pesticide (Page 9 col 2 paragraph 3 "To examine whether synergistic combinations might transpose to the divergent genetic networks of pathogenic fungi, we tested 18 synergistic combinations from the CM [cryptagen matrix] against the human pathogens C. neoformans, C. gattii, C. albicans, C. parapsilosis, and A. fumigatus. [] Focused dose-response assays were carried out over four concentrations for each compound and synergism assessed using Bliss independence at 48 and 72 hr (see Supplemental Information for details on each species)").
Response to Arguments under 35 USC § 102
Applicant’s arguments filed 4/2/2026 are fully considered but they are not persuasive.
Applicant asserts that "Wildenhain does not teach or suggest a classifier that has been 'trained over at least one synergistic chemical interaction between compounds of at least one composition' as claimed in amended claims 1, 27, and 29" and thus cannot anticipate these claims (Remarks 4/2/2026 pages 2-3). Examiner notes several citations above in section "Claim Rejections - 35 USC 102" from Wildenhain that suggest the alleged missing teachings, but are provided here for convenience: page 2 Abstract "This work demonstrates that machine learning methods trained on unbiased chemical-genetic interaction data may be widely applicable for the discovery of synergistic combinations in different species"; Page 5 col 1 paragraph 2 "In order to develop computational approaches for synergy prediction, we required a large unbiased dataset of pairwise chemical combinations and growth phenotypes. We therefore used the CGM data to seed an experimental matrix of chemical-chemical interaction data for training and evaluation of synergy prediction algorithms"; and Page 7 col 2 last paragraph "The random forest model was trained on one-third of the CM data with 5-fold cross-validation, and optimized for tree size and variable split points (Figure S3E). This algorithm, termed SONARGNR (for genetic, NBL and random forest), yielded synergy scores that predicted synergistic interactions in the CM with an AUC of 0.87 (Figure 3G). The score distributions for synergistic and non-synergistic chemical pairs were well separated for a subset of synergistic combinations; however, synergy for more than half of all combinations was not predicted by SONARGNR (Figures 3H, 3I, and S3F)".
Therefore, the rejection of independent claims 1, 27, 29, and 31 under 35 USC 102 is maintained. Claims 2-4, 6-7, 10, 12, 13, 15, 20, 22, and 24 depend from these independent claims; therefore, their rejection is likewise maintained.
Claim Rejections - 35 USC § 103
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 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.
Claims 17, 19, and 21 rejected under 35 U.S.C. 103 as being unpatentable over Wildenhain et al. (Wildenhain et al. "Prediction of synergism from chemical-genetic interactions by machine learning." Cell Systems 1.6 (2015): 383-395) as applied to claims 1-7, 10, 12, 13, 15, 20, 22, 24, 27, 29, and 31 above, in view of Kar et al. (Kar et al. "Exploration of computational approaches to predict the toxicity of chemical mixtures." Toxics 7.1 (2019): 15).
Wildenhain et al. are applied to claims 1-7, 10, 12, 13, 15, 20, 22, 24, 27, 29, and 31.
Regarding claim 17, Wildenhain teaches the method of Claim 1 on which this claim depends/these claims depend.
Wildenhain does not explicitly teach receiving a third representation of a third compound and excluding an excluded composition comprising the third compound from prediction based on determining at least one of: a chemical feature of the third compound matches an exclusion rule, an availability value corresponding to the third compound being less than a threshold, a similarity metric between the third compound and a fourth compound being greater than a threshold, and a toxicity indication of the third compound matches a toxicity criterion.
However, Kar teaches a model for toxicity prediction using tertiary mixtures and using different models that can limit bioavailability of specified chemicals demonstrating an exclusion of certain reagents in the model (Page 13 paragraph 2 "The developed model was further employed by authors for toxicity prediction of 2,340 compounds consisting of single, binary and tertiary halogenated mixtures as well as perfluoroalkyl substances (PFASs)" and "Cipullo et al. [43] employed two machine learning (ML) models, including random forest (RF) and artificial neural networks (NN) to predict temporal bioavailability followed by toxicity prediction employing predicted bioavailability features as the input of complex chemical mixtures (Figure 7)" suggest using more than two input compounds for the mixture as well as using bioavailability as a restricting feature).
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Wildenhain as taught by Kar in order to computationally assess chemical mixtures by predicting toxicity (page 1 abstract "the present review explains the importance of evaluation of a mixture’s toxicity, the role of computational approaches to assess the toxicity, followed by types of in silico methods. Additionally, successful application of in silico tools in a mixture’s toxicity predictions is explained in detail"). One skilled in the art would have a reasonable expectation of success because both approaches use in silico method for assessing toxicity of chemical mixtures.
Regarding claim 19, Wildenhain teaches the method of Claim 1 on which this claim depends/these claims depend.
Wildenhain does not explicitly teach selecting at least one of the first and second chemical features from the group consisting of: representations of aromaticity, representations of electronegativity, representations of polarity, representations of hydrophilicity/hydrophobicity, and representations of hybridizations of at least one of the pesticidal and synergistic compounds.
However, Kar teaches utilizing these chemical features and more (Page 7 Table 1 contains descriptions of the various "Representative Example of Descriptors or Computational Method" including structural representation (aromaticity), molecular property descriptors and molecule field analysis (electronegativity, polarity, hydro-interaction), and the 5D and 6D encompassing ligand-receptor models).
Regarding claim 21, Wildenhain teaches the method of Claim 1 on which this claim depends/these claims depend.
Wildenhain does not explicitly teach the at least one training pest shares a pesticidal mode of action with at least one of the one or more pests.
However, Kar teaches that any mechanism of action may be modeled for the purposes of predicting toxicity (Page 16 paragraph 1 "Another important point to remember, considering physicochemical parameters related to the mechanism of action, is that the developed model can replicate the toxicity response in a mathematical equation. Such an equation could be used for predictions for untested compounds/mixtures" suggests that any mode of action may be implemented in the prediction model).
Response to Arguments under 35 USC § 103
Applicant’s arguments filed 4/2/2026 are fully considered but they are not persuasive.
Applicant asserts the same argument for Kar as was made for Wildenhain, above (Remarks 4/2/2026 page 3). Examiner notes that the Office Action filed 12/5/2025 did not explicitly mention Kar teaching or suggesting a classifier that has been trained over at least one synergistic chemical interaction between compounds of at least one composition, and therefore the argument is moot.
Therefore, the rejection of claims 17, 19, and 21 under 35 USC 103 is maintained.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-4, 6-7, 10, 12, 13, 15, 17, 19-22, 24, 27, and 29 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 25, and 26 of US Patent Application 17/827,363 in view of Wildenhain et al. (Wildenhain et al. "Prediction of synergism from chemical-genetic interactions by machine learning." Cell Systems 1.6 (2015): 383-395). Although the claims at issue are not identical, they are not patentably distinct from each other because both involve obtaining first and second sample data (claim 1), training a machine learning model on the data (claim 1), generating a prediction (claim 1), producing a prediction of synergy between the first and second input chemical (claim 25), and predicting synergistic pesticidal efficacy against a target pest (claim 26).
While 17/827,363 does not explicitly teach the limitation of transforming the input data by compression, feature selection, and/or transcoding to generate encoded representations of candidate pesticidal compositions which are amenable to classification by classifier (as interpreted above), it would have been obvious to one of ordinary skill in the art to modify these methods, with those taught by Wildenhain as described above for claim 1 of the instant application, in order to generate training and test data sets for the interaction model (Page 4 Figure 1 Legend (A) "Data generation and analysis workflow. 4,915 unique molecules from four different chemical libraries were screened against a panel of 195 S. cerevisiae deletion strains (termed sentinels) to identify compounds that inhibit the growth of specific deletion strains (termed cryptagens). Pairwise combinations of 128 structurally diverse cryptagens from the chemical genetic matrix (CGM) were screened in a 128 x 128 cryptagen matrix (CM) to identify synergistic compound pairs. The CGM dataset and chemical structural features were used to build a NBL to predict compound activity likelihoods for each sentinel strain" demonstrates feature selection of the data). One skilled in the art would have a reasonable expectation of success because both approaches are modeling synergistic chemical interactions for efficacy against target pests.
Response to Arguments under Double Patenting
Applicant’s arguments filed 4/2/2026 are fully considered but they are not persuasive.
Applicant asserts the same argument for the Double Patenting rejection as was made for the 35 USC 102 rejection, above (Remarks 4/2/2026 page 8). Examiner again notes the several citations above in section "Claim Rejections - 35 USC 102" from Wildenhain that teach or suggest the alleged missing teachings. Applicant further asserts that US-17827363 does "not teach or suggest at least generating encoded representations of a composition based on encoding first and second chemical features of a pesticidal compound and a synergistic compound" (Remarks 4/2/2026 page 8). Examiner notes that it was explicitly recited in the Office Action filed 12/5/2025 that US-17827363 does not explicitly teach this limitation and that it would have been obvious to one of ordinary skill in the art to modify these methods, with those taught by Wildenhain as described above for claim 1 of the instant application.
Therefore, the rejection of claims 1-4, 6-7, 10, 12, 13, 15, 17, 19-22, 24, 27, and 29 under 35 USC 103 is maintained.
Conclusion
No claims are allowed.
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 TH REE-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 finaI action.
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/R.A.P./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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