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 1-20 are pending and examined on the merits.
Priority
The instant application filed on 10/17/2022 claims the benefit of priority to U.S. Provisional Application No. 63/264,640, filed 11/29/2021; U.S. Provisional Application No. 63/264,641, filed 11/29/2021; U.S. Provisional Application No. 63/264,642, filed 11/29/2021; and U.S. Provisional Application No. 63/264,643, filed 11/29/2021. Thus, the effective filing date of the claims is 11/29/2021.
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.
Information Disclosure Statement
The information disclosure statements (IDS) filed on 10/17/2022, 5/8/2023, 7/10/2024, and 7/24/2025 have been entered and considered. A signed copy of the corresponding 1449 form has been included with this Office action.
The listing of references in the specification (para.0029-30) is not a proper information disclosure statement. 37 CFR 1.98(b) requires a list of all patents, publications, or other information submitted for consideration by the Office, and MPEP § 609.04(a) states, "the list may not be incorporated into the specification but must be submitted in a separate paper." Therefore, unless the references have been cited by the examiner on form PTO-892, they have not been considered.
Specification
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code:
Para.0086, "DimeNet (github.com/gasteigerjo/dimenet)"
Para.0181, "SMILES representations were converted into mordred descriptors, (github.com/mordred-descriptor/mordred)"
Para.0186, "The open-source BayesianOptimization (github.com/fmfn/BayesianOptimization)"
Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
The abstract of the disclosure is objected to because it contains 151 words (the maximum word count limit is 150 words). A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1, 12, and 20 recites "identifying, based on the utility function, one or more particular points within the embedding space as corresponding to high utility metrics". The metes and bounds of "high utility metrics" is not clear because the degree of "high" is not particularly claimed, or no threshold for the utility metric to be considered "high" is described. The instant specification mentions one example in para.0165 "At block 2740, process 2700 may identify, based on the utility function, one or more particular points within the embedding space as corresponding to high utility metrics. For example, in FIG. 26, graph 2628 shows a utility function with star 2632 indicating the maximum. The values associated with that maximum may be one point of the one or more particular points", however that would only be one particular point, as opposed to the claims "one or more particular points". To further prosecution, and to keep the claim language as consistent between claims as possible, the limitation is interpreted as "identifying, based on the utility function, one or more particular points within the embedding space as corresponding to the highest utility metrics".
Claim 8 and 19 recites (similar to claims 1, 12, and 20) "identifying the one or more particular points further comprises identifying one or more reactants as corresponding to the high utility metrics". The metes and bounds of "high utility metrics" is not clear because the degree of "high" is not particularly claimed, or no threshold for the utility metric to be considered "high" is described. The instant specification mentions one example in para.0165 "At block 2740, process 2700 may identify, based on the utility function, one or more particular points within the embedding space as corresponding to high utility metrics. For example, in FIG. 26, graph 2628 shows a utility function with star 2632 indicating the maximum. The values associated with that maximum may be one point of the one or more particular points", however that would only be one particular point, as opposed to the claims "one or more particular points". To further prosecution, and to keep the claim language as consistent between claims as possible, the limitation is interpreted as "identifying the one or more particular points further comprises identifying one or more reactants as corresponding to the highest utility metrics".
All other claims depend from independent claims 1, 12, or 20, and therefore are also rejected under 35 USC 112(b).
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 1-20 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:
Claim 1, 12, 20: “determining a set of reaction inputs for a depolymerization reaction” provides a judgement (determining a set of inputs requires a judgement regarding what inputs to include in the set) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
“the embedded representation of the structure of the reactant is identified as a set of coordinate values within an embedding space” provides for organizing information (identifying a set of coordinate values within an embedding space involves sorting or structuring data) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
“constructing a predictive function to predict reaction-characteristic values from embedded representations of reactant structures, wherein constructing the predictive function uses the first data set” provides a mathematical calculation (constructing a function [para.0161-162] involves mathematical calculations) that is considered a mathematical concept, which is an abstract idea.
“evaluating a utility function that transforms a given point within the embedding space into a utility metric that represents a degree to which identifying an experimentally derived reactant-characteristic value for the given point is predicted to facilitate training a more accurate version of the predictive function” provides a mathematical calculation (evaluating a function involves maximizing a function [para.0137] which involves mathematical calculations) that is considered a mathematical concept, which is an abstract idea.
“identifying, based on the utility function, one or more particular points within the embedding space as corresponding to the highest utility metrics” (as interpreted above) provides an evaluation (identifying points having the highest metrics involves evaluating and comparing said metrics) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 6, 17: “determining that the one or more particular points are equivalent to one or more coordinate values of the set of coordinate values within the embedding space in the plurality of first data elements” provides a comparison (determining equivalency of values involves comparing said values) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 7, 18: “each particular point of the one or more particular points is not the same as any set of coordinate values within the embedding space in the plurality of first data elements” provides a comparison (determining equivalency of values involves comparing said values) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 8, 19: “identifying the one or more particular points further comprises identifying one or more reactants as corresponding to the highest utility metrics” (as interpreted above) provides an evaluation (identifying points having the highest metrics involves evaluating and comparing said metrics) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 9: “comparing the adjusted amount to a threshold value” provides a comparison (comparing an amount to a threshold value) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 11: “the predictive function was constructed at least in part using training data corresponding to a set of molecules that were selected using Bayesian optimization” provides a mathematical calculation (selecting molecules using Bayesian optimization 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 12-20 recite performing some aspects of the analysis on “A system comprising: one or more data processors; and a non-transitory computer readable storage medium containing instructions” (claim 12) or “A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions” (claim 20), 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 1-20 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, 12, 20: “accessing, by a computing device, a first data set” provides insignificant extra-solution activities (accessing data is a pre-solution activity involving data gathering steps) that do not serve to integrate the judicial exceptions into a practical application.
“outputting, by the computing device, a result that identifies, for each particular point of the one or more particular points, a reactant corresponding to the particular point or a reactant structure corresponding to the particular point” provides insignificant extra-solution activities (outputting data is a post-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 4, 15: “performing an experiment using the reactant corresponding to the point previously identified using the utility function to determine at least a reaction-characteristic value in the plurality of first data elements” provides insignificant extra-solution activities (performing a validation experiment is a post-solution activity involving sample manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 6, 17: “outputting a message conveying that the one or more particular points represent a converged solution” provides insignificant extra-solution activities (outputting data is a post-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 8, 19: “outputting, by the computing device, an experimental procedure including the one or more reactants” provides insignificant extra-solution activities (outputting data is a post-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 9: “accessing an inventory data store including an amount of the reactant, adjusting the amount using the one or more particular points to determine an adjusted amount” provides insignificant extra-solution activities (accessing and adjusting data are pre-solution activities involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
“outputting an order for an additional amount of the reactant when the adjusted amount is below the threshold value” provides insignificant extra-solution activities (outputting data is a post-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 10: “updating the predictive function using a second data set, the second data set including a plurality of second data elements, a portion of the plurality of second data elements determined from performing an experiment using the reactant or the reactant structure identified by the outputted result” provides insignificant extra-solution activities (updating a function with experimentally derived data is a post-solution activity involving data and sample gathering and manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 12: “A system comprising: one or more data processors; and a non-transitory computer readable storage medium containing instructions” 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 20: “A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions” provides insignificant extra-solution activities (running instructions on generic computer components) that do not serve to integrate the judicial exceptions into a practical application.
The steps for accessing, adjusting, and outputting data, and performing experiments and updating models with the data from said experiments 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 gathering, data manipulation, and sample 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 1-20 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 system comprising: one or more data processors; and a non-transitory computer readable storage medium containing instructions” (claim 12) or “A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions” (claim 20) 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 accessing, adjusting, and outputting data, and performing experiments and updating models with the data from said experiments 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 demonstrated to be well-understood, routine, and conventional as evidenced by: Murray et al. (Organic & biomolecular chemistry 14.8 (2016): 2373-2384; page 3 col 1 paragraph 2 "The technique of ‘Design of Experiments’ is a statistical approach to reaction optimisation that allows the variation of multiple factors simultaneously in order to screen ‘reaction space’ for a particular process. Importantly, this enables the evaluation of a large number of reaction parameters in a relatively small number of experiments. Whilst this technique is routinely applied by process chemists in a wide range of industries, and also by academics working in engineering disciplines,7 it is rarely used in academic chemistry"), and Lukic (Journal of medical biochemistry 36.3 (2017): 220; page 4 col 2 paragraph 2 "An electronic stock is a feature we are still developing in our laboratory. It is conceptualized as an inventory management through LIS. When completed, it would enable monitoring of stock status for each article in laboratory and monitoring the expiry dates. Furthermore, it would facilitate ordering of inventory to prevent shortage, but also overstock of any item in the laboratory. Thus, we expect that this feature will optimize inventory management and ensure product availability".
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 1-20 are not patent eligible.
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 1-20 rejected under 35 U.S.C. 103 as being unpatentable over Koge et al. (Molecular informatics 40.2 (2021): 2000203) in view of Kumar et al. (MRS Communications 11.4 (2021): 377-390).
Regarding claim 1, 12, and 20, Koge teaches accessing, by a computing device, a first data set that includes a plurality of first data elements, each of the plurality of first data elements characterizing the depolymerization reaction and including an embedded representation of a structure of a reactant of the depolymerization reaction and a reaction-characteristic value that characterizes a reaction between the reactant and a particular polymer, wherein the embedded representation of the structure of the reactant is identified as a set of coordinate values within an embedding space (Page 1 abstract col 2 "Our method enables molecular structures and physical properties to be embedded locally and continuously into VAEs’ latent space while maintaining the consistency of the relationship between the structural features and the physical properties of molecules to yield better predictions").
Koge also teaches constructing a predictive function to predict reaction-characteristic values from embedded representations of reactant structures, wherein constructing the predictive function uses the first data set (Page 2 col 2 last paragraph "f(zi) denotes the predicted value of the physical property regression model corresponding to the i-th input data, and y is the true value. b and g are hyperparameters. Linear regression f(zi) predicts a physical property value from a latent vector zi corresponding to the i-th input data xi").
Koge also teaches evaluating a utility function that transforms a given point within the embedding space into a utility metric that represents a degree to which identifying an experimentally derived reactant-characteristic value for the given point is predicted to facilitate training a more accurate version of the predictive function (Page 2 col 2 last paragraph "we applied metric learning into a drug/material design model using VAEs. Metric learning is a learning method that matches a distance similarity in a label space with a distance similarity in an embedding space by a neural network. In this study, we propose a learning method that combines log ratio loss[12] with the loss function of VAE Eq. (2)").
Koge also teaches optimizing an anchor molecule in the latent space which would return (identify) the highest utility metrics (Page 3 figure 2 legend "By optimizing the positions of the anchor, positive, and negative latent vectors, VAE latent space M becomes closer to the Chemical space P.D(X,Y) represents the distance between X and Y").
Koge does not explicitly teach a method of determining a set of reaction inputs for a depolymerization reaction; nor outputting, by the computing device, a result that identifies, for each particular point of the one or more particular points, a reactant corresponding to the particular point or a reactant structure corresponding to the particular point.
However, Kumar suggests application of their method to depolymerization reactions, and teaches identifying polymer candidates (Page 5 col 2 first paragraph "As a predictive tool, it is possible to identify promising hypothetical polymer candidates, where selection can be made for monomer synthesis, polymerization, and reaction conditions under flow behavior. It is possible to use neural networks to start from a substantially limited amount of data for polymeric properties and expand or narrow the scope of explored constructs by real-time feedback from actual laboratory synthesis and thermophysical property measurements. While this has been applied in forward polymerization reactions, there is a significant interest in applying similar neural networks for designing faster depolymerization reactions on the basis of autocatalysis").
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 Koge as taught by Kumar in order to apply the same predictive tools for forward polymerization reactions to depolymerization reactions (Page 5 col 2 first paragraph "While this has been applied in forward polymerization reactions, there is a significant interest in applying similar neural networks for designing faster depolymerization reactions on the basis of autocatalysis"). One skilled in the art would have a reasonable expectation of success because both methods are using machine learning for predicting reactants for depolymerization reactions.
Regarding claims 2 and 13, Koge in view of Kumar teach the methods of Claims 1 and 12 on which this claim depends/these claims depend, respectively. Koge also teaches the one or more particular points are predefined discrete points (Page 3 Figure 2 shows a predefined 2D graph containing discrete coordinates).
Regarding claims 3 and 14, Koge in view of Kumar teach the methods of Claims 1 and 12 on which this claim depends/these claims depend, respectively. Koge also teaches the first data set comprises a point previously identified using the utility function (An anchor point may be previously identified using the functions of Koge (Page 3 figure 2 legend "By optimizing the positions of the anchor, positive, and negative latent vectors, VAE latent space M becomes closer to the Chemical space P.D(X,Y) represents the distance between X and Y").
Regarding claims 4, 10, and 15, Koge in view of Kumar teach the methods of Claims 3 and 14 on which this claim depends/these claims depend, respectively. Kumar also teaches performing an experiment using the reactant corresponding to the point previously identified using the utility function to determine at least a reaction-characteristic value in the plurality of first data elements; and updating the predictive function using a second data set, the second data set including a plurality of second data elements, a portion of the plurality of second data elements determined from performing an experiment using the reactant or the reactant structure identified by the outputted result (Page 5 col 2 first paragraph "It is possible to use neural networks to start from a substantially limited amount of data for polymeric properties and expand or narrow the scope of explored constructs by real-time feedback from actual laboratory synthesis and thermophysical property measurements).
Regarding claims 5 and 16, Koge in view of Kumar teach the methods of Claims 1 and 12 on which this claim depends/these claims depend, respectively. Kumar also teaches the reactants in the plurality of first data elements comprise an ionic liquid or a solvent, the reaction-characteristic values in the plurality of first data elements comprise yield or conversion (Page 4 col 1 paragraph 2 "A majority of the works discussed above are based on diffusion controlled reactions, where diffusion of reacting species controls the reaction rates. However, polymerization and depolymerization reactions may not be diffusion limited in the presence of explicit ionic charges present either on the solvents such as in ionic liquids[34,35] or on the monomers being polymerized (e.g., in polymerized ionic liquids[36] and polyzwitterions[37])").
Regarding claims 6-7 and 17-18, Koge in view of Kumar teach the methods of Claims 1 and 12 on which this claim depends/these claims depend, respectively. Determining equivalency of coordinates and indicating a converged solution is obvious because it is a predictable use of prior art elements using optimization algorithms.
Regarding claims 8-9 and 19, Koge in view of Kumar teach the methods of Claims 1 and 12 on which this claim depends/these claims depend, respectively. Manually designing experimental procedures including specified reactants, as well as ordering reagents when below some specified amount are both routine tasks in any chemical or biological laboratory as evidenced by Murray et al. (Organic & biomolecular chemistry 14.8 (2016): 2373-2384; page 3 col 1 paragraph 2 "The technique of ‘Design of Experiments’ is a statistical approach to reaction optimisation that allows the variation of multiple factors simultaneously in order to screen ‘reaction space’ for a particular process. Importantly, this enables the evaluation of a large number of reaction parameters in a relatively small number of experiments. Whilst this technique is routinely applied by process chemists in a wide range of industries, and also by academics working in engineering disciplines,7 it is rarely used in academic chemistry"), and Lukic (Journal of medical biochemistry 36.3 (2017): 220; page 4 col 2 paragraph 2 "An electronic stock is a feature we are still developing in our laboratory. It is conceptualized as an inventory management through LIS. When completed, it would enable monitoring of stock status for each article in laboratory and monitoring the expiry dates. Furthermore, it would facilitate ordering of inventory to prevent shortage, but also overstock of any item in the laboratory. Thus, we expect that this feature will optimize inventory management and ensure product availability"), and therefore would be obvious to a person having ordinary skill in the art to apply these routine methods to those of Koge and Kumar.
Regarding claim 11, Koge in view of Kumar teach the methods of Claim 1 on which this claim depends/these claims depend, respectively. Koge also teaches the predictive function was constructed at least in part using training data corresponding to a set of molecules that were selected using Bayesian optimization (Page 1 col 2 first paragraph "By using a Bayesian optimization (BO)[7] search on the latent space based on a physical property value, we can identify molecular structures that have a certain desirable property as SMILES sequences using Recurrent neural networks (RNN)").
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 1-20 rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-19 of US-12378383 in view of Koge et al. (Molecular informatics 40.2 (2021): 2000203) and Kumar et al. (MRS Communications 11.4 (2021): 377-390). Although the claims at issue are not identical, they are not patentably distinct from each other because both involve accessing data sets containing embedded representations of reactant structures in a chemical reaction, constructing predictive functions to predict reaction-characteristic values based on the embedded data, and outputting an identified reactant or reactants for a particular chemical reaction.
While US-12378383 does not explicitly teach a method for calculating a metric representing an experimentally derived reactant-characteristic value using a utility function, it would have been obvious to one of ordinary skill in the art to modify these methods, with those taught by Koge as described above for claims 1, 12, and 20 of the instant application, in order to yield better predictions of physical properties of molecules (Page 1 abstract col 2 "Our method enables molecular structures and physical properties to be embedded locally and continuously into VAEs’ latent space while maintaining the consistency of the relationship between the structural features and the physical properties of molecules to yield better predictions"). One skilled in the art would have a reasonable expectation of success because both methods are utilizing embedded representations of chemical structures for predicting properties of chemical reactions.
While US-12378383 does not explicitly teach a method for application to depolymerization reactions, it would have been obvious to one of ordinary skill in the art to modify these methods, with those taught by Kumar as described above for claims 1, 12, and 20 of the instant application, in order to apply the same predictive tools for forward polymerization reactions to depolymerization reactions (Page 5 col 2 first paragraph "While this has been applied in forward polymerization reactions, there is a significant interest in applying similar neural networks for designing faster depolymerization reactions on the basis of autocatalysis"). One skilled in the art would have a reasonable expectation of success because both methods are using machine learning for predicting reactants for chemical reactions.
Claims 1-20 rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-20 of US-12291608 in view of Koge et al. (Molecular informatics 40.2 (2021): 2000203). Although the claims at issue are not identical, they are not patentably distinct from each other because both involve accessing data sets containing embedded representations of reactant structures in a depolymerization reaction, constructing predictive functions to predict reaction-characteristic values based on the embedded data, and outputting an identified reactant or reactants for a particular chemical reaction.
While US-12291608 does not explicitly teach a method for calculating a metric representing an experimentally derived reactant-characteristic value using a utility function, it would have been obvious to one of ordinary skill in the art to modify these methods, with those taught by Koge as described above for claims 1, 12, and 20 of the instant application, in order to yield better predictions of physical properties of molecules (Page 1 abstract col 2 "Our method enables molecular structures and physical properties to be embedded locally and continuously into VAEs’ latent space while maintaining the consistency of the relationship between the structural features and the physical properties of molecules to yield better predictions"). One skilled in the art would have a reasonable expectation of success because both methods are utilizing embedded representations of chemical structures for predicting properties of chemical reactions.
Claims 1-20 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of US-20230167264. Although the claims at issue are not identical, they are not patentably distinct from each other because both involve accessing data sets containing embedded representations of reactant structures in a depolymerization reaction, constructing predictive functions to predict reaction-characteristic values based on the embedded data, calculating a metric representing an experimentally derived reactant-characteristic value using a utility function, and outputting an identified reactant or reactants for a particular chemical reaction
Claims 1-20 rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-20 of US-20250236717 in view of Koge et al. (Molecular informatics 40.2 (2021): 2000203). Although the claims at issue are not identical, they are not patentably distinct from each other because both involve accessing data sets containing embedded representations of reactant structures in a depolymerization reaction, constructing predictive functions to predict reaction-characteristic values based on the embedded data, and outputting an identified reactant or reactants for a particular chemical reaction.
While US-20250236717 does not explicitly teach a method for calculating a metric representing an experimentally derived reactant-characteristic value using a utility function, it would have been obvious to one of ordinary skill in the art to modify these methods, with those taught by Koge as described above for claims 1, 12, and 20 of the instant application, in order to yield better predictions of physical properties of molecules (Page 1 abstract col 2 "Our method enables molecular structures and physical properties to be embedded locally and continuously into VAEs’ latent space while maintaining the consistency of the relationship between the structural features and the physical properties of molecules to yield better predictions"). One skilled in the art would have a reasonable expectation of success because both methods are utilizing embedded representations of chemical structures for predicting properties of chemical reactions.
Claims 1-20 rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-20 of US-20250326912 in view of Koge et al. (Molecular informatics 40.2 (2021): 2000203). Although the claims at issue are not identical, they are not patentably distinct from each other because both involve accessing data sets containing embedded representations of reactant structures in a depolymerization reaction, constructing predictive functions to predict reaction-characteristic values based on the embedded data, and outputting an identified reactant or reactants for a particular chemical reaction.
While US-20250326912 does not explicitly teach a method for calculating a metric representing an experimentally derived reactant-characteristic value using a utility function, it would have been obvious to one of ordinary skill in the art to modify these methods, with those taught by Koge as described above for claims 1, 12, and 20 of the instant application, in order to yield better predictions of physical properties of molecules (Page 1 abstract col 2 "Our method enables molecular structures and physical properties to be embedded locally and continuously into VAEs’ latent space while maintaining the consistency of the relationship between the structural features and the physical properties of molecules to yield better predictions"). One skilled in the art would have a reasonable expectation of success because both methods are utilizing embedded representations of chemical structures for predicting properties of chemical reactions.
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Jiang et al., CN-101339180; Machine learning application for decomposition of reactive chemical species
Sarmento et al., RU-2694321; Training neural network with chemical structures for predictive modeling
St. John et al., "Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost." Nature communications 11.1 (2020): 2328; Application to depolymerization predictions
Conclusion
No claims are allowed.
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Robert A. Player whose telephone number is 571-272-6350. The examiner can normally be reached Mon-Fri, 8am-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Larry D. Riggs can be reached at 571-270-3062. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/R.A.P./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
Prosecution Insights