DETAILED ACTION
The Applicant’s response, received 03 November 2025 has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
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 the Claims
Claims 1-5 and 7-27 are pending.
Claims 1-5 and 7-27 are rejected. ii
Priority
The effective filing date of the claimed invention is 01 October 2019.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11 August 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Claim Interpretation
The claim limitation interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, in the Office action mailed 09 July 2025 with regard to claims 2-5, 7-14, 18, 22, 23, and 27 is maintained in view of the amendment received 03 November 2025.
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
software module, in claims 2-5, 7-14, 18, 22, 23, and 27.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
The written description discloses a corresponding structure for the non-structural generic placeholder “software module” (as shown in ¶ [0062] in the Specification) in claims 2-5, 7-14, 18, 22, 23, and 27 at ¶¶ [00183] - [00198], particularly at ¶ [00190] where it shows a computing device with computer components including a processor, memory, input/output (I/O) controller, display adapter, network interface, and mass storage devices.
Claim 11 recites the limitation “a retrosynthesis of the reaction product.” The term retrosynthesis is interpreted to mean a data analysis technique (e.g., in silico) to determine precursor structures or reactions that lead to the synthesis of that product.
Claim Rejections - 35 USC § 112
The rejection of claims 1, 2-5, 7-23, 26, and 27 under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, in the Office action mailed 09 July 2025 is withdrawn in view of the Applicant’s arguments/remarks (pages 13-15) received 03 November 2025.
The rejection of claims 4, 5, and 27 under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, in the Office action mailed 09 July 2025 is withdrawn in view of the amendment received 03 November 2025.
Claim Rejections - 35 USC § 101
The rejection of claims 1-5 and 7-27 under 35 U.S.C. 101 in the Office action mailed 09 July 2025 is maintained with modification in view of the amendment received 03 November 2025.
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 1-5 and 7-27 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mental processes, i.e., concepts performed in the human mind (e.g., observation, evaluation, judgement, opinion); and (b) mathematical concepts (e.g., mathematical relationships, formulas or equations, mathematical calculations).
Subject matter eligibility evaluation in accordance with MPEP 2106.
Eligibility Step 1: Step 1 of the eligibility analysis asks: Is the claim to a process, machine, manufacture or composition of matter?
Claims 1-5, 7-23, 26, and 27 are directed to a method (i.e., a process) for automating the determination of chemical synthesis pathways; claim 24 is directed to a system comprising at least one processor and memory (i.e., a machine or manufacture) for automating the determination of chemical synthesis pathways; and claim 25 is directed to a non-transitory, computer-readable medium (i.e., a machine or manufacture).
Therefore, these claims are encompassed by the categories of statutory subject matter, and thus, satisfy the subject matter eligibility requirements under step 1.
[Step 1: YES]
Eligibility Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception.
Eligibility Step 2A Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim.
Independent claim 1 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
generating a first model by machine learning (i.e., mental processes and mathematical concepts);
training the first model with training data including a plurality of known positive reactions (i.e., mental processes and mathematical concepts);
a first plurality of reactions for synthesizing the first molecular structure by the trained first model automatically expanding the first molecular structure to create a set of reaction nodes and chemical compound nodes with directional links, the set including a plurality of pathways that produce the first molecular structure, and at least one of the first plurality of reactions being created by the trained first model and not pre-existing in any location accessible by the computing system, and at least one automatic expansion being performed by the trained first model on a subset of chemical compound nodes selected according to a search policy requiring (i.e., mental processes and mathematical concepts):
perform a cost estimate for each available chemical compound node of a plurality of available chemical compound nodes to determine an estimated cost for producing a chemical compound represented by the chemical compound node (i.e., mental processes and mathematical concepts),
rank the plurality of available chemical compound nodes according to their cost estimates (i.e., mental processes and mathematical concepts),
select a subset of available chemical compound nodes from the plurality of available chemical compound nodes based on: 1) each selected available chemical compound nodes having a cost estimate less than non-selected available chemical compound nodes, 2) a number of selected available chemical compound nodes being determined by the search policy, and 3) the number of selected available chemical compound nodes being less in number than the plurality of available chemical compound nodes (i.e., mental processes and mathematical concepts); and
expand only the selected subset of available chemical compound nodes (i.e., mental processes);
extracting, from the set of reaction nodes and chemical compound nodes, at least one first pathway producing the first molecular structure (i.e., mental processes);
predicting, for each extracted first pathway, a cost for producing the first molecular structure according to the extracted first pathway (i.e., mental processes and mathematical concepts); and
ranking each extracted first pathway according to the predicted cost to determine an extracted first pathway with a lowest predicted cost (i.e., mental processes and mathematical concepts).
Independent claim 24 recites a system comprising at least one processor and memory for performing abstract ideas recited by the method of independent claim 1.
Independent claim 25 recites a non-transitory, computer-readable medium comprising instructions that when executed by a processor of a computing system comprising at least one processor and memory, cause the computing system to perform the abstract ideas recited by the method of independent claim 1.
Dependent claims 2-5, 7-23, 26, and 27 further recite the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas, as noted below.
Dependent claim 2 further recites:
adheres to the constraint in generating the first plurality of reactions (i.e., mental processes).
Dependent claim 3 further recites:
wherein the constraint is defined with reference to the first molecular structure (i.e., mental processes); and
adhering to the constraint in generating the first plurality of reactions (i.e., mental processes).
Dependent claim 4 further recites:
selecting an extracted first pathway (i.e., mental processes);
selecting, from the selected first pathway, a first compound within the selected first pathway (i.e., mental processes);
comparing the first compound to compounds within a database of commercially available compounds (i.e., mental processes);
based on the comparison, choosing a second compound (i.e., mental processes);
substituting the second compound for the first compound in the first listed pathway (i.e., mental processes);
revising any reaction between the second compound and the first molecular structure in the first listed pathway to account for a difference between the second compound and the first compound, the revising resulting in a second pathway and a change to the first molecular structure such that the result of the second pathway is the second molecular structure (i.e., mental processes and mathematical concepts); and
associating the second pathway with the first listed pathway, wherein the providing the listing including each first pathway in an order determined by the ranking includes listing the second pathway with the associated first listed pathway (i.e., mental processes).
Dependent claim 5 further recites:
selecting an extracted first pathway (i.e., mental processes); and
selecting the first compound (i.e., mental processes).
Dependent claim 7 further recites:
wherein an automatic expansion includes (i.e., mental processes):
selecting from the selected subset, a chemical compound node to be expanded;
generating, using the model, at least one additional reaction producing a chemical compound represented by the selected chemical compound node (i.e., mental processes and mathematical concepts);
adding, for each proposed additional reaction, a reaction node to the set, and adding a directional link from the reaction node to the selected chemical compound node (i.e., mental processes and mathematical concepts); and
adding, for each substrate in each proposed additional reaction, a chemical compound node to the set, and adding a directional link from the added chemical compound node to the reaction node representing the additional reaction (i.e., mental processes and mathematical concepts).
Dependent claim 9 further recites:
extracting the at least one first pathway from the expanded set (i.e., mental processes).
Dependent claim 10 further recites:
training a statistical model to predict reaction feasibility using known reaction data and infeasible reaction data (i.e., mental processes and mathematical concepts);
determining a probability of success for each reaction node in an extracted pathway by evaluating each reaction node using the statistical model trained to predict reaction feasibility using known reaction data and infeasible reaction data (i.e., mental processes and mathematical concepts).
Dependent claim 11 further recites:
discarding substrates to leave only reaction products (i.e., mental processes);
generating, using the first molecular structure and the model generated by machine learning using known reactions, for each of the reaction products, a reaction that is a first step in a retrosynthesis of the reaction product (i.e., mental processes and mathematical concepts);
comparing the generated reactions to the set of reactions known to occur to determine a set of generated reactions that do not conform to properties of the set of reactions known to occur (i.e., mental processes); and
adding the set of generated reactions that do not conform to the infeasible reaction data (i.e., mental processes).
Dependent claim 12 further recites:
searching template graphs of the known reactions for product subgraphs that match a product subgraph of the first molecular structure (i.e., mental processes);
generating, for each matching product subgraph, a proposed set of substrate subgraphs (i.e., mental processes);
removing invalid chemical compounds from the proposed set of substrates and the related product subgraph (i.e., mental processes); and
extracting a template from each remaining product subgraph and generated set of substrate subgraphs, a reaction template (i.e., mental processes).
Dependent claim 13 further recites:
wherein at least one of the first plurality of reactions for synthesizing the first molecular structure is initially a single step pathway for synthesizing the first molecular structure and the initial single step pathway is expanded to a multi-step pathway by:
1) designating a substrate from the initial single step pathway as a target molecular structure (i.e., mental processes);
2) generating, using the target molecular structure and the model, at least one single step pathway for synthesizing the designated target molecular structure (i.e., mental processes and mathematical concepts); and
3) adding the at least one proposed single step pathway to the first plurality of reactions (i.e., mental processes).
Dependent claim 14 further recites:
repeating steps 1-3 of claim 13 for each substrate in the first plurality of reactions until determining that the substrate is found in a database of commercially available compounds, or performing a maximum number of iterations of steps 1-3 in claim 13 for the substrate (i.e., mental processes and mathematical concepts).
Dependent claim 15 further recites:
wherein at least one first pathway producing the first molecular structure is a multi-step pathway including a plurality of single step pathways (i.e., mental processes).
Dependent claim 16 further recites:
ranking an initial subset of the first plurality of reactions, wherein the initial single step pathway is selected from the initial subset of the first plurality of reactions as being a highest-ranked reaction (i.e., mental processes and mathematical concepts).
Dependent claim 17 further recites:
wherein a subset of the first plurality of reactions includes reactions that become intermediate reactions in one or more of the extracted first pathways (i.e., mental processes).
Dependent claim 19 further recites:
providing, for an extracted first pathway, an estimate of difficulty in synthesizing the first molecular structure according to the extracted first pathway, the estimate being based at least in part on an analysis of each reaction in the extracted first pathway (i.e., mental processes and mathematical concepts).
Dependent claim 20 further recites:
wherein the estimate is also based on the predicted cost of the extracted first pathway (i.e., mental processes and mathematical concepts).
Dependent claim 21 further recites:
generating a first plurality of reactions for synthesizing the first molecular structure includes creating an estimate of reaction feasibility for each step in a pathway of the first plurality of reactions (i.e., mental processes and mathematical concepts); and
extracting, from the first plurality of reactions, at least one first pathway producing the first molecular structure includes using the estimates of reaction feasibility in determining which at least one first pathway to extract (i.e., mental processes and mathematical concepts).
Dependent claim 22 further recites:
creating, using the model, a first estimate of reaction feasibility for each of a first subset of steps in the first plurality of reactions (i.e., mental processes and mathematical concepts); and
creating a second estimate of reaction feasibility for each of a second subset of steps in the first plurality of reactions by:
determining a reaction template associated with the step (i.e., mental processes), determining a first number of feasible reactions in a reference dataset that are associated with the same reaction template (i.e., mental processes and mathematical concepts), determining a second number of infeasible reactions in the reference dataset that are associated with the same reaction template (i.e., mental processes and mathematical concepts), dividing the first number by a sum of the first and second numbers, the result of the division being the second estimate of reaction feasibility (i.e., mental processes and mathematical concepts).
Dependent claim 23 further recites:
generating the first plurality of reactions for synthesizing the first molecular structure, at least one of the first plurality of reactions being created by the trained first model (i.e., mental processes and mathematical concepts);
extracting from the set of reaction nodes and chemical compound nodes, at least one first pathway producing the first molecular structure (i.e., mental processes);
predicting a cost for each first pathway (i.e., mental processes and mathematical concepts); and
ranking each extracted first pathway according to the predicted cost to determine an extracted first pathway with a lowest predicted cost (i.e., mental processes and mathematical concepts).
Dependent claim 26 further recites:
generating negative reactions using a neural network, wherein training the first model with training data including a plurality of known positive reactions includes using known positive reactions, known negative reactions, and generated negative reactions (i.e., mental processes and mathematical concepts).
Dependent claim 27 further recites:
the model includes a generator model and a discriminator model (i.e., mental processes and mathematical concepts);
generating the first model by machine learning includes using a plurality of known positive reactions, a plurality of known negative reactions, and a plurality of generated negative reactions (i.e., mental processes) and includes:
training the generator model using known positive reactions (i.e., mental processes and mathematical concepts);
training the discriminator model using known positive reactions, known negative reactions, and generated negative reactions (i.e., mental processes and mathematical concepts); and
generating, using the first molecular structure and the first model, a first plurality of reactions for synthesizing the first molecular structure, at least one of the first plurality of reactions being created and not retrieved from a database includes:
generating, using the first molecular structure and the generator model, a second plurality of reactions for synthesizing the first molecular structure, at least one of the second plurality of reactions being created by the generator model and not retrieved from a database (i.e., mental processes and mathematical concepts); and
reviewing, by the discriminator model, the second plurality of reactions and discriminating between feasible and unfeasible reactions and collecting the feasible reactions as the first plurality of reactions (i.e., mental processes and mathematical concepts).
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pen and paper (e.g., rank the plurality of available chemical compound nodes according to their cost estimates), and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas (e.g., training the first model with training data including a plurality of known positive reactions) are abstract ideas regardless of whether or not the limitations are practical to perform in the human mind.
Therefore, claims 1-5 and 7-27 recite an abstract idea.
[Step 2A Prong One: YES]
Eligibility Step 2A Prong Two: In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that when examined as a whole integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d)(I)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A Prong One are not integrated into a practical application because of the reasons noted below.
Dependent claims 3, 7, 9, 10, 13, 15-17, 19, 20, and 22 do not recite any elements in addition to the judicial exception, and thus are part of the judicial exception.
The additional elements in independent claim 1 include:
a computing system including at least one processor, memory, and mass storage;
receiving a first molecular structure (i.e., receiving data);
providing the first molecular structure to the trained first model (i.e., inputting data);
computationally generating;
providing to a computer device, a listing including each first pathway in an order determined by the ranking (i.e., inputting data); and
performing chemical synthesis of the extracted first pathway with the lowest predicted cost.
The additional elements in independent claim 24 include:
a system comprising at least one processor and memory;
receiving a first molecular structure (i.e., receiving data);
providing the first molecular structure to the trained first model (i.e., inputting data);
computationally generating;
providing a listing including each first pathway in an order determined by the ranking (i.e., outputting data).
The additional elements in independent claim 25 include:
a non-transitory, computer-readable medium;
a computing system comprising at least one processor and memory;
receiving a first molecular structure (i.e., receiving data);
providing the first molecular structure to the trained first model (i.e., inputting data);
computationally generating;
providing a listing including each first pathway in an order determined by the ranking (i.e., outputting data).
The additional elements in dependent claims 2, 4, 5, 8, 11, 12, 14, 18, 21, 23, 26, and 27 include:
receiving, in addition to the first molecular structure, a constraint on the generating the first plurality of reactions (i.e., receiving data) (claim 2);
database of commercially available compounds (i.e., accessing data) (claims 4 and 14);
a user selecting the first pathway (i.e., inputting data) (claim 5);
displaying on a computer display (claim 8);
receiving a set of reactions known to occur (i.e., receiving data) (claim 11);
computationally generating (claims 12, 21, and 23);
providing (i.e., outputting data), on a computer monitor, the listing as an interactive display of each first pathway in the order determined by the ranking (claim 18);
computing system (claims 21, 26, and 27);
receiving a first molecular structure (i.e., receiving data) (claim 23); providing a listing including each first pathway in an order determined by the ranking (i.e., inputting data) (claim 23); obtaining the ranking each first pathway according to the predicted cost (i.e., outputting data) (claim 23); obtains the first molecular structure (i.e., receiving data) (claim 23).
The additional elements of a computing system including at least one processor, memory, and mass storage; a non-transitory, computer-readable medium; computationally generating; displaying on a computer display; and an interactive display; invoke a computer and/or computer-related components merely as tools for use in the claimed process, and/or amount to no more than mere instructions to apply the exceptions using a generic computer (MPEP 2106.05(f)), and therefore are not an improvement to computer functionality itself, or an improvement to any other technology or technical field, and thus, do not integrate the judicial exceptions into a practical application (MPEP 2106.04(d)(1)).
The additional elements of receiving data and/or inputting data and/or outputting data and/or accessing data are merely pre-solution (e.g., gathering data) and/or post-solution activities used in the claimed process – nominal additions to the claims that do not meaningfully limit the claims, and therefore do not add more than insignificant extra-solution activity to the judicial exceptions (MPEP 2106.05(g)).
The additional element of performing chemical synthesis amounts to mere instructions to apply an exception, because this type of recitation is equivalent to the words "apply it". The claim limitation attempts to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, because the limitation of the extracted first pathway “with the lowest predicted cost” could mean performing synthesis of any pathway with a “lowest cost,” since there is not a restriction, limit, or indication as to what the cost is actually referring to and how it relates to the synthesized compound. Therefore, this additional element does not integrate the judicial exceptions into a practical application (MPEP 2106.05(f)).
Thus, the additionally recited elements merely invoke a computer and/or computer-related components as a tool, and/or amount to insignificant extra-solution activity, and/or amount to mere instructions to apply an exception, and as such, when all limitations in claims 1-5 and 7-27 have been considered as a whole, the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application, and therefore claims 1-5 and 7-27 are directed to an abstract idea (MPEP 2106.04(d)).
[Step 2A Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
Dependent claims 3, 7, 9, 10, 13, 15-17, 19, 20, and 22 do not recite any elements in addition to the judicial exception(s).
The additional elements recited in independent claims 1, 24, and 25 and dependent claims 2, 4, 5, 8, 11, 12, 14, 18, 21, 23, 26, and 27 are identified above, and carried over from Step 2A: Prong Two along with their conclusions for analysis at Step 2B. Any additional element or combination of elements that was considered to be insignificant extra-solution activity at Step 2A: Prong Two was re-evaluated at Step 2B, because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and all additional elements and combination of elements were evaluated to determine whether any additional elements or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP 2106.05(d).
The additional elements of a computing system including at least one processor, memory, and mass storage; a non-transitory, computer-readable medium; computationally generating; displaying on a computer display; an interactive display; receiving data and/or inputting data and/or outputting data and/or accessing data; are conventional computer components and/or functions (see MPEP at 2106.05(b) and 2106.05(d)(II) regarding conventionality of computer components and computer processes).
The additional element of performing chemical synthesis (claim 1) is conventional. Evidence for the conventionality is shown by Ley et al. (Angewandte Chemie International Edition, 2015, Vol. 54, pp. 3449-3464, as cited in the Office action mailed 09 July 2025). Ley et al. reviews organic synthesis and the way that new technologies and machines have found use as methods for transforming the field (Abstract). Ley et al. further shows that organic synthesis is at the heart of many molecular assembly processes (page 3450, col. 2, para. 2); and discusses various aspects of machine-assisted approaches to improve synthesis processes (page 3451, col. 2, para. 2; and throughout the paper).
Therefore, when taken alone, all additional elements in claims 1-5 and 7-27 do not amount to significantly more than the above-identified judicial exception(s). Even when evaluated as a combination, the additional elements fail to transform the exception(s) into a patent-eligible application of that exception. Thus, claims 1-5 and 7-27 are deemed to not contribute an inventive concept, i.e., amount to significantly more than the judicial exception(s) (MPEP 2106.05(II)).
[Step 2B: NO]
Response to Arguments
The Applicant’s arguments received 03 November 2025 have been fully considered, but are not persuasive.
The Applicant summarizes the USPTO Memorandum of August 4, 2025 in view of the Office action mailed 09 July 2025 on pages 16-19 of the Remarks, and states that aspects of the Office action, e.g., characterizing limitations as mental processes and/or mathematical processes is contrary to the guidance of the Memorandum. The Applicant further analogizes the instant claim limitations reciting “generating…a first model by machine learning” to Example 39 on page 18 (top) of the Remarks, and further states (para. 2) that if the training of a neural network of Example 39 does not recite a judicial exception, then for similar reasons the limitations of amended claim 1 also do not recite judicial exceptions. The Applicant further states (paras. 4-5) that the claim limitations of “computationally” and “by the computing system executing the first model and using the first molecular structure” have been removed by the Examiner from the claim language addressed in this section of the rejection, and that no reason is given for their removal, and that this may have been due to an overgeneralization of the claim language for the purposes of determining whether it recites an abstract idea.
These arguments are not persuasive, in part for reasons already in the record, and further because the USPTO Memorandum of August 4, 2025 did not provide any new USPTO practice or procedure and is meant to be consistent with existing USPTO guidance, which was followed in the examination of the instant claims, as noted in the above rejection. Furthermore, regarding the Applicant’s argument that the claim limitations of “computationally” and “by the computing system executing the first model and using the first molecular structure” have been removed by the Examiner from the claim language, it is noted that Step 2A of the eligibility analysis requires identifying limitations that are judicial exceptions at Prong One and limitations that are additional elements at Prong Two, and because claims can recite a mental process even if they are claimed as being performed on a computer (MPEP 2106.04(a)(2)(III)(C)), elements of the claim such as the computing system are identified as additional elements at Prong Two, whereas certain steps claimed as being performed on the computing system, e.g., using a model, are identified as judicial exceptions at Prong One. Still further, regarding the Applicant’s attempt at analogizing the instant claims with Example 39, it is noted that Example 39 considers a hypothetical method for training a neural network for facial detection, and is only intended to be illustrative of the claim analysis performed using MPEP 2106, and of the particular issues noted in the Example. Example 39 should be interpreted based on the fact patterns set forth in the Example's claim, as other fact patterns may have different eligibility outcomes, as evidenced in the rejection of the instant claims above. In particular, Example 39 comprises steps of applying one or more transformations to each digital facial image (e.g., mirroring, rotating, smoothing, or contrast reduction) to create a modified set of digital facial images; creating a first training set comprising the collected set of digital facial images, the modified set of digital facial images, and a set of digital non-facial images; training the neural network in a first stage using the first training set; creating a second training set for a second stage of training comprising the first training set and digital non-facial images that are incorrectly detected as facial images after the first stage of training; and training the neural network in a second stage using the second training set. While the exemplified analysis of Example 39 concludes that the claim does not recite any judicial exceptions (e.g., mental processes or mathematical concepts), this conclusion is determined from the particular fact pattern of the hypothetical claimed process, and should not be generalized as an axiom that claim limitations reciting training a neural network do not recite judicial exceptions. The Applicant is directed to the July 2024 Subject Matter Eligibility Examples (examples 47 and 48) for updated guidance on the eligibility analysis of claims that recite limitations specific to artificial intelligence.
The states on pages 19-22 of the Remarks that the streamlined analysis is applicable to the present amended claims, and that when viewed as a whole, amended independent claims 1, 24, and 25 each recite subject matter such that it is clear that the claims do not seek to tie up all techniques for determining pluralities of chemical reactions and chemical synthesis routes, and they certainly do not seek to tie up all techniques by a preponderance of the evidence.
These arguments are not persuasive, in part for the reasons provided in response to a similar argument (e.g., see Office action mailed 09 July 2025, pp. 27-29) and reiterated here, i.e., first, eligibility is not self-evident (MPEP 2106.06(a)) and there is not a clear improvement to a technology or to computer functionality (MPEP 2106.06(b)). Second, while preemption is the concern underlying the judicial exceptions, it is not a standalone test for determining eligibility, and instead, questions of preemption are inherent in and resolved by the two-part framework of the eligibility analysis in the rejection above. Third, the eligibility of the claims is not self-evident because when viewed as a whole, the claims recite conventional computer components that execute processes that comprise judicial exceptions from the mental process and mathematical concept groupings of abstract ideas, as noted in the rejection above at Eligibility Step 2A Prong One. Furthermore, a clear improvement to a technology or to computer functionality is not evident because the limitations that result in the claimed systems and methods for determining pluralities of chemical reactions and chemical synthesis routes comprise judicial exceptions (as identified at Eligibility Step 2A Prong One in the rejection above) that are not integrated into a practical application at Eligibility Step 2A Prong Two when the claims are considered as a whole when using one or more of the considerations introduced at MPEP 2106.04(d) subsection I, and discussed in more detail in MPEP §§ 2106.04(d)(1), 2106.04(d)(2), 2106.05(a) through (c) and 2106.05(e) through (h). Therefore, any purported improvement is in the abstract idea itself (e.g., determining pluralities of chemical reactions and chemical synthesis routes), and not an improvement in the functioning of a computer, or an improvement to any other technology or technical field.
Regarding Step 2A Prong One, the Applicant states on pages 22-27 of the Remarks that the Applicant does not concede that limitations such as generating a first model, extracting, predicting, and ranking (Remarks, page 22) are mental processes, further states that the Applicant does not concede that the claims can recite a mental process even if they are claimed as being performed on a computer (Remarks, page 22, bottom), and that limitations such as “training…the first model” and “computationally generating…a first plurality of reactions” are not abstract because they do not recite a mathematical process and they cannot be practically performed by the human mind. The Applicant further states (page 25, bottom) that the method of claim 1 has been reduced to practice and estimates that the code for the computer system required three full-time employees working at least one and a half years and resulted in over 40,000 lines of code, and (page 26, top) the claim necessitates computer technology.
These arguments are not persuasive, because first, the MPEP states at MPEP 2106.04(a)(2)(III)(C) that claims can recite a mental process even if they are claimed as being performed on a computer. Second, when determining whether a claim recites a mathematical concept (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations), examiners should consider whether the claim recites a mathematical concept or merely limitations that are based on or involve a mathematical concept. A claim does not recite a mathematical concept (i.e., the claim limitations do not fall within the mathematical concept grouping), if it is only based on or involves a mathematical concept. Furthermore, it is important to note that a mathematical concept need not be expressed in mathematical symbols, because words used in a claim operating on data to solve a problem can serve the same purpose as a formula (MPEP 2106.04(a)(2)(I)). Third, regarding the Applicant’s argument that the claim “necessitates computer technology to perform steps that are far too complex and computationally intensive to be replicated by a human mind using mental processes,” the amount of data and/or the amount of time to perform the process steps, in and of themselves is not a limitation which takes a process out of the realm of the human mind. It is the process performed on that data which is the mental step, and mental steps identified in the claims do not have to be the fastest, most efficient, or require specialized computing elements. Thus, although the amount of data may be considered to be significantly large and take considerable time and effort to process manually, the use of a computer to perform the claimed method at a rate and accuracy that can far outstrip the mental performance of a skilled artisan does not change the nature of the activity being performed (i.e., an abstract idea), and therefore does not materially alter the patent eligibility of the claimed subject matter.
Regarding Step 2A, Prong Two (pages 27-37 of the Remarks), the Applicant summarizes the eligibility analysis guidelines at Prong Two and references TQP Development (Remarks, page 28) as one example of an additional element that reflects an improvement, and analogizes the instant claims to Thales and TQP Development with regard to reflecting an improvement to a technology or technical field (Remarks, page 29). The Applicant further states (page 30, top) that by creating and training a first model, the computing system of the instant claims is no longer generic, and is a system uniquely adapted to perform the steps of the claimed method, and that by itself, is an improvement to computer functionality. The Applicant further states that the step of performing chemical synthesis provides a practical application (Remarks, page 30). The Applicant provides further arguments for a practical application of the judicial exceptions on pages 31-37 of the Remarks.
These arguments are not persuasive, because first, regarding the Applicant’s assertion that the subject matter of amended claims 1, 24, and 25 reflect an improvement to a technology or a technical field, specifically an improvement to technology for chemical compound synthesis of a target compound, the improvements that the Applicant points to in the foregoing argument comprise the judicial exceptions that are identified at Eligibility Step 2A Prong One in the rejection above, and because a judicial exception alone is not eligible subject matter, if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application. As noted at Eligibility Step 2A Prong Two in the rejection above, the claims are determined to recite abstract ideas when evaluated at Eligibility Step 2A Prong One that are subsequently determined to not be integrated into a practical application when evaluated at Eligibility Step 2A Prong Two using one or more of the considerations introduced in subsection I at 2106.04(d) of the MPEP, and discussed in more detail in MPEP §§ 2106.04(d)(1), 2106.04(d)(2), 2106.05(a) through (c) and 2106.05(e) through (h). Second, the claims do not result in an improvement to computer functionality, because the claims invoke a computer merely as a tool for performing the recited abstract ideas (e.g., determining pluralities of chemical reactions and chemical synthesis routes), and therefore any purported improvement is in the abstract idea itself, and not an improvement in the functioning of a computer, or an improvement to any other technology or technical field.
Regarding Eligibility Step 2B (Remarks, pages 37-43), the Applicant states that the eligibility analysis of record under Step 2B is improperly limited to additional elements (Remarks, page 38-40) and does not provide for considering the additional elements in combination with limitations purported to be judicial exceptions, and therefore the claims are not considered as a whole. The Applicant further states (Remarks, page 40-42) that the analysis under Step 2B has not met its evidentiary burden to show well-understood, routine, or conventional elements, and further states (Remarks, pages 42-43) that the claims provide an inventive concept that improves technology and that the ordered combination is not conventional.
These arguments are not persuasive, because first, the evaluation of whether additional elements integrate a judicial exception into a practical application is determined at Eligibility Step 2A Prong Two (as discussed in foregoing responses to arguments) and is separate from the search for an inventive concept at Eligibility Step 2B. Second, a conclusion of whether a claim is eligible at Step 2B requires that all relevant considerations be evaluated, which comprises steps of: (1) carrying over the identification of any additional element(s) in the claim from Step 2A Prong Two; (2) carrying over the conclusions from Step 2A Prong Two on the considerations discussed in MPEP §§ 2106.05(a) - (c), (e) (f) and (h); (3) re-evaluating any additional element or combination of elements that was considered to be insignificant extra-solution activity per MPEP § 2106.05(g), because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and (4) evaluating whether any additional element or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP § 2106.05(d). As noted in the rejection above, when all additional elements in claims 1-5 and 7-27 have been evaluated individually and in combination at Eligibility Step 2B, they are deemed to not contribute an inventive concept, i.e., amount to significantly more than the judicial exceptions (MPEP 2106.05(II)). Third, the instant claimed improvement to the technology of designing organic synthesis pathways for desired organic molecules is a purported improvement to the abstract idea (data analysis), and not an improvement to computer functionality itself, or an improvement to another technology or technical field. Fourth, with regard to the Applicant’s attempt to analogize the instant independent claims Diamond v. Diehr, fact patterns differ between the instant claims and Diamond v. Diehr, as evidenced in the rejection of the instant claims above, not least because the additional element in Diamond v. Diehr (i.e., the press mold) uses and/or interacts with the judicial exception (i.e., the repeated calculations of the cure time by use of the Arrhenius equation) in a way that integrates the recited judicial exceptions into a practical application. However, the instant claim additional element of performing chemical synthesis is not analogous with Diamond v. Diehr, not least because the data outputting step is not controlling the chemical synthesis step. Fifth, with regard to the Applicant’s argument that the ordered combination is not conventional, it is reiterated that it is the additional elements that are evaluated individually and in combination at Eligibility Step 2B to determine whether the additional elements provide significantly more than the recited judicial exceptions (i.e., an inventive concept), as noted and discussed in the above rejection.
Claim Rejections - 35 USC § 102
The rejection of claims 1, 2, 3, 4, 5, 7-11, 13-17, 19-21, and 23-27 under 35 U.S.C. 102(a)(1) as being anticipated by Madrid et al. in the Office action mailed 09 July 2025 is withdrawn in view of the amendment received 03 November 2025.
Response to Arguments
The Applicant’s arguments/remarks received 03 November 2025 have been fully considered, and are persuasive.
The Applicant states on page 45 (bottom) of the Remarks that the Madrid reference fails to disclose each and every feature of the amended independent claims.
The Applicant’s arguments are persuasive.
Claim Rejections - 35 USC § 103
The rejection of claim 12 under 35 U.S.C. 103 as being unpatentable over Madrid et al. as applied to claims 1, 2, 3, 4, 5, 7-11, 13-17, 19-21, and 23-27 under 35 U.S.C. 102 above, and further in view of Sankar et al. in the Office action mailed 09 July 2025 is withdrawn in view of the amendment received 03 November 2025.
The rejection of claim 18 under 35 U.S.C. 103 as being unpatentable over Madrid et al. as applied to claims 1, 2, 3, 4, 5, 7-11, 13-17, 19-21, and 23-27 under 35 U.S.C. 102 above, and further in view of Konze et al. in the Office action mailed 09 July 2025 is withdrawn in view of the amendment received 03 November 2025.
The amendment received 03 November 2025 has been fully considered, however after further consideration, new grounds of rejection are raised under 35 U.S.C. 103 in view of the amendment.
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 1-5, 7-11, 13-17, and 19-27 are rejected under 35 U.S.C. 103 as being unpatentable over Madrid et al. (WO 2019/156872, as cited in the Information Disclosure Statement (IDS) received 24 August 2021, previously cited) in view of Blakemore et al. (“Organic synthesis provides opportunities to transform drug discovery.” Nature Chemistry, (2018), Vol. 10, pp. 383-394, newly cited).
Independent claims 1, 24, and 25 broadly encompass designing organic synthesis pathways for desired organic molecules using systems and methods for training and executing a machine learning model to automate the determination and display of chemical synthesis pathways and subsequent providing of a ranking that represents the total estimated cost of pathway execution, including the cost of starting materials and the risk of synthesis failure. Dependent claims 2-5, 7-23, 26, and 27 further define the training of the model and the use of the model, e.g., to assemble proposed reactions into multi-reaction synthetic pathways (e.g., as search tree structures) and rank these pathways (e.g., according to a determined cost).
Madrid et al. is directed to the computational generation of chemical synthesis routes and methods, in particular, retrosynthetic methods for determining one or more optimal synthetic routes to generate a target compound.
Blakemore et al. is directed to the topic of synthetic organic chemistry from the perspective of the pharmaceutical industry, and its application in the discovery of transformational medicines.
Regarding claims 1, 24, and 25, Madrid et al. shows using computer hardware, software, or a combination of software and hardware, and a non-transitory computer-readable storage medium having processor-executable instructions (para. [0013]) for performing retrosynthetic methods for determining one or more optimal synthetic routes to generate a target compound (Abstract) using artificial intelligence techniques including machine learning (para. [0022]). Madrid et al. further shows selecting a target compound for which synthetic plans can be determined that would summarize some or all reasonable routes for the synthesis of the target compound (para. [0019]); receiving a target compound as input to a route engine that applies reaction transformations derived from the reactions retrosynthetically to generate one or more synthetic routes (para. [0037]); routes can be generated using known chemical reactions and/or computationally generated chemical reactions (para. [0019], lines 10-13; and para. [0038]); a predicted reaction may be provided to one or more machine learning classifiers to assess whether the predicted reaction involving a reagent would be successful, and if the predicted reaction would be successful, the predicted reaction can be included or excluded from route generation (para. [0038]); limiting (constraints) the feedstocks and reactions to consider (para. [0046], bottom); the route engine can determine minimum-cost routes and initialize a priority (rank) queue with reactions that can activate from feedstocks (para(s). [0047] – [0050]); using a cost function (para. [0063]); the route engine can compare the total costs (of predicted reactions) and select the route having the lowest cost (para. [0048]); and determining, based on a predetermined number of reactions and a cost function, an optimal synthetic route from the plurality of synthetic routes (para. [0068]).
Further regarding claims 1, 24, and 25, Madrid et al. further shows a method for identifying one or more synthetic routes for producing a target compound comprising training one or more machine learning classifiers on a portion of a plurality of known chemical reactions (para. [0006]).
Madrid et al. further shows that some or all reactions of the known reactions are defined as positive or negative (para. [0027]).
Madrid et al. further shows various depictions of reaction nodes and chemical compound nodes with directional links to produce a molecular structure (FIG. 11; and FIGs. 6, 7, 8, and 10).
Madrid et al. further shows that the machine learning generated route was executed (i.e., chemical synthesis was performed) on a multi-step flow synthesizer affording Diazepam in 78% yield on 161 mg scale (para. [0070]).
Regarding claims 1, 24, and 25, Madrid et al. does not show an automatic expansion that expands only the selected subset of least-cost chemical compounds.
Regarding claims 1, 24, and 25, Blakemore et al. shows that organic synthesis is still a rate-limiting factor in drug-discovery projects, and that new technologies such as machine-assisted approaches and artificial intelligence for synthesis planning have the potential to dramatically accelerate the drug-discovery process (Abstract) and further shows that experimental synthesis represents the most time-consuming aspect of medicinal chemistry so improvements there, along with in silico predictions and compound design, have great potential to increase efficiency and reduce the cost of preparing novel molecules, and thus, there is good potential for scientific and financial return on investments made in fundamental organic synthesis research (page 383, col. 1, para. 1).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Madrid et al. by incorporating methods for in silico predictions and compound design, as discussed by Blakemore et al. One of ordinary skill in the art would have been motivated to combine the methods of Madrid et al. with methods for in silico predictions and compound design, i.e., expanding only the selected subset of least-cost chemical compounds, because Blakemore et al. shows that the use of artificial intelligence for synthesis planning had the potential to dramatically accelerate the drug-discovery process and the potential to increase efficiency and reduce the cost of preparing novel molecules. This modification would have had a reasonable expectation of success given that both Madrid et al. and Blakemore et al. discuss using in silico techniques for organic synthesis planning.
Regarding claims 2 and 3, Madrid et al. further shows limiting (constraints) the feedstocks and reactions to consider (para. [0046], bottom).
Regarding claim 4, Madrid et al. further shows revising reaction routes by repeating a process of determining the reactions that produce the reactants to limit the feedstocks and reactions to consider (para. [0046]); and comparing different reactants in different reaction routes to initialize a priority queue of reactions based on minimum-cost routes (para. [0047]).
Regarding claim 5, Madrid et al. further shows a user interface for providing inputs to the route engine, which can comprise a target compound and optimal routes (para. [0040]).
Regarding claim 7, Madrid et al. further shows that for each compound (as reactant) one reaction away from the target compound (and/or downstream/upstream reactant), reaction transformations can be modified (para. [0039]) and can be repeated until a maximum number of new reaction is reached, building a route network with each new route, wherein the sequence of chemical reactions may be referred to as a route, and the route network for a target compound may be represented in a tree data structure and output for display (para. [0042]). Madrid et al. further shows an example tree data structure wherein the target compound may be positioned at the center of the tree data structure and each edge may comprise a reaction and each node may comprise a compound (reactant) (para. [0043]; and FIG. 6).
Regarding claim 8, Madrid et al. further shows that the route network for a target compound may be represented in a tree data structure and output for display (para. [0042]).
Regarding claim 9, Madrid et al. further shows a route engine may apply the target compound to one or more known reaction transformations to generate a predicted reaction, and then the predicted reaction may be provided to the one or more machine learning classifiers to assess whether the predicted reaction involving a reagent would be successful, and if the prediction is that the predicted reaction would be successful, the predicted reaction can be included in route generation, and if the prediction is that the predicted reaction would not be successful, the predicted reaction can be excluded from route generation (para. [0038]).
Regarding claim 10, Madrid et al. further shows the trained machine learning model is used to predict whether new data exhibits statistical relationships (para. [0022]); and a trained machine learning classifier generates a prediction with a probability of being correct (para. [0035]); and that determining the one or more optimal synthetic routes from the plurality of synthetic routes may be based on one or more parameters, wherein the one or more parameters comprise the likelihood of reaction success (para. [0062]).
Regarding claim 11, Madrid et al. further shows that known reactions may be accessed via various reaction databases (para. [0021]); excluding reactants with no path to the target compound (para. [0046]); a first stage that starts with the target compound and identifies the chemical reactions that produce the target compound, and determines the reactants of these reactions beginning at a distance of 1 to the target compound (para. [0046]); comparing reactions and reactants (para. [0047]); and differentiating negative reactions from positive reactions in the set of known reactions (para. [0027]).
Regarding claim 13, Madrid et al. further shows that for each compound (as reactant) one reaction away from the target (i.e., a single step), the route engine can generate one or more sequences of chemical reactions designed to result in the creation of the target compound (i.e., a multi-step pathway) (para. [0042]).
Regarding claim 14, Madrid et al. further shows performing a maximum number of iterations (i.e., repeated until a maximum number of new reactions is reached (para. [0042]); and accessing reaction databases (para. [0021]); and retrosynthetically determining a plurality of synthetic routes may be based on one or mora parameters, that comprise available feedstock, or available chemical substances (para. [0067]).
Regarding claim 15, Madrid et al. further shows a route for creating Diazepam, which is a multi-step pathway comprised of a plurality of single-step pathways (para. [0070]; and FIG. 10).
Regarding claim 16, Madrid et al. further shows that the route engine can initialize a priority queue with reactions, wherein next reactions can be added to the priority queue and the process repeated until the target compound is reached (para. [0047]).
Regarding claim 17, Madrid et al. further shows an example tree data structure comprised of a plurality of routes, wherein the target compound may be positioned at the center of the tree data structure, wherein each edge may comprise a reaction and each node may comprise a compound (reactant), and wherein nodes can represent chemical intermediates (para. [0043]; and FIG. 6).
Regarding claim 19, Madrid et al. further shows that determining the one or more optimal synthetic routes from the plurality of synthetic routes may be based on one or more parameters, wherein the one or more parameters comprise one or more of available feedstock, available chemical substances, available equipment, yield, financial cost, time, reaction conditions, or likelihood of reaction success (i.e., difficulty) (para. [0062]).
Regarding claim 20, Madrid et al. further shows that determining the one or more optimal synthetic routes from the plurality of synthetic routes may be based on one or more parameters, wherein the one or more parameters comprise one or more of available feedstock, available chemical substances, available equipment, yield, financial cost, time, reaction conditions, or likelihood of reaction success (i.e., difficulty) (para. [0062]).
Regarding claim 21, Madrid et al. further shows that determining the one or more optimal synthetic routes from the plurality of synthetic routes may be based on one or more parameters, wherein the one or more parameters comprise one or more of available feedstock, available chemical substances, available equipment, yield, financial cost, time, reaction conditions, or likelihood of reaction success (i.e., difficulty) (para. [0062]). Madrid et al. further shows that the method can comprise determining, based on the plurality of known chemical reactions, one or more known chemical reactions that result in a target compound (para. [0065]); determining, based on chemical reaction transformations, one or more predicted chemical reactions that result in the target compound (para. [0066]); and retrosynthetically determining a plurality of synthetic routes wherein each synthetic route may result in the target compound, wherein at least one synthetic route comprises at least one or more predicted chemical reactions (para. [0067]).
Regarding claim 23, Madrid et al. further shows receiving a target compound as input (para. [0021]); applying reaction transformations derived from the reactions retrosynthetically to generate one or more synthetic routes (para. [0021]); the route(s) having minimal cost can be identified as the optimal route(s) (i.e., ranking according to predicted cost) (para. [0049]); and a computational method for identifying one or more existing or novel chemical synthesis routes for producing a target compound comprising determining a plurality of known chemical reactions and/or a plurality of novel chemical reactions (i.e., not retrieved from a database) (para. [0052]).
Regarding claim 26, Madrid et al. further shows determining, from the plurality of novel chemical reactions, a plurality of predicted chemical reactions, based on a trained classifier, wherein the trained classifier may be trained on data derived from a plurality of chemical reactions known to be successful and a plurality of chemical reactions known to be unsuccessful that are instances of a given chemical transformation (para. [0053]); and wherein the machine learning classifier can be one or more of an artificial neural network (para. [0022]).
Regarding claim 27, Madrid et al. further shows using artificial intelligence techniques (e.g., a machine learning classifier such as a neural network) for generating the predicted reactions (paras. [0022] & [0032]) that may generate one or more synthetic routes (para. [0037]; (i.e., analogous to the “generator model” as described at para. [0048] in the Applicant’s specification)); and wherein determining the one or more optimal synthetic routes from the plurality of synthetic routes may be based on one or more parameters, wherein the one or more parameters comprise one or more of available feedstock, available chemical substances, available equipment, yield, financial cost, time, reaction conditions, or likelihood of reaction success (i.e., difficulty) (para. [0062]; (i.e., analogous to the “discriminator model” as described at para. [0048] in the Applicant’s specification)); and a computational method for identifying one or more existing or novel chemical synthesis routes for producing a target compound comprising determining a plurality of known chemical reactions and/or a plurality of novel chemical reactions (i.e., not retrieved from a database) (para. [0052]). Madrid et al. further shows multiple machine-learning-based classification models may be combined into single machine learning-based classification model, and similarly, a machine learning-based classifier may represent a single classifier containing a single or a plurality of machine learning-based classification models and/or multiple classifiers containing a single or a plurality of machine learning-based classification models (para. [0033]).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Madrid et al. in view of Blakemore et al. as applied to claims 1-5, 7-11, 13-17, and 19-27 above, and further in view of Sankar et al. (Bioinformatics, 2017, Vol. 33(24), pp. 3955-3963, as cited in the Office action mailed 09 July 2025).
Sanker et al. is directed to predicting novel metabolic pathways through subgraph mining, in particular, mining the repertoire of biochemical transformations from reaction databases, and applying that knowledge to predict reactions to synthesize new molecules.
Regarding claim 12, Madrid et al. in view of Blakemore et al. as applied to claims 1-5, 7-11, 13-17, and 19-27 above, does not show using subgraphs for computationally generating a plurality of reactions for synthesizing a target molecule.
Regarding claim 12, Sankar et al. et al. shows predicting novel metabolic pathways through subgraph mining (Title) and further shows using a method based on subgraph mining to predict a series of bio-chemical transformations, which can convert between two (even previously unseen) molecules, wherein the method is based on subgraph edit distance to map reactants and products, using only their chemical structures (Abstract). Sankar et al. further shows a reaction rule network to identify and rank predicted pathways using graph operators, i.e., graph addition and graph subtraction to define the rules for finding a pathway from a reactant to a target molecule (page 3959, Section 2.5).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Madrid et al. in view of Blakemore et al. as applied to claims 1-5, 7-11, 13-17, and 19-27 above, by incorporating subgraphs to predict reactions to synthesize new molecules as shown by Sankar et al., and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Madrid et al. in view of Blakemore et al. as applied to claims 1-5, 7-11, 13-17, and 19-27 above, and Sankar et al., because Sankar et al. shows a method to rapidly predict reactions for synthesizing new molecules using subgraphs, which relies only on the structures of the molecules, without demanding additional information such as thermodynamics or hand-curated reactant mapping (Abstract). This modification would have had a reasonable expectation of success given that both Madrid et al. in view of Blakemore et al. as applied to claims 1-5, 7-11, 13-17, and 19-27 above, and Sankar et al. are directed toward providing solutions to retrosynthesis queries, i.e., predicted pathways from reactants to target molecules.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Madrid et al. in view of Blakemore et al. as applied to claims 1-5, 7-11, 13-17, and 19-27 above, and further in view of Konze et al. (Journal of Chemical Information and Modeling, 2019 (Published 12 August), Vol. 59, pp. 3782-3793, as cited in the Office action mailed 09 July 2025).
Konze et al. is directed to a computational technique that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space that accelerates the discovery of novel chemical matter in drug discovery campaigns.
Regarding claim 18, Madrid et al. in view of Blakemore et al. as applied to claims 1-5, 7-11, 13-17, and 19-27 above, does not show providing a listing on a monitor as an interactive display.
Regarding claim 18, Konze et al. shows the combination of retrosynthetic analysis, reaction-based enumeration, and robust filtering in an easy-to-use graphical user interface (GUI; page 3783, col. 2, para. 2) that allows user-specified depth of retrosynthetic tree analysis (page 3784, col. 1, para. 2); and a range of user-specified criteria (page 3785, col. 2, para. 3).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Madrid et al. in view of Blakemore et al. as applied to claims 1-5, 7-11, 13-17, and 19-27 above, by incorporating a graphical user interface (GUI, i.e., interactive display) as shown by Konze et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Madrid et al. in view of Blakemore et al. as applied to claims 1-5, 7-11, 13-17, and 19-27 above and Konze et al., because Konze et al. shows a reaction-based enumeration tool that enables the rapid exploration of synthetically tractable ligands that employs an easy-to-use graphical user interface to provide a streamlined approach for rapidly creating and evaluating large sets of synthetically tractable, lead-like, potent ligands that are of significant interest in drug discovery. This modification would have had a reasonable expectation of success given that both Madrid et al. in view of Blakemore et al. as applied to claims 1-5, 7-11, 13-17, and 19-27 above and Konze et al. are directed to the development of computational tools for retrosynthetic analysis of possible paths from a target molecule to the starting materials.
Response to Arguments
The Applicant’s arguments/remarks received 03 November 2025 have been fully considered. With respect to the Applicant’s comments and arguments to the previous rejections of record, the arguments appear to be directed to new embodiments of the newly added limitations, and do not argue the analysis of the references as they apply to the claim embodiments previously pending. The Examiner acknowledges the failure of the cited references to teach the limitations. However, in an updated search of the art, a new rejection has been made in view of the amendments.
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 THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/S.W.B./
Examiner, Art Unit 1687
/Joseph Woitach/Primary Examiner, Art Unit 1687