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 1-4, 7-8 are currently pending and under exam herein.
Claims 1-4, 7-8 are rejected.
Priority
The instant application claims priority from foreign application CN202111570217.2 filed on 12/21/2021. Thus, the effective filing date of the instant application is 12/21/2021
Response to Arguments 112(b)
Applicant sufficiently amended Claim 2 by removing the term “quasi-prediction.” The rejection is withdrawn.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4, 7-8 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) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion).
Subject matter eligibility evaluation in accordance with MPEP 2106:
Eligibility Step 1: Claims 1-4, 7-8 are directed to a bioretrosynthetic method based on an AND-OR tree and single-step reaction template prediction and are eligible.
[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:
A bioretrosynthetic method based on an AND-OR tree and single-step reaction template prediction, comprising the following steps: Sl: selecting an OR node from a pre-constructed AND-OR tree, and using a molecule corresponding to the OR node as a product molecule to be predicted (mental process)
wherein the pre-constructed AND-OR tree comprises two types of nodes: AND node and OR node; the AND node represents a reaction template, and the OR node represents a molecule (mental process, this limits what the mental process is done on)
predicting k templates in a preset template set that are most likely to synthesize the product molecule using a pre-constructed single-step reaction template prediction model, forming a template set Top-k, and assigning a weight value ranged between 0 and 1 to each template (mental process, mathematics)
wherein the preset template set is constructed based on a metabolic reaction structure in a known metabolic reaction data set (mental process, mathematics, this limits what the mental process and or mathematical process is done on)
wherein the pre-constructed single-step reaction template prediction model is a multi- classification model constructed by a Gaussian Naive Bayes classifier or a convolutional neural network ResNet50 based on the preset template set;wherein step S2 comprises:S2.1: predicting a probability that all reaction templates in the preset template set are capable of producing an input product molecule using the constructed multi-classification model; andS2.2: selecting top k reaction templates with the highest probabilities to form the template set Top-k, and setting a weight value of each reaction template in the template set Top-k as a corresponding probability value; (mental process, mathematics)
expanding the pre-constructed AND-OR tree, specifically comprising: adding each template in the Top-k as a new AND node to the AND-OR tree to obtain newly-added k AND nodes, and using the OR node selected in step S 1 as a parent node of the newly-added k AND nodes (mental process, mathematics)
adding each reaction substrate molecule corresponding to each newly-added AND node to the AND-OR tree as an OR node to obtain newly-added OR nodes, with the newly-added AND node as a parent node of the newly-added OR nodes (mental process, mathematics)
determining whether there is an AND node in step S3, wherein a substrate molecule corresponding to a child node of the AND node belongs to a known metabolite set: Sink- Compounds set; if there is an AND node, finding a biosynthetic pathway, stopping an iterative retrosynthetic process, and generating the biosynthetic pathway (mental process, mathematics)
if there is no AND node, determining whether the maximum number of iterations has been reached; if the maximum number of iterations is reached, stopping the iterative retrosynthetic process (mental process, mathematics)
repeating steps S1 to S4 until a biosynthetic pathway is found or the maximum number of iterations is reached; (mental process, mathematics)
Dependent claim 2 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein in step Si: in the AND-OR tree, a root node and a leaf node each are an OR node, and an intermediate node is an AND node or an OR node; a child node of each AND node is an OR node, representing all substrate molecules in a reaction template (mental process, mathematics, this limits what the mental process and or mathematical process is done on)
a child node of each non-leaf OR node in the AND-OR tree is an AND node (mental process, mathematics, this limits what the mental process and or mathematical process is done on)
each AND node represents a reaction template capable of producing a molecule corresponding to a parent node of the AND node, and the root node of the AND-OR tree is a target molecule node for prediction of a biosynthetic pathway to be predicted (mental process, mathematics, this limits what the mental process and or mathematical process is done on)
an initially-constructed AND- OR tree comprises only one root node, corresponding to the target molecule of a biosynthetic pathway to be predicted. (mental process, mathematics, this limits what the mental process and or mathematical process is done on)
Dependent claim 3 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein in step S1, each node of the AND-OR tree has a weight value; (mathematical process)
wherein in step S1, each node of the AND-OR tree has a weight value; a weight value of the AND node is a weight value of a corresponding reaction template, indicating a prediction probability of the corresponding reaction template (mathematical process)
OR nodes other than the root node each have a weight value of the parent node except for the OR nodes, and the root node has a weight value of 1 (mathematical process).
Dependent claim 4 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein in step S1, the selected OR node is a leaf node in the AND-OR tree that does not belong to the known Sink-Compounds set and has a maximum weight value (mental process, mathematics, this limits what the mental process and or mathematical process is done on)
if there are a plurality of the leaf nodes with the maximum weight value, one of the leaf nodes is selected randomly as the OR node. (mental process, mathematics, this limits what the mental process and or mathematical process is done on)
Dependent claim 7 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein in step S4, a process of generating the biosynthetic pathway comprises the following steps: (1) checking whether each leaf node in the AND-OR tree is in the Sink-Compounds set (mental process)
marking the leaf node in the Sink-Compounds as "true", and marking the leaf node not in the Sink- Compounds as "false" (mental process)
(2) for a non-leaf AND node in the AND-OR tree, marking the non-leaf AND node as "true" if and only if each child node of the non-leaf AND node is marked as "true", otherwise marking the non-leaf AND node as "false"; and for a non-leaf OR node in the AND-OR tree, marking the non-leaf OR node as "true" if and only if the non-leaf OR node comprises at least one child node marked as "true", otherwise marking the non-leaf OR node as "false" (mental process)
(3) if the root node is marked as "false", it indicates that no synthetic pathway has been found, outputting "No Solution"; otherwise, deleting all nodes marked as "false" in the AND-OR tree, and remaining a subtree representing a synthetic pathway of a target molecule. (mental process)
Dependent claim 8 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
a retrosynthesis planning module used for selecting an OR node from a pre-constructed AND- OR tree, and using a molecule corresponding to the OR node as a product molecule to be predicted; wherein the pre-constructed AND-OR tree comprises two types of nodes, an AND node and the OR node (mental process)
AND node represents a reaction template, and the OR node represents a molecule (mental process)
a reaction template prediction module used for predicting k templates in a preset template set that are most likely to synthesize the product molecule using a pre-constructed single-step reaction template prediction model, forming a template set Top-k, and assigning a weight value ranged between 0 and 1 to each template (mental process, mathematical process)
wherein the preset template set is constructed based on a metabolic reaction structure in a known metabolic reaction data set; (mental process)
wherein the pre-constructed single-step reaction template prediction model is constructed by a Gaussian Naive Bayes classifier or a convolutional neural network ResNet5O based on the preset template set; (mathematical concept)
wherein predicting the k templates in the preset template set, forming the template set Top-k, and assigning the weight value ranged between 0 and 1 to each template comprises: predicting a probability that all reaction templates in the preset template set are capable of producing an input product molecule using the constructed multi-classification model; (, mathematical concept)
and selecting top k reaction templates with the highest probabilities to form the template set Top- k, and setting a weight value of each reaction template in the template set Top-k as a corresponding probability value (mental process, mathematical concept)
an AND-OR tree extension module used for expanding the pre-constructed AND-OR tree, specifically comprising: adding each template in the Top-k as a new AND node to the AND-OR tree to obtain newly-added k AND nodes, and using the OR node selected in the retrosynthesis planning module as a parent node of the newly-added k AND nodes (mental process, mathematical process)
adding each reaction substrate molecule corresponding to each newly-added AND node to the AND-OR tree as an OR node to obtain newly-added OR nodes, with the newly-added AND node as a parent node of the newly- added OR nodes (mental process)
biosynthetic pathway generation module used for determining whether there is an AND node in the AND-OR tree obtained by the AND-OR tree extension module, wherein a substrate molecule corresponding to a child node of the AND node belongs to a known metabolite set: Sink- Compounds set (mental process)
if there is an AND node, finding a biosynthetic pathway, stopping an iterative retrosynthetic process, and generating the biosynthetic pathway; if there is no AND node, determining whether the maximum number of iterations has been reached; if the maximum number of iterations is reached, stopping the iterative retrosynthetic process (mental process)
if the maximum number of iterations is not reached, repeating steps of the retrosynthesis planning module to the biosynthetic pathway generation module until a biosynthetic pathway is found or the maximum number of iterations is reached (mental process, mathematical process)
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 pencil and paper, 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 are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Therefore, claims 1-4, 7-8 recite an abstract idea as the dependent claims will inherit the abstract ideas from the independent claims.
[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); MPEP 2106.05(a-h)). 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.
The additional element in independent claim 1 includes:
implemented by at least one processor and a memory storing a pre- constructed single-step reaction template prediction model, a pre-constructed AND-OR tree and instructions that, when executed by the at least one processor, cause the at least one processor to perform operations
nodes; wherein each reaction substrate molecule of the newly-added AND nodes is obtained by calling a function in an open source library RDChiral, and in the function, an input parameter is a SMILES sequence of the reaction template and the product molecule, and an output is a list of the corresponding substrate molecules;
and visually displaying the biosynthetic pathway for synthesizing a target molecule.
The additional element in independent claim 8 includes:
at least one processor and a memory storing a pre-constructed single-step reaction template prediction model, apre-constructed AND-OR tree and instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising
wherein each reaction substrate molecule of the newly-added AND nodes is obtained by calling a function in an open source library RDChiral, and in the function, an input parameter is a SMILES sequence of the reaction template and the product molecule, and an output is a list of the corresponding substrate molecules
and visually displaying the biosynthetic pathway for synthesizing a target molecule.
The additional elements of implemented by at least one processor and a memory storing a pre- constructed single-step reaction template prediction model, a pre-constructed AND-OR tree and instructions that, when executed by the at least one processor, cause the at least one processor to perform operations (Claim 1), nodes; wherein each reaction substrate molecule of the newly-added AND nodes is obtained by calling a function in an open source library RDChiral, and in the function, an input parameter is a SMILES sequence of the reaction template and the product molecule, and an output is a list of the corresponding substrate molecules; (claim 1) visually displaying the biosynthetic pathway for synthesizing a target molecule (Claim 1) at least one processor and a memory storing a pre-constructed single-step reaction template prediction model, apre-constructed AND-OR tree and instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising (Claim 8), wherein each reaction substrate molecule of the newly-added AND nodes is obtained by calling a function in an open source library RDChiral, and in the function, an input parameter is a SMILES sequence of the reaction template and the product molecule, and an output is a list of the corresponding substrate molecules (claim 8), and visually displaying the biosynthetic pathway for synthesizing a target molecule. (claim 8) merely invoke a computer as a tool and does not improve the computer (see MPEP 2106.04(d) and 2106.05(a)), and/or amount to insignificant extra-solution data gathering activity.
The additionally recited elements merely invoke a computer as a tool, and/or amount to insignificant extra-solution data gathering activity, and as such, when all limitations in claims 1-4, 7-8 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 1-4, 7-8 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.
The additional elements recited in claims 1-4, 7-8 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 implemented by at least one processor and a memory storing a pre- constructed single-step reaction template prediction model, a pre-constructed AND-OR tree and instructions that, when executed by the at least one processor, cause the at least one processor to perform operations (Claim 1), nodes; wherein each reaction substrate molecule of the newly-added AND nodes is obtained by calling a function in an open source library RDChiral, and in the function, an input parameter is a SMILES sequence of the reaction template and the product molecule, and an output is a list of the corresponding substrate molecules; (claim 1) visually displaying the biosynthetic pathway for synthesizing a target molecule (Claim 1) at least one processor and a memory storing a pre-constructed single-step reaction template prediction model, a pre-constructed AND-OR tree and instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising (Claim 8), wherein each reaction substrate molecule of the newly-added AND nodes is obtained by calling a function in an open source library RDChiral, and in the function, an input parameter is a SMILES sequence of the reaction template and the product molecule, and an output is a list of the corresponding substrate molecules (claim 8), and visually displaying the biosynthetic pathway for synthesizing a target molecule. (claim 8) are conventional and merely invoke a computer as a tool used in the recited judicial exceptions (see MPEP 2106.05(a)), and/or amount to insignificant extra-solution data gathering activity. Evidence for conventionality is shown by González-Medina et al. (González-Medina et al. Open Chemoinformatic Resources to Explore the Structure, Properties and Chemical Space of Molecules. RSC Advances 2017, 7 (85), 54153–54163) which is a review for different chemoinformatic tools including RDKIT and various visualization modules (pg. 54160, col. 2 paragraph 2)
When taken alone, all additional elements in claims 1-4, 7-8 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 1-9 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 101
Applicant states “The amended claim 1 provides a method for bioretrosynthetic prediction based on AND-OR tree and single-step reaction template prediction model, which is used to predict reaction through the single-step reaction template prediction model without complicated parameter settings such as thermodynamics, cofactors, and enzyme performance or complex domain knowledge, so as to improve the efficiency for selecting the biosynthetic pathway for synthesizing a target molecule, and plan this biosynthetic pathway based on the AND-OR tree search, so as to select the metabolic reactions most likely to participate in the biosynthetic pathway, thus improving the actual availability of the generated biosynthetic pathway.” The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 (2012) ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same)
The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand."); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016) (holding that claims to a mental process of "translating a functional description of a logic circuit into a hardware component description of the logic circuit" are directed to an abstract idea, because the claims "read on an individual performing the claimed steps mentally or with pencil and paper"). Mental processes performed by humans with the assistance of physical aids such as pens or paper are explained further below with respect to point B.
Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘‘[W]ith the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016) (holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer"). Mental processes recited in claims that require computers are explained further below with respect to point C.
Because both product and process claims may recite a "mental process", the phrase "mental processes" should be understood as referring to the type of abstract idea, and not to the statutory category of the claim. The courts have identified numerous product claims as reciting mental process-type abstract ideas, for instance the product claims to computer systems and computer-readable media in Versata Dev. Group. v. SAP Am., Inc., 793 F.3d 1306, 115 USPQ2d 1681 (Fed. Cir. 2015). This concept is explained further below with respect to point D.
• a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)
claims to "comparing BRCA sequences and determining the existence of alterations," where the claims cover any way of comparing BRCA sequences such that the comparison steps can practically be performed in the human mind, University of Utah Research Foundation v. Ambry Genetics, 774 F.3d 755, 763, 113 USPQ2d 1241, 1246 (Fed. Cir. 2014)
3. Using a computer as a tool to perform a mental process. An example of a case in which a computer was used as a tool to perform a mental process is Mortgage Grader, 811 F.3d. at 1324, 117 USPQ2d at 1699. The patentee in Mortgage Grader claimed a computer-implemented system for enabling borrowers to anonymously shop for loan packages offered by a plurality of lenders, comprising a database that stores loan package data from the lenders, and a computer system providing an interface and a grading module. The interface prompts a borrower to enter personal information, which the grading module uses to calculate the borrower’s credit grading, and allows the borrower to identify and compare loan packages in the database using the credit grading. 811 F.3d. at 1318, 117 USPQ2d at 1695. The Federal Circuit determined that these claims were directed to the concept of "anonymous loan shopping", which was a concept that could be "performed by humans without a computer." 811 F.3d. at 1324, 117 USPQ2d at 1699. Another example is Berkheimer v. HP, Inc., 881 F.3d 1360, 125 USPQ2d 1649 (Fed. Cir. 2018), in which the patentee claimed methods for parsing and evaluating data using a computer processing system. The Federal Circuit determined that these claims were directed to mental processes of parsing and comparing data, because the steps were recited at a high level of generality and merely used computers as a tool to perform the processes. 881 F.3d at 1366, 125 USPQ2d at 1652-53.
Examiner asserts applicant is merely using a computer to facilitate an abstract idea. This can be done by any skilled chemist on pencil or paper. There is no requirement to actually synthesize any molecule or to make anything. It is simply collecting, analyzing and displaying chemical data. There is no improvement to a general computer or particular machine nor is it integrated into a practical application. The present invention, under the broadest reasonable interpretation, just makes and displays a prediction.
Examiner also asserts that Gaussian Naive Bayes classifier is math please see MPEP sections below.
It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a ‘‘process of organizing information through mathematical correlations’’ are directed to an abstract idea); and Bancorp Servs., LLC v. Sun Life Assurance Co. of Can. (U.S.), 687 F.3d 1266, 1280, 103 USPQ2d 1425, 1434 (Fed. Cir. 2012) (identifying the concept of ‘‘managing a stable value protected life insurance policy by performing calculations and manipulating the results’’ as an abstract idea) (MPEP 2106.04(a))
A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation (MPEP 2106.04(a))
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-4, 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (Chen et al. Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search, arxiv, 6/29/2020) in further view of Koch et al. (Koch et al. Reinforcement Learning for Bioretrosynthesis. ACS Synthetic Biology 2019, 9 (1), 157–168) in further view of Girsang et al. (Girsang, A. S.; Yunanda, R.; Syahputra, M. E.; Peranginangin, E. Convolutional Neural Network Using Res-Net For Organic and Anorganic Waste Classification. 2022 IEEE International Conference of Computer Science and Information Technology (ICOSNIKOM) 2022, 01–06.) in further view of Ishida et al. (Ishida, S.; Terayama, K.; Kojima, R.; Takasu, K.; Okuno, Y. Prediction and Interpretable Visualization of Retrosynthetic Reactions Using Graph Convolutional Networks. Journal of Chemical Information and Modeling 2019, 59 (12), 5026–5033). The italicized text corresponds to the instant claim limitations.
With respect to the limitations of Claims 1, Chen et al. teaches that based on the AND-OR tree representation, we propose an A*-like planning algorithm which is guided by a neural network learned from past retrosynthesis planning experiences. (pg. 2, col. 1, paragraph 5, bioretrosynthetic method based on an AND-OR tree and single-step reaction template prediction implemented by at least one processor and a memory storing a pre- constructed single-step reaction template prediction model, a pre-constructed AND-OR tree and instructions that, when executed by the at least one processor, cause the at least one processor to perform operations (Claim 1)) Chen et al. also teaches the search tree of PNS is an AND-OR tree T, where each AND node needs all its children to be proved, while OR node requires at least one to be satisfied. Each node u ∈ T is associated with a proof number pn(u) that defines the minimum number of leaf nodes to be proved in order to prove u. Similarly, the disproof number dn(u) finds the minimum number of leaf nodes needed to disprove u. With such definition, we can recursively define these numbers for internal nodes. The application of one-step retrosynthesis model B on molecule m can be represented using one block of AND-OR tree (denoted as AND-OR stump), with molecule node as ‘OR’ node and reaction node as ‘AND’ node. This is because a molecule m can be synthesized using any one of its children reactions (or-relation), and each reaction node requires all of its children molecules (and-relation) to be ready (pg. 3, col. 2, par. 2-3) and S(l: selecting an OR node from a pre-constructed AND-OR tree, and using a molecule corresponding to the OR node as a product molecule to be predicted; wherein the pre-constructed AND-OR tree comprises two types of nodes: AND node and OR node; the AND node represents a reaction template, and the OR node represents a molecule (Claim 1)) Chen et al. also teaches the use of probability scores in order to rank top-k predictions and a template-based MLP model for one-step retrosynthesis. Following literature, they formulate the one-step retrosynthesis as a multi-class classification problem, where given a molecule as product, the goal is to predict possible reaction templates. Reactants are obtained by applying the predicted templates to product molecule. There are in total ∼ 380K distinct templates. Throughout all experiments, we take the top-50 templates predicted by MLP model and apply them on each product to get corresponding reactant lists (pg. 7, col. 1, paragraph 2, S2: predicting k templates in a preset template set that are most likely to synthesize the product molecule using a pre-constructed single-step reaction template prediction model, forming a template set Top-k, and assigning a weight value ranged between 0 and 1 to each template (Claim 1)) After picking the node m with minimum cost estimation, they will expand the search tree with k one-step retrosynthesis proposals. Specifically, for each proposed retrosynthesis they create a reaction node, and for each molecule, they create a molecule node under the reaction node. This will create an AND-OR stump under node m. (pg. 4, col. 2 paragraph 5, S3: expanding the pre-constructed AND-OR tree, specifically comprising: adding each template in the Top-k as a new AND node to the AND-OR tree to obtain newly-added k AND nodes, and using the OR node selected in step S 1 as a parent node of the newly-added k AND nodes; adding each reaction substrate molecule corresponding to each newly-added AND node to the AND-OR tree as an OR node to obtain newly-added OR nodes, with the newly-added AND node as a parent node of the newly-added OR nodes (Claim 1)) Chen et al. also teaches that given a single target molecule t ∈ M and an initial set of molecules I ⊂ M, we are interested in synthesizing t via a sequence of chemical reactions using reactants that are from or can be synthesized by I. In this case, I corresponds to a set of molecules that are commercially available. (pg. 2, col. 2, Retrosynthesis planning, paragraph 1, S4: determining whether there is an AND node in step S3, wherein a substrate molecule corresponding to a child node of the AND node belongs to a known metabolite set: Sink- Compounds set; if there is an AND node, finding a biosynthetic pathway, stopping an iterative retrosynthetic process, and generating the biosynthetic pathway; and if there is no AND node, determining whether the maximum number of iterations has been reached; if the maximum number of iterations is reached, stopping the iterative retrosynthetic process; and if the maximum number of iterations is not reached, repeating steps S1 to S4 until a biosynthetic pathway is found or the maximum number of iterations is reached (Claim 1)).
With respect to the limitations of Claim 1, Chen et al. formulates the one-step retrosynthesis as a multi-class classification problem, where given a molecule as product, the goal is to predict possible reaction templates. Reactants are obtained by applying the predicted templates to product molecule. There are in total ∼ 380K distinct templates. Throughout all experiments, we take the top-50 templates predicted by MLP model and apply them on each product to get corresponding reactant lists. (pg. 7, col. 1, paragraph 2, step S2 comprises: S2.1: predicting a probability that all reaction templates in the preset template set are capable of producing an input product molecule using the constructed multi-classification model; and S2.2: selecting top k reaction templates with the highest probabilities to form the template set Top-k, and setting a weight value of each reaction template in the template set Top-k as a corresponding probability value (Claim 1))
With respect to the limitations of Claim 1, Chen et al. further extracts reaction templates with RDChiral for all reactions and discard those whose reactants cannot be obtained by applying reaction templates to their products (pg. 7, col. 1, paragraph 1, step S3, each reaction substrate molecule of the newly-added AND nodes is obtained by calling a function in an open source library RDChiral; and in the function, an input parameter is a SMILES sequence of the reaction template and the product molecule, and an output is a list of the corresponding substrate molecules (Claim 1), ).
With respect to the limitations of Claims 1, Chen et al. teaches a retrosynthetic tree diagram which includes visualization of a synthesis pathway (pg. 14, Figure 7, and visually displaying the biosynthetic pathway for synthesizing a target molecule. (Claim 1)).
With respect to the limitations of Claim 2, Chen et al. teaches the application of one-step retrosynthesis model B on molecule m can be represented using one block of AND-OR tree (denoted as AND-OR stump), with molecule node as ‘OR’ node and reaction node as ‘AND’ node. This is because a molecule m can be synthesized using any one of its children reactions (or-relation), and each reaction node requires all of its children molecules (and-relation) to be ready (pg. 3, col. 2, Represent retrosynthesis planning using AND-OR tree, paragraph 1, wherein in step Si: in the AND-OR tree, a root node and a leaf node each are an OR node, and an intermediate node is an AND node or an OR node; a child node of each AND node is an OR node, representing all substrate molecules in a reaction template; a child node of each non-leaf OR node in the AND-OR tree is an AND node; each AND node represents a reaction template capable of producing a molecule corresponding to a parent node of the AND node, and the root node of the AND-OR tree is a target molecule node of a biosynthetic pathway to be predicted; and an initially-constructed AND- OR tree comprises only one root node, corresponding to the target molecule of a biosynthetic pathway to be predicted (Claim 2)).
With respect to the limitations of Claim 3, Chen et al. assigns a value function to each node for reactions and molecules as well as probabilities of each reaction and gives a cost needed to synthesize each molecule. (pg. 5, col. 1, paragraph 3, wherein in step S1, each node of the AND-OR tree has a weight value; a weight value of the AND node is a weight value of a corresponding reaction template, indicating a prediction probability of the corresponding reaction template; and OR nodes other than the root node each have a weight value of the parent node except for the OR nodes, and the root node has a weight value of 1. (Claim 3))
With respect to the limitations of Claim 4, Chen et al. explains the frontier contains all the molecule nodes in T that haven’t been expanded before. Since they want to minimize the total cost of the final solution, an ideal option to expand next would be the molecule node which is part of the best synthesis plan. The selection of the frontier nodes is the cost equivalent of selecting the maximum probability, as minimizing cost is equivalent to maximizing probability (pg. 4, col. 2, paragraph 2, wherein in step S1, the selected OR node is a leaf node in the AND-OR tree that does not belong to the known Sink-Compounds set and has a maximum weight value; and if there are a plurality of the leaf nodes with the maximum weight value, one of the leaf nodes is selected randomly as the OR node (Claim 4)).
With respect to the limitations of Claim 7, Chen et al. teaches the search tree of PNS is an AND-OR tree T, where each AND node needs all its children to be proved, while OR node requires at least one to be satisfied. Each node u ∈ T is associated with a proof number pn(u) that defines the minimum number of leaf nodes to be proved in order to prove u. Similarly, the disproof number dn(u) finds the minimum number of leaf nodes needed to disprove u. This corresponds to true and false depending if the node is proved or unproved (pg. 3, col. 2, paragraph 2, wherein in step S4, a process of generating the biosynthetic pathway comprises the following steps: (1) checking whether each leaf node in the AND-OR tree is in the Sink-Compounds set; marking the leaf node in the Sink-Compounds as "true", and marking the leaf node not in the Sink- Compounds as "false";(2) for a non-leaf AND node in the AND-OR tree, marking the non-leaf AND node as "true" if and only if each child node of the non-leaf AND node is marked as "true", otherwise marking the non-leaf AND node as "false"; and for a non-leaf OR node in the AND-OR tree, marking the non-leaf OR node as "true" if and only if the non-leaf OR node comprises at least one child node marked as "true", otherwise marking the non-leaf OR node as "false"; and (3) if the root node is marked as "false", it indicates that no synthetic pathway has been found, outputting "No Solution"; otherwise, deleting all nodes marked as "false" in the AND-OR tree, and remaining a subtree representing a synthetic pathway of a target molecule (Claim 7)).
With respect to the limitations of Claims 8, Chen et al. teaches that based on the AND-OR tree representation, we propose an A*-like planning algorithm which is guided by a neural network learned from past retrosynthesis planning experiences. (pg. 2, col. 1, paragraph 5, A bioretrosynthetic system based on an AND-OR tree and single-step reaction template prediction, comprising at least one processor and a memory storing a pre-constructed single-step reaction template prediction model, apre-constructed AND-OR tree and instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising (Claim 1)) and the search tree of PNS is an AND-OR tree T, where each AND node needs all its children to be proved, while OR node requires at least one to be satisfied. Each node u ∈ T is associated with a proof number pn(u) that defines the minimum number of leaf nodes to be proved in order to prove u. Similarly, the disproof number dn(u) finds the minimum number of leaf nodes needed to disprove u. With such definition, we can recursively define these numbers for internal nodes. The application of one-step retrosynthesis model B on molecule m can be represented using one block of AND-OR tree (denoted as AND-OR stump), with molecule node as ‘OR’ node and reaction node as ‘AND’ node. This is because a molecule m can be synthesized using any one of its children reactions (or-relation), and each reaction node requires all of its children molecules (and-relation) to be ready (pg. 3, col. 2, par. 2-3 ,a retrosynthesis planning module used for selecting an OR node from a pre-constructed AND- OR tree, and using a molecule corresponding to the OR node as a product molecule to be predicted; wherein the pre-constructed AND-OR tree comprises two types of nodes, an AND node and the OR node; the AND node represents a reaction template, and the OR node represents a molecule) Chen et al. also teaches the use of probability scores in order to rank top-k predictions and a template-based MLP model for one-step retrosynthesis. Following literature, they formulate the one-step retrosynthesis as a multi-class classification problem, where given a molecule as product, the goal is to predict possible reaction templates. Reactants are obtained by applying the predicted templates to product molecule. There are in total ∼ 380K distinct templates. Throughout all experiments, we take the top-50 templates predicted by MLP model and apply them on each product to get corresponding reactant lists (pg. 7, col. 1, paragraph 2, a reaction template prediction module used for predicting k templates in a preset template set that are most likely to synthesize the product molecule using a pre-constructed single-step reaction template prediction model, forming a template set Top-k, and assigning a weight value ranged between 0 and 1 to each template (Claim 8)). Chen et al. also teaches that after picking the node m with minimum cost estimation, they will expand the search tree with k one-step retrosynthesis proposals. Specifically, for each proposed retrosynthesis they create a reaction node, and for each molecule, they create a molecule node under the reaction node. This will create an AND-OR stump under node m. (pg. 4, col. 2 paragraph 5, an AND-OR tree extension module used for expanding the pre-constructed AND-OR tree, specifically comprising: adding each template in the Top-k as a new AND node to the AND-OR tree to obtain newly-added k AND nodes, and using the OR node selected in the retrosynthesis planning module as a parent node of the newly-added k AND nodes; adding each reaction substrate molecule corresponding to each newly-added AND node to the AND-OR tree as an OR node to obtain newly-added OR nodes, with the newly-added AND node as a parent node of the newly- added OR nodes (Claim 8)). Chen et al. also teaches that given a single target molecule t ∈ M and an initial set of molecules I ⊂ M, we are interested in synthesizing t via a sequence of chemical reactions using reactants that are from or can be synthesized by I. In this case, I corresponds to a set of molecules that are commercially available. (pg. 2, col. 2, Retrosynthesis planning, paragraph 1, a biosynthetic pathway generation module used for determining whether there is an AND node in the AND-OR tree obtained by the AND-OR tree extension module, wherein a substrate molecule corresponding to a child node of the AND node belongs to a known metabolite set: Sink- Compounds set; if there is an AND node, finding a biosynthetic pathway, stopping an iterative retrosynthetic process, and generating the biosynthetic pathway; if there is no AND node, determining whether the maximum number of iterations has been reached; if the maximum number of iterations is reached, stopping the iterative retrosynthetic process; if the maximum number of iterations is not reached, repeating steps of the retrosynthesis planning module to the biosynthetic pathway generation module until a biosynthetic pathway is found or the maximum number of iterations is reached (Claim 8)).
With respect to the limitations of Claim 8, Chen et al. formulates the one-step retrosynthesis as a multi-class classification problem, where given a molecule as product, the goal is to predict possible reaction templates. Reactants are obtained by applying the predicted templates to product molecule. There are in total ∼ 380K distinct templates. Throughout all experiments, we take the top-50 templates predicted by MLP model and apply them on each product to get corresponding reactant lists. (pg. 7, col. 1, paragraph 2, step S2 comprises: S2.1: predicting a probability that all reaction templates in the preset template set are capable of producing an input product molecule using the constructed multi-classification model; and S2.2: selecting top k reaction templates with the highest probabilities to form the template set Top-k, and setting a weight value of each reaction template in the template set Top-k as a corresponding probability value (Claim 8))
With respect to the limitations of Claim 8, Chen et al. further extracts reaction templates with RDChiral for all reactions and discard those whose reactants cannot be obtained by applying reaction templates to their products (pg. 7, col. 1, paragraph 1, step S3, each reaction substrate molecule of the newly-added AND nodes is obtained by calling a function in an open source library RDChiral; and in the function, an input parameter is a SMILES sequence of the reaction template and the product molecule, and an output is a list of the corresponding substrate molecules, (Claim 8)).
With respect to the limitations of Claims 8, Chen et al. teaches a retrosynthetic tree diagram which includes visualization of a synthesis pathway (pg. 14, Figure 7, and visually displaying the biosynthetic pathway for synthesizing a target molecule. (Claim 8)).
Chen et al. does not explicitly teach
wherein the preset template set is constructed based on a metabolic reaction structure in a known metabolic reaction data set; (Claim 1)
wherein the preset template set is constructed based on a metabolic reaction structure in a known metabolic reaction data set (Claim 8)
wherein the pre-constructed single-step reaction template prediction model is a multi- classification model constructed by a Gaussian Naive Bayes classifier or a convolutional neural network ResNet50 based on the preset template set (claim 1 and claim 8)
However, these limitations were known at the time as taught by Koch et al. in view of Ishida et al. in view of Girsang et al.
With respect to the limitations of Claims 1 and 8, Koch et al. teaches that chemical similarity between query (applied on a new substrate) and the native chemical transformation has been used in various studies. They adapted the strategy that proceeds in a 2-step evaluation of the reaction. In a first step, before rule application, similarity between query and native substrates is calculated. After rule application, similarity between native and query products is also calculated. This allows accounting of similarity in a manner straightforward to use with mono component reaction rules. Using this metric allows us to select chemical reactions similar to the ones present in metabolic databases, increasing our chances that this predicted reaction can be catalyzed. (pg. 158, col. 1, paragraph 6) and that after validating biological relevance of predictions for metabolic engineering, we tested RetroPath RL on a larger data set. Their previous tool was tested on the LASER database that compiles successful metabolic engineering projects, completed with compounds taken from the Metabolic Engineering journal (pg. 160, col. 2, paragraph 2, wherein the preset template set is constructed based on a metabolic reaction structure in a known metabolic reaction data set; (Claim 1) wherein the preset template set is constructed based on a metabolic reaction structure in a known metabolic reaction data set (Claim 8)).
With respect to the limitations of Claims 1 and 8, Ishida et al. improvement of retrosynthetic reaction prediction and interpretability of the prediction. In this paper, we propose an interpretable prediction framework using graph convolutional networks (GCN) for retrosynthetic reaction prediction and integrated gradients (IG) for visualization of contributions to the prediction to address these challenges. As a result, from the viewpoint of balanced accuracies, our model showed better performances than the approach using an extended-connectivity fingerprint. Furthermore, IG-based visualization of the GCN prediction successfully highlighted reaction-related atoms. They use a CNN for retrosynthetic planning (abstract, wherein the pre-constructed single-step reaction template prediction model is a multi- classification model constructed by a Gaussian Naive Bayes classifier or a convolutional neural network ResNet50 based on the preset template set (claim 1 and claim 8)
With respect to the limitations of Claims 1 and 8, Girsang et al. teaches CNN-ResNet with efficient genetic algorithm (EGA) to optimize the hyperparameter. The EGA algorithm utilizes transfer learning techniques in its algorithm so that the optimization process on CNN can achieve unified accuracy values quickly (abstract, wherein the pre-constructed single-step reaction template prediction model is a multi- classification model constructed by a Gaussian Naive Bayes classifier or a convolutional neural network ResNet50 based on the preset template set (claim 1 and claim 8)
It would be obvious to a person having ordinary skill in the art to combine the retrosynthesis methods of Chen et al. with Koch et al. because both are in the same field of endeavor and are attempting to solve the same problem. Chen et al. with Koch et al. in order to improve the results. A person of ordinary skill in the art would understand how to put them together. Ishida et al. adds the use of a CNN architecture for improved retrosynthesis. It would be obvious to modify the machine learning architecture. Girsang et al. specifically adds the use of a ResNeet50 CNN architecture for a chemistry problem. A person of ordinary skill in the art would be motivated to try the number of finite architecture combinations that existed in machine learning predictions for chemical data.
There is a reasonable expectation of success because the method does not change and Kock et al. just suggests to use a different dataset with the method of Chen et al. while Ishida et al. adds the use of a CNN for retrosynthetic predictions and Girsang et al. specifically adds the use of a ResNeet50 CNN architecture. Each part works individually so they are expected to work when put together.
Response to Arguments 103
Applicant sufficiently amended the claims in order to overcome the previous rejection. The previous arguments are moot. Ishida et al. in view of Girsang et al. Badds the “neural network with a Gaussian Naive Bayes classifier or ResNet5O” The previous rejection is moot.
Conclusion
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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/C.H.B./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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