DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 09/09/2025 has been entered.
Office Action Overview
Claim Status
Canceled:
3, 4, and 6-8
Pending:
1, 2, 5, and 9
Withdrawn:
none
Examined:
1, 2, 5, and 9
Independent:
1, 9
Amended:
1, 9
New:
none
Allowable:
none
Objected to:
none
Rejections applied
Abbreviations
X
112/b Indefiniteness
PHOSITA
"a Person Having Ordinary Skill In The Art before the effective filing date of the claimed invention"
112/b "Means for"
BRI
Broadest Reasonable Interpretation
112/a Enablement,
Written description
CRM
"Computer-Readable Media" and equivalent language
112 Other
IDS
Information Disclosure Statement
X
102, 103
JE
Judicial Exception
X
101 JE(s)
112/a
35 USC 112(a) and similarly for 112/b, etc.
101 Other
N:N
page:line
Double Patenting
MM/DD/YYYY
date format
Priority
As detailed in the 05/22/2020 filing receipt, this application claims priority to as early as 03/18/2019, the filing date of parent Japanese application JP2019-050148. At this point in examination, all claims have been interpreted as being accorded this priority date.
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. See paper entered 06/24/2020.
Withdrawal/Revision of Objections/Rejections
In view of the amendment and remarks received 09/09/2025:
• The 101 rejection of claims 1, 2, 5, and 9 in the Office Action mailed 06/10/2025 is maintained with revision.
• The 103 rejection of claims 1, 2, 5, and 9 in the Office Action mailed 06/10/2025 is withdrawn and a new 103 rejection is put forth below..
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1, 2, 5, and 9 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims depending from rejected claims are rejected similarly, unless otherwise noted, and any amendments in response to the following rejections should be applied throughout the claims, as appropriate
In claim 1 line 40 and claim 9 line 37 the connection is unclear between the “calculating” and “synthetic pathway” in the recitation of “calculating the synthetic pathway”. It is not clear in what way a synthetic pathway, itself, would be calculated. It is suggested to amend to recite “calculating the synthetic pathway design”, to reflect the Specification 32:3. The claims will be interpreted as suggested to amend.
In claim 1 line 32-34 and claim 9 line 28-30, it is unclear what the conversion parameter is converting in the recitation, “the updated conversion parameter being fed back to execute the conversion of the first dispersedly represented numerical vector and the second dispersedly represented numerical vector”, because in the convert (converting in claim 9) step of claims 1 and 9, the conversion parameter converts first and second compound structure notation character strings into first and second dispersedly represented numerical vectors; the conversion parameter does not convert the first and second dispersedly represented numerical vectors into something else. It is suggested to amend to recite “the updated conversion parameter being fed back to execute the conversion of the first compound structure notation character string and second compound structure notation character string into the first dispersedly represented numerical vector and the second dispersedly represented numerical vector”. The claims will be interpreted as suggested to amend.
Claim 9 line 32 recites the limitation "the display" in the recitation of “displaying, on the display, a diagram…” There is insufficient antecedent basis for this limitation in the claim. It is suggested to delete the phrase “on the display” and amend to recite: “displaying
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, 2, 5, and 9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to one or more judicial exceptions without significantly more.
MPEP 2106 details the following framework to analyze Subject Matter Eligibility:
• Step 1: Are the claims directed to a category of statutory subject matter (a process, machine, manufacture, or composition of matter)? (see MPEP § 2106.03)
• Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e. an abstract idea, a law of nature, or a natural phenomenon? (see MPEP § 2106.04(a)). Note, the MPEP at 2106.04(a)(2) & 2106.04(b) further explains that abstract ideas and laws of nature are defined as:
• mathematical concepts, (mathematical formulas or equations, mathematical
relationships and mathematical calculations);
• certain methods of organizing human activity (fundamental economic practices
or principles, managing personal behavior or relationships or interactions between
people); and/or
• mental processes (procedures for observing, evaluating, analyzing/ judging and
organizing information).
• laws of nature and natural phenomena are naturally occurring principles/ relations that
are naturally occurring or that do not have markedly different characteristics compared to
what occurs in nature.
• Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application? (see MPEP § 2106.04(d))
• Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept? (see MPEP § 2106.05)
Regarding Step 1: Yes, claims 1, 2, 5, and 9 are directed to a related system and method, and therefore to a category of statutory subject matter. (See MPEP § 2106.03).
Regarding Step 2A, Prong One: Claims 1, 2, 5, and 9 recite judicial exceptions of mathematical concepts and mental processes as follows:
Independent claims 1 and 9 recite mental processes of: to convert, using a conversion parameter, respectively, a first and second compound structure notation character string into a first and second dispersedly represented numerical vector, both numerical vectors having two or more real number values as elements (also considered to inherently recite a mathematical concept, as generating dispersedly represented numerical vectors requires mathematical operations as described in the Specification, 14:25 through 15:12); and considering the information that the first (and second) compound structure notation character string indicates a first (and second) chemical structure of a first (and a second) compound among a plurality of compounds; to generate a biological reaction characteristic vector between the first dispersedly represented numerical vector and the second dispersedly represented numerical vector as a basis for the design (designing in claim 9) of the synthetic pathway for synthesizing a target substance; considering the information of the generated biological reaction characteristic vector indicates an enzymatic reaction; considering the information of the database of the generated biological reaction characteristic vector and previously generated biological reaction characteristic vectors of biological reactions; to calculate a similarity of biological reactions based on the biological reaction characteristic vectors of the biological reactions (also considered to inherently recite a mathematical concept, as calculating similarities requires mathematical operations as described in the Specification, 20:5-7); considering the information of associating the biological reaction characteristic vector of a same enzymatic reaction group with enzyme numbers; estimating an enzyme number indicating a biological reaction; considering the information of notation information indicating chemical structures of the plurality of compounds and a biological reaction characteristic vector of the enzymatic reaction group (also considered to inherently recite a mathematical concept, as discussed in Specification 20:1-10); updating and using the updated the conversion parameter to convert the first dispersedly represented numerical vector and the second dispersedly represented numerical vector, (also considered to inherently recite a mathematical concept, see Specification 24:5-7); and calculating the synthetic pathway for synthesizing a target substance on the basis of a maximum pathway number, a target compound, and/or an initial compound (also considered to recite a mathematical concept).
Independent claims 1 and 9 inherently recite mathematical concepts of: perform machine learning on the biological reaction characteristic vectors by associating a biological reaction characteristic vector of a same enzymatic reaction group with enzyme numbers (discussed at Specification 20:1-10); perform machine learning using notation information indicating chemical structures of the plurality of compounds and a biological reaction characteristic vector of the enzymatic reaction group (discussed at Specification 24:5-7, and 24:12-16) and plotting vector points in a two dimensional space (also considered a mental process).
Dependent claim 2 recites a mental process and inherent mathematical concept of: to set the numerical vectors as compound structure characteristic vectors with a fixed-dimensional vector having a plurality of real number values as elements (discussed at Specification 11:20-25, and 13:6-17); and a mental process of: considering the information that differences in structures of compounds are represented by differences in one or more of a plurality of real number values.
Dependent claim 5 recites the mental processes of: label at least two enzymatic reactions as one enzymatic reaction class; and an inherent mathematical concept of: perform machine learning (discussed at Specification 24:4-11 ).
To summarize Step 2A, Prong 1, the claims recite judicial exceptions in the form of abstract ideas, including both mental processes and mathematical concepts. Mental processes recited in claims 1, 2, 5, and 9 include, e.g., converting compound structure notation character strings into dispersedly represented numerical vectors having two or more real number values as elements using a conversion parameter; generating a biological reaction characteristic vector; calculating a similarity of biological reactions; etc. These limitations each include an embodiment which could be performed in the human mind, or with pen and paper, as there is nothing recited as to the complexity of converting strings to vectors, etc., which would preclude performing the steps in the human mind or with pen and paper. Additionally, mathematical concepts are recited throughout the claims, e.g., in converting strings to numerical vectors; calculating a similarity of biological reactions; performing machine learning; plotting vector points, calculating synthetic pathways, etc.; there are inherent mathematical concepts in the conversion to numerical data, data calculations, and machine learning, as discussed in the Specification. at p.11, p.13-15, p.20, and p.24. Such analysis performed mentally, or with paper and pencil, may take considerable time and effort, and although a general-purpose computer can perform these calculations at a rate and accuracy that can far exceed the mental performance of a skilled artisan, the nature of the activity is essentially the same, and therefore constitutes an abstract idea. (See MPEP 2106.04(a)(2)(I) and (III).)
Therefore, the claims recite elements that constitute a judicial exception in the form of abstract ideas. (Step 2A, Prong One: Yes.)
Regarding Step 2A, Prong Two: Because the claims do recite judicial exceptions, direction under Step 2A, Prong Two provides that the claims must be examined further to determine whether they integrate the abstract ideas into a practical application. A claim can be said to integrate a judicial exception into a practical application when it applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The Step 2A, Prong Two analysis is performed in order to determine if the abstract idea is integrated into a practical application [see MPEP § 2106.04(d) and (d)(I)] by analyzing the additional elements of the claim, alone or in combination, for the following considerations:
• an improvement to technology [see MPEP 2106.04(d) & (d)(1); and 2106.05(a)]
• a particular therapy/prophylaxis [see MPEP 2106.04(d) & (d)(2); and 2106.05(e)]
• a particular machine [see MPEP 2106.04(d) and 2106.05(b)]
• a particular transformation [see MPEP 2106.04(d) and 2106.05(c)]
• or for applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment [see MPEP 2106.04(d) and 2106.05(e)].
Additional elements of data gathering and outputting: Claims 1 and 9 recite the additional elements of storing data; and displaying data. Data gathering steps and outputting steps, to include data storage and display of data, are additional elements which perform functions of inputting, collecting, and outputting the data needed to carry out the abstract idea. These steps are considered insignificant extra-solution activity, and are not sufficient to integrate an abstract idea into a practical application as they do not impose any meaningful limitation on the abstract idea or how it is performed, nor do they provide an improvement to the field [see MPEP § 2106.04(d)(I) and 2106.05(g)].
Additional elements of computer components: Claims 1, 2, and 5 recite a processor; claims 1 and 9 recite a display; claim 1 recites a memory storing instructions. The claims require only generic computer components, which do not improve computer technology, and do not integrate the recited judicial exception into a practical application (see MPEP § 2106.04(d)(1) and MPEP § 2106.05(f)).
The claims have been further analyzed with respect to Step 2A, Prong Two, and no additional elements have been found, alone or in combination, that would integrate the judicial exception into a practical application. (Step 2A, Prong Two: No).
Step 2B analysis: Because the additional claim elements do not integrate the abstract idea into a practical application, the claims are further examined under Step 2B, which evaluates whether the additional elements, individually and in combination, amount to significantly more than the judicial exception itself by providing an inventive concept. An inventive concept is furnished by an element or combination of elements that is recited in the claim in addition to the judicial exception, and is sufficient to ensure that the claim, as a whole, amounts to significantly more than the judicial exception itself (see MPEP § 2106.05).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exceptions because the claims recite additional elements that are well-understood, routine, and conventional. Those additional elements are as follows:
Additional element of data gathering and outputting: The additional element of storing data and/or displaying data of claim 1 and 9 does not cause the claims to rise to the level of significantly more than the judicial exception. The courts have recognized receiving or transmitting data over a network; and storing and retrieving information in memory; [see MPEP§2106.05(d)(II)], as well- understood, routine, conventional activity when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity [see MPEP 2106.05(g)]. Therefore, the data storage and display steps are shown to be well-understood, routine, and conventional, and as a result, the additional element of data storage and display does not provide an inventive concept needed to amount to significantly more than the judicial exception.
Additional elements of computer components: Claims 1, 2, 5, and 9 recite the additional elements of a processor, a memory, and/or a display do not cause the claims to rise to the level of significantly more than the judicial exception; these are conventional computers, generically claimed and discussed at Specification 8:15-21, and 9:20 through 10:11. The processor, memory, and display do not cause the claims to rise to the level of significantly more than the judicial exception as they do not provide an inventive concept.
Further regarding the conventionality of additional elements, the MPEP at 2106.05(b) and 2106.05(d) presents several points relevant to conventional computers and data gathering steps in regard to Step 2A Prong 2 and Step 2B, including:
• A general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions, does not qualify as a particular machine (see 2106.05(b)(I)), as in the case of claim 1, 2, 5, and 9, which are interpreted to recite conventional computer components.
• Integral use of a machine to achieve performance of a method may integrate the recited judicial exception into a practical application or provide significantly more, in contrast to where the machine is merely an object on which the method operates, which does not integrate the exception into a practical application or provide significantly more (see 2106.05(b)(II). In the instant claims, the recited processor and memory are used in converting compound structure character notation strings, generating biological reaction characteristic vectors, calculating similarities of biological reactions, performing machine learning, etc.; as such, the processor and memory act only as a tool to perform the steps of data analysis, and do not integrate the exception into a practical application or provide significantly more.
• Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not integrate a judicial exception or provide significantly more (see 2106.05(b)(III). The processor and memory of claim 1 used in performing data analysis does not impose meaningful limitations on the claims.
• The courts have recognized “receiving or transmitting data over a network”, “performing repetitive calculations”, and “storing and retrieving information in memory”, as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (see MPEP 2106.05(d)(II)). The storing of data in claim 1 is recited in a generic manner.
All limitations of claims 1, 2, 5, and 9 have been analyzed with respect to Step 2B, and none provides a specific inventive concept, as they all fail to rise to the level of significantly more than the identified judicial exception, and thus do not transform the judicial exception into a patent eligible application of the exceptions. Step2B: NO. Therefore, the claims, when the limitations are considered individually and as a whole, are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Response to Applicant Arguments - 35 USC § 101
The Applicant's arguments filed 09/09/2025 have been fully and respectfully considered but they are not persuasive.
Regarding Step 2A Prong Two, the Applicant asserts, p.8-13 of 20, that claim 1 sets forth an improvement to technology (p.8), and discusses the technical problem concerning biological reaction structure data in predicting biological reactions (p.8). The Applicant further asserts the improvement is recited in the claims…for example, claim 1 recites…perform machine learning,…update the conversion parameter,…display a diagram…(with the vectors) plotted as points…calculate the synthetic pathway…and store the calculated synthetic pathway in the database…(p.11-12). The Applicant asserts use of specifically adapted biological reaction characteristic vectors allows compound structures and their biological reactions to be processed efficiently and unambiguously, thereby providing a reliable basis and input to an improved design process for synthetic pathways (p.12); the Applicant notes the potential technical effect solves the technical problem of improving the efficiency and accuracy of the design of synthetic pathways, going beyond the alleged abstract idea and beyond using a computer as a tool (p.12). The Applicant asserts claim 1 is not directed to an abstract idea because claim 1 includes additional elements that integrate the abstract idea into a practical application, …and the additional elements apply the abstract idea in a meaningful way by improving prediction of biological reactions in synthetic pathway design (p.13).
The argument is not persuasive, respectfully, because there has not yet been shown an improvement to technology. At present, the improvement to the claimed method of generating vectors, estimating enzymes, and using machine learning may represent an improvement to the abstract idea of data analysis, but does not yet show an improvement to technology (See MPEP 2106.05(a) and 2106.04(d)). It is important to keep in mind that an improvement in the abstract idea itself is not an improvement in technology [see MPEP 2106.05(a)(II)]. Further, the additional elements of data gathering and generic computer components are not sufficient to integrate the judicial exception into a practical application, or provide significantly more, even when considering the claim as a whole, and as such there is not yet considered to be a practical application recited at Step 2A Prong Two of the 101 analysis.
Regarding Step 2B, the Applicant asserts, p.13-15 of 20, that claim 1 recites meaningful unconventional elements that amount to significantly more than the alleged abstract idea, and states that an inventive concept can be found in the non-conventional and non-generic arrangement of the features of the claims, while pointing to BASCOM Global Internet Services, Inc., v. AT&T Mobility LLC, AT&T Corp., and Ancora Techs. v. HTC Am. The Applicant further asserts, p.15, that even if claim 1 includes an abstract idea, the claim includes additional elements that singly and as an ordered combination amount to significantly more than the mere abstract idea.
The argument is not persuasive because the additional elements amount to either insignificant extra solution activity of data gathering and outputting, or to conventional computer components, and as such do not provide an improvement to computer technology or other technology; there is no single or ordered combination of these additional elements that would provide significantly more, even when the claims are considered as a whole. It is also noted that the fact pattern differs between BASCOM (method and system for allowing an ISP to perform content filtering of information retrieved from the Internet) and the instant application (method for processing biological reaction information using structure notation and vectors to estimate enzyme numbers indicating biological reactions), and between Ancora (method of restricting software operation within a license limitation), and the instant application. Additionally, the judicial exceptions identified at Step 2A, Prong One are not integrated into a practical application at Step 2A, Prong Two; still further, the claims do not recite additional elements that amount to significantly more than the judicial exceptions at Step 2B when those additional elements are evaluated individually and in combination to determine whether they contribute an inventive concept.
Claim Rejections - 35 USC § 103
The rejection of claims 1, 2, 5, and 9 in the Office Action mailed 06/10/2025, as being unpatentable over Mallory in view of Fooshee, is withdrawn in view of the amendment and remarks received 09/09/2025.
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.
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, 2, 5, and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Mallory, (In PACIFIC SYMPOSIUM ON BIOCOMPUTING 2018: Proceedings of the Pacific Symposium, pages 56-67 (2018), cited on the IDS received 13 July 2022), in view of “k-means clustering.”, Wikipedia, The Free Encyclopedia, Wikimedia Foundation, 03/17/2019, (https://web.archive.org/web/20190317055759/https://en.wikipedia.org/wiki/K-means_clustering; cited on the attached Form PTO-892)), pages 1-12; cited on the attached Form PTO-892) (hereafter referred to as “k-means clustering document”).
Independent claims 1 and 9 are respectively directed to a biological info processing system (comprising a processor, memory, and display) and method to (note: bold added by Examiner for ease of organization): convert first and second compound structure notation character strings respectively indicating first and second chemical structures of first and second compounds among a plurality of compounds into first and second dispersedly represented numerical vectors each of two or more real numbers, using a conversion parameter; generate a biological reaction characteristic vector, indicating an enzymatic reaction, between the first and second dispersedly represented numerical vectors as a basis for the design (designing a synthetic pathway in claim 9) of a synthetic pathway for synthesizing a target substance; and store the generated biological reaction characteristic vector with previously generated biological reaction characteristic vectors of reactions in a database; calculate a similarity of biological reactions based on biological reaction characteristic vector of the biological reaction; perform machine learning on the biological reaction characteristic vectors by associating a biological reaction characteristic vectors of a same enzymatic reaction group with enzyme numbers, and estimate an enzyme number indicating a biological reaction; and perform machine learning using notation information indicating chemical structures of the plurality of compounds and a biological reaction characteristic vector of the enzymatic reaction group, and update the conversion parameter, the updated conversion parameter being fed back to execute the conversion of the first and the second dispersedly represented numerical vectors; display a diagram with the biological reaction characteristic vector, and the first and the second dispersedly represented numerical vectors are plotted as points in a two dimensional space, and calculate the synthetic pathway for synthesizing a target substance on the basis of at least one user-set condition including a maximum pathway number, a target compound, and/or an initial compound, and store the calculated synthetic pathway in the database. (Bold emphasis added by examiner)
Dependent claim 2 further recites to set the numerical vectors as compound structure characteristic vectors with a fixed dimensional vector having real number values, and differences in structures of compounds are represented by differences in one or more real number values. Dependent claim 5 further recites to: virtually label two or more enzymatic reactions as one enzymatic reaction class, and perform machine learning.
Regarding claims 1, 2, and 9, Mallory teaches a pipeline for constructing a vector space for chemical reactions; this pipeline includes data processing (inherently showing a memory and a processor), vector space construction and characterization, and chemical reaction and drug querying (p.58). Mallory teaches the pipeline takes SMILES (i.e., compound structure notation character strings) as input (i.e., generates molecular fingerprints, and embeds the molecular fingerprints using kernel principal component analysis; and storage of each compound using MACCS keys (i.e., real numbers) to encode (i.e., convert using a conversion parameter) molecular structure in a condensed bit vector (i.e., of real numbers) (i.e. dispersedly represented numerical vectors); and teaches their Transformed Vector Space Matrix (TVSM), had final dimensions of 11,893 by 8 (p.59). Mallory teaches for each drug-metabolite pair, construction of a difference vector by subtracting the drug vector from the metabolite vector, and computing the similarity between the drug transformation vector and each reaction difference vector in the dataset (p.61, paragraph 1). (This shows converting compound structure notation character strings into numerical vectors of real number values using a conversion parameter of claims 1 and 9, and vectors with real number values where differences in structure are represented by difference in real numbers of claim 2).
Further regarding claims 1 and 9, Mallory teaches “a pipeline for comparing and characterizing chemical transformations using continuous vector representations of molecular structure learned using unsupervised representation learning” (i.e., K-means clustering)(abstract, p.56). Mallory teaches constructing a vector for each reaction (i.e., a biological reaction characteristic vector) by subtracting vector A from vector B in TVSM (Transformed Vector Space Matrix); and teaches a pipeline for querying reactions for drug-metabolite pairs; for each drug-metabolite pair, they subtract the drug vector from the metabolite vector to construct a difference vector. This was repeated for all reactions in the dataset to create a ranked list (i.e., a database) of reactions most similar to the original drug metabolite (i.e., target substance) query (p.60, text and fig.2) Mallory teaches that to evaluate the effectiveness of reaction clusters created using KMeans, they performed an enrichment analysis to characterize clusters by enzymes that catalyze the reactions, to glean what reaction types were characteristic of each cluster using enzymes as a proxy for the reaction type. The analyses was performed using data from MetaCyc: both unigram and bigram enzyme names, Enzyme Consortium (EC) class numbers, and Gene Ontology (GO) codes for Molecular Function (p.60). Mallory teaches results from enrichment analysis on select clusters (table 1, p.63) (including Enzyme Consortium (EC) numbers and GO enrichment) from KMeans clustering (e.g., estimate an enzyme number), such that “cluster 20 mapped to GO:0047893, which is consistent with EC number 2.4.1 hexosyltransferase and cluster 23 mapped to GO:0008934 inositol monophosphate 1-phosphatase activity, which is consistent with EC number 3.1.3” (lower half of p.62). Mallory teaches “(One) can use this technique to detect the transformation pathway from one compound to another, based on enzyme vectors. This automatic construction (i.e., designing) of drug-related pathways would aid current manual curation efforts for pathway construction at drug databases like PharmGKB37 and provide an initial automatically constructed pathway for other users that do not require a high quality curated pathway for their work”, (top of p.66). Mallory teaches computing the similarity between the drug transformation vector and each reaction difference vector in their dataset, resulting in a ranked list of all reactions for each drug-metabolite pair based on similarity between the drug and reaction difference vectors (p.61) Mallory also teaches calculating a Tanimoto similarity (aka Jaccard index) as the kernel function for kernel PCA using the molecular fingerprints.(p.59). (Showing generating a biological reaction characteristic vector indicating an enzymatic reaction for pathway design (designing) to synthesize target substances; calculating a similarity of biological reactions and associating biological reaction characteristic vectors of same enzymatic group with enzyme numbers, and estimating an enzyme number indicating a biological reaction of claims 1 and 9).
Regarding the claim 1 and 9 steps of “display, on the display, a diagram in which the biological reaction characteristic vector, the first dispersedly represented numerical vector, and the second dispersedly represented numerical vector are plotted as points in a two or three-dimensional space” and “calculating the synthetic pathway”, Mallory teaches displaying of compounds (i.e., dispersedly represented numerical vectors of target and initial compounds) by 2D and 3D plotting, respectively, in fig.3A and 3B (p.61); and 2D displaying of reaction level vectors (i.e., biological reaction characteristic vectors) in fig. 5A and 5B (p.62).
Mallory teaches in evaluating the effectiveness of reaction clusters created using KMeans, performing an enrichment analysis to characterize clusters by enzymes that catalyze the reactions (i.e., labelling enzymatic reactions) (p.60), (showing virtual labeling of enzymatic reactions of claim 5).
Regarding machine learning and/or to update a conversion parameter of claim 1, 5, and 9, Mallory teaches using “unsupervised representation learning” (p.56, paragraph 1) (i.e., machine learning); and application of KMeans clustering to reaction vectors constructed using MetaCyc reactions, with construction of vectors for each reaction by subtracting vector A from vector B from TVSM for all reactions A → B in react_list (p.60, section 2.3.2). Because Mallory employs K-Means clustering, it would be inherent that updating weight/parameters occurs, as typically, K-means clustering algorithms, such as is used in Mallory, inherently use an iterative refinement technique that has an assignment step and an update step, which would include updating of parameters, shown by ”k-means clustering document” (p.1-3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the features of Mallory, for a pipeline that uses condensed bit vectors encoded from SMILES notation information as input, and the resulting vector space matrix, Transformed Vector Space Matrix (TVSM), in constructing a vector space for chemical reactions, constructing difference vectors, characterizing the reaction clusters created using KMeans into (predicted) enzyme numbers, displaying compound and reaction vector points in a display, with inherent updating of parameters as shown in the “k-means clustering document”. This is because Mallory states their method shows meaningful clusters for molecules and reactions in the transformed vector space based on chemical similarity, and that this data can be used to understand drug metabolism (p.66). The method of Mallory, which includes the K-means clustering algorithm, would inherently include an iterative refinement technique that has an assignment step and an update step, as the “k-means clustering document” states the common (k-means clustering) algorithm uses an iterative refinement technique (p.2), and k-means clustering is a popular method for cluster analysis (p.1). One would have had a reasonable expectation of success in combining because Mallory includes use of the K-means clustering algorithm as in the “k-means clustering document”, and as such, the combination would have been obvious.
Response to Arguments - Claim Rejections under 35 USC § 103
The Applicant's arguments filed 09/09/2025 have been fully and respectfully considered but they are not persuasive.
The Applicant asserts, on p.17-18 of 20, that Mallory does not disclose "display, on the display, a diagram in which the biological reaction characteristic vector, the first dispersedly represented numerical vector, and the second dispersedly represented numerical vector are plotted as points in a two or three- dimensional space, and, where applicable, one or more lines representing reaction pathways."
The argument is not persuasive because regarding the claim 1 and 9 steps of “display, on the display, a diagram in which the biological reaction characteristic vector, the first dispersedly represented numerical vector, and the second dispersedly represented numerical vector are plotted as points in a two or three-dimensional space”, Mallory teaches displaying of compounds (i.e., dispersedly represented numerical vectors of target and initial compounds) by 2D and 3D plotting, respectively, in fig.3A and 3B (p.61); and 2D displaying of reaction level vectors (i.e., biological reaction characteristic vectors) in fig. 5A and 5B (p.62).
The Applicant asserts, on p.18-19 of 20, that Mallory does not disclose machine learning or use of a conversion parameter, and reiterates the previous Office Action, which states “Mallory does not explicitly show machine learning and/or to adjust (to update) a conversion parameter”, and that this deficiency is not cured by Fooshee (p.18). The Applicant further asserts, p.18, paragraph 4, none of the documents considers the use of machine learning and back-propagation techniques for improving the simulation and processing of biological reactions. The Applicant additionally asserts, p.19, paragraph 1, that Fooshee does not disclose perform machine learning…, and update the conversion parameter, the updated conversion parameter being fed back to execute the conversion of the first dispersedly represented numerical vector and the second dispersedly represented numerical vector," as set forth in claim 1.
The argument is not persuasive because: Regarding the instant 103 rejection, Fooshee is not included in the instant 103 rejection; however, Mallory and the “k-means clustering document” are relied upon for teaching machine learning and updating of a conversion parameter of claim 1, 5, and 9, as Mallory teaches using “unsupervised representation learning” (p.56, paragraph 1); and application of KMeans clustering to reaction vectors constructed using MetaCyc reactions, with construction of vectors for each reaction by subtracting vector A from vector B from TVSM for all reactions A → B in react_list (p.60, section 2.3.2). Because Mallory employs KMeans clustering, it would be inherent that updating weight/conversion parameters occurs, as typically K-means clustering algorithms, such as is used in Mallory, inherently use an iterative refinement technique that has an assignment step and an update step, which would include updating of parameters, as shown by the “k-means clustering document”. Additionally, regarding the term back-propagation, this term is not recited in the claims or in the Specification.
Conclusion
No claim is allowed.
This Office action is a Non-Final action. A shortened statutory period for reply to this action is set to expire THREE MONTHS from the mailing date of this action.
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/M.A.V./Examiner
Art Unit 1687
/G. STEVEN VANNI/Primary patents examiner
Art Unit 1686