DETAILED ACTION
The Applicant’s filing, received 22 November 2022, has been fully considered. The following rejections and/or objections constitute the complete set presently being applied to the instant application.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1-20 are pending.
Claims 1-20 are rejected.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
This application is a CON of PCT/CN2022/078336, filed 28 February 2022,
which claims benefit of foreign application CHINA 202110260343.1, filed 10 March 2021.
Therefore, the effective filing date of the claimed invention is 10 March 2021.
Information Disclosure Statement
The information disclosure statements (IDS) received 30 January 2023, 03 October 2023, 08 August 2024, and 25 March 2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements have been considered by the examiner.
Drawings
The drawings received 22 November 2022 are objected to, as noted below.
The drawings are objected to for failing to comply with 37 C.F.R. 1.84(t) because:
the sheets of drawings should be numbered in consecutive Arabic numerals, starting with 1, and the number of each sheet should be shown by two Arabic numerals placed on either side of an oblique line, with the first being the sheet number and the second being the total number of sheets of drawings, with no other marking.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea 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: Step 1 of the eligibility analysis asks: Is the claim to a process, machine, manufacture or composition of matter?
Claims 1-13 recite a method for processing molecular scaffold transitions (i.e., a process); claims 14-17 recite an electronic device comprising one or more processors and a memory (i.e., a machine and/or a manufacture); and claims 18-20 recite a non-transitory computer-readable storage medium (i.e., a machine and/or a manufacture).
Therefore, these claims are encompassed by the categories of statutory subject matter, and thus, satisfy the subject matter eligibility requirements under step 1.
[Step 1: YES]
Eligibility Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception.
Eligibility Step 2A Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim.
Independent claim 1 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
generating, according to a connection graph structure corresponding to a reference drug molecule, an atomic latent vector corresponding to the reference drug molecule (i.e., mental processes and mathematical concepts);
performing atom masking processing on the atomic latent vector to obtain a scaffold latent vector and a sidechain latent vector included in the atomic latent vector (i.e., mental processes and mathematical concepts);
generating a target scaffold latent vector with a target transition degree between the scaffold latent vector and the target scaffold latent vector according to a spatial distribution of the scaffold latent vector (i.e., mental processes and mathematical concepts); and
generating a transitioned drug molecule according to the target scaffold latent vector and the sidechain latent vector (i.e., mental processes and mathematical concepts, e.g., the generated molecule could be represented as a data string, e.g., a SMILES string, or as graph data in the form of a set of mathematical matrices).
Independent claim 14 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
generating, according to a connection graph structure corresponding to a reference drug molecule, an atomic latent vector corresponding to the reference drug molecule (i.e., mental processes and mathematical concepts);
performing atom masking processing on the atomic latent vector to obtain a scaffold latent vector and a sidechain latent vector included in the atomic latent vector (i.e., mental processes and mathematical concepts);
generating a target scaffold latent vector with a target transition degree between the scaffold latent vector and the target scaffold latent vector according to a spatial distribution of the scaffold latent vector (i.e., mental processes and mathematical concepts); and
generating a transitioned drug molecule according to the target scaffold latent vector and the sidechain latent vector (i.e., mental processes and mathematical concepts, e.g., the generated molecule could be represented as a data string, e.g., a SMILES string, or as graph data in the form of a set of mathematical matrices).
Independent claim 18 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
generating, according to a connection graph structure corresponding to a reference drug molecule, an atomic latent vector corresponding to the reference drug molecule (i.e., mental processes and mathematical concepts);
performing atom masking processing on the atomic latent vector to obtain a scaffold latent vector and a sidechain latent vector included in the atomic latent vector (i.e., mental processes and mathematical concepts);
generating a target scaffold latent vector with a target transition degree between the scaffold latent vector and the target scaffold latent vector according to a spatial distribution of the scaffold latent vector (i.e., mental processes and mathematical concepts); and
generating a transitioned drug molecule according to the target scaffold latent vector and the sidechain latent vector (i.e., mental processes and mathematical concepts, e.g., the generated molecule could be represented as a data string, e.g., a SMILES string, or as graph data in the form of a set of mathematical matrices).
Dependent claims 2-13, 15-17, 19 and 20 further recite the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas, as noted below.
Dependent claim 2 further recites:
a node in the connection graph structure represents an atom in the reference drug molecule (i.e., mental processes and mathematical concepts); and
generating the atomic latent vector (i.e., mathematical concepts) comprises:
determining node information of each node in the connection graph structure through a graph encoder according to a node feature and a side feature included in the connection graph structure, the node feature representing an atomic feature in the reference drug molecule, and the side feature representing a feature between atoms in the reference drug molecule (i.e., mathematical concepts);
generating latent vectors of each node according to the node information of each node and node features of each node (i.e., mathematical concepts); and
generating the atomic latent vector corresponding to the reference drug molecule according to the latent vectors of each node and the atom included in the reference drug molecule (i.e., mathematical concepts).
Dependent claim 3 further recites:
the graph encoder comprises a plurality of cascaded hidden layers (i.e., mathematical concepts); and
determining the node information of each node in the connection graph structure (i.e., mathematical concepts) comprises:
in accordance with (i) a node feature of a first node in the connection graph structure, (ii) a node feature of a second node in the connection graph structure, and (iii) side information between the first node and another node in a first hidden layer, determining information between the first node and the second node in a second hidden layer, wherein the first node is any node in the connection graph structure, the second node is any neighbor node of the first node in the connection graph structure, the another node is a neighbor node of the first node and excludes the second node, and the second hidden layer is a hidden layer next to the first hidden layer (i.e., mathematical concepts);
determining side information between the first node and the second node in the second hidden layer according to the side information between the first node and the another node in the first hidden layer and the information between the first node and the second node in the second hidden layer, wherein side information between two nodes in the connection graph structure in an initial hidden layer is obtained according to a node feature of one of the two nodes and a side feature between the two nodes (i.e., mathematical concepts); and
summing side information corresponding to each node in the plurality of hidden layers to obtain the node information of each node (i.e., mathematical concepts).
Dependent claim 4 further recites:
determining a bit vector corresponding to the reference drug molecule, the bit vector having a length that corresponds to a number of atoms comprised in the reference drug molecule, and a bit value corresponding to a scaffold atom in the bit vector has a first value (i.e., mental processes and mathematical concepts); and
filtering an atomic latent vector corresponding to the reference drug molecule according to the bit vector to obtain a latent vector of the scaffold atom and a latent vector of the sidechain atom (i.e., mental processes and mathematical concepts); and
performing multi-head attention processing on the latent vector of the scaffold atom to obtain the scaffold latent vector (i.e., mathematical concepts); and
performing multi-head attention processing on the latent vector of the sidechain atom to obtain the sidechain latent vector (i.e., mathematical concepts).
Dependent claim 5 further recites:
determining a first distance between the scaffold latent vector and the cluster centers of each scaffold cluster (i.e., mental processes and mathematical concepts);
determining a target scaffold cluster to which a scaffold of the reference drug molecule belongs according to the first distance (i.e., mental processes and mathematical concepts); and
determining a Gaussian mixture distribution to which the scaffold latent vector belongs according to the cluster center of the target scaffold cluster (i.e., mental processes and mathematical concepts).
Dependent claim 6 further recites:
performing random sampling processing on the target scaffold cluster according to the target transition degree to obtain an offset corresponding to the target transition degree (i.e., mental processes and mathematical concepts); and
adding the scaffold latent vector and the offset corresponding to the target transition degree to obtain the target scaffold latent vector (i.e., mental processes and mathematical concepts).
Dependent claim 7 further recites:
multiplying, when the target transition degree is a first transition degree, a variance of the target scaffold cluster and a first vector obtained by random sampling to obtain a first offset (i.e., mental processes and mathematical concepts); and
using the first offset as an offset corresponding to the first transition degree, the first transition degree representing scaffold crawling (i.e., mental processes and mathematical concepts).
Dependent claim 8 further recites:
selecting a first scaffold cluster from the plurality of scaffold clusters when the target transition degree is a second transition degree, wherein a distance between the first scaffold cluster and the cluster center of the target scaffold cluster is less than or equal to a first set value and the second transition degree represents scaffold hopping (i.e., mental processes and mathematical concepts); and
generating a second offset according to a product of the variance of the first scaffold cluster and a second vector obtained by random sampling, the cluster center of the target scaffold cluster, and the cluster center of the first scaffold cluster (i.e., mental processes and mathematical concepts); and
using the second offset as an offset corresponding to the second transition degree (i.e., mental processes and mathematical concepts).
Dependent claim 9 further recites:
selecting a second scaffold cluster from the plurality of scaffold clusters when the target transition degree is a third transition degree, a distance between the second scaffold cluster and the cluster center of the target scaffold cluster being greater than or equal to a second set value (i.e., mental processes and mathematical concepts); and
generating a third offset according to a product of a variance of the second scaffold cluster and a third vector obtained by random sampling, a cluster center of the target scaffold cluster, and the cluster center of the second scaffold cluster, and using the third offset as an offset corresponding to the third transition degree (i.e., mental processes and mathematical concepts).
Dependent claim 10 further recites:
generating the transitioned drug molecule according to the target scaffold latent vector, the sidechain latent vector, the target and the target activity value of the specified reference drug molecule (i.e., mathematical concepts).
Dependent claim 11 further recites:
performing molecular filtration processing of physicochemical property according to the transitioned drug molecule to obtain a drug-like drug molecule (i.e., mental processes and mathematical concepts);
docking the drug-like drug molecule to the eutectic structure (i.e., mathematical concepts);
removing a drug molecule that does not match the eutectic structure through a binding mode of the drug-like drug molecule and the eutectic structure to obtain a filtered drug molecule (i.e., mental processes); and
verifying a compound according to docking of the filtered drug molecule and the eutectic structure (i.e., mental processes).
Dependent claim 12 further recites:
the method is implemented through a machine learning model (i.e., mental processes and mathematical concepts); and
the method further comprises:
determining a second distance between the sample scaffold latent vector of the sample molecule and the cluster centers of each scaffold cluster, and determining a scaffold cluster to which a sample scaffold of the sample molecule belongs according to the second distance (i.e., mental processes and mathematical concepts); and
generating a distance-based cross-entropy loss according to the distance between the sample scaffold latent vector and the cluster center of the scaffold cluster to which the sample scaffold belongs (i.e., mental processes and mathematical concepts);
generating a loss function of the machine learning model according to the cross-entropy loss and a predicted loss of the machine learning model for the sample molecule (i.e., mental processes and mathematical concepts); and
adjusting a parameter of the machine learning model based on the loss function (i.e., mental processes and mathematical concepts).
Dependent claim 13 further recites:
the machine learning model comprises a decoder (i.e., mental processes and mathematical concepts); and
the method further comprises:
inputting, after the sample scaffold latent vector and a sample sidechain latent vector corresponding to the sample molecule are obtained through the machine learning model, the sample scaffold latent vector, the sample sidechain latent vector, and a target molecule corresponding to the sample molecule to the decoder (i.e., mental processes); and
determining the predicted loss according to output of the decoder and the target molecule (i.e., mental processes and mathematical concepts).
Dependent claim 15 further recites:
a node in the connection graph structure represents an atom in the reference drug molecule (i.e., mental processes and mathematical concepts);
generating the atomic latent vector comprises:
determining node information of each node in the connection graph structure through a graph encoder according to a node feature and a side feature included in the connection graph structure, the node feature representing an atomic feature in the reference drug molecule, and the side feature representing a feature between atoms in the reference drug molecule (i.e., mathematical concepts);
generating latent vectors of each node according to the node information of each node and node features of each node (i.e., mathematical concepts); and
generating the atomic latent vector corresponding to the reference drug molecule according to the latent vectors of each node and the atom included in the reference drug molecule (i.e., mathematical concepts).
Dependent claim 16 further recites:
determining a bit vector corresponding to the reference drug molecule, the bit vector having a length that corresponds to a number of atoms comprised in the reference drug molecule, and a bit value corresponding to a scaffold atom in the bit vector has a first value (i.e., mental processes and mathematical concepts); and
filtering an atomic latent vector corresponding to the reference drug molecule according to the bit vector to obtain a latent vector of the scaffold atom and a latent vector of the sidechain atom (i.e., mental processes and mathematical concepts); and
performing multi-head attention processing on the latent vector of the scaffold atom to obtain the scaffold latent vector (i.e., mathematical concepts); and
performing multi-head attention processing on the latent vector of the sidechain atom to obtain the sidechain latent vector (i.e., mathematical concepts).
Dependent claim 17 further recites:
determining a first distance between the scaffold latent vector and the cluster centers of each scaffold cluster (i.e., mental processes and mathematical concepts);
determining a target scaffold cluster to which a scaffold of the reference drug molecule belongs according to the first distance (i.e., mental processes and mathematical concepts); and
determining a Gaussian mixture distribution to which the scaffold latent vector belongs according to the cluster center of the target scaffold cluster (i.e., mental processes and mathematical concepts).
Dependent claim 19 further recites:
the operations are implemented through a machine learning model (i.e., mental processes and mathematical concepts); and
the operations further comprise:
determining a second distance between the sample scaffold latent vector of the sample molecule and the cluster centers of each scaffold cluster, and determining a scaffold cluster to which a sample scaffold of the sample molecule belongs according to the second distance (i.e., mental processes and mathematical concepts); and
generating a distance-based cross-entropy loss according to the distance between the sample scaffold latent vector and the cluster center of the scaffold cluster to which the sample scaffold belongs (i.e., mental processes and mathematical concepts);
generating a loss function of the machine learning model according to the cross-entropy loss and a predicted loss of the machine learning model for the sample molecule (i.e., mental processes and mathematical concepts); and
adjusting a parameter of the machine learning model based on the loss function (i.e., mental processes and mathematical concepts).
Dependent claim 20 further recites:
the machine learning model comprises a decoder (i.e., mental processes and mathematical concepts); and
the operations further comprise:
inputting, after the sample scaffold latent vector and a sample sidechain latent vector corresponding to the sample molecule are obtained through the machine learning model, the sample scaffold latent vector, the sample sidechain latent vector, and a target molecule corresponding to the sample molecule to the decoder (i.e., mental processes); and
determining the predicted loss according to output of the decoder and the target molecule (i.e., mental processes and mathematical concepts).
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pen and paper (e.g., generating, according to a connection graph structure corresponding to a reference drug molecule, an atomic latent vector corresponding to the reference drug molecule), and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas (e.g., generating a target scaffold latent vector with a target transition degree between the scaffold latent vector and the target scaffold latent vector according to a spatial distribution of the scaffold latent vector) are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Therefore, claims 1-20 recite an abstract idea.
[Step 2A Prong One: YES]
Eligibility Step 2A Prong Two: In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that when examined as a whole integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d)(I); 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.
Dependent claims 2-4, 6-9, 13, 15, 16 and 20 do not recite any elements in addition to the judicial exception, and thus are part of the judicial exception.
The additional elements in independent claim 1 include:
an electronic device.
The additional elements in independent claim 14 include:
an electronic device comprising one or more processors and a memory.
The additional elements in independent claim 18 include:
a non-transitory computer-readable storage medium, storing one or more instructions; and
one or more processors of an electronic device.
The additional elements in dependent claims 5, 10, 11, 12, 17 and 19 include:
obtaining a plurality of scaffold clusters, wherein cluster centers of each scaffold cluster in the plurality of scaffold clusters fit a Gaussian mixture distribution (i.e., obtaining data) (claim 5);
obtaining a target and a target activity value of the specified reference drug molecule (i.e., obtaining data) (claim 10);
obtaining a eutectic structure corresponding to the reference drug molecule (i.e., obtaining data) (claim 11);
synthesizing a compound according to docking of the filtered drug molecule and the eutectic structure (claim 11);
obtaining a sample scaffold latent vector corresponding to a sample molecule, and obtaining a plurality of scaffold clusters, cluster centers of each scaffold cluster in the plurality of scaffold clusters fitting a Gaussian mixture distribution (i.e., obtaining data) (claim 12);
obtaining a plurality of scaffold clusters, wherein cluster centers of each scaffold cluster in the plurality of scaffold clusters fit a Gaussian mixture distribution (i.e., obtaining data) (claim 17); and
obtaining a sample scaffold latent vector corresponding to a sample molecule, and obtaining a plurality of scaffold clusters, cluster centers of each scaffold cluster in the plurality of scaffold clusters fitting a Gaussian mixture distribution (i.e., obtaining data) (claim 19).
The additional elements of an electronic device (claim 1); an electronic device comprising one or more processors and a memory (claim 14); a non-transitory computer-readable storage medium, storing one or more instructions (claim 18); and one or more processors of an electronic device (claim 18); invoke a computer and/or computer-related components merely as tools for use in the claimed process, such that they amount to no more than mere instructions to apply the exceptions using a generic computer (MPEP 2106.05(f)), and therefore are not an improvement to computer functionality itself, or an improvement to any other technology or technical field, and thus, do not integrate the judicial exceptions into a practical application (MPEP 2106.04(d)(1)).
The additional element of obtaining data (claims 5, 10, 11, 12, 17 and 19) is merely a pre-solution activity of gathering data for use in the claimed process – a nominal or tangential addition to the claims that does not meaningfully limit the claims, and therefore does not add more than insignificant extra-solution activity to the judicial exceptions (MPEP 2106.05(g)).
The additional element of synthesizing a compound according to docking of the filtered drug molecule and the eutectic structure (claim 11) does not amount to more than mere instructions to implement an abstract idea (i.e., does not amount to more than a recitation of the words “apply it” (or an equivalent)). The additional element must do more than simply state the judicial exception (i.e., the discovered properties) while adding the words equivalent to “apply it” (i.e., “synthesizing a compound according to…”) (MPEP 2106.05(f)).
Thus, the additionally recited elements merely invoke a computer and/or computer related components as tools; and/or amount to insignificant extra-solution activity; and/or do not amount to more than mere instructions to implement an abstract idea; and as such, when all limitations in claims 1-20 have been considered as a whole (i.e., the analysis takes into consideration all the claim limitations and how those limitations interact and impact each other when evaluating whether the exception is integrated into a practical application), the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application, and therefore claims 1-20 are directed to an abstract idea (MPEP 2106.04(d)).
[Step 2A Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
Dependent claims 2-4, 6-9, 13, 15, 16 and 20 do not recite any elements in addition to the judicial exception(s).
The additional elements recited in independent claims 1, 14 and 18 and dependent claims 5, 10, 11, 12, 17 and 19 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 an electronic device (claim 1); an electronic device comprising one or more processors and a memory (claim 14); a non-transitory computer-readable storage medium, storing one or more instructions (claim 18); and one or more processors of an electronic device (claim 18); and obtaining data (claims 5, 10, 11, 12, 17 and 19); are conventional computer components and/or functions (see MPEP at 2106.05(b) and 2106.05(d)(II) regarding conventionality of computer components and computer processes).
The additional element of synthesizing an AI-designed compound (claim 11) is conventional. Evidence of conventionality is shown by:
Chen et al. (“Has Drug Design Augmented by Artificial Intelligence Become a Reality.” Trends in Pharmacological Sciences, 2019, vol. 40, no. 11, pp. 806-808).
Chen et al. reviews the application of artificial intelligence (AI) to drug discovery and shows that generative molecular design based on deep learning is a particular area of attention (Abstract). While Chen et al. does discuss a recently published novel approach (i.e., Zhavoronkov et al.), Chen et al. shows that early de novo design methods enumerated large virtual libraries and then explored the chemical space through docking and similarity/pharmacophore searches, as well as transformational rules, and further shows that with advances in deep learning methods, generative modeling has emerged as a de novo design method (page 807, col. 1). Chen et al. further shows that the conventional process from initialization of a drug discovery project to identifying a candidate drug for preclinical study usually takes 3-5 years, and hundreds to thousands of compounds need to be synthesized and tested (page 806, col. 3, para. 2).
Therefore, when taken alone (i.e., individually), all additional elements in claims 1-20 do not amount to significantly more than the above-identified judicial exception(s). Even when evaluated as an ordered combination, the additional elements fail to transform the exception(s) into a patent-eligible application of that exception. Thus, claims 1-20 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]
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.
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 1-10 and 12-20 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (“Multi-objective de novo drug design with conditional graph generative model.” Journal of Cheminformatics, 2018, vol. 10:33, pp. 1-24) and Lim et al. (“Scaffold-based molecular design using graph generative model.” arXiv:1905.13639v1 [cs.LG] 31 May 2019, pp. 1-33) and Rigoni et al. ("Conditional Constrained Graph Variational Autoencoders for Molecule Design," 2020 IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, ACT, Australia, pp. 729-736).
Independent claims 1, 14, and 18 broadly provide a method, a device, and a non-transitory storage medium, respectively, for processing a molecular scaffold transition to generate a transitioned drug molecule, and more particularly, generating, according to a connection graph structure corresponding to a reference drug molecule, an atomic latent vector corresponding to the reference drug molecule, performing atom masking processing on the atomic latent vector to obtain a scaffold latent vector and a sidechain latent vector included in the atomic latent vector, generating a target scaffold latent vector with a target transition degree between the scaffold latent vector and the target scaffold latent vector according to a spatial distribution of the scaffold latent vector, and generating a transitioned drug molecule according to the target scaffold latent vector and the sidechain latent vector.
Dependent claims 2-10, 12, 13, 15-17, 19, and 20 further define aspects of the graph structure, e.g., the information and features associated with the graph nodes, and further define the generation of the vectors corresponding to the reference drug molecule, e.g., latent vectors and bit vectors, and processing the vectors, e.g., by using a machine learning model and multi-head attention processing.
Li et al. is directed to a method for multi-objective de novo drug design using a conditional graph generative model.
Lim et al. is directed to a method for scaffold-based molecular design using a graph generative model.
Rigoni et al. is directed to a method for using conditional constrained graph variational autoencoders for molecule design.
Regarding independent claims 1, 14, and 18, Li et al. shows employing a conditional graph generative model for the application of drug design tasks that is suitable for generation based on multiple objectives, including the generation of compounds containing a given scaffold (Abstract). Li et al. further shows a schematic representation of the molecule generation process, starting with the addition of an atom, and at each step, a graph transition (append, connect, or terminate) is sampled and performed on the intermediate molecule structure, and the probability for sampling each transition is calculated and parametrized using a deep neural network (page 3, Figure 2). Li et al. further shows that the method was aimed at obtaining a bi-directional mapping between molecule space and a continuous latent space so that operations on molecules can be achieved by manipulating the latent representation (page 2, col. 1, para. 1), and deep generative models that can directly output molecular graphs, i.e., Li et al. focuses on sequential graph generators, which build a graph by iteratively refining its intermediate structure, wherein the process starts from an empty graph, a graph transition is selected from the set of all available transition actions based on the generation history, and the selection is done by sampling from a probability distribution which is parametrized by a deep network (page 3, col. 1, para. 1), and further wherein the mapping determines all available graph transitions at each step (page 3, col. 1, para. 2). Li et al. further shows that the model learned about the side chain characteristics of each scaffold (page 16, col. 2, para. 1).
Regarding independent claims 1, 14, and 18, Li et al. does not show generating an atomic latent vector corresponding to the reference drug molecule; or performing atom masking processing on the atomic latent vector.
Regarding independent claims 1, 14, and 18, Lim et al. shows that the goal of graph encoding is to generate a latent vector z of the entire graph G of a whole molecule, and for the initial node and edge features, choosing the atom types and bond types of the molecule (page 6, para. 3; and Figure 1).
Regarding independent claims 1, 14, and 18, Rigoni et al. shows sampling the atom type from the distribution returned by a function applying a binary mask in order to remove certain atoms (page 732, col. 2, para. 2, middle of bulleted list).
Regarding dependent claims 2-10, 12, 13, 15-17, 19 and 20, Li et al. and Lim et al. and Rigoni et al. do not explicitly show using the same method steps as the instant claims in order to generate a transitioned drug molecule according to a target scaffold latent vector and a sidechain latent vector.
However, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Li et al. by incorporating methods for generating latent vectors encoding for the node and edge features (i.e., the atom types and bond types), as shown by Lim et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Li et al. with the methods of Lim et al., because Lim et al. shows methods for performing graph encoding using latent vectors. This modification would have had a reasonable expectation of success given that both Li et al. and Lim et al. disclose methods for scaffold-based generation of molecules using graph generative models.
It would have been further prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Li et al. by incorporating methods for masking atoms, as shown by Rigoni et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Li et al. with the methods of Rigoni et al., because Rigoni et al. shows that using a binary mask on atoms can improve the accuracy of the model. This modification would have had a reasonable expectation of success given that both Li et al. shows drug design with a conditional graph generative model and Rigoni et al. shows molecule design using a graph variational autoencoder approach, which Rigoni et al. presents as delivering the best trade-off between generative capabilities and ease of training.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Li et al. and Lim et al. and Rigoni et al. as applied to claims 1-10 and 12-20 above, and further in view of Luo et al. (“Computer Aided Screening of PI3K Inhibitor Molecules from Database.” (2020) In: Hung, J., Yen, N., Chang, JW. (eds) Frontier Computing. FC 2019. Lecture Notes in Electrical Engineering, vol 551. Springer, Singapore. Pp. 746-753).
Dependent claim 11 further defines the claimed method with additional steps after generating the transitioned drug molecule, in particular, performing molecular filtration processing of physicochemical property according to the transitioned drug molecule to obtain a drug-like drug molecule; obtaining a eutectic structure corresponding to the reference drug molecule; docking the drug-like drug molecule to the eutectic structure; removing a drug molecule that does not match the eutectic structure through a binding mode of the drug-like drug molecule and the eutectic structure to obtain a filtered drug molecule; and synthesizing and verifying a compound according to docking of the filtered drug molecule and the eutectic structure.
Luo et al. is directed to computer-aided screening of molecules from a database.
Regarding dependent claim 11, Li et al. and Lim et al. and Rigoni et al. as applied to claims 1-10 and 12-20 above, do not show the limitations of claim 11.
Regarding dependent claim 11, Luo et al. shows virtual screening of molecules (Abstract) using molecular docking software (page 747, para. 4), and further shows an interaction between a ligand and the protein in the eutectic structure (page 749, para. 4; and Figs. 1(a) and 1(b)), and using the computer-aided method to synthesize molecules (page 747, para. 1).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Li et al. and Lim et al. and Rigoni et al. as applied to claims 1-10 and 12-20 above, by incorporating methods for computer-aided screening using molecular docking and eutectic structures, as shown by Luo et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Li et al. and Lim et al. and Rigoni et al. as applied to claims 1-10 and 12-20 above, with the methods of Luo et al., because Luo et al. shows a virtual screening model for molecular compounds. This modification would have had a reasonable expectation of success given that both Li et al. and Lim et al. and Rigoni et al. as applied to claims 1-10 and 12-20 above, and Luo et al. disclose methods for discovering new molecular structures with pharmacological properties.
Conclusion
No claims are 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 application.
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/S.W.B./Examiner, Art Unit 1687
/Joseph Woitach/Primary Examiner, Art Unit 1687
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