DETAILED ACTION
This Office Action is in response to Preliminary Amendment filed on April 12, 2024 for Application # 18/399,450 filed on December 28, 2023 in which claims 1-10 are presented for examination.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of claims
Claims 1-10 are pending, of which claims 1-10 are rejected under 35 U.S.C. 101 and also claims 1-10 are rejected under 35 U.S.C. 103. Claims 1-10 are rejected under 35 U.S.C. 112(a).
No claims are amended.
Claims 11-17 are canceled.
No claims are newly added.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. KR10-2023-0043303, filed on May 10, 2024.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-10 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention. Independent claims 1, 9 and 10 cites “negative molecular model”, examiner did not find support in the specification on enablement on how the negative molecular model will be compared or matched to the source molecular model or target molecular model. Examiner requests applicant to identify sections of specification which describes the enablement.
Independent claims 2-8 are rejected under the same rationale as the parent claims since the independent claims inherit the deficiencies of the parent. .
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 1-10 are rejected under 35 U.S.C. 101. because the claims are directed to an abstract idea; and because the claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than the abstract idea, see Alice Corporation Pty. Ltd. v. CLS Bank International, et al, 573 U.S. (2014). In determining whether the claims are subject matter eligible, the Examiner applies the 2019 USPTO Patent Eligibility Guidelines. (2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, Jan. 7, 2019.)
Step 1: Is the claim to a process, machine, manufacture, or composition of matter? Yes—Claims 1-10 recite a method and apparatus respectively.
The analysis of claims 1, 9 and 10 is as follows:
Step 2A, prong one: Does claims 1, 9 and 10 recite an abstract idea, law of nature or natural phenomenon? Yes—the limitations of “obtaining a training dataset including a source molecular model, a target molecular model whose structural similarity with the source molecular model exceeds a first threshold, and a negative molecular model whose structural similarity with one or more models of the source molecular model or the target molecular model is smaller than or equal to the first threshold;
training a molecular generation model to adjust a distance between the source molecular model and the target molecular model based on the training dataset and a first loss function; and
training the molecular generation model to adjust at least one distance of a distance between the source molecular model and the negative molecular model, and a distance between the target molecular model and the negative molecular model based on the training dataset and a second loss function different from the first loss function” as drafted, are mental steps based on various processes can be performed in a human mind of training the molecular model applying loss function to generate a chemical compound (acts of thinking, decision making). These limitations, therefore fall within the human mind processes group and with a pen & paper.
Step 2A, prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? No—the judicial exception is not integrated into a practical application as just stated as related to the technical field of computer science . Although the claim recites that the recited functionality includes “method”, “computer” and “storage medium”, these computer components are recited at a high-level of generality such that it amounts to no more than a mere instructions to apply the exception using generic computer component. In addition, the claim recites “obtaining a training dataset including a source molecular model, a target molecular model whose structural similarity with the source molecular model exceeds a first threshold, and a negative molecular model whose structural similarity with one or more models of the source molecular model or the target molecular model is smaller than or equal to the first threshold;
training a molecular generation model to adjust a distance between the source molecular model and the target molecular model based on the training dataset and a first loss function; and
training the molecular generation model to adjust at least one distance of a distance between the source molecular model and the negative molecular model, and a distance between the target molecular model and the negative molecular model based on the training dataset and a second loss function different from the first loss function” are mere training machine learning model from the source molecular model to get target molecular model (i.e., providing chemical compound); the computers that perform those functions and the mental steps are recited at a high level of generality that do not impose a meaningful limitation on the judicial exception and are insufficient to integrate the mental steps into a practical application. Although the claim recites the additional functionality “training the molecular generation model to adjust at least one distance of a distance between the source molecular model and the negative molecular model, and a distance between the target molecular model and the negative molecular model based on the training dataset and a second loss function different from the first loss function “, training machine learning model also recited at a high level of generality and merely generally link to respective technological environments (e.g., training molecular model) and therefore likewise amounts to no more than a mere instructions to apply the exception using generic computer components and is insufficient to integrate the steps into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No— The recitation in the preamble is insufficient to transform a judicial exception to a patentable invention because the preamble elements are recited at a high level of generality that simply links to a field of use, see MPEP 2106.05(h). The claimed extra-solution of operation based on loss function (algorithm) is acknowledged to be well-understood, routine, conventional activity (see, e.g., court recognized WURC examples in MPEP 2106.05(d)(II)(i). Similarly, the obtaining and training are also recited at a high level of generality and merely generally link to respective technological environments. The claim thus recites computing components only at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Taken alone, their additional elements do not amount to significantly more than the above- identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
For the reasons above, claims 1, 9 and 10 are rejected as being directed to non-patentable subject matter under §101.
The analysis of claims 2-8 are as follows:
Step 2A, prong one: Does claims 2-8 recite an abstract idea, law of nature or natural phenomenon? Yes—the limitations of “claim 2 recites wherein the training of the molecular generation model to adjust the distance between the source molecular model and the target molecular model based on the training dataset and the first loss function includes: training the molecular generation model to decrease the distance between the source molecular model and the target molecular model based on the training dataset and the first loss function.
Claim 3 recites wherein the training of the molecular generation model to adjust the at least one distance of the distance between the source molecular model and the negative molecular model, and the distance between the target molecular model and the negative molecular model based on the training dataset and the second loss function different from the first loss function includes: training the molecular generation model to increase the at least one distance of the distance between the source molecular model and the negative molecular model, and the distance between the target molecular model and the negative molecular model, based on the training dataset and the second loss function different from the first loss function.
Claim 4 recites training the molecular generation model such that a molecular model whose structural similarity with the source molecular model exceeds a second threshold is output from the source molecular model, based on the training dataset and a reward function.
Claim 5 recites wherein the training of the molecular generation model such that the molecular model whose structural similarity with the source molecular model exceeds the second threshold is output from the source molecular model, based on the training dataset and the reward function includes: obtaining an output molecular model by entering the source molecular model into the molecular generation model; and calculating a positive weight or a negative weight associated with the output molecular model and assigning the positive weight or the negative weight to the molecular generation model, based on whether structural similarity between the output molecular model and the source molecular model exceeds the second threshold as a result of comparing the output molecular model and the source molecular model.
Claim 6 recites wherein the calculating of the positive weight or the negative weight associated with the output molecular model and the assigning of the positive weight or the negative weight to the molecular generation model, based on whether the structural similarity between the output molecular model and the source molecular model exceeds the second threshold as the result of comparing the output molecular model and the source molecular model includes: calculating the positive weight or the negative weight associated with the output molecular model and the assigning the positive weight or the negative weight to the molecular generation model, based on whether the structural similarity between the output molecular model and the source molecular model exceeds the second threshold, and whether a chemical property score of the output molecular model exceeds a chemical property score of the source molecular model as the results of comparing the output molecular model and the source molecular model.
Claim 7 recites wherein the training of the molecular generation model such that the molecular model whose structural similarity with the source molecular model exceeds the second threshold is output from the source molecular model, based on the training dataset and the reward function includes: training the molecular generation model such that a molecular model whose structural similarity with the source molecular model exceeds the second threshold and which has a chemical property score greater than a chemical property score of the source molecular model, is output from the source molecular model, based on the training dataset and the reward function.
Claim 8 recites wherein a chemical property score of the target molecular model is greater than a chemical property score of the source molecular model” as drafted, are mental steps based on various processes can be performed in a human mind of training the molecular model applying loss function to generate a chemical compound (acts of thinking, decision making). These limitations, therefore fall within the human mind processes group and with a pen & paper.
Step 2A, prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? No—the judicial exception is not integrated into a practical application as just stated as related to the technical field of computer science . Although the claim recites that the recited functionality includes “method”, “computer” and “storage medium”, these computer components are recited at a high-level of generality such that it amounts to no more than a mere instructions to apply the exception using generic computer component. In addition, the claim recites “claim 2 recites wherein the training of the molecular generation model to adjust the distance between the source molecular model and the target molecular model based on the training dataset and the first loss function includes: training the molecular generation model to decrease the distance between the source molecular model and the target molecular model based on the training dataset and the first loss function.
Claim 3 recites wherein the training of the molecular generation model to adjust the at least one distance of the distance between the source molecular model and the negative molecular model, and the distance between the target molecular model and the negative molecular model based on the training dataset and the second loss function different from the first loss function includes: training the molecular generation model to increase the at least one distance of the distance between the source molecular model and the negative molecular model, and the distance between the target molecular model and the negative molecular model, based on the training dataset and the second loss function different from the first loss function.
Claim 4 recites training the molecular generation model such that a molecular model whose structural similarity with the source molecular model exceeds a second threshold is output from the source molecular model, based on the training dataset and a reward function.
Claim 5 recites wherein the training of the molecular generation model such that the molecular model whose structural similarity with the source molecular model exceeds the second threshold is output from the source molecular model, based on the training dataset and the reward function includes: obtaining an output molecular model by entering the source molecular model into the molecular generation model; and calculating a positive weight or a negative weight associated with the output molecular model and assigning the positive weight or the negative weight to the molecular generation model, based on whether structural similarity between the output molecular model and the source molecular model exceeds the second threshold as a result of comparing the output molecular model and the source molecular model.
Claim 6 recites wherein the calculating of the positive weight or the negative weight associated with the output molecular model and the assigning of the positive weight or the negative weight to the molecular generation model, based on whether the structural similarity between the output molecular model and the source molecular model exceeds the second threshold as the result of comparing the output molecular model and the source molecular model includes: calculating the positive weight or the negative weight associated with the output molecular model and the assigning the positive weight or the negative weight to the molecular generation model, based on whether the structural similarity between the output molecular model and the source molecular model exceeds the second threshold, and whether a chemical property score of the output molecular model exceeds a chemical property score of the source molecular model as the results of comparing the output molecular model and the source molecular model.
Claim 7 recites wherein the training of the molecular generation model such that the molecular model whose structural similarity with the source molecular model exceeds the second threshold is output from the source molecular model, based on the training dataset and the reward function includes: training the molecular generation model such that a molecular model whose structural similarity with the source molecular model exceeds the second threshold and which has a chemical property score greater than a chemical property score of the source molecular model, is output from the source molecular model, based on the training dataset and the reward function.
Claim 8 recites wherein a chemical property score of the target molecular model is greater than a chemical property score of the source molecular model” are mere training machine learning model from the source molecular model to get target molecular model (i.e., providing chemical compound); the computers that perform those functions and the mental steps are recited at a high level of generality that do not impose a meaningful limitation on the judicial exception and are insufficient to integrate the mental steps into a practical application. Although the claim recites the additional functionality “training the molecular generation model to adjust at least one distance of a distance between the source molecular model and the negative molecular model, and a distance between the target molecular model and the negative molecular model based on the training dataset and a second loss function different from the first loss function “, training machine learning model also recited at a high level of generality and merely generally link to respective technological environments (e.g., training molecular model) and therefore likewise amounts to no more than a mere instructions to apply the exception using generic computer components and is insufficient to integrate the steps into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No— The recitation in the preamble is insufficient to transform a judicial exception to a patentable invention because the preamble elements are recited at a high level of generality that simply links to a field of use, see MPEP 2106.05(h). The claimed extra-solution of operation based on loss function (algorithm) is acknowledged to be well-understood, routine, conventional activity (see, e.g., court recognized WURC examples in MPEP 2106.05(d)(II)(i). Similarly, the obtaining and training are also recited at a high level of generality and merely generally link to respective technological environments. The claim thus recites computing components only at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Taken alone, their additional elements do not amount to significantly more than the above- identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
For the reasons above, claims 2-8 are rejected as being directed to non-patentable subject matter under §101.
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 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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-10 are rejected under 35 U.S.C. 103 as being unpatentable over Yoo et al. US 2022/0383993 A1 (hereinafter ‘Yoo’) in view of Sarshogh et al. US 2022/0318596 A1 (hereinafter ‘Sarshogh’) (IDS 12/28/2023)
As per claim 1, Yoo disclose, A training method (Yoo: paragraph 0007: disclose method that is one of AI-based molecular structure designing) for a molecular generation model (Yoo: paragraph 0050: disclose generate a designed molecular structure ‘model’) performed by at least one processor (Yoo: paragraph 0044: disclose a processor of a general-purpose computer), the method comprising:
obtaining a training dataset including a source molecular model (Yoo: paragraph 0009: disclose obtaining information associated with a source molecule), a target molecular model whose structural similarity (Yoo: paragraph 0051: disclose newly generated molecular structure ‘target molecular model’ and the existing molecular structure ‘source molecular model’ are determined for similarity) with the source molecular model (Yoo: paragraph 0009: disclose obtaining, using the learning model, information associated with a modified partial structure corresponding to the target partial structure, and outputting result information in which the target partial structure is replaced by the modified partial structure in the source molecule) exceeds a first threshold (Yoo: paragraph 0051: disclose newly generated molecular structure ‘target molecular model’ and the existing molecular structure ‘source molecular model’ are determined for similarity, and when the similarity is greater than a specific value ‘threshold’), and a negative molecular model whose structural similarity with one or more models of the source molecular model or the target molecular model (Yoo: paragraph 0052: disclose main part and a secondary part of a corresponding material, and restriction on property change depending on a structure, Examiner equates this to negative molecular model as being used to change the properties to get the target molecular model and paragraph 0116: disclose generating a modified molecule, a latent vector space of a generative model using a genetic algorithm may be searched, and molecules may be generated according to various scores for new scaffolds generated as a result of the search. This is called iterative exploration, and the iterative exploration is a very efficient search method compared to batch search) is smaller than or equal to the first threshold (Yoo: paragraph 0052: disclose similarity of less than or equal to a specific value ‘threshold’ to the existing molecular structure in the previous step).
It is noted, however, Yoo did not specifically detail the aspects of
training a molecular generation model to adjust a distance between the source molecular model and the target molecular model based on the training dataset and a first loss function; and
training the molecular generation model to adjust at least one distance of a distance between the source molecular model and the negative molecular model, and a distance between the target molecular model and the negative molecular model based on the training dataset and a second loss function different from the first loss function as recited in claim 1.
On the other hand, Sarshogh achieved the aforementioned limitations by providing mechanisms of
training a molecular generation model to adjust a distance between the source molecular model and the target molecular model based (Sarshogh: paragraph 0126: disclose modified ‘adjust’ weights and identifying molecule graphs within a threshold distance of each other ‘source and target molecular model’ in the molecule embedding space. Examiner equates this teaching to limitation of adjust distance to modify weights) on the training dataset and a first loss function (Sarshogh: paragraph 0130: disclose loss function applied to the output of the decoder and a character-based representation of the input molecule graph to modify weights of the molecule embedding model encoder and decoder for mapping of the molecule graph to the embedding space); and
training the molecular generation model to adjust at least one distance of a distance between the source molecular model and the negative molecular model (Sarshogh: paragraph 0126: disclose modified ‘adjust’ weights and identifying molecule graphs within a threshold distance of each other ‘source and negative molecular model’ in the molecule embedding space. Examiner equates this teaching to limitation of adjust distance to modify weights), and a distance between the target molecular model and the negative molecular model based on the training dataset and a second loss function different from the first loss function (Sarshogh: paragraph 0135: disclose calculating a loss between the produced character-based representation and the label, backpropagating gradient of the loss function to the embedding model for each example in the training data batch, and modifying learnable weights of the embedding model based on the back propagation. Examiner equates the loss function taught in this teaching as second loss function).
Yoo and Sarshogh are analogous art because they are from the “same field of endeavor” and both from the same “problem-solving area”. Namely, they are both from the field of “Machine Learning System”.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine the systems of Yoo and Sarshogh because they are both directed to machine learning system and both are from the same field of endeavor. The skilled person would therefore regard it as a normal option to include the restriction features of Sarshogh with the method described by Yoo in order to solve the problem posed.
The motivation for doing so would have been to identify other molecules that have properties similar to the properties the first molecule (Sarshogh: paragraph 0001).
Therefore, it would have been obvious to combine Sarshogh with Yoo to obtain the invention as specified in instant claim 1.
As per claim 2, most of the limitations of this claim have been noted in the rejection of claim 1 above.
It is noted, however, Yoo did not specifically detail the aspects of
wherein the training of the molecular generation model to adjust the distance between the source molecular model and the target molecular model based on the training dataset and the first loss function includes: training the molecular generation model to decrease the distance between the source molecular model and the target molecular model based on the training dataset and the first loss function as recited in claim 2.
On the other hand, Sarshogh achieved the aforementioned limitations by providing mechanisms of
wherein the training of the molecular generation model to adjust the distance between the source molecular model and the target molecular model based on the training dataset and the first loss function includes: training the molecular generation model to decrease the distance between the source molecular model and the target molecular model (Sarshogh: paragraph 0126: disclose modified ‘adjust’ weights and identifying molecule graphs within a threshold distance of each other ‘source and target molecular model’ in the molecule embedding space. Examiner equates this teaching to limitation of adjust distance to modify weights) based on the training dataset and the first loss function (Sarshogh: paragraph 0130: disclose loss function applied to the output of the decoder and a character-based representation of the input molecule graph to modify weights of the molecule embedding model encoder and decoder for mapping of the molecule graph to the embedding space).
As per claim 3, most of the limitations of this claim have been noted in the rejection of claim 1 above.
It is noted, however, Yoo did not specifically detail the aspects of
wherein the training of the molecular generation model to adjust the at least one distance of the distance between the source molecular model and the negative molecular model, and the distance between the target molecular model and the negative molecular model based on the training dataset and the second loss function different from the first loss function includes: training the molecular generation model to increase the at least one distance of the distance between the source molecular model and the negative molecular model , and the distance between the target molecular model and the negative molecular model, based on the training dataset and the second loss function different from the first loss function as recited in claim 3.
On the other hand, Sarshogh achieved the aforementioned limitations by providing mechanisms of
wherein the training of the molecular generation model to adjust the at least one distance of the distance between the source molecular model and the negative molecular model, and the distance between the target molecular model and the negative molecular model based on the training dataset and the second loss function different from the first loss function includes: training the molecular generation model to increase the at least one distance of the distance between the source molecular model and the negative molecular model (Sarshogh: paragraph 0126: disclose modified ‘adjust’ weights and identifying molecule graphs within a threshold distance of each other ‘source and negative molecular model’ in the molecule embedding space. Examiner equates this teaching to limitation of adjust distance to modify weights), and the distance between the target molecular model and the negative molecular model, based on the training dataset and the second loss function different from the first loss function (Sarshogh: paragraph 0135: disclose calculating a loss between the produced character-based representation and the label, backpropagating gradient of the loss function to the embedding model for each example in the training data batch, and modifying learnable weights of the embedding model based on the back propagation. Examiner equates the loss function taught in this teaching as second loss function).
As per claim 4, most of the limitations of this claim have been noted in the rejection of claim 1 above. In addition, Yoo disclose, training the molecular generation model such that a molecular model whose structural similarity with the source molecular model exceeds a second threshold is output from the source molecular model, based on the training dataset (Yoo: paragraph 0052: disclose main part and a secondary part of a corresponding material, and restriction on property change depending on a structure, Examiner equates this to negative molecular model as being used to change the properties to get the target molecular model and paragraph 0116: disclose generating a modified molecule, a latent vector space of a generative model using a genetic algorithm may be searched, and molecules may be generated according to various scores for new scaffolds generated as a result of the search. This is called iterative exploration, and the iterative exploration is a very efficient search method compared to batch search) and a reward function (Yoo: paragraph 0103: disclose increase the accuracy of scoring through the deep generative workflow. Examiner equates accuracy to reward function).
As per claim 5, most of the limitations of this claim have been noted in the rejection of claims 1 and 4 above. In addition, Yoo disclose, wherein the training of the molecular generation model such that the molecular model whose structural similarity with the source molecular model exceeds the second threshold is output from the source molecular model, based on the training dataset (Yoo: paragraph 0052: disclose main part and a secondary part of a corresponding material, and restriction on property change depending on a structure, Examiner equates this to negative molecular model as being used to change the properties to get the target molecular model and paragraph 0116: disclose generating a modified molecule, a latent vector space of a generative model using a genetic algorithm may be searched, and molecules may be generated according to various scores for new scaffolds generated as a result of the search. This is called iterative exploration, and the iterative exploration is a very efficient search method compared to batch search) and the reward function (Yoo: paragraph 0103: disclose increase the accuracy of scoring through the deep generative workflow. Examiner equates accuracy to reward function) includes;
based on whether structural similarity between the output molecular model and the source molecular model exceeds the second threshold (Yoo: paragraph 0052: disclose similarity of less than or equal to a specific value ‘threshold’ to the existing molecular structure in the previous step) as a result of comparing the output molecular model and the source molecular model (Yoo: paragraph 0052: disclose main part and a secondary part of a corresponding material, and restriction on property change depending on a structure, Examiner equates this to negative molecular model as being used to change the properties to get the target molecular model and paragraph 0116: disclose generating a modified molecule, a latent vector space of a generative model using a genetic algorithm may be searched, and molecules may be generated according to various scores for new scaffolds generated as a result of the search. This is called iterative exploration, and the iterative exploration is a very efficient search method compared to batch search)
It is noted, however, Yoo did not specifically detail the aspects of
obtaining an output molecular model by entering the source molecular model into the molecular generation model; and calculating a positive weight or a negative weight associated with the output molecular model and assigning the positive weight or the negative weight to the molecular generation model as recited in claim 5.
On the other hand, Sarshogh achieved the aforementioned limitations by providing mechanisms of
obtaining an output molecular model by entering the source molecular model into the molecular generation model; and calculating a positive weight or a negative weight associated with the output molecular model and assigning the positive weight or the negative weight to the molecular generation model (Sarshogh: paragraph 0057: disclose edge weights may be based on features of the neighboring nodes of the node in the input graph and the features in the input graph).
As per claim 6, most of the limitations of this claim have been noted in the rejection of claims 1, 4 and 5 above. In addition, Yoo disclose, wherein the calculating of the positive weight or the negative weight associated with the output molecular model and the assigning of the positive weight or the negative weight to the molecular generation model (Yoo: paragraph 0052: disclose main part and a secondary part of a corresponding material, and restriction on property change depending on a structure, Examiner equates this to negative molecular model as being used to change the properties to get the target molecular model and paragraph 0116: disclose generating a modified molecule, a latent vector space of a generative model using a genetic algorithm may be searched, and molecules may be generated according to various scores for new scaffolds generated as a result of the search. This is called iterative exploration, and the iterative exploration is a very efficient search method compared to batch search), based on whether the structural similarity between the output molecular model and the source molecular model (Yoo: paragraph 0052: disclose similarity of less than or equal to a specific value ‘threshold’ to the existing molecular structure in the previous step) exceeds the second threshold as the result of comparing the output molecular model and the source molecular model includes: based on whether the structural similarity between the output molecular model and the source molecular model exceeds the second threshold (Yoo: paragraph 0052: disclose main part and a secondary part of a corresponding material, and restriction on property change depending on a structure, Examiner equates this to negative molecular model as being used to change the properties to get the target molecular model and paragraph 0116: disclose generating a modified molecule, a latent vector space of a generative model using a genetic algorithm may be searched, and molecules may be generated according to various scores for new scaffolds generated as a result of the search. This is called iterative exploration, and the iterative exploration is a very efficient search method compared to batch search), and whether a chemical property score of the output molecular model exceeds a chemical property score of the source molecular model as the results of comparing the output molecular model and the source molecular model (Yoo: paragraph 0056: disclose applied to narrow the range of the desired ‘chemical’ property and to perform scoring according to the degree to which a generated molecule corresponds to the desired ‘chemical’ property).
It is noted, however, Yoo did not specifically detail the aspects of
calculating the positive weight or the negative weight associated with the output molecular model and the assigning the positive weight or the negative weight to the molecular generation model as recited in claim 6.
On the other hand, Sarshogh achieved the aforementioned limitations by providing mechanisms of
calculating the positive weight or the negative weight associated with the output molecular model and the assigning the positive weight or the negative weight to the molecular generation model (Sarshogh: paragraph 0057: disclose edge weights may be based on features of the neighboring nodes of the node in the input graph and the features in the input graph).
As per claim 7, most of the limitations of this claim have been noted in the rejection of claims 1 and 4 above. In addition, Yoo disclose, wherein the training of the molecular generation model such that the molecular model whose structural similarity with the source molecular model exceeds the second threshold is output from the source molecular model, based on the training dataset and the reward function includes (Yoo: paragraph 0052: disclose main part and a secondary part of a corresponding material, and restriction on property change depending on a structure, Examiner equates this to negative molecular model as being used to change the properties to get the target molecular model and paragraph 0116: disclose generating a modified molecule, a latent vector space of a generative model using a genetic algorithm may be searched, and molecules may be generated according to various scores for new scaffolds generated as a result of the search. This is called iterative exploration, and the iterative exploration is a very efficient search method compared to batch search): training the molecular generation model such that a molecular model whose structural similarity with the source molecular model exceeds the second threshold and which has a chemical property score greater than a chemical property score of the source molecular model (Yoo: Paragraph 0087: disclose compound satisfying a target property value may have a high score), is output from the source molecular model, based on the training dataset and the reward function (Yoo: paragraph 0103: disclose increase the accuracy of scoring through the deep generative workflow. Examiner equates accuracy to reward function).
As per claim 8, most of the limitations of this claim have been noted in the rejection of claim 1 above. In addition, Yoo disclose, wherein a chemical property score of the target molecular model is greater than a chemical property score of the source molecular model (Yoo: Paragraph 0087: disclose compound satisfying a target property value may have a high score).
As per claim 9, most of the limitations of this claim have been noted in the rejection of claim 1 above. In addition, Yoo disclose, A computer-readable recording medium (Yoo: Paragraph 0198: disclose a storage, which examiner equates to computer-readable medium) which records a computer program to perform the training method for a molecular generation model (Yoo: Paragraph 0044: disclose computer program instructions may also be stored in a computer-usable or computer-readable memory) according to claim 1: remaining limitations in this claim 9 are similar to the limitations in claim 1. Therefore, examiner rejects these remaining limitations under the same rationale as limitations rejected under claim 1.
As per claim 10, Yoo disclose, A training device for a molecular generation model, the training device comprising:
a memory configured to store data associated with the molecular generation model (Yoo: paragraph 0044: disclose computer-readable memory that may direct a computer or other programmable data processing equipment); and
at least one processor connected to the memory and configured to train the molecular generation model (Yoo: paragraph 0044: disclose processor of a general-purpose computer or a special purpose computer),
wherein the at least one processor includes instructions, the instructions, when executed by the at least one processor, causing the at least one processor to (Yoo: paragraph 0044: disclose computer program instructions may also be stored in a computer-usable or computer-readable memory that may direct a computer or other programmable data processing equipment): remaining limitations in this claim 10 are similar to the limitations in claim 1. Therefore, examiner rejects these remaining limitations under the same rationale as limitations rejected under claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Pub. US 2021/0174910 A1 disclose “METHOD AND APPARATUS FOR GENERATING NEW CHEMICAL STRUCTURE USING NEURAL NETWORK”
US Pub. US 2022/0238190 A1 disclose “Method for designing molecules with desired properties, involves providing known molecules with known properties as first dataset, establishing another molecules with structure as second data set, and selecting molecules from multiple data sets based on score to provide multiple scored datasets”
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/PAVAN MAMILLAPALLI/
Primary Examiner, Art Unit 2159
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