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 1-13 are currently pending and under exam herein.
Claims 1-13 are rejected.
Drawings
The Drawings filed on 7/14/2022 were considered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/14/2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
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 4 is 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.
The term “approximately” in claim 4 is a relative term which renders the claim indefinite. The term “approximately” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The specification gives multiple values of 10%, 5%, and 1% which is unclear to the exact range they are claiming.
Claim 12 is 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.
A broad range or limitation together with a narrow range or limitation that falls within the broad range or limitation (in the same claim) may be considered indefinite if the resulting claim does not clearly set forth the metes and bounds of the patent protection desired. See MPEP § 2173.05(c). In the present instance, dependent claim 12 recites the broad recitation “without the experimentally measured retention time”. Independent claim 1 cites the narrower recitation “applying the measured retention time in the liquid chromatography column to the machine learning model.” The claim(s) are considered indefinite because there is a question or doubt as to whether the feature introduced by such narrower language is (a) merely exemplary of the remainder of the claim, and therefore not required, or (b) a required feature of the claims.
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 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion).
Subject matter eligibility evaluation in accordance with MPEP 2106:
Eligibility Step 1: Claims 1-13 are directed to a machine learning drug evaluation using liquid chromatographic testing
[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:
constructing a machine learning model trained from a database of small molecule physicochemical properties including a known physicochemical property for each molecule and a known retention time in a liquid chromatography column to create a learned association between the physicochemical property and liquid chromatography retention time (Mathematical Concept)
applying the measured retention time in the liquid chromatography column to the machine learning model to obtain a predicted physicochemical property for the candidate small molecule (Mathematical Concept)
selecting one or more candidate small molecules having a target value of the physicochemical property from the machine learning model (Mathematical Concept, mental process)
Dependent claim 2 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the database of small molecule physicochemical properties is a small molecule retention time (SMRT) dataset including International Chemical Identifier (InChi) codes, and extracted data are converted to Simplified Molecular Input Line Entry System (SMILES) notation to extract physico-chemical properties as a query to a ChEMBL database (mathematical concept, this just limits what the math is done on, or the database the machine learning model is trained on).
Dependent claim 3 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the physicochemical property is lipophilicity (mathematical concept, this just limits what the math is done on, or the database the machine learning model is trained on).
Dependent claim 4 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the target lipophilicity is between approximately 1 and approximately 3 (mathematical concept, this just limits what the math is done on, or the database the machine learning model is trained on).
Dependent claim 5 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
where the database of small molecule physicochemical properties includes acid dissociation constant (pKa) and polar surface area (mathematical concept, this just limits what the math is done on, or the database the machine learning model is trained on).
Dependent claim 6 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the machine learning model comprises a Random Forest Regression algorithm (mathematical concept, this just limits the type of math being done when training the machine learning model).
Dependent claim 7 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the machine learning model comprises a Gradient Boosting algorithm (mathematical concept, this just limits the type of math being done when training the machine learning model).
Dependent claim 8 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the machine learning model comprises a Support Vector Machine algorithm (mathematical concept, this just limits the type of math being done when training the machine learning model).
Dependent claim 10 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the machine learning model is further trained by one or more indicators of computed molecular descriptors for the candidate small molecule
Dependent claim 11 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the indicators of computed molecular descriptors include one or more computed parameters of mass, dipole moment, atomic composition, Morgan fingerprint, Tanimoto similarity (mathematical concept, this just limits how the machine learning model is trained).
Dependent claim 12 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the machine learning models are trained without the experimentally measured retention time descriptor in the liquid chromatography column to predict the lipophilicity (mathematical concept, this just limits how the machine learning model is trained).
Dependent claim 13 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the machine learning models are trained with the experimentally measured retention time descriptor in the liquid chromatography column to predict the lipophilicity (mathematical concept, this just limits what the machine learning model is predicting).
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pencil and paper, and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Therefore, claims 1-13 recite an abstract idea as the dependent claims will inherit the abstract ideas from the independent claims.
[Step 2A Prong One: YES]
Eligibility Step 2A Prong Two: In determining whether a claim is directed to a judicial exception, further
examination is performed that analyzes if the claim recites additional elements that when examined as a
whole integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that
integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception
in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements
are analyzed to determine if the abstract idea is integrated into a practical application (MPEP
2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract
idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A Prong One are not integrated into a practical application because of the reasons noted below.
The additional element in independent claim 1 includes:
A machine learning system for predicting a physicochemical property of candidate small molecules for pharmaceuticals comprising
applying a candidate small molecule having an unknown physicochemical property and unknown retention time to a liquid chromatography column and measuring the retention time of the candidate small molecule in the liquid chromatography column
testing the selected candidate small molecules for pharmaceutical activity.
The additional element in dependent claim 9 includes:
wherein the machine learning model comprises a Deep Neural Network algorithm.
The additional elements of applying a candidate small molecule having an unknown physicochemical property and unknown retention time to a liquid chromatography column and measuring the retention time of the candidate small molecule in the liquid chromatography column (claim 1) and testing the selected candidate small molecules for pharmaceutical activity (claim 1) are insignificant extra-solution activity that are part of the data gathering process used in the recited judicial exceptions (see MPEP 2106.05(g)). The additional elements of a machine learning system for predicting a physicochemical property of candidate small molecules for pharmaceuticals comprising (claim 1) wherein the machine learning model comprises a Deep Neural Network algorithm (claim 9) merely invokes a computer as a tool and does not improve the technology of a generic computer (see MPEP 2106.05(a))
Claims 1-13 do not recite any elements in addition to the judicial exception, and thus are part of the judicial exception.
Thus, the additionally recited elements merely invoke a computer as a tool, and/or amount to insignificant extra-solution data gathering activity, and as such, when all limitations in claims 1-13 have been considered as a whole, the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application, and therefore claims 1-13 are directed to an abstract idea (MPEP 2106.04(d)).
[Step 2A Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
The additional elements recited in claim 1 and claim 9 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 applying a candidate small molecule having an unknown physicochemical property and unknown retention time to a liquid chromatography column and measuring the retention time of the candidate small molecule in the liquid chromatography column (claim 1), testing the selected candidate small molecules for pharmaceutical activity (claim 1), a machine learning system for predicting a physicochemical property of candidate small molecules for pharmaceuticals comprising (claim 1), wherein the machine learning model comprises a Deep Neural Network algorithm (claim 9) are conventional and part of the data gathering process used in the recited judicial exceptions (see MPEP 2106.05(g)) and merely invokes a computer as a tool and does not improve the technology of a generic computer (see MPEP 2106.05(a)). Evidence for conventionality is shown by Munro et al. (Munro, K. et al. Artificial Neural Network Modelling of Pharmaceutical Residue Retention Times in Wastewater Extracts Using Gradient Liquid Chromatography-High Resolution Mass Spectrometry Data. Journal of Chromatography A 2015, 1396, 34–44) (abstract) which train a deep neural network to predict retention time of pharmaceutical compounds. Additional evidence for conventionality is shown by Taskinen et al. (Taskinen, J.; Yliruusi, J. Prediction of Physicochemical Properties Based on Neural Network Modelling. Advanced Drug Delivery Reviews 2003, 55 (9), 1163–1183.) which uses neural networks to predict physicochemical properties. Additional evidence for conventionality is shown by Fu et al. (Fu, Y.; Luo, J.; Qin, J.; Yang, M. Screening Techniques for the Identification of Bioactive Compounds in Natural Products. Journal of Pharmaceutical and Biomedical Analysis 2019, 168, 189–200.) (abstract) which is a review for screening methods of determining bioactivity of natural products. Additional evidence for conventionality is shown by, Bouwmeester et al. (Bouwmeester, R.; Martens, L.; Degroeve, S. Comprehensive and Empirical Evaluation of Machine Learning Algorithms for Small Molecule LC Retention Time Prediction. Analytical Chemistry 2019, 91 (5), 3694–3703.) (abstract), a review for machine learning prediction of retention times in liquid chromatography.
Claims 1-13 do not recite any elements in addition to the judicial exception. Therefore, when taken alone, all additional elements in claims 1-13 do not amount to significantly more than the above-identified judicial exception(s). Even when evaluated as a combination, the additional elements fail to transform the exception(s) into a patent-eligible application of that exception. Thus, claims 1-13 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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1,2, 3, 5, 6, 8, 9, 10, 11, 13 are rejected under 35 U.S.C. 103 as being unpatentable over Domingo-Almenara et al. (Domingo-Almenara et al. The METLIN Small Molecule Dataset for Machine Learning-Based Retention Time Prediction. Nature Communications 2019, 10 (1)) in further view of Valkó (Valkó, K. L. Lipophilicity and Biomimetic Properties Measured by HPLC to Support Drug Discovery. Journal of Pharmaceutical and Biomedical Analysis 2016, 130, 35–54) in further view of Wijewardhane et al. (ChemRxiv, 2021, Graph Neural Networks Bootstrapped for Synthetic Selection and Validation of Small Molecule Immunomodulators). The italicized text corresponds to the instant claim limitations.
With respect to the limitations of Claims 1,2, 3, 5, 6, 8, 9, 10, 11, 13, Domingo-Almenara et al. teaches that machine learning (ML) has played and still plays a key role at different levels in fields as diverse as quantum mechanics, physical chemistry, biophysics or physiology. In chemoinformatics, ML has been widely adopted in the design of quantitative structure–activity relationship (QSAR) models aimed at predicting specific properties such as bioactivity, toxicity or small molecule-protein binding affinity. These models enable screening for molecules with specific properties and their development has been possible given the availability of public datasets. In that sense, datasets with a wide set of examples from which an ML model can learn are necessary to build accurate ML-based prediction models (pg. 2, Introduction paragraph 1, A machine learning system for predicting a physicochemical property of candidate small molecules for pharmaceuticals (Claim 1)). Domingo-Almenara et al. also teaches the METLIN’s SMRT dataset includes the RT in seconds, the PubChem numbers, the molfile containing the structures (SDF format), and molecular descriptors and extended connectivity fingerprints (ECFP) calculated with Dragon 7. ECFP together with their respective RT were used as input data for the deep-learning regression model. (pg. 2, Results, paragraph 1-2, constructing a machine learning model trained from a database of small molecule physicochemical properties including a known physicochemical property for each molecule and a known retention time in a liquid chromatography column to create a learned association between the physicochemical property and liquid chromatography retention time (Claim 1)). Domingo-Almenara et al. also teaches RP chromatography with high performance liquid chromatography–mass spectrometry (HPLC–MS) was used to acquire RT data for a total of 80,038 small molecules (pg. 2, Results, paragraph 1, applying a candidate small molecule having an unknown physicochemical property and unknown retention time to a liquid chromatography column and measuring the retention time of the candidate small molecule in the liquid chromatography column (Claim 1)) Domingo-Almenara et al. also teaches the METLIN’s SMRT dataset includes the RT in seconds, the PubChem numbers, the molfile containing the structures (SDF format), and molecular descriptors and extended connectivity fingerprints (ECFP) calculated with Dragon 7. ECFP together with their respective RT were used as input data for the deep-learning regression model. The MELTIN database also contains smiles and inchi codes of the corresponding molecules. (pg. 2, Results, paragraph 1-2, wherein the database of small molecule physicochemical properties is a small molecule retention time (SMRT) dataset including International Chemical Identifier (InChi) codes, and extracted data are converted to Simplified Molecular Input Line Entry System (SMILES) notation to extract physico-chemical properties as a query to a ChEMBL database (Claim 2)). Domingo-Almenara et al. also teaches RP chromatography with high performance liquid chromatography–mass spectrometry (HPLC–MS) was used to acquire RT data for a total of 80,038 small molecules. See MPEP 2111.05 and Lowry, 32 F.3d at 1583-84, 32 USPQ2d at 1035. When the computer-readable medium merely serves as a support for information or data, no functional relationship exists (pg. 2, Results, paragraph 1, where the database of small molecule physicochemical properties includes acid dissociation constant (pKa) and polar surface area (Claim 5)). Domingo-Almenara et al. also teaches other non-deep ML methods such as random forest regression using fingerprints. The random forest regression yielded a lower accuracy than the DLM (pg. 2, Application of deep learning for RT prediction, paragraph 2, wherein the machine learning model comprises a Random Forest Regression algorithm (Claim 6)). Domingo-Almenara et al. also teaches a deployed deep-learning regression model (DLM) (pg. 2, Application of deep learning for RT prediction, paragraph 1, wherein the machine learning model comprises a Deep Neural Network algorithm (Claim 9)) Domingo-Almenara et al. also teaches the METLIN’s SMRT dataset includes the RT in seconds, the PubChem numbers, the molfile containing the structures (SDF format), and molecular descriptors and extended connectivity fingerprints (ECFP) calculated with Dragon 7. ECFP together with their respective RT were used as input data for the deep-learning regression model. (pg. 2, Results, paragraph 1-2, wherein the machine learning model is further trained by one or more indicators of computed molecular descriptors for the candidate small molecule (Claim 10)). Domingo-Almenara et al. also teaches the METLIN’s SMRT dataset includes the RT in seconds, the PubChem numbers, the molfile containing the structures (SDF format), and molecular descriptors and extended connectivity fingerprints (ECFP) calculated with Dragon 7. ECFP together with their respective RT were used as input data for the deep-learning regression model. ECFP are equivalent to morgan fingerprints (pg. 2, Results, paragraph 1-2, wherein the indicators of computed molecular descriptors include one or more computed parameters of mass, dipole moment, atomic composition, Morgan fingerprint, Tanimoto similarity (Claim 11)).
Domingo-Almenara et al. does not explicitly teach applying the measured retention time in the liquid chromatography column to the machine learning model to obtain a predicted physicochemical property for the candidate small molecule (Claim 1) selecting one or more candidate small molecules having a target value of the physicochemical property from the machine learning model (Claim 1), testing the selected candidate small molecules for pharmaceutical activity (Claim 1), wherein the physicochemical property is lipophilicity (Claim 3), wherein the machine learning model comprises a Support Vector Machine algorithm (Claim 8),
Valkó teaches that the chromatographic properties measured at early stages of the drug discovery process provide an easy assessment of lipophilicity, oral absorption, volume of distribution, drug efficiency (pg. 50, conclusion paragraph 1, applying the measured retention time in the liquid chromatography column to the machine learning model to obtain a predicted physicochemical property for the candidate small molecule (Claim 1)) Valkó also teaches that properties, such as lipophilicity, protein binding, phospholipid binding, and acid/base character can be incorporated in the design of molecules with the right biological distribution and pharmacokinetic profile to become an effective drug (abstract, selecting one or more candidate small molecules having a target value of the physicochemical property from the machine learning model (Claim 1)) Valkó teaches that the chromatographic properties measured at early stages of the drug discovery process provide an easy assessment of lipophilicity, oral absorption, volume of distribution, drug efficiency (pg. 50, conclusion paragraph 1, wherein the physicochemical property is lipophilicity (Claim 3)) Valkó teaches that the chromatographic properties measured at early stages of the drug discovery process provide an easy assessment of lipophilicity, oral absorption, volume of distribution, drug efficiency (pg. 50, conclusion paragraph 1, wherein the machine learning models are trained with the experimentally measured retention time descriptor in the liquid chromatography column to predict the lipophilicity (Claim 13)). Wijewardhane et al. also teaches a Bootstrapped EGNN model was used to select compounds for synthesis and experimental validation with predicted high and low potency to inhibit PD-1/PD-L1 interaction (abstract, selecting one or more candidate small molecules having a target value of the physicochemical property from the machine learning model (Claim 1) and testing the selected candidate small molecules for pharmaceutical activity (Claim 1). Wijewardhane et al. also teaches compared the cross-validated EGNN model with GNN, Support Vector Machine (SVM), and Random Forest (RF) baseline models trained with Incyte training data, using their test set performances (pg18, paragraph 2, wherein the machine learning model comprises a Support Vector Machine algorithm (Claim 8)) A person having ordinary skill in the art would be motivated to combine the machine learning database and model for retention time prediction taught by Domingo-Almenara et al. with the machine learning models that predict lipophilicity from HPLC retention times with the knowledge of selecting favorable candidates for further testing taught by Wijewardhane et al. because all the works are in the same field of endeavor and all address predicting properties of compounds for drug discovery. Therefore a person of ordinary skill in the art would be motivated to combine the prior art. In addition, there is a reasonable expectation of success because the underlying function of each model does not change just the data being used to train the model and the type of machine learning model used.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Domingo-Almenara et al. in further view of Valkó in further view of Wijewardhane et al. as applied to claims 1,2, 3, 5, 6, 8, 9, 10, 11, 13 above in further view of Datta et al. (Datta, R.; Das, D.; Das, S. Efficient Lipophilicity Prediction of Molecules Employing Deep-Learning Models. Chemometrics and Intelligent Laboratory Systems 2021, 213, 104309.) The italicized text corresponds to the instant claim limitations.
The limitations of claims 1,2, 3, 5, 6, 8, 9, 10, 11, 13 have been taught by Domingo-Almenara et al. in further view of Valkó in further view of Wijewardhane et al. above.
Domingo-Almenara et al. in further view of Valkó in further view of Wijewardhane et al. does not explicitly teach wherein the target lipophilicity is between approximately 1 and approximately 3 (Claim 4). However, these limitations were known in the art at the time of the effective filing date of the invention, as taught by Datta et al. which teaches the optimum range of lipophilicity of compounds to be successful as drugs is found to have logP value between 1 and 3 (pg. 1, Introduction, paragraph 2, wherein the target lipophilicity is between approximately 1 and approximately 3 (Claim 4)).
A person having ordinary skill in the art would use the machine learning model to predict chemical properties with retention time taught by Domingo-Almenara et al. in further view of Valkó in further view of Wijewardhane et al. with an optimal lipophilicity value between 1 and 3 taught by Datta et al. because it limits the range of prediction and will make a better machine learning model as well as being in the same field of endeavor. There is a reasonable expectation of success because the models function does not change and only the optimized prediction value is changed.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Domingo-Almenara et al. in further view of Valkó in further view of Wijewardhane et al. as applied to claims 1,2, 3, 5, 6, 8, 9, 10, 11, 13 above in further view of Wang et al. (Wang, Y et al. In Silico Prediction of Human Intravenous Pharmacokinetic Parameters with Improved Accuracy. Journal of Chemical Information and Modeling 2019, 59 (9), 3968–3980.) The italicized text corresponds to the instant claim limitations.
The limitations of claims 1,2, 3, 5, 6, 8, 9, 10, 11, 13 have been taught by Domingo-Almenara et al. in further view of Valkó in further view of Wijewardhane et al. above.
Domingo-Almenara et al. in further view of Valkó in further view of Wijewardhane et al. does not explicitly teach wherein the machine learning model comprises a Gradient Boosting algorithm (Claim 7). However, these limitations were known in the art at the time of the effective filing date of the invention, as taught by Wang et al. teaches gradient boosting machine is a family of powerful machine-learning techniques whose learning procedure consecutively fits new models to provide more an accurate estimate of the response variable (pg. 3970, Model Building, paragraph 1, wherein the machine learning model comprises a Gradient Boosting algorithm (Claim 7)).
A person having ordinary skill in the art would use the machine learning model to predict chemical properties with retention time taught by Domingo-Almenara et al. in further view of Valkó in further view of Wijewardhane et al. with a gradient boosting algorithm taught by Wang et al. because it would be obvious for a person skilled in the art to try a variety of machine learning models in orderto find the best model. In addition, there is a reasonable expectation of success because gradient boosting is another type of supervised machine learning and therefore can work on the same type of data structure. In addition, Wang et al. teaches gradient boosting machine is a family of powerful machine-learning techniques whose learning procedure consecutively fits new models to provide more an accurate estimate of the response variable (pg. 3970, Model Building, paragraph 1) and therefore a person having ordinary skill in the art would be motivated to use that machine learning model.
Conclusion
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/C.H.B./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/ Supervisory Patent Examiner, Art Unit 1687