DETAILED ACTION
This non-final rejection is responsive to communication filed January 19, 2024. Claims 1-20 are pending in this 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 .
Priority
This application is a 371 of PCT/IB2022/057222 08/03/2022, and PCT/IB2022/057222 has PRO 63/203,965 08/05/2021.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on April 16, 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because the broadest reasonable interpretation of a claim drawn to a computer readable medium typically covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable medium. As such, the broadest reasonable interpretation of the computer readable medium covers a signal per se, and therefore claim 20 is rejected as covering non-statutory subject matter.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites “determining a plurality of molecular descriptors, each molecular descriptor of the plurality of molecular descriptors associated with at least one of an atomic scale property, a molecular scale property, and a compound-scale property; selecting, by a selection operator and based on a minimization of an error, a subset of the plurality of molecular descriptors that affect a polymer surface energy; and predicting…a surface energy of an input polymer”. The broadest reasonable interpretation of these steps is that the steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. For example, a user mentally determine molecular descriptors, select a subset of molecular descriptors based on a selection operator and minimization of an error, and predict a surface energy. Further, these concepts are mathematical concepts.
This judicial exception is not integrated into a practical application. The additional elements of training a machine learning model to predict a polymer surface energy of a given polymer based on the subset of molecular descriptors and predicting, via the trained machine learning model, a surface energy of an input polymer are recited at a high level such that they provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application, and the claim is directed to the judicial exception.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the recitations of a training a machine learning model and using the trained machine learning model to make a prediction amounts to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer, which does not provide an inventive concept.
Dependent claim 2 recites: wherein the polymer is a homopolymer. This limitation further defines the data that is mentally processed, and thus are directed to a mental process. There are no additional elements in claim 2, and thus this judicial exception is not integrated into a practical application and the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Dependent claims 3-4 and 12-13 recite: wherein the atomic scale property comprises a count of relevant atoms, wherein the relevant atoms comprise at least one of a halogen, oxygen, a three-fold coordinated carbon, or a four-fold coordinated carbon. These limitations further describe the molecular descriptors that are determined and thus recite a mental process. There are no additional elements in claims 3-4 and 12-13, and thus this judicial exception is not integrated into a practical application and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Dependent claims 5-6 and 14-15 recite: wherein the molecular scale property comprises a count of functional groups, wherein the count of functional groups comprises a count of at least one of an aldehyde group, an acid group, or an aromatic group. These limitations further describe the molecular descriptors that are determined and thus recite a mental process. There are no additional elements in claims 5-6 and 14-15, and thus this judicial exception is not integrated into a practical application and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Dependent claims 7 and 16 recite: wherein the compound scale property comprises at least one of a van der Waals surface area, a topological surface area, or a fraction of rotatable bonds. This limitation further describes the molecular descriptors that are determined and thus recite a mental process. This limitation further describes a mathematical concept. There are no additional elements in claims 7 and 16, and thus this judicial exception is not integrated into a practical application and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Dependent claims 8 and 17 recite: wherein the selection operator comprises a least absolute shrinkage and selection operator (LASSO). This limitation represents a mathematical concept. There are no additional elements in claims 8 and 17, and thus this judicial exception is not integrated into a practical application and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Dependent claims 9, 10 and 18 recite: wherein the machine learning model comprises a Gaussian Process Regression (GPR) comprising a radial basis function (RBF) kernel, wherein the GPR further comprises a five-fold cross-validation. These limitations represent mathematical concepts applied using a computer. This judicial exception is not integrated into a practical application and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the machine learning model is recited at a high level such that it provides nothing more than mere instructions to implement an abstract idea on a generic computer.
Claim 11 recites “determining a plurality of molecular descriptors, each molecular descriptor of the plurality of molecular descriptors associated with at least one of an atomic scale property, a molecular scale property, and a compound-scale property; selecting, by a selection operator and based on a minimization of an error, a subset of the plurality of molecular descriptors that affect a polymer surface energy; and predicting…a surface energy of an input polymer”. The broadest reasonable interpretation of these steps is that the steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. For example, a user mentally determine molecular descriptors, select a subset of molecular descriptors based on a selection operator and minimization of an error, and predict a surface energy. Further, these concepts are mathematical concepts.
This judicial exception is not integrated into a practical application. The additional element of predicting, via the trained machine learning model, a surface energy of an input polymer is recited at a high level such that it provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). Even when viewed in combination, this additional element does not integrate the recited judicial exception into a practical application, and the claim is directed to the judicial exception.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the recitation of predicting, via a trained machine learning model, a surface energy amounts to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, this additional element represents mere instructions to implement an abstract idea or other exception on a computer, which does not provide an inventive concept.
Dependent claim 19 recites: wherein the machine learning model is trained with a training dataset comprising a plurality polymers, wherein the plurality polymers comprises a plurality of polymer classes comprising at least one of a polyoxide, a polyvinyl, a polyolefin, a polyamide, or a polyether, wherein the training dataset further comprises a plurality of chemical moieties, wherein the plurality of chemical moieties includes at least one of hydrogen, carbon, nitrogen, oxygen, sulfur, silicon, fluorine, chlorine, or bromine, wherein the training dataset further comprises at least one experimentally determined surface energy of at least one of the plurality of polymers. These additional elements further describes the trained machine learning model. This judicial exception is not integrated into a practical application and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the trained machine learning model is recited at a high level such that it provides nothing more than mere instructions to implement an abstract idea on a generic computer.
Claim 20 recites “determine a plurality of molecular descriptors, each molecular descriptor of the plurality of molecular descriptors associated with at least one of an atomic scale property, a molecular scale property, and a compound-scale property; select, by a selection operator and based on a minimization of an error, a subset of the plurality of molecular descriptors that affect a polymer surface energy; predict…a surface energy of an input polymer; and output the predicted surface energy”. The broadest reasonable interpretation of these steps is that the steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. For example, a user mentally determine molecular descriptors, select a subset of molecular descriptors based on a selection operator and minimization of an error, predict a surface energy, and manually output the predicted surface energy. Further, these concepts are mathematical concepts.
This judicial exception is not integrated into a practical application. The additional elements of “predict, via the trained machine learning model, a surface energy of an input polymer” and “one or more processors” to perform claimed steps are recited at a high level such that they provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application, and the claim is directed to the judicial exception.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the recitations of “predict, via the trained machine learning model, a surface energy of an input polymer” and “one or more processors” to perform claimed steps amount to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer, which does not provide an inventive concept.
Claim Rejections - 35 USC § 103
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 1-7, 11-16, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Thompson-Colón et al. (US 2021/0233618 A1) (‘Thompson’) in view of Zare et al. (US 2016/0251490 A1) (‘Zare’).
With respect to claim 1, Thompson teaches a method comprising:
determining a plurality of molecular descriptors, each molecular descriptor of the plurality of molecular descriptors associated with at least one of an atomic scale property, a molecular scale property, and a compound-scale property (paragraphs 66-67, 78, and 106);
selecting, by a selection operator and based on a minimization of an error, a subset of the plurality of molecular descriptors that affect a polymer property (paragraph 93 and 95);
training a machine learning model to predict a polymer property of a given polymer based on the subset of molecular descriptors (paragraphs 94-96 and 99); and
predicting, via the trained machine learning model, a property of an input polymer (paragraphs 103 and 111).
Thompson does not explicitly teach a polymer property being a surface energy.
Zare teaches a polymer property being a surface energy (paragraph 115) and determining the surface energy of a polymer (paragraphs 58-59 and 115).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the invention to have modified Thompson to determine/predict surface energy as taught by Zare because Thompson teaches predicting different physical properties of a polymer (paragraphs 46-47 and 81-82) and suggests that other properties may also be predicted. Because surface energy is a physical property of a polymer, it would have been obvious to incorporate surface energy as one the properties of a polymer in Thompson to achieve predictable results of predicting physical properties using a predictive model.
With respect to claim 2, Thompson in view of Zare teaches wherein the polymer is a homopolymer (Thompson, paragraphs 81-82 and 106; Zare, paragraph 60).
With respect to claims 3 and 12, Thompson in view of Zare teaches wherein the atomic scale property comprises a count of relevant atoms (Thompson, paragraph 68).
With respect to claims 4 and 13, Thompson in view of Zare teaches wherein the relevant atoms comprise at least one of a halogen, oxygen, a three-fold coordinated carbon, or a four-fold coordinated carbon (Thompson, paragraph 68).
With respect to claims 5 and 14, Thompson in view of Zare teaches wherein the molecular scale property comprises a count of functional groups (Thompson, paragraph 69).
With respect to claims 6 and 15, Thompson in view of Zare teaches wherein the count of functional groups comprises a count of at least one of an aldehyde group, an acid group, or an aromatic group (Thompson, paragraph 69).
With respect to claims 7 and 16, Thompson in view of Zare teaches wherein the compound scale property comprises at least one of a van der Waals surface area, a topological surface area, or a fraction of rotatable bonds (Thompson, paragraphs 72 and 106).
With respect to claim 11, Thompson teaches a method comprising:
determining a plurality of molecular descriptors, each molecular descriptor of the plurality of molecular descriptors associated with at least one of an atomic scale property, a molecular scale property, and a compound-scale property (paragraphs 66-67, 78, and 106);
selecting, by a selection operator and based on a minimization of an error, a subset of the plurality of molecular descriptors that affect a polymer property (paragraph 93 and 95);
predicting, via a trained machine learning model (paragraphs 94-96 and 99), a property of an input polymer (paragraphs 103 and 111).
Thompson does not explicitly teach a polymer property being a surface energy.
Zare teaches a polymer property being a surface energy (paragraph 115) and determining the surface energy of a polymer (paragraphs 58-59 and 115).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the invention to have modified Thompson to determine/predict surface energy as taught by Zare because Thompson teaches predicting different physical properties of a polymer (paragraphs 46-47 and 81-82) and suggests that other properties may also be predicted. Because surface energy is a physical property of a polymer, it would have been obvious to incorporate surface energy as one the properties of a polymer in Thompson to achieve predictable results of predicting physical properties using a predictive model.
With respect to claim 19, Thompson in view of Zare teaches wherein the machine learning model is trained with a training dataset comprising a plurality polymers, wherein the plurality polymers comprises a plurality of polymer classes comprising at least one of a polyoxide, a polyvinyl, a polyolefin, a polyamide, or a polyether (Thompson, paragraph 106; Zare, paragraph 60), wherein the training dataset further comprises a plurality of chemical moieties, wherein the plurality of chemical moieties includes at least one of hydrogen, carbon, nitrogen, oxygen, sulfur, silicon, fluorine, chlorine, or bromine (Thompson, paragraph 68), wherein the training dataset further comprises at least one experimentally determined surface energy of at least one of the plurality of polymers (Thompson, paragraphs 94-96 and 99; Zare, paragraphs 58-59 and 115).
With respect to claim 20, Thompson teaches computer readable medium comprising instructions that when executed cause one or more processors (paragraphs 8-9 and 62) to:
determine a plurality of molecular descriptors, each molecular descriptor of the plurality of molecular descriptors associated with at least one of an atomic scale property, a molecular scale property, and a compound-scale property (paragraphs 66-67, 78, and 106);
select, by a selection operator and based on a minimization of an error, a subset of the plurality of molecular descriptors that affect a polymer property (paragraph 93 and 95);
predict, via a trained machine learning model (paragraphs 94-96 and 99), a property of an input polymer (paragraphs 103 and 111); and
output the predicted property (paragraph 116).
Thompson does not explicitly teach a polymer property being a surface energy.
Zare teaches a polymer property being a surface energy (paragraph 115) and determining the surface energy of a polymer (paragraphs 58-59 and 115).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the invention to have modified Thompson to determine/predict surface energy as taught by Zare because Thompson teaches predicting different physical properties of a polymer (paragraphs 46-47 and 81-82) and suggests that other properties may also be predicted. Because surface energy is a physical property of a polymer, it would have been obvious to incorporate surface energy as one the properties of a polymer in Thompson to achieve predictable results of predicting physical properties using a predictive model.
Claims 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Thompson-Colón et al. (US 2021/0233618 A1) (‘Thompson’) in view of Zare et al. (US 2016/0251490 A1) (‘Zare’) as applied to claims 1 and 11 above, and further in view of Joshi et al. (US 2023/0160863 A1) (‘Joshi’).
With respect to claims 8 and 17, Thompson in view of Zare teaches selecting, by a selection operator.
Thompson in view of Zare does not explicitly teach wherein the selection operator comprises a least absolute shrinkage and selection operator (LASSO).
Joshi teaches wherein the selection operator comprises a least absolute shrinkage and selection operator (LASSO) (paragraph 63).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the invention to have modified Thompson to use a LASSO selection operator as taught by Joshi because it would only entail swapping the selection operator of Thompson with the LASSO operator of Joshi to enable predictable results of selecting a subset of descriptors.
Claims 9-10 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Thompson-Colón et al. (US 2021/0233618 A1) (‘Thompson’) in view of Zare et al. (US 2016/0251490 A1) (‘Zare’) as applied to claims 1 and 11 above, and further in view of Kojaku et al. (US 2013/0179380 A1) (‘Kojaku’).
With respect to claims 9 and 18, Thompson in view of Zare teaches a machine learning model.
Thompson in view of Zare does not explicitly teach wherein the machine learning model comprises a Gaussian Process Regression (GPR) comprising a radial basis function (RBF) kernel.
Kojaku teaches wherein the machine learning model comprises a Gaussian Process Regression (GPR) comprising a radial basis function (RBF) kernel (paragraphs 28, 37 and 121).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the invention to have modified Thompson to use a GPR model comprising RBF as taught by Kojaku because RBF kernels are widely used in non-linear prediction methods and can universally provide highly accurate predictions (Kojaku, paragraphs 28 and 37).
With respect to claim 10, Thompson in view of Zare and Kojaku wherein the GPR further comprises a five-fold cross-validation (paragraph 119).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALICIA M WILLOUGHBY whose telephone number is (571)272-5599. The examiner can normally be reached 9-5:30, EST, M-F.
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/ALICIA M WILLOUGHBY/Primary Examiner, Art Unit 2156 June 19, 2026
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