DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-20 are currently pending and under examination herein.
Claims 1-20 are rejected.
Priority
The instant application also claims benefit to U.S. provisional application No. 63409549 filed on 09/23/2022. Domestic benefit is acknowledged. As such, the effective filing date of claims 1-20 is 09/23/2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/24/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. A signed copy of a list of references cited from each IDS is included in this Office Action.
Drawings
Color photographs and color drawings are not accepted in utility applications unless a petition filed under 37 CFR 1.84(a)(2) is granted. Any such petition must be accompanied by the appropriate fee set forth in 37 CFR 1.17(h), one set of color drawings or color photographs, as appropriate, if submitted via the USPTO patent electronic filing system or three sets of color drawings or color photographs, as appropriate, if not submitted via the via USPTO patent electronic filing system, and, unless already present, an amendment to include the following language as the first paragraph of the brief description of the drawings section of the specification:
The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.
Color photographs will be accepted if the conditions for accepting color drawings and black and white photographs have been satisfied. See 37 CFR 1.84(b)(2).
Specification
The specification submitted on 9/24/2023 is accepted.
Duplicate Claim Warning
Applicant is advised that should claim 4 be found allowable, claim 5 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
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 1-9, 15, and 18-20 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 written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
With respect to claims 1-9 and 18-20, the claims recite a prediction pipeline in which molecular information is provided to or used by an artificial neural network to generate a prediction regarding formation of a deep eutectic solvent. However, it is unclear from the specification how the claimed prediction pipeline is able to perform the claimed prediction of whether a candidate mixture will form a deep eutectic solvent, particularly where the claims require an artificial neural network to generate such a prediction. Although the specification generally identifies SMILES or vectorized representations of molecular structures as inputs to the neural network, it does not appear to adequately describe the particular molecular features or representations derived from those inputs and used by the neural network to generate the claimed prediction. Particularly, Cappelluti et al. (Cappelluti, F., Mariani, A., Bonomo, M., Damin, A., Bencivenni, L., Passerini, S., Carbone, M., & Gontrani, L. (2022). Stepping away from serendipity in Deep Eutectic Solvent formation: Prediction from precursors ratio. Journal of Molecular Liquids, 367, 120443.) discloses a specific predictive molecular procedure where classical molecular-dynamics simulations are required to assess the extent of DES formation for each mixture (see “3. Conclusion” where thermal and spectroscopic measurements allow for the ability to undoubtedly categorize certain molecules as DES or a simple mixture of precursors; see also the requirement for ΔTm derivation for a mixture to be categorized as DES). Thus, Cappelluti provides an example where candidate molecules alone do not constitute predictive methodology, but rather the reference identifies specific molecular-dynamics-based analysis that provides the computational information to generate the prediction. As the claimed invention does not sufficiently describe the particular molecular features or representations derived from those inputs in the prediction pipeline, there is a lack of written description.
Additionally, with respect to claim 15, the claim recites “providing an accurate score to the set of predictive deep eutectic solvents; and, providing the accurate score to the artificial neural network to recursively train the artificial neural network”. Although the specification does describe an element of active learning in paragraph [0041], there is no description as to what an “accurate score” is, how it is generated, or how the score itself recursively trains the artificial neural network.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
In accordance with MPEP 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature, or natural phenomenon (Step 2A, Prong 1).
Claim 1 recites predicting their probability of formation using a set of artificial neural network algorithms and the initial group of molecules, selecting a first mixture from the set of mixtures predicted by the set of artificial neural network algorithms, wherein the first mixture comprises molecules not present in the training portion, comparing test mixture within the set of mixtures with the testing portion to provide a confidence score for each artificial neural network algorithm in the set of artificial neural network algorithms.
Claim 7 recites generating a predicted mixture using a first artificial neural network algorithm, comparing a probability of formation of the generated set of mixtures with the testing portion includes, comparing a first stability value of the test mixture with a second stability value of the first mixture.
Claim 18 recites providing an artificial neural network trained with a dataset of existing deep eutectic solvents having a training portion and a testing portion wherein the training portion has a record size larger than that of the testing portion.
Claim 19 recites generating a confidence value for each of the predicted deep eutectic solvents in the set of predicted deep eutectic solvents.
The limitations reciting predicting their probability of formation using a set of artificial neural network algorithms and the initial group of molecules and generating a confidence value for each of the predicted deep eutectic solvents in the set of predicted deep eutectic solvents; and providing an artificial neural network trained with a dataset of existing deep eutectic solvents having a training portion and a testing portion wherein the training portion has a record size larger than that of the testing portion equate to verbal equivalents of mathematical calculations and fall under the “mathematical concept” grouping of ideas.
The limitations of selecting a first mixture from the set of mixtures predicted by the set of artificial neural network algorithms, wherein the first mixture comprises molecules not present in the training portion, comparing test mixture within the set of mixtures with the testing portion to provide a confidence score for each artificial neural network algorithm in the set of artificial neural network algorithms; comparing a probability of formation of the generated set of mixtures with the testing portion includes, comparing a first stability value of the test mixture with a second stability value of the first mixture fall under the “mental process” grouping of ideas. Selection, comparison, and evaluation are all steps that can be performed practically in the human mind or with a pen and paper and are therefore mental processes. The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 and Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016). As such, claims 1-20 recite abstract ideas.
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). This judicial exception is not integrated into a practical application because the claims do not recite additional elements that reflects an improvement to technology or applies or uses the recited judicial exception in some other meaningful way. Rather, the instant claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment. Specifically, the claims recite the following additional elements:
Claims 1 and 10 recites a computerized method of predicting the formation of deep eutectic solvents, from a mixture of molecules comprising: providing an initial dataset of existing deep eutectic solvents, divided into a training portion and a testing portion; providing an initial group of molecules, providing a set of representations for the initial group of molecules, generating a set of mixtures, and, providing the set of artificial neural network algorithms in confidence score order to a user.
Claim 2 recites providing the initial dataset having a set of compounds using a simplified molecular input line entry system.
Claim 3 recites providing the initial group of molecules using a simplified molecular input line entry system
Claim 4 recites providing the initial group of molecules using a vectorized representation of the initial group of molecules.
Claim 5 recites providing the initial group of molecules using a vectorized representation of the initial group of molecules.
Claim 6 recites providing the initial dataset wherein each deep eutectic solvent has a designation of stable or not stable.
Claim 7 recites generating a predicted mixture using a first artificial neural network algorithm.
Claims 8 and 11 recites wherein the training portion has a set of records numbering greater than 50% of the records in the initial dataset.
Claims 9 and 12 recites the training portion has a set of records in a range of 50% to 90% of the records in the initial dataset.
Claim 14 recites displaying a subset of the set of predictive deep eutectic solvents having a stability probability higher than 50%.
Claim 15 recites the set of predictive natural deep eutectic solvents is a first set of predictive deep eutectic solvents; providing a desired molecule; generating a second set of predictive deep eutectic solvents according to an artificial neural network and the desired molecule; providing an accurate score to the set of predictive deep eutectic solvents; and, providing the accurate score to the artificial neural network to recursively train the artificial neural network.
Claim 16 recites the method of claim 10 wherein the artificial neural network includes a binary classifier.
Claim 17 recites the training portion includes randomly generated deep eutectic solvents.
Claim 18 recites a computerized method of predicting deep eutectic mixtures from a molecule comprising: providing an initial molecule, generating a set of predicted deep eutectic solvents according to an artificial neural network and the initial molecule, generating a set of predicted deep eutectic solvents and, displaying the set of predicted deep eutectic solvents to a user.
Claim 20 recites displaying a subset from the set of predicted natural deep eutectic solvents having a confidence value greater than a predetermined value.
The limitations reciting providing an initial dataset of existing deep eutectic solvents, divided into a training portion and a testing portion; providing an initial group of molecules, providing a set of representations for the initial group of molecules; generating a set of mixtures; providing the set of artificial neural network algorithms in confidence score order to a user; generating a predicted mixture using a first artificial neural network algorithm; providing a desired molecule; generating a second set of predictive deep eutectic solvents according to an artificial neural network and the desired molecule; providing an accurate score to the set of predictive deep eutectic solvents; providing the accurate score to the artificial neural network to recursively train the artificial neural network; providing an initial molecule, generating a set of predicted deep eutectic solvents according to an artificial neural network and the initial molecule, generating a set of predicted deep eutectic solvents; displaying the set of predicted deep eutectic solvents to a user; and displaying a subset from the set of predicted natural deep eutectic solvents having a confidence value greater than a predetermined value all constitute data gathering, processing, or outputting which equates to insignificant extra solution activity. Of note, the courts have ruled in Electric Power Group, LLC V. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016) that the collection, analysis, and display of data are considered insignificant extra-solution activity and does not integrate the judicial exception into a practical application (see MPEP 2106.05(g)).
The limitations reciting providing the initial dataset having a set of compounds using a simplified molecular input line entry system; providing the initial group of molecules using a simplified molecular input line entry system; providing the initial group of molecules using a vectorized representation of the initial group of molecules; providing the initial dataset wherein each deep eutectic solvent has a designation of stable or not stable; the training portion has a set of records numbering greater than 50% of the records in the initial dataset; the training portion has a set of records in a range of 50% to 90% of the records in the initial dataset; wherein the artificial neural network includes a binary classifier; and the training portion includes randomly generated deep eutectic solvents merely serve to further limit the insignificant extra solution activity and do not integrate the abstract idea into a practical application.
Furthermore, there are no limitations that indicate that the claimed computer, processor, input device or computer-readable medium require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. As such, claims 1-20 do not integrate the abstract idea into a practical application.
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic field-of-use and/or technological environment. The instant claims recite the following additional elements:
Claims 1 and 10 recites a computerized method of predicting the formation of deep eutectic solvents, from a mixture of molecules comprising: providing an initial dataset of existing deep eutectic solvents, divided into a training portion and a testing portion; providing an initial group of molecules, providing a set of representations for the initial group of molecules, generating a set of mixtures, and, providing the set of artificial neural network algorithms in confidence score order to a user.
Claim 2 recites providing the initial dataset having a set of compounds using a simplified molecular input line entry system.
Claim 3 recites providing the initial group of molecules using a simplified molecular input line entry system
Claim 4 recites providing the initial group of molecules using a vectorized representation of the initial group of molecules.
Claim 5 recites providing the initial group of molecules using a vectorized representation of the initial group of molecules.
Claim 6 recites providing the initial dataset wherein each deep eutectic solvent has a designation of stable or not stable.
Claim 7 recites generating a predicted mixture using a first artificial neural network algorithm.
Claims 8 and 11 recites wherein the training portion has a set of records numbering greater than 50% of the records in the initial dataset.
Claims 9 and 12 recites the training portion has a set of records in a range of 50% to 90% of the records in the initial dataset.
Claim 14 recites displaying a subset of the set of predictive deep eutectic solvents having a stability probability higher than 50%.
Claim 15 recites the set of predictive natural deep eutectic solvents is a first set of predictive deep eutectic solvents; providing a desired molecule; generating a second set of predictive deep eutectic solvents according to an artificial neural network and the desired molecule; providing an accurate score to the set of predictive deep eutectic solvents; and, providing the accurate score to the artificial neural network to recursively train the artificial neural network.
Claim 16 recites the method of claim 10 wherein the artificial neural network includes a binary classifier.
Claim 17 recites the training portion includes randomly generated deep eutectic solvents.
Claim 18 recites a computerized method of predicting deep eutectic mixtures from a molecule comprising: providing an initial molecule, generating a set of predicted deep eutectic solvents according to an artificial neural network and the initial molecule, generating a set of predicted deep eutectic solvents and, displaying the set of predicted deep eutectic solvents to a user.
Claim 20 recites displaying a subset from the set of predicted natural deep eutectic solvents having a confidence value greater than a predetermined value.
The limitations reciting providing the initial dataset having a set of compounds using a simplified molecular input line entry system; providing the initial group of molecules using a vectorized representation of the initial group of molecules; providing the initial group of molecules using a vectorized representation of the initial group of molecules; providing the initial dataset wherein each deep eutectic solvent has a designation of stable or not stable; displaying a subset of the set of predictive deep eutectic solvents having a stability probability higher than 50%; and displaying a subset from the set of predicted natural deep eutectic solvents having a confidence value greater than a predetermined value all amount to generic data computing activity which is well-understood, routine, and conventional functions. Specifically, the courts have identified steps of receiving data over a network or storing and retrieving information in memory as conventional computer functions in Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC V. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., V. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. V. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); and Versata Dev. Group, Inc. V. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). In addition, the courts have also ruled that both electronic record keeping and storing and retrieving information in memory are well-understood, routine, and conventional (see Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining "shadow accounts"); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log) and Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93). Although there are further limitations on the type of data transmitted or displays of data, the recited functions still amount to generic computing activities which are conventional.
The limitations reciting the set of predictive natural deep eutectic solvents is a first set of predictive deep eutectic solvents; providing a desired molecule; generating a second set of predictive deep eutectic solvents according to an artificial neural network and the desired molecule; providing an accurate score to the set of predictive deep eutectic solvents; and, providing the accurate score to the artificial neural network to recursively train the artificial neural network; generating a predicted mixture using a first artificial neural network algorithm are well-understood, routine, and conventional. As explained with respect to Step 2A, Prong Two, without sufficient details of the neural network and recited at a high level of generality, the recited limitations are at best mere instructions to apply the abstract ideas which cannot provide an inventive concept. See MPEP 2106.05(f). In addition, the recitations of generating data (e.g. solvent data, score, or predicted mixture) from the neural network also amount to receiving or transmitting data over a network and are well understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept.
Additionally, the limitations reciting providing an initial dataset of existing deep eutectic solvents, divided into a training portion and a testing portion; providing an initial group of molecules, providing a set of representations for the initial group of molecules, generating a set of mixtures, and, providing the set of artificial neural network algorithms in confidence score order to a user; generating a predicted mixture using a first artificial neural network algorithm; the training portion has a set of records numbering greater than 50% of the records in the initial dataset; the training portion has a set of records in a range of 50% to 90% of the records in the initial dataset; wherein the artificial neural network includes a binary classifier; the training portion includes randomly generated deep eutectic solvents; and providing an initial molecule, generating a set of predicted deep eutectic solvents according to an artificial neural network and the initial molecule, generating a set of predicted deep eutectic solvents and, displaying the set of predicted deep eutectic solvents to a user amount to well-understood, conventional, and routine activities for the reasons aforementioned, but also evidenced by Brownlee, where the train-test-split architecture has been widely used to estimate the performance of machine learning algorithms, particularly with binary classification and provides a subsequent confidence score (e.g. mean absolute error) (see “Train-Test Split to Evaluate Machine Learning Models” in attached document).
There are no additional elements that comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-20 are not patent eligible.
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.
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.
The present rejection(s) reference specific passages from cited prior art. However,
Applicant is advised that the rejections are based on the entirety of each cited prior art. That is,
each cited prior art reference “must be considered in its entirety”. (See MPEP 2141.02(VI))
Therefore, Applicant is advised to review all portions of the cited prior art if traversing a
rejection based on the cited prior art.
Claims 1-20 are free from prior art.
Regarding claim 1, Khajeh (Quantitative structure-property relationship for melting and freezing points of deep eutectic solvents. Journal of Molecular Liquids, 321, 114744) teaches:
A computerized method of predicting the formation of deep eutectic solvents, from a mixture of molecules (see “Abstract”; see page 1) comprising:
providing an initial dataset of existing deep eutectic solvents, divided into a training portion and a testing portion (see Fig. 1 where the data collection and data base development step is divided into a training set and a test set; see also “2.1 Dataset” for discussion on page 2));
providing an initial group of molecules, providing a set of representations for the initial group of molecules (see “2.2 Molecular Descriptors” with the step of randomly generating the initial population of descriptors where descriptors of hydrogen bond donor and cation are calculated per each deep eutectic solvents; see also Fig. 1),
generating a set of mixtures (synthesized DES is based on Abbott et al. which defines a deep eutectic solvent as a mix between a quaternary salt and a hydrogen bond donor in “1. Introduction” on page ,
Although Khajeh is the closest piece of prior art of record, Khajeh does not teach predicting their probability of formation using a set of artificial neural network algorithms and the initial group of molecules, selecting a first mixture from the set of mixtures predicted by the set of artificial neural network algorithms, wherein the first mixture comprises molecules not present in the training portion, comparing test mixture within the set of mixtures with the testing portion to provide a confidence score for each artificial neural network algorithm in the set of artificial neural network algorithms, and, providing the set of artificial neural network algorithms in confidence score order to a user. Therefore, claim 1 is free from prior art. Claims 2-20 are free from prior art by virtue of dependency.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Hansen (Benworth B. Hansen, Stephanie Spittle, Brian Chen, Derrick Poe, Yong Zhang, Jeffrey M. Klein, Alexandre Horton, Laxmi Adhikari, Tamar Zelovich, Brian W. Doherty, Burcu Gurkan, Edward J. Maginn, Arthur Ragauskas, Mark Dadmun, Thomas A. Zawodzinski, Gary A. Baker, Mark E. Tuckerman, Robert F. Savinell, Joshua R. Sangoro; Deep Eutectic Solvents: A Review of Fundamentals and Applications. Chem. Rev. 10 February 2021; 121 (3): 1232–1285) discloses that machine learning is a popular new technique that has been implemented in deep eutectic solvent modeling pipelines, particularly, using artificial neural networks (see “5.2.4. Machine Learning” from pages 1265-1266).
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/P.N./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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