DETAILED ACTION
Applicant’s response, filed 5/26/2026, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-20 are currently pending and examined on the merits.
Priority
The instant application claims priority to U.S. Provisional Application 62/890,976 filed on 23 August 2019. At this point in examination, the effective filing date of claims 1-20 is 23 August 2019.
Drawings
The objection to Fig. 4 in the drawings filed 2/22/2022 is withdrawn in view of the amendments filed 5/26/2026.
Claim Objections
The objections to claims 9 and 15 are withdrawn in view of the claim amendments filed 5/26/2026.
Claim Rejections - 35 USC § 112
The previous rejections to claims 1-2 under 35 U.S.C. § 112(b) are withdrawn in view of the claim amendments filed 5/26/2026.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion). Any newly recited portions herein are necessitated by claim amendment.
Subject matter eligibility evaluation in accordance with MPEP 2106:
Eligibility Step 1: Claims 1-7 are directed to a system (machine). Claims 8-14 are directed to a method (process) of generating a predicted electrostatic solvation free energy of a protein. Claims 15-20 are directed to a system (machine). Therefore, these claims are encompassed by the categories of statutory subject matter, and thus satisfy the subject matter eligibility requirements under Step 1.
[Step 1: YES]
Eligibility Step 2A: First, it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception.
Eligibility Step 2A, Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth described in the claim.
Claims 1-20 recite the following steps which fall within the mental processes and/or mathematical concepts groups of abstract ideas, as noted below.
Independent claim 1 further recites:
identify a set of compounds based on one or more of a defined target clinical application, a set of target characteristics, and a defined class of compounds (i.e., mental processes);
pre-process each compound of the set of compounds to generate respective sets of feature data (i.e., mental processes);
process the sets of feature data with a trained Poisson-Boltzmann machine learning model to produce a plurality of predicted electrostatic solvation free energies for each compound of the set of compounds, wherein the sets of feature data include multi-weighted colored subgraph centralities (i.e., mental processes, mathematical concepts);
identify a subset of the set of compounds based on the plurality of predicted electrostatic solvation free energies (i.e., mental processes).
Dependent claim 2 further recites:
assign rankings to each compound of the set of compounds, wherein assigning a ranking to a given compound of the set of compounds for a given characteristic of the set of target characteristics (i.e., mental processes);
comparing a first predicted electrostatic solvation free energy corresponding to the given compound to other predicted electrostatic solvation free energies of other compounds of the set of compounds, wherein the ordered list is ordered according to the assigned rankings (i.e., mental processes).
Dependent claim 3 further recites:
calculate a plurality of multi-weighted colored subgraph centralities for the first protein (i.e., mental processes, mathematical concepts);
generate a feature vector that includes the multi-weighted colored subgraph centralities, wherein one of the sets of feature data includes the feature vector (i.e., mental processes);
process the feature vector with the Poisson-Boltzmann machine learning model to generate a predicted electrostatic solvation free energy of the first protein (i.e., mental processes, mathematical concepts).
Dependent claim 4 further recites:
to calculate a first multi-weighted colored subgraph centrality of the plurality of multi-weighted colored subgraph centralities for the first protein (i.e., mental processes, mathematical concepts);
define vertices for atoms of the first protein (i.e., mental processes);
define first edges corresponding to pairwise atomic interactions between the atoms of the first protein using a generalized Lorentz function (i.e., mental processes, mathematical concepts);
calculate first atomic centralities for each of the atoms of the first protein (i.e., mental processes, mathematical concepts);
sum the first atomic centralities to generate the first multi-weighted colored subgraph centrality (i.e., mental processes, mathematical concepts).
Dependent claim 5 further recites:
to calculate a second multi-weighted colored subgraph centrality of the plurality of multi-weighted colored subgraph centralities for the first protein (i.e., mental processes, mathematical concepts);
define second edges corresponding to pairwise atomic interactions between the atoms of the first protein using a generalized exponential function (i.e., mental processes, mathematical concepts);
calculate second atomic centralities for each of the atoms of the first protein (i.e., mental processes, mathematical concepts);
sum the second atomic centralities to generate the second multi-weighted colored subgraph centrality (i.e., mental processes, mathematical concepts).
Dependent claims 6, 13, and 19 further recite:
wherein the generalized exponential function and the generalized Lorentz function are weighted based on atomic rigidity (i.e., mental processes, mathematical concepts).
Dependent claims 7, 14, and 20 further recite:
wherein the generalized exponential function and the generalized Lorentz function are weighted based on atomic charge (i.e., mental processes, mathematical concepts).
Independent claim 8 further recites:
calculating, by a processor, a plurality of multi-weighted colored subgraph centralities for a protein (i.e., mental processes, mathematical concepts);
generating, by the processor, a feature vector that includes the multi-weighted colored subgraph centralities (i.e., mental processes);
executing, by the processor, a Poisson-Boltzmann machine learning model to process the feature vector to generate a predicted electrostatic solvation free energy of the protein (i.e., mental processes, mathematical concepts).
Dependent claim 9 further recites:
calculating, by the processor, a second plurality of multi-weighted colored subgraph centralities for a second protein (i.e., mental processes, mathematical concepts);
generating, by the processor, a second feature vector that includes the second multi-weighted colored subgraph centralities (i.e., mental processes);
executing, by the processor, a Poisson-Boltzmann machine learning model to process the second feature vector to generate a second predicted electrostatic solvation free energy of the second protein (i.e., mental processes, mathematical concepts).
Dependent claim 10 further recites:
assigning, by the processor, rankings to the protein and the second protein based on the first predicted electrostatic solvation free energy and the second predicted electrostatic solvation free energy (i.e., mental processes);
generating, by the processor, an ordered list that includes the protein and the second protein based on the rankings (i.e., mental processes).
Dependent claim 11 further recites:
calculating, by the processor, a first multi-weighted colored subgraph centrality of the plurality of multi-weighted colored subgraph centralities for the protein (i.e., mental processes, mathematical concepts);
defining, by the processor, vertices for atoms of the protein (i.e., mental processes);
defining, by the processor, first edges corresponding to pairwise atomic interactions between the atoms of the protein using a generalized Lorentz function (i.e., mental processes, mathematical concepts);
defining, by the processor, first atomic centralities for each of the atoms of the protein (i.e., mental processes);
summing, by the processor, the first atomic centralities to generate the first multi-weighted colored subgraph centrality (i.e., mental processes, mathematical concepts).
Dependent claim 12 further recites:
calculating, by the processor, a second multi-weighted colored subgraph centrality for the protein (i.e., mental processes, mathematical concepts);
defining, by the processor, second edges corresponding to pairwise atomic interactions between the atoms of the protein using a generalized exponential function (i.e., mental processes, mathematical concepts);
calculating, by the processor, second atomic centralities for each of the atoms of the protein (i.e., mental processes, mathematical concepts);
summing, by the processor, the second atomic centralities to generate the second multi-weighted colored subgraph centrality (i.e., mental processes, mathematical concepts).
Independent claim 15 further recites:
generate feature data corresponding to the protein (i.e., mental processes);
process the feature data with a trained Poisson-Boltzmann machine learning model to produce a predicted electrostatic solvation free energy of the protein (i.e., mental processes, mathematical concepts).
Dependent claim 16 further recites:
calculate multi-weighted colored subgraph centralities for the protein (i.e., mental processes, mathematical concepts);
generate a feature vector that includes the multi-weighted colored subgraph centralities, wherein the feature data includes the feature vector (i.e., mental processes);
process the feature vector with the Poisson-Boltzmann machine learning model to generate the predicted electrostatic solvation free energy of the protein (i.e., mental processes, mathematical concepts).
Dependent claim 17 further recites:
to calculate a first multi-weighted colored subgraph centrality of the multi-weighted colored subgraph centralities (i.e., mental processes, mathematical concepts);
define vertices for atoms of the protein (i.e., mental processes);
define first edges corresponding to pairwise atomic interactions between the atoms of the protein using a generalized Lorentz function (i.e., mental processes, mathematical concepts);
calculate the first atomic centralities for each of the atoms of the protein (i.e., mental processes, mathematical concepts);
sum the first atomic centralities to generate the first multi-weighted colored subgraph centrality (i.e., mental processes, mathematical concepts).
Dependent claim 18 further recites:
to calculate a second multi-weighted colored subgraph centrality of the multi-weighted colored subgraph centralities (i.e., mental processes, mathematical concepts);
define second edges corresponding to pairwise atomic interactions between the atoms of the protein using a generalized exponential function (i.e., mental processes, mathematical concepts);
calculate second atomic centralities for each of the atoms of the protein (i.e., mental processes, mathematical concepts);
sum the second atomic centralities to generate the second multi-weighted colored subgraph centrality (i.e., mental processes, mathematical concepts).
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a 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 1-20 recite an abstract idea.
[Step 2A, Prong One: YES]
Eligibility Step 2A, Prong Two: In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that, when examined as a whole, integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A, Prong One are not integrated into a practical application because of the reasons noted below.
Claims 1, 10, and 15 recite the additional non-abstract elements of data gathering:
display an ordered list of the subset of the set of compounds via an electronic display (claim 1);
causing, by the processor, the ordered list to be displayed at a user device (claim 10);
receive an identifier corresponding to a protein (claim 15).
which are each a data gathering step, or a description of the data gathered.
Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect the data needed to carry out the JE. The data gathering does not impose any meaningful limitation on the JE, or how the JE is performed. The additional limitation (data gathering) must have more than a nominal or insignificant relationship to the identified judicial exception. (MPEP 2106.04/.05, citing Intellectual Ventures LLC v. Symantee Corp, McRO, TLI communications, OIP Techs. Inc. v. Amason.com Inc., Electric Power Group LLC v. Alstrom S.A.).
Claims 1, 10, and 15 recite the additional non-abstract element (EIA) of a general-purpose computer system or parts thereof:
a system comprising: a non-transitory computer-readable memory and a processor configured to execute instructions stored on the non-transitory computer-readable memory (claims 1 and 15);
an electronic display (claim 1);
a user device (claim 10).
The EIA do not provide any details of how specific structures of the computer elements are used to implement the JE. The claims require nothing more than a general-purpose computer to perform the functions that constitute the judicial exceptions. The computer elements of the claims do not provide improvements to the functioning of the computer itself (as in DDR Holdings, LLC v. Hotels.com LP); they do not provide improvements to any other technology or technical field (as in Diamond v. Diehr); nor do they utilize a particular machine (as in Eibel Process Co. v. Minn. & Ont. Paper Co.). Hence, these are mere instructions to apply the JE using a computer, and therefore the claim does not recite integrate that JE into a practical application.
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 1-20 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. Claims 1, 10, and 15 contain additional elements that would not integrate a judicial exception into a practical application and are further probed for inventive concept in Step 2B.
[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.
With respect to claims 1, 10, and 15: The limitations identified above as non-abstract elements (EIA) related to data gathering do not rise to the level of significantly more than the judicial exception. Activities such as data gathering do not improve the functioning of a computer, or comprise an improvement to any other technical field. The limitations do not require or set forth a particular machine, they do not affect a transformation of matter, nor do they provide an unconventional step (citing McRO and Trading Technologies Int’l v. IBG). Data gathering steps constitute a general link to a technological environment. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp.,).
With respect to claims 1, 10, and 15: The limitations identified above as non-abstract elements (EIA) related to general-purpose computer systems do not rise to the level of significantly more than the judicial exception. These elements do not improve the functioning of the computer itself, or comprise an improvement to any other technical field (Trading Technologies Int’l v. IBG, TLI Communications). They do not require or set forth a particular machine (Ultramercial v. Hulu, LLC., Alice Corp. Pty. Ltd v. CLS Bank Int’l), they do not affect a transformation of matter, nor do they provide an unconventional step. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp., CyberSource v. Retail Decisions, Parker v. Flook, Versata Development Group v. SAP America).
[Step 2B: NO]
Therefore, claims 1-20 are patent ineligible under 35 U.S.C. § 101.
Response to Arguments
Applicant's arguments, see pages 10-14, filed 5/26/2026, with respect to the rejection of claims 1-20 under 35 U.S.C. 101, have been fully considered but they are not persuasive.
With respect to the Applicant’s argument that processing sets of feature data with a trained Poisson-Boltzmann machine learning model to produce predicted electrostatic solvation free energies for each compound of a set of compounds recited in claim 1 and calculating multi-weighted colored subgraph centralities using multiple kernel parameters (generalized Lorentz and exponential functions) for proteins recited in claims 4-7, 11-14, and 17-20 are not operations that can practically be performed in the human mind and involve computational complexity that precludes mental performance (pg. 10-11 of Applicant’s Remarks), this argument is not persuasive. Applicant argues that predicting electrostatic solvation free energies using a machine learning model and calculating multi-weighted colored subgraph centralities is performed on proteins containing thousands of atoms, which is not commensurate in scope with the claims. Under the broadest reasonable interpretation (BRI), large proteins are a set of compounds that can be of any size. Therefore, compounds can be selected by their relevant characteristics, characteristics of those compounds can be selected as feature data, and a subset of the compounds can be identified based on their predicted energies as recited in the claims because these are all steps that involve simple comparisons or evaluations between data values, which can be practically performed in the human mind. See MPEP 2106.04(a).
With respect to the Applicant’s argument that the processing of feature data through a trained Poisson-Boltzmann machine learning model is not merely a mental process or mathematical concept, but an additional element that results in improved computer functionality (pg. 12 of Applicant’s Remarks), this argument is not persuasive. Under Step 2A, Prong One, the claims are evaluated for whether they recite an abstract idea, law of nature, or natural phenomenon. Processing feature data with a machine learning model is a mental process or mathematical concept, which are abstract ideas. Therefore, this operation cannot be evaluated as an additional element for whether it integrates the judicial exception into a practical application, such as improvements to the functioning of a computer, under Step 2A, Prong Two. See MPEP 2106.04.
Applicant argues that the artificial neural network recited in claim 1 of USPTO Subject Matter Eligibility Example 47 (Anomaly Detection) is found eligible because there is no mathematical concept recited in the claim and thus is comparable to the trained Poisson-Boltzmann machine learning model, which also does not recite mathematical formulas or calculations used to train the model (pg. 12 of Applicant’s Remarks). Applicant asserts that USPTO Subject Matter Eligibility Example 48 (Speech Separation), which evaluates that synthesizing speech waveforms is merely based on or involves a mathematical concept but does not recite a mathematical concept, supports the analysis above (pg. 12-13 of Applicant’s Remarks). This argument is not persuasive. Example 47 recites an application specific integrated circuit (ASIC) for an artificial neural network (ANN) comprising neurons and synaptic circuits, where the ANN is trained to detect anomalies. Example 48 recites artificial intelligence-based methods of analyzing speech signals and separating desired speech from extraneous or background speech. These scenarios are different and not analogous to the instant claims because the instant claims only recite a machine learning model used to predict electrostatic solvation free energies of compounds, which do not include the structural recitations as in Example 47, claim 1, and Example 48. Furthermore, processing feature data with a Poisson-Boltzmann machine learning model to produce predicted electrostatic solvation free energy as recited in claims 1, 8, and 15 reads as inputting feature data into a series of mathematical equations that make up the machine learning model to output electrostatic solvation free energy. Therefore, this limitation is a verbal equivalent for a mathematical concept under the broadest reasonable interpretation (BRI). See MPEP 2106.04(a)(2).
With respect to the Applicant’s argument that the claims recite a specific technical improvement to the functioning of computer-implemented electrostatic analysis systems and are directed to a practical application of predicting electrostatic solvation free energy for compound analysis and identification (pg. 13-14 of Applicant’s Remarks), this argument is not persuasive. Processing feature data with a trained Poisson-Boltzmann machine learning model to produce predicted electrostatic solvation free energy as recited in claims 1, 8, and 15 is evaluated as a mental process or mathematical concept and do not contain additional elements that can integrate the claim into a practical application. It is important to note that the judicial exception alone cannot provide the improvement and that the improvement can only be provided by one or more additional elements. See MPEP 2106.05(a). Additionally, Applicant cites technical problems such as high-dimensional biomolecular data in machine learning prediction, which is narrower than the claims. Therefore, Applicant’s arguments are not commensurate in scope with the claimed invention. Furthermore, identifying compounds and assigning rankings to proteins recited in claims 1 and 10, respectively, are mental process steps, which cannot integrate the claims into a practical application. Displaying an ordered list of compounds and assigning rankings to proteins recited in claims 1 and 10, respectively, are data gathering steps. Additional elements identified as data gathering steps do not affect how the steps of the abstract idea are performed; they provide the data which is acted upon by the limitations of the JE. These data gathering steps do not apply, rely on, or use the steps identified as making up the JE. Rather, the method steps avail themselves of the data gathered. The data gathering in the claims constitutes insignificant pre-solution activity. See MPEP § 2106.05(g).
Therefore, the rejection to claims 1-20 under 35 U.S.C. § 101 is maintained with modifications as necessitated by amendment of the claims, filed 5/26/2026.
Claim Rejections - 35 USC § 103
The rejection of claims 1-5, 8-12, and 16-18 under 35 U.S.C. 103 as being unpatentable over Nguyen et al. (Journal of Computer-Aided Molecular Design, 2019, 33, 1-22), as provided in the IDS filed 8/6/2025, referred to as Nguyen [A], is withdrawn in view of the arguments filed 5/26/2026.
The rejection of claims 6, 13, 15, and 19 under 35 U.S.C. 103 as being unpatentable over Nguyen et al. (Journal of Computer-Aided Molecular Design, 2019, 33, 1-22), referred to as Nguyen [A], as applied to claims 1-5, 8-12, and 16-18 above, in view of Bramer et al. (The Journal of Chemical Physics, 2018, 148(5), 1-14), is withdrawn in view of the arguments filed 5/26/2026.
The rejection of claims 7, 14, and 20 under 35 U.S.C. 103 as being unpatentable over Nguyen et al. (Journal of Computer-Aided Molecular Design, 2019, 33, 1-22), referred to as Nguyen [A], as applied to claims 1-5, 8-12, and 16-18 above, in view of Nguyen et al. (International Journal for Numerical Methods in Biomedical Engineering, 2018, 35(3), 1-28); refer to as Nguyen [B], is withdrawn in view of the arguments filed 5/26/2026.
Response to Arguments
Applicant’s arguments, see pages 14-16, filed 5/26/2026, with respect to claims 1, 8, and 15 have been fully considered and are persuasive. The rejections of claims 1-20 have been withdrawn.
With respect to the Applicant’s argument that Nguyen [A] fails to disclose or suggest a trained Poisson-Boltzmann machine learning model that predicts electrostatic solvation free energy as recited by claims 1, 8, and 15, and that Nguyen [A]’s free energy prediction does not correspond to electrostatic solvation free energy (pg. 15-16 of Applicant’s Remarks), this argument is persuasive. Therefore, the rejections of claims 1-20 are withdrawn. Examiner notes that claims 1-20 are free from the prior art as the prior art does not teach nor fairly suggest using machine learning models to predict electrostatic solvation free energy as recited in claims 1, 8, and 15.
Conclusion
No claims are allowed.
It is noted that claims 1-20 are free from the prior art as the prior art does not teach nor fairly suggest processing sets of feature data with a trained Poisson-Boltzmann machine learning model to produce predicted electrostatic solvation free energies for each compound of a set of compounds in claim 1, executing a Poisson-Boltzmann machine learning model to process a feature vector to generate a predicted electrostatic solvation free energy of a protein in claim 8, and processing feature data with a trained Poisson-Boltzmann machine learning model to produce a predicted electrostatic solvation free energy of a protein in claim 15. The closest prior art is Lim et al. (Chemical Science, 2019, 10(36), 8306-8315). Lim et al. discloses Delfos (deep learning model for solvation free energies in generic organic solvents), which is a machine learning-based quantitative structure-property relationship (QSPR) method that predicts solvation free energies for various organic solute and solvent systems by encoding chemical structures of input compounds into feature vectors and using the feature vectors to calculate solvation free energy (pg. 8306, Abstract, lines 4-11; pg. 8313, col. 1, para. 2). However, Lim et al. is silent to processing feature data with a machine learning model to predict electrostatic solvation free energy in particular, as required by the instant claims.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jammy Luo whose telephone number is (571)272-2358. The examiner can normally be reached Monday - Friday, 9:00 AM - 5:00 PM EST.
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/J.N.L./Examiner, Art Unit 1686
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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