DETAILED ACTION
Applicant's response, filed 16 March 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 .
Priority
The instant application claims benefit of priority to U.S. Provisional Application No. 63/142,074
filed on 01/27/2021. The claim to the benefit of priority is acknowledged. As such, the effective filing
date of claims 1-20 is 01/27/2021.
Claim Status
Claims 1-20 are pending.
Claims 1-20 are rejected.
Drawings
Response to Amendment
In view of applicant’s amendments to the drawings, previous objections to the drawings over color drawings are withdrawn.
Claim Objections
Response to Amendment
In view of applicant’s amendments to the claims, specifically claim 11, previous objections to the claims over minor informalities are withdrawn.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-10 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claims contain 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. Claim 1 has been amended to specify the limitations of creating a first and second random molecule, however the specification provides no detail or support of the limitation. More specifically, the specification in paragraphs [0036] and [0041] use the word synthesize and synthesis as directed to molecules but only in regards to descriptions of ease of synthesis or description of the problem that is attempted to be solved, i.e. “For example, Remdisivir® has shown great potential as a candidate drug for COVID-19 throughout the current global pandemic due to its binding affinity to the virus' ACE2 receptor, but presents challenges in the production of an adequate global supply due to the complexity required to synthesize the molecule”.
Furthermore, the specification in paragraphs [0034], [0037], [0057], [0080], and [0084] uses the words create/creates/created, however the latter three uses do not pertain to the creation of a molecule, and paragraph [0037] is using it in terms of something that has already been created. This leaves paragraph [0034] which states “Present embodiments provide for targeted molecular design wherein a user may be presented with newly-designed molecules that are automatically organized, easy to understand, along with sortable measurements thereof, allowing the user to immediately view side-by-side comparisons of all relevant properties in the newly-designed molecules. In one embodiment, "Fully Autonomous Molecular Evolution" (FAME) may execute a program to continuously measure all newly generated molecules, strategically select the top molecules based on the closeness of their properties as a fit to the desired molecule properties, for example those generated molecules having the lowest binding affinity value , i.e., for example, the lowest Ko value, where Ko is one measure of equilibrium disassociation constant (and thus the highest likelihood to bind to the receptor), those having the lowest molecular weight, etc., and use these molecules and their properties to continuously retrain itself to create molecules having better binding affinity values, lower molecular weight, etc.”, which is merely an intended use not the actual creation of the molecule itself. Therefore, it is not disclosed within the specification that the method is itself synthesizing new molecules, but is rather performing analysis on the already synthesized molecules, and as such there is no support within the description for the synthesis of the molecules themselves.
Claim Rejections - 35 USC § 101
Response to Amendment
In view of applicant’s amendments to the claims, previous rejections under 35 U.S.C. 101 have been reviewed, updated, and provided below. In the case of claims 1-10 rejections under 35 U.S.C. 101 have been withdrawn.
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 11-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more. The claims recite a method of designing molecules using properties/features of molecules to modify and then score and rank candidate molecules. The judicial exception is not integrated into a practical application because while claims 11-20 attempt to integrate the exception into a practical application, said application is either generically recited computer elements that do not add a meaningful limitation to the abstract idea or it is insignificant extra solution activity and merely implementing the abstract idea on a computer. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer elements only store and retrieve information in memory as well as perform basic calculations that are known to be well-understood, routine and conventional computer functions as recognized by the decisions listed in MPEP § 2106.05(d).
Framework with which to Analyze Subject Matter Eligibility:
Step 1: Are the claims directed to a category of stator subject matter (a process, machine, manufacture, or composition of matter)? [See MPEP § 2106.03]
Claims are directed to statutory subject matter, specifically a method (Claims 1-20)
Step 2A Prong One: Do the claims recite a judicially recognized exception, i.e., an abstract idea, a law of nature, or a natural phenomenon? [See MPEP § 2106.04(a)]
The claims herein recite abstract ideas, specifically mental processes and mathematical concepts.
With respect to the Step 2A Prong One evaluation, the instant claims are found herein to recite abstract ideas that fall into the grouping of mental processes and mathematical concepts.
Claim 11: Adding one or more head start molecules to a molecular database, measuring the added molecules against metrics, assigning scores for each secondary metric, selecting one or more head start molecules, training a model using the head start molecules, and generating one or more generations of new molecules, are processes of aquiring/retaining, designing, partitioning/selecting, and calculating/comparing information/data that can all be done with pen and paper or in the human mind and are therefore, abstract ideas, specifically mental processes.
Claim 12: Designating a defined number of head start molecules to be used for training is a process of partitioning/selecting information/data that can all be done with pen and paper or in the human mind and are therefore, abstract ideas, specifically mental processes.
Claim 13: Designating a secondary defined number of head start molecules to be used for training is a process of partitioning/selecting information/data that can all be done with pen and paper or in the human mind and are therefore, abstract ideas, specifically mental processes.
Claim 14: The second defined number being less than the first defined number is a process of partitioning/selecting information/data that can all be done with pen and paper or in the human mind and are therefore, abstract ideas, specifically mental processes.
Claim 15: Selecting a receptor, selecting a portion of a new molecule, and determining the binding affinity are processes of partitioning/selecting, and calculating information/data that can all be done with pen and paper or in the human mind and are therefore, abstract ideas, specifically mental processes.
Claim 16: Posing the new molecule in different poses, and determining the binding affinity in each pose are processes of designing, comparing, and calculating information/data that can all be done with pen and paper or in the human mind and are therefore, abstract ideas, specifically mental processes.
Claim 17: Selecting a first defined number of new molecules, and generating a second generation of new molecules are processes of partitioning/selecting, and designing information/data that can all be done with pen and paper or in the human mind and are therefore, abstract ideas, specifically mental processes.
Claim 18: Selecting a second defined number of new molecules, and generating a second generation of new molecules are processes of partitioning/selecting, and designing information/data that can all be done with pen and paper or in the human mind and are therefore, abstract ideas, specifically mental processes.
Claim 19: Randomly selecting a head start molecule, and generating new molecules are processes of partitioning/selecting, and designing information/data that can all be done with pen and paper or in the human mind and are therefore, abstract ideas, specifically mental processes.
Claim 20: Displaying a table are processes of showing information/data that can all be done with pen and paper or in the human mind and are therefore, abstract ideas, specifically mental processes.
Step 2A Prong Two: If the claims recite a judicial exception under prong one, then is the judicial exception integrated into a practical application? [See MPEP § 2106.04(d) and MPEP § 2106.05(a)-(c) & (e)-(h)]
Because the claims do recite judicial exceptions, direction under Step 2A Prong Two provides that the claims must be examined further to determine whether they integrate the abstract ideas into a practical application.
The following claims recite the following additional elements in the form of non-abstract elements:
Claim 11: Accessing a molecular database is an insignificant extra solution activity, specifically mere data gathering (See Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989) and Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis)) [See MPEP § 2106.05(g)].
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept? [See MPEP § 2106.05]
Because the additional claim elements do not integrate the abstract idea into a practical application, the claims are further examined under Step 2B, which evaluates whether the additional elements, individually and in combination, amount to significantly more than the judicial exception itself by providing an inventive concept.
The claims do not recite additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that are generic, conventional or nonspecific. These additional elements include:
The additional element of accessing a molecular database, is a process of storing and retrieving information in memory as described with paragraph [0053] of the specification, and is thus an insignificant extra solutional activity, specifically mere data gathering, that is recognized as well understood, routine and conventional by the courts (See Storing and retrieving information in memory, 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) [See MPEP § 2106.05(g)]. Therefore, taken both individually and as whole, the additional element does not amount to significantly more than the judicial exception by providing an inventive concept.
Therefore, claims 11-20, when the limitations are considered individually and as a whole, are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant’s arguments, see pages 13-14 of the Remarks, filed 3/16/2026, with respect to the rejection of claims 1-10 under 35 U.S.C. 101 have been fully considered and are persuasive. Specifically the claim integrates non-abstract elements into the claims that cannot be performed within the human mind and are significantly more, specifically in the creation of a first and second molecule. The rejection of claims 1-10 has been withdrawn.
Applicant's arguments filed 3/16/2026 have been fully considered but they are not persuasive.
Applicant asserts on page 13 of the Remarks filed 3/16/2026 that all claims include the generating of a molecule and cites “providing a random molecule” from claim 11. However, the providing of a random molecule is not
Claim Rejections - 35 USC § 102
Response to Amendment
In view of applicant’s amendments to the claims, previous rejections under 35 U.S.C. 102 for anticipation under the prior art, have been withdrawn.
Response to Arguments
Applicant’s arguments, see pages 9-11 of the Remarks, filed 3/16/2026, with respect to the rejection of claims 1-5 and 7-10 have been fully considered and are persuasive. The rejection of claims 1-5 and 7-10 has been withdrawn.
Claim Rejections - 35 USC § 103
Response to Amendment
In view of applicant’s amendments to the claims, previous rejections under 35 U.S.C. 103 for obviousness under the prior art, have been reviewed, updated, and provided below.
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.
Claims 1-5 and 7-10 are rejected under 35 U.S.C. 103 as being unpatentable over Fleishman et al. (US 20170032079 A1) and Nilsson et al. (Annual Review Biophysical Biomolecular Structure (2005) 91-118).
Claim 1 is directed to a method of designing molecules having one or more desired properties via creating/altering compounds and comparing the properties of designed compounds with desired thresholds to score and evaluate them.
Fleishman et al. teaches in claim 6 “A method of computationally designing a modified polypeptide chain starting from an original polypeptide chain”, in claim 1 “wherein said substitutions are modifying the designed protein relative to a corresponding wild type protein, as determined by at least one of: a thermal denaturation temperature of the designed protein being equal or higher than a thermal denaturation temperature of the wild type protein; a solubility of the designed protein being equal or higher than a solubility of the wild type protein; a degree of misfolding of the designed protein being equal or lower than a degree of misfolding of the wild type protein; a half-life of the designed protein being equal or longer than a half-life of the wild type protein; a specific activity of the designed protein being equal or higher than a specific activity of the wild type protein; and a recombinant expression level of the designed protein being equal or higher than a recombinant expression level of the wild type protein”, reading on a method of designing molecules having one or more desired properties, comprising: providing a plurality of known molecules having known properties as a first dataset. Fleishman et al. teaches in claim 6 “the method comprising: (i) determining unsubstitutable positions and substitutable positions in an amino acid Commented [OW22]: Claim 3 is included below but not mentioned here sequence of the original polypeptide chain; (ii) determining at least one position-specific amino acid alternative for each of said substitutable positions, and determining a position-specific stability scoring for each of said amino acid alternative; (iii) combinatorially generating a plurality of designed sequences, each of said designed sequences corresponds to a modified polypeptide chain; (iv) sorting said plurality of designed structures according to a minimized energy scoring, said minimized energy scoring is determined by subjecting each of said designed structures to an energy minimization”, reading on based on the properties of the known molecules, creating a plurality of new molecules having a structure different than that of at least one of the known molecules as a second data set; evaluating the properties of the second dataset of molecules with respect to the desired properties to provide a score. Fleishman et al. teaches in paragraph [0083] “…the input of the iterative combinatorial design step of the method provided herein, according to some embodiments of the present invention…”, and in paragraph [0118] “…energy minimization may include iterations of rotamer sampling (repacking) followed by side chain and backbone minimization”, an iteration or iterative process being in which the process is repeated until some specified stopping point thereby reading on selecting a plurality of molecules from the second data set based on the score thereof to provide a nth scored dataset; based on the properties of the second molecules, creating a plurality of new molecules in the nth data set; selecting a plurality of molecules from the nth data set based on the score thereof to provide a nth + 1 scored dataset; and repeating the acts of creating a plurality of new molecules based on the nth+1 data set to create the nth+2 data set; selecting a plurality of molecules from the nth +2 data set based on the score thereof to provide a nth + 3 scored dataset and using the second random molecule to introduce mutation. Furthermore, Fleishman et al. teaches in paragraph [0305] “The amino acid sequences of the selected modified polypeptide chains can be used to produce the corresponding proteins, using any protein synthesizer or a biologic recombinant expression system. Thus, according to another aspect of some embodiments of the present invention, there is provided a method of producing a designed protein”, reading on creating a first molecule and creating a second random molecule. Fleishman et al. teaches in paragraph [0317] “In some embodiments of the present invention, the structural fold of the designed protein is that of an antibody”.
Nilsson et al. teaches in the abstract “Proteins have become accessible targets for chemical synthesis. The basic strategy is to use native chemical ligation, Staudinger ligation, or other orthogonal chemical reactions to couple synthetic peptides. The ligation reactions are compatible with a variety of solvents and proceed in solution or on a solid support. Chemical synthesis enables a level of control on protein composition that greatly exceeds that attainable with ribosome-mediated biosynthesis”, reading on combining the first random molecule with the plurality of known molecules and combining the selected plurality of molecules from the second dataset with the second random molecules, to create a set of second molecules.
It would have been obvious at the time of first filing to have modified the teachings of Fleishman et al. for the method of designing molecules, with the teachings of Nilsson et al. for the use of protein synthesis through the binding of molecules together as the latter teaches in the abstract “Chemical synthesis enables a level of control on protein composition that greatly exceeds that attainable with ribosome-mediated biosynthesis. Accordingly, the chemical synthesis of proteins is providing previously unattainable insight into the structure and function of proteins”. One would have had a reasonable expectation of success given that both of the articles are directed to the creation of new proteins, one through the use of in silico methods and the other through the use of chemical synthesis (combining molecules), and while the former teaches the synthesis of proteins it does not specify or limit the methods used – paragraph [0305] “The amino acid sequences of the selected modified polypeptide chains can be used to produce the corresponding proteins, using any protein synthesizer”. Therefore, it would have been obvious at the time of first filing to have modified the teachings of each and to be successful.
Claim 2 is directed to the method of claim 1 but further specifies the displaying of a score for each designed molecule.
Fleishman et al. teaches in paragraph [0212] “For each acceptance threshold the output list contains all amino acid alternatives that had a ΔΔG value more negative than the acceptance threshold”, reading on for each designed molecule, the score thereof with respect to the property(s).
Claim 3 is directed to the method of claim 2 and thus claim 1, but further specifies that there be a primary and secondary property.
Fleishman et al. teaches in the abstract “A method for designing and selecting a protein having a stabilized structure compared to a corresponding wild type protein, and proteins having at least six amino acid substitutions with respect to a corresponding wild type protein, designed for improved thermal stability, improved specific activity and/or improved expression levels, are provided herein”, in paragraph [0003] “The present invention, in some embodiments thereof, relates to computational chemistry and computational protein design and, more particularly, but not exclusively, to proteins designed for stability and a method of computationally designing and selecting an amino-acid sequence having desired properties”, in paragraph [0089] “proteins designed for stability and a method of computationally designing and selecting an amino-acid sequence having desired properties”, and in paragraph [0012] “The invention, according to some embodiments thereof, is directed at designed proteins, having a non-naturally occurring, man-made amino acid sequence, at least to some extent and at least in one polypeptide chain thereof, that are more stable and exhibit several modified characteristics”, reading on wherein the properties include a primary property and a secondary property.
Claim 4 is directed to the method of claim 3 and thus claim 1, but further specifies that the score be a weighted score that includes multiple property scores.
Fleishman et al. teaches in paragraph [0112] “According to some embodiments, prior to its use in the method presented herein, the template structure is subjected to a global energy minimization, afforded by weighted fitting thereof…”, reading on wherein the secondary property score is a weighted score which includes both a primary metric score and an additional property score.
Claim 5 is directed to the method of claim 3 and thus claim 1, but further specifies providing a target receptor and the primary property being binding affinity.
Fleishman et al. teaches in paragraph [0291] “According to some embodiments of the present invention, the modification of the designed protein relative to the corresponding wild type protein is determined by specific activity…”, and in paragraph [0292] “…the specific activity of an enzyme can be determined by an enzymatic activity assay, and the specific activity of a binding protein can be determined by a binding assay”, reading on providing a target receptor, and the primary property is the binding affinity of the designed molecules to the target receptor.
Claim 7 is directed to the method of claim 1 but further specifies training a model using known molecules and generating one or more new molecules using the trained model.
Fleishman et al. teaches in the abstract “A method for designing and selecting a protein having a stabilized structure compared to a corresponding wild type protein, and proteins having at least six amino acid substitutions with respect to a corresponding wild type protein, designed for improved thermal stability, improved specific activity and/or improved expression levels, are provided herein”, paragraph [0112] “According to some embodiments, prior to its use in the method presented herein, the template structure is subjected to a global energy minimization, afforded by weighted fitting thereof…”, and paragraph [0114] “The term “weight fitting”, according to some embodiments of any of the embodiment of the present invention, refers to a one or more computational structure refinement procedures or operations, aimed at optimizing geometrical, spatial and/or energy criteria by minimizing polynomial functions based on predetermined weights, restraints and constrains… An exemplary energy minimization procedure, according to some embodiments of the present invention, is the cyclic-coordinate descent (CCD), which can be implemented with the default all-atom energy function in the Rosetta™ software suite for macromolecular modeling”, reading on further comprising training a model using the known molecules; and generating one or more new molecules using the trained model.
Claim 8 is directed to the method of claim 1 but further specifies training the model using both known and user provided molecules and generating new molecules using the model.
Fleishman et al. teaches in paragraph [0132] “It is noted that the method is not limited to any particular sequence database, search method, identity determination algorithm, and any set of criteria for qualifying homologous sequences. However, the quality of the results obtained by use of the method depends to some extent on the quality of the input sequence data”, reading on further comprising training a model using the known molecules and user provided molecules having known properties; and generating one or more new molecules using the trained model.
Claim 9 is directed to the method of claim 7 and thus claim 1, but further specifies generating molecules using the trained model.
Fleishman et al. teaches in paragraph [0010] “Another approach suggested a method for combinatorial design that is based on iterations between sequence redesign and backbone minimization…”, and in paragraph [0084] “…simplified illustrations of the output of the single position scanning step and the input of the iterative combinatorial design step of the method provided herein…”, and in claim 6 “the method comprising: (i) determining unsubstitutable positions and substitutable positions in an amino acid Commented [OW22]: Claim 3 is included below but not mentioned here sequence of the original polypeptide chain; (ii) determining at least one position-specific amino acid alternative for each of said substitutable positions, and determining a position-specific stability scoring for each of said amino acid alternative; (iii) combinatorially generating a plurality of designed sequences, each of said designed sequences corresponds to a modified polypeptide chain; (iv) sorting said plurality of designed structures according to a minimized energy scoring, said minimized energy scoring is determined by subjecting each of said designed structures to an energy minimization”, reading on further comprising generating one or more new molecules using the trained model using the known molecules of the first dataset and at least a portion of the molecules of the second dataset.
Claim 10 is directed to the method of claim 8 and thus claim 1, but further specifies generating molecules using the trained model.
Fleishman et al. teaches in paragraph [0132] “It is noted that the method is not limited to any particular sequence database, search method, identity determination algorithm, and any set of criteria for qualifying homologous sequences. However, the quality of the results obtained by use of the method depends to some extent on the quality of the input sequence data”, paragraph [0083] “…the input of the iterative combinatorial design step of the method provided herein, according to some embodiments of the present invention…”, and in claim 6 “the method comprising: (i) determining unsubstitutable positions and substitutable positions in an amino acid Commented [OW22]: Claim 3 is included below but not mentioned here sequence of the original polypeptide chain; (ii) determining at least one position-specific amino acid alternative for each of said substitutable positions, and determining a position-specific stability scoring for each of said amino acid alternative; (iii) combinatorially generating a plurality of designed sequences, each of said designed sequences corresponds to a modified polypeptide chain; (iv) sorting said plurality of designed structures according to a minimized energy scoring, said minimized energy scoring is determined by subjecting each of said designed structures to an energy minimization”, reading on further comprising generating one or more new molecules using the trained model using the known molecules and the user provided known molecules of the first dataset and at least a portion of the molecules of the second dataset.
Claims 6 and 11-20 are rejected under 35 U.S.C. 103 as being unpatentable over Fleishman et al. (US 20170032079 A1) and Nilsson et al. (Annual Review Biophysical Biomolecular Structure (2005) 91-118) as applied to claims 1-5 and 7-10 above, and further in view of Dal Ben et al. (Bioorganic & Medicinal Chemistry (2010) 7923-7930).
Claim 6 is directed to the method of claim 5 and thus claim 1, but further specifies posing the molecule in different poses with respect to the receptor and finding binding affinities with respect to each pose.
Fleishman et al. and Nilsson et al. teach the method of claims 1-5 and 7-10 as previously described.
Fleishman et al. and Nilsson et al. do not teach posing the molecule in different poses with respect to the receptor and finding binding affinities with respect to each pose.
Dal Ben et al. teaches on page 7929, column 1, paragraph 2 “For this analysis we employed AR structural models based on the A2AAR crystal structure and optimized with Ado manual docking and Monte Carlo analysis. Each AR model was then used for docking analysis of the analyzed compounds”, and on page 7924, column 2, paragraph 4 “Poses generated by the placement methodology were scored using two available methods implemented in MOE, the London dG scoring function which estimates the free energy of binding of the ligand from a given pose, and Affinity dG Scoring which estimates the enthalpic contribution to the free energy of binding. Top docking pose of each compound was then subjected to MMFF9432–38 energy minimization”, reading on further comprising posing the designed molecules in different poses with respect to the target receptor, and determining the binding affinity of the designed molecule with respect to each pose.
It would have been obvious at the time of invention to a person skilled in the art to modify the teachings of Fleishman et al. for the method of claims 1 and 5, with the teachings of Dal Ben et al. for the incorporation of docking as the latter teaches on page 7926, column 1, paragraph 2 “The docking poses highlight these differences in ligand–receptor contact”, speaking to the differences in pocket contact with various amino acids and/or subgroups. One would have had a reasonable expectation of success given that binding affinity and the insertion pose of ligand/receptor is prima facie obviously correlated, but additionally in the abstract Dal Ben outlines how they are trying to understand binding affinities using molecular modeling, while Fleishman et al. is using molecular modeling in terms of binding affinity (along with other features) to design proteins. Therefore, it would have been obvious to a person of ordinary skill in the art to incorporate the teachings of each and to be successful.
Claim 11 is directed to a method of targeted molecular design using user added molecules to train a model to generate new molecules.
Fleishman et al. teaches teaches in claim 6 “the method comprising: (i) determining unsubstitutable positions and substitutable positions in an amino acid Commented [OW22]: Claim 3 is included below but not mentioned here sequence of the original polypeptide chain; (ii) determining at least one position-specific amino acid alternative for each of said substitutable positions, and determining a position-specific stability scoring for each of said amino acid alternative; (iii) combinatorially generating a plurality of designed sequences, each of said designed sequences corresponds to a modified polypeptide chain; (iv) sorting said plurality of designed structures according to a minimized energy scoring, said minimized energy scoring is determined by subjecting each of said designed structures to an energy minimization”, and in claim 1 “wherein said substitutions are modifying the designed protein relative to a corresponding wild type protein, as determined by at least one of: a thermal denaturation temperature of the designed protein being equal or higher than a thermal denaturation temperature of the wild type protein; a solubility of the designed protein being equal or higher than a solubility of the wild type protein; a degree of misfolding of the designed protein being equal or lower than a degree of misfolding of the wild type protein; a half-life of the designed protein being equal or longer than a half-life of the wild type protein; a specific activity of the designed protein being equal or higher than a specific activity of the wild type protein; and a recombinant expression level of the designed protein being equal or higher than a recombinant expression level of the wild type protein”, reading on measuring the added one or more head start molecules against one or more metrics, including a primary metric and at least one secondary metric, wherein the metrics relate to at least one of the binding affinity of a molecule to a target receptor and an additional metric. Fleishman et al. teaches in paragraph [0132] “It is noted that the method is not limited to any particular sequence database, search method, identity determination algorithm, and any set of criteria for qualifying homologous sequences. However, the quality of the results obtained by use of the method depends to some extent on the quality of the input sequence data”, paragraph [0083] “…the input of the iterative combinatorial design step of the method provided herein, according to some embodiments of the present invention…”, in paragraph [0112] “According to some embodiments, prior to its use in the method presented herein, the template structure is subjected to a global energy minimization, afforded by weighted fitting thereof…”, and in the abstract “A method for designing and selecting a protein having a stabilized structure compared to a corresponding wild type protein, and proteins having at least six amino acid substitutions with respect to a corresponding wild type protein, designed for improved thermal stability, improved specific activity and/or improved expression levels, are provided herein”, reading on selecting one or more head start molecules based on the assigned scores for each of the primary metric, the at least one secondary metric, and a random molecule selected from the one or more head start molecules; training a model using the selected one or more head start molecules; and generating one or more generations of new molecules based on the trained model. Fleishman et al. teaches in paragraph [0123] “Once an original polypeptide chain has been identified, and a corresponding template structure has been provided, the method requires assembling a database of qualifying homologous amino acid sequences related to the amino acid sequence of the original polypeptide chain”, use of a database inherently reads on adding one or more head start molecules to a molecular database, as adding to a database is a universal and fundamental database operation that is widely understood and implemented across various technologies and fields (standard and expected functionality of any database system). Fleishman et al. teaches in paragraphs [0014]-[0020] “wherein the substitutions are modifying the designed protein relative to a corresponding wild type protein, as determined by at least one of…a thermal denaturation temperature…a solubility of the designed protein…a degree of misfolding of the designed protein…a half-life of the designed protein…a specific activity of the designed protein…a recombinant expression level…”, reading on providing a value of a primary metric and a value of at least one secondary metric, wherein the metrics relate to at least one of the binding affinity of a molecule to a target receptor and an additional metric. Fleishman et al. teaches in paragraph [0229] “For each combinatorial design iteration, the final output is a single MSVTS”, reading on providing the structure of a molecule having the desired value of the primary metric and the desired value of the at least one secondary metric.
Fleishman et al. and Nilsson et al. do not teach adding the measured molecules to a master results table.
Dal Ben et al. teaches on page 7924, column 1, paragraph 2 “The list of analyzed compounds is reported in Table 1”, reading on adding the measured one or more head start molecules to a master results table.
It would have been obvious at the time of invention to a person skilled in the art to modify the teachings of Fleishman et al. and Nilsson et al. for the method of claims 1 and 5, with the teachings of Dal Ben et al. for compiling a list of the analyzed compounds to create a master results table as it would enable the centralization of scattered information into a single, comprehensive source, improving data quality, streamlining future analytics and facilitating easier future decision making. One would have had a reasonable expectation of success given that all that’s being done is compiling results. Therefore, it would have been obvious to a person of ordinary skill in the art to incorporate the teachings of each and to be successful.
Claim 12 is directed to the method of claim 11 but further specifies designating a number of the molecules that have the highest scores and using those molecules for training.
Fleishman et al., Nilsson et al., and Dal Ben et al. teach the method of claim 11 as previously described.
Fleishman et al. teaches in paragraph [0010] “Another approach suggested a method for combinatorial design that is based on iterations between sequence redesign and backbone minimization…”, and in paragraph [0084] “…simplified illustrations of the output of the single position scanning step and the input of the iterative combinatorial design step of the method provided herein…”, and in claim 6 “the method comprising: (i) determining unsubstitutable positions and substitutable positions in an amino acid Commented [OW22]: Claim 3 is included below but not mentioned here sequence of the original polypeptide chain; (ii) determining at least one position-specific amino acid alternative for each of said substitutable positions, and determining a position-specific stability scoring for each of said amino acid alternative; (iii) combinatorially generating a plurality of designed sequences, each of said designed sequences corresponds to a modified polypeptide chain; (iv) sorting said plurality of designed structures according to a minimized energy scoring, said minimized energy scoring is determined by subjecting each of said designed structures to an energy minimization”, reading on further comprising designating a first defined number of the head start molecules having the highest scores for the primary metric and using those first defined number of head start molecules having the highest scores for the primary metric as the selected one or more head start molecules for training the model.
Claim 13 is directed to the method of claim 12 and thus claim 11, but further specifies designating a number of the molecules that have the highest scores in a second metric and using those mo0lecules for training.
Fleishman et al., Nilsson et al., and Dal Ben et al. teach the method of claim 11 as previously described.
Fleishman et al. teaches in paragraph [0010] “Another approach suggested a method for combinatorial design that is based on iterations between sequence redesign and backbone minimization…”, and in paragraph [0084] “…simplified illustrations of the output of the single position scanning step and the input of the iterative combinatorial design step of the method provided herein…”, and in claim 6 “the method comprising: (i) determining unsubstitutable positions and substitutable positions in an amino acid Commented [OW22]: Claim 3 is included below but not mentioned here sequence of the original polypeptide chain; (ii) determining at least one position-specific amino acid alternative for each of said substitutable positions, and determining a position-specific stability scoring for each of said amino acid alternative; (iii) combinatorially generating a plurality of designed sequences, each of said designed sequences corresponds to a modified polypeptide chain; (iv) sorting said plurality of designed structures according to a minimized energy scoring, said minimized energy scoring is determined by subjecting each of said designed structures to an energy minimization”, reading on further comprising additionally designating a second defined number of the head start molecules having the highest scores for the at least one secondary metric and using those second defined number of head start molecules having the highest scores for the at least one secondary metric as additional selected one or more head start molecules for training the model.
Claim 14 is directed to the method of claim 13 and thus claim 11, but further specifies that the second defined number is less than the first defined number.
Fleishman et al., Nilsson et al., and Dal Ben et al. teach the method of claim 11 as previously described.
Fleishman et al. teaches in paragraph [0010] “Another approach suggested a method for combinatorial design that is based on iterations between sequence redesign and backbone minimization…”, and in paragraph [0084] “…simplified illustrations of the output of the single position scanning step and the input of the iterative combinatorial design step of the method provided herein…”, and in claim 6 “the method comprising: (i) determining unsubstitutable positions and substitutable positions in an amino acid Commented [OW22]: Claim 3 is included below but not mentioned here sequence of the original polypeptide chain; (ii) determining at least one position-specific amino acid alternative for each of said substitutable positions, and determining a position-specific stability scoring for each of said amino acid alternative; (iii) combinatorially generating a plurality of designed sequences, each of said designed sequences corresponds to a modified polypeptide chain; (iv) sorting said plurality of designed structures according to a minimized energy scoring, said minimized energy scoring is determined by subjecting each of said designed structures to an energy minimization”, any iterative process that contains multiple thresholds would inherently have a second threshold filtering output that is less than the first threshold filtering output. Therefore, this reads on wherein the second defined number is less than the first defined number.
Claim 15 is directed to the method of claim 11 but further specifies providing a target receptor and the primary property being binding affinity.
Fleishman et al., Nilsson et al., and Dal Ben et al. teach the method of claim 11 as previously described.
Fleishman et al. teaches in paragraph [0291] “According to some embodiments of the present invention, the modification of the designed protein relative to the corresponding wild type protein is determined by specific activity…”, and in paragraph [0292] “…the specific activity of an enzyme can be determined by an enzymatic activity assay, and the specific activity of a binding protein can be determined by a binding assay”, reading on providing a target receptor, and the primary property is the binding affinity of the designed molecules to the target receptor.
Claim 16 is directed to the method of claim 15 and thus claim 11, but further specifies posing the molecule in different poses with respect to the receptor and finding binding affinities with respect to each pose.
Fleishman et al., Nilsson et al., and Dal Ben et al. teach the method of claim 11 as previously described.
Fleishman et al. and Nilsson et al. do not teach posing the molecule in different poses with respect to the receptor and finding binding affinities with respect to each pose.
Dal Ben et al. teaches on page 7929, column 1, paragraph 2 “For this analysis we employed AR structural models based on the A2AAR crystal structure and optimized with Ado manual docking and Monte Carlo analysis. Each AR model was then used for docking analysis of the analyzed compounds”, and on page 7924, column 2, paragraph 4 “Poses generated by the placement methodology were scored using two available methods implemented in MOE, the London dG scoring function which estimates the free energy of binding of the ligand from a given pose, and Affinity dG Scoring which estimates the enthalpic contribution to the free energy of binding. Top docking pose of each compound was then subjected to MMFF9432–38 energy minimization”, reading on further comprising posing the designed molecules in different poses with respect to the target receptor, and determining the binding affinity of the designed molecule with respect to each pose.
Claim 17 is directed to the method of claim 12 and thus claim 11, but further specifies designating a number of the molecules that have the highest scores, using those mo0lecules for training, and generating a second generation of molecules.
Fleishman et al., Nilsson et al., and Dal Ben et al. teach the method of claim 11 as previously described.
Fleishman et al. teaches in the abstract “A method for designing and selecting a protein having a stabilized structure compared to a corresponding wild type protein, and proteins having at least six amino acid substitutions with respect to a corresponding wild type protein, designed for improved thermal stability, improved specific activity and/or improved expression levels, are provided herein”, paragraph [0112] “According to some embodiments, prior to its use in the method presented herein, the template structure is subjected to a global energy minimization, afforded by weighted fitting thereof…”, and paragraph [0114] “The term “weight fitting”, according to some embodiments of any of the embodiment of the present invention, refers to a one or more computational structure refinement procedures or operations, aimed at optimizing geometrical, spatial and/or energy criteria by minimizing polynomial functions based on predetermined weights, restraints and constrains… An exemplary energy minimization procedure, according to some embodiments of the present invention, is the cyclic-coordinate descent (CCD), which can be implemented with the default all-atom energy function in the Rosetta™ software suite for macromolecular modeling”, reading on after generating one or more new molecules based on the trained model as a first generation of new molecules, selecting the first defined number of new molecules from the first generation of new molecules, the first defined number of new molecules from the first generation of new molecules being those with the highest score against the primary metrics; and generating a second generation of new molecules using the first defined number of new molecules with the trained model.
Claim 18 is directed to the method of claim 12 and thus claim 11, but further specifies designating a number of the molecules that have the highest scores in a second metric, using those mo0lecules for training, and generating a second generation of molecules.
Fleishman et al., Nilsson et al., and Dal Ben et al. teach the method of claim 11 as previously described.
Fleishman et al. teaches in the abstract “A method for designing and selecting a protein having a stabilized structure compared to a corresponding wild type protein, and proteins having at least six amino acid substitutions with respect to a corresponding wild type protein, designed for improved thermal stability, improved specific activity and/or improved expression levels, are provided herein”, paragraph [0112] “According to some embodiments, prior to its use in the method presented herein, the template structure is subjected to a global energy minimization, afforded by weighted fitting thereof…”, and paragraph [0114] “The term “weight fitting”, according to some embodiments of any of the embodiment of the present invention, refers to a one or more computational structure refinement procedures or operations, aimed at optimizing geometrical, spatial and/or energy criteria by minimizing polynomial functions based on predetermined weights, restraints and constrains… An exemplary energy minimization procedure, according to some embodiments of the present invention, is the cyclic-coordinate descent (CCD), which can be implemented with the default all-atom energy function in the Rosetta™ software suite for macromolecular modeling”, reading on further comprising after generating one or more new molecules based on the trained model as a first generation of new molecules, selecting the second defined number of new molecules from the first generation of new molecules, the first defined number of new molecules from the first generation of new molecules being those with the highest score against the secondary metric; and generating a second generation of new molecules using the first defined number of new molecules and the second defined number of new molecules with the trained model.
Claim 19 is directed to the method of claim 18 and thus claim 11, but further specifies randomly selecting a user specified molecule, generating a second-generation of molecules and further training the model.
Fleishman et al., Nilsson et al., and Dal Ben et al. teach the method of claim 11 as previously described.
Fleishman et al. and Nilsson et al. do not teach randomly selecting a user specified molecule.
Dal Ben et al. teaches on page 7924, column 2, paragraph 3 “During the Monte Carlo conformational searching, the input structure was modified by random changes in torsion angles, and molecular position”, reading on further comprising randomly selecting a head start molecule, and generating a second generation of new molecules using the first defined number of new molecules, the second defined number of new molecules, and the random molecule with the trained model.
Claim 20 is directed to the method of claim 19 and thus claim 11, but further specifies displaying a table of the molecules and their corresponding metric/score.
Fleishman et al., Nilsson et al., and Dal Ben et al. teach the method of claim 11 as previously described.
Fleishman et al. and Nilsson et al. do not teach displaying a table of the molecules and their corresponding metric/score.
Dal Ben et al. teaches on page 7924, column 1, paragraph 2 “The list of analyzed compounds is reported in Table 1”, reading on further comprising displaying a table comprising each new molecule and the score thereof against the primary metric and the one or more secondary metrics.
Response to Arguments
Applicant's arguments filed 3/16/2026 have been fully considered but they are not persuasive.
Applicant asserts on pages 11-13 of the Remarks filed 3/16/2026 as the cited prior art does not cure the deficiencies introduced by the newly recited limitations within the amendments. While applicant is partially correct in so far as the previously recited art did not cure said deficiencies, newly recited prior does provide for an obviousness rejection over the previously recited prior art in view of the newly recited art (Nilsson et al.).
Double Patenting
Response to Amendment
In view of applicant’s amendments to the claims, previous rejections of claims 1-10 under double patenting have been reviewed, updated, and provided below.
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-10 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-12 of U.S. Patent Application No. 17/584,073. Although the claims at issue are not identical, they are not patentably distinct from each other because while the language may not be exactly the same and the steps are slightly different in terms of specifying what and when, the overall processes are the same.
Application: 17/584,053
Application: 17/584,073
Claim 1: A method of designing molecules having one or more desired properties, comprising: providing a plurality of known molecules having known properties as a first dataset; create a first random molecule; combine the first random molecule with the plurality of known molecules based on the properties of the known molecules, creating a plurality of new molecules having a structure different than that of at least one of the known molecules as a second data set using the random molecule to introduce mutation; evaluating the properties of the second dataset of molecules with respect to the desired properties to provide a score; create a second random molecule selecting a plurality of molecules from the second data set based on the score thereof to provide a nth scored dataset by combining the selected plurality of molecules from the second dataset with the second random molecules, to create a set of second molecules; based on the properties of the second molecules, creating a plurality of new molecules in the nth data set using the second random molecule to introduce mutation; selecting a plurality of molecules from the nth data set based on the score thereof to provide a nth + 1 scored dataset; and repeating the acts of creating a plurality of new molecules based on the nth+1 data set to create the nth+2 data set; selecting a plurality of molecules from the nth +2 data set based on the score thereof to provide a nth + 3 scored dataset.
Claim 1: A method of generating at least one of the chemical and physical structure of at least one molecule having a property, comprising: providing an initial molecule having at least one of a chemical structure and a physical structure; selecting at least a first attribute of the initial molecule relating to a first property thereof; evaluating the performance of the first molecule with respect to the first property thereof; modifying at least a portion of the at least one of a chemical structure and a physical structure of the initial molecule to form a first modified molecule; predicting the performance of the first modified molecule, upon further modification thereof, with respect to the performance of that first modified molecule with respect to the first property thereof; and based on the predicted performance, further modifying the first modified molecule.
Claim 2: The method of claim 1, further comprising: modifying at least a portion of the at least one of a chemical structure and a physical structure of the initial molecule to form second through nth modified molecules, where n is a positive integer; and predicting the performance of the second through n-1 modified molecules, upon further modification thereof, with respect to the performance of that second through n-1 modified molecules with respect to the property thereof, and based on the predicted performance, further modify each of the first through n-1 modified molecules to generate the nth modified molecule.
Claim 8: The method of claim 1, further comprising; selecting a second attribute of the initial molecule relating to a second property thereof; evaluating the performance of the molecule with respect to the first and the second property thereof; modifying at least a portion of the at least one of a chemical structure and a physical structure of the initial molecule to form a first modified molecule; predicting the performance of the first modified molecule, upon further modification thereof, with respect to the performance of that first modified molecule with respect to the first and the second property thereof.
Claim 2: The method of claim 1, further comprising displaying, for each designed molecule, the score thereof with respect to the property(s).
Claim 1: A method of generating at least one of the chemical and physical structure of at least one molecule having a property, comprising: providing an initial molecule having at least one of a chemical structure and a physical structure; selecting at least a first attribute of the initial molecule relating to a first property thereof; evaluating the performance of the first molecule with respect to the first property thereof; modifying at least a portion of the at least one of a chemical structure and a physical structure of the initial molecule to form a first modified molecule; predicting the performance of the first modified molecule, upon further modification thereof, with respect to the performance of that first modified molecule with respect to the first property thereof; and based on the predicted performance, further modifying the first modified molecule.
Claim 3: The method of claim 2, wherein the properties include a primary property and a secondary property.
Claim 1: A method of generating at least one of the chemical and physical structure of at least one molecule having a property, comprising: providing an initial molecule having at least one of a chemical structure and a physical structure; selecting at least a first attribute of the initial molecule relating to a first property thereof; evaluating the performance of the first molecule with respect to the first property thereof; modifying at least a portion of the at least one of a chemical structure and a physical structure of the initial molecule to form a first modified molecule; predicting the performance of the first modified molecule, upon further modification thereof, with respect to the performance of that first modified molecule with respect to the first property thereof; and based on the predicted performance, further modifying the first modified molecule.
Claim 2: The method of claim 1, further comprising: modifying at least a portion of the at least one of a chemical structure and a physical structure of the initial molecule to form second through nth modified molecules, where n is a positive integer; and predicting the performance of the second through n-1 modified molecules, upon further modification thereof, with respect to the performance of that second through n-1 modified molecules with respect to the property thereof, and based on the predicted performance, further modify each of the first through n-1 modified molecules to generate the nth modified molecule.
Claim 8: The method of claim 1, further comprising; selecting a second attribute of the initial molecule relating to a second property thereof; evaluating the performance of the molecule with respect to the first and the second property thereof; modifying at least a portion of the at least one of a chemical structure and a physical structure of the initial molecule to form a first modified molecule; predicting the performance of the first modified molecule, upon further modification thereof, with respect to the performance of that first modified molecule with respect to the first and the second property thereof.
Claim 4: The method of claim 3, wherein the secondary property score is a weighted score which includes both a primary metric score and an additional property score.
Specification Paragraph [0006] Herein are provided methods and non-transitory computer media configured to generate molecules by repeatedly modifying a molecular structure of a molecule, and predicting, after at least one modification of the molecule to create an intermediate molecule structure prior to the generation of a final molecule structure, the properties of the molecule with respect to specified properties, and weightings of those properties, or of the molecule with respect to those properties.
Claim 5: The method of claim 3, further comprising: providing a target receptor, and the primary property is the binding affinity of the designed molecules to the target receptor.
Claim 6: The method of claim 1, wherein the property thereof is binding energy.
Claim 6: The method of claim 5, further comprising posing the designed molecules in different poses with respect to the target receptor, and determining the binding affinity of the designed molecule with respect to each pose.
Claim 7: The method of claim 1, wherein the property thereof is the location of a potential chemical binding site with respect to the topography of the nth molecule.
Claim 7: The method of claim 1, further comprising training a model using the known molecules; and generating one or more new molecules using the trained model.
Specification Paragraph [0059]: A "Model Checkpoints" folder may save at least one Hierarchical Data Format version 5 (HDF5) file, which include the system's Neural Network Component 5 training checkpoints. A "Model Training" folder contain data received by one or more Experience Replay Buffers, such as numerical representations of final prepared inputs, selected actions, numerical vector of Final Molecular Measurement Scores, total final reward scores, and other data used to train the Neural Network Component.
Claim 1: A method of generating at least one of the chemical and physical structure of at least one molecule having a property, comprising: providing an initial molecule having at least one of a chemical structure and a physical structure; selecting at least a first attribute of the initial molecule relating to a first property thereof; evaluating the performance of the first molecule with respect to the first property thereof; modifying at least a portion of the at least one of a chemical structure and a physical structure of the initial molecule to form a first modified molecule; predicting the performance of the first modified molecule, upon further modification thereof, with respect to the performance of that first modified molecule with respect to the first property thereof; and based on the predicted performance, further modifying the first modified molecule.
Claim 8: The method of claim 1, further comprising training a model using the known molecules and user provided molecules having known properties; and generating one or more new molecules using the trained model.
Specification Paragraph [0059]: A "Model Checkpoints" folder may save at least one Hierarchical Data Format version 5 (HDF5) file, which include the system's Neural Network Component 5 training checkpoints. A "Model Training" folder contain data received by one or more Experience Replay Buffers, such as numerical representations of final prepared inputs, selected actions, numerical vector of Final Molecular Measurement Scores, total final reward scores, and other data used to train the Neural Network Component.
Claim 1: A method of generating at least one of the chemical and physical structure of at least one molecule having a property, comprising: providing an initial molecule having at least one of a chemical structure and a physical structure; selecting at least a first attribute of the initial molecule relating to a first property thereof; evaluating the performance of the first molecule with respect to the first property thereof; modifying at least a portion of the at least one of a chemical structure and a physical structure of the initial molecule to form a first modified molecule; predicting the performance of the first modified molecule, upon further modification thereof, with respect to the performance of that first modified molecule with respect to the first property thereof; and based on the predicted performance, further modifying the first modified molecule.
Claim 9: The method of claim 7, further comprising generating one or more new molecules using the trained model using the known molecules of the first dataset and at least a portion of the molecules of the second dataset.
Specification Paragraph [0059]: A "Model Checkpoints" folder may save at least one Hierarchical Data Format version 5 (HDF5) file, which include the system's Neural Network Component 5 training checkpoints. A "Model Training" folder contain data received by one or more Experience Replay Buffers, such as numerical representations of final prepared inputs, selected actions, numerical vector of Final Molecular Measurement Scores, total final reward scores, and other data used to train the Neural Network Component.
Claim 1: A method of generating at least one of the chemical and physical structure of at least one molecule having a property, comprising: providing an initial molecule having at least one of a chemical structure and a physical structure; selecting at least a first attribute of the initial molecule relating to a first property thereof; evaluating the performance of the first molecule with respect to the first property thereof; modifying at least a portion of the at least one of a chemical structure and a physical structure of the initial molecule to form a first modified molecule; predicting the performance of the first modified molecule, upon further modification thereof, with respect to the performance of that first modified molecule with respect to the first property thereof; and based on the predicted performance, further modifying the first modified molecule.
Claim 10: The method of claim 8, further comprising generating one or more new molecules using the trained model using the known molecules and the user provided known molecules of the first dataset and at least a portion of the molecules of the second dataset.
Specification Paragraph [0059]: A "Model Checkpoints" folder may save at least one Hierarchical Data Format version 5 (HDF5) file, which include the system's Neural Network Component 5 training checkpoints. A "Model Training" folder contain data received by one or more Experience Replay Buffers, such as numerical representations of final prepared inputs, selected actions, numerical vector of Final Molecular Measurement Scores, total final reward scores, and other data used to train the Neural Network Component.
Response to Arguments
Applicant's arguments filed 3/16/2026 have been fully considered but they are not persuasive.
Applicant asserts on page 14 of the Remarks filed 3/16/2026, that upon allowance of claims of either/both applications, that a terminal disclaimer will be filed. No terminal disclaimer has been filed and accepted, and as such the rejection of claims 1-10 under double patenting is not withdrawn.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 KEENAN NEIL ANDERSON-FEARS whose telephone number is (571)272-0108. The examiner can normally be reached M-Th, alternate F, 8-5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Karlheinz Skowronek can be reached at 571-272-9047. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/K.N.A./ Examiner, Art Unit 1687
/OLIVIA M. WISE/ Supervisory Patent Examiner, Art Unit 1685
Prosecution Insights