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 . Claims 1-8 are presented in the case.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on application CN202310787676.9 filed in China on 06/29/2023. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement submitted on 12/29/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3-4 and 7-8 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 3 recites the limitation “wherein said each atomic ensemble consisting of either a pair of atoms or a group of two or more atoms, with two atomic ensembles distinguishable if and only if the atomic type of either one of an atom of one atomic ensemble is different from the atomic type of either one of an atom of the other atomic ensemble”. It appears two atoms is also a pair, should this say three or more atoms? Also what comparison is occurring? Is the type being different?. This is unclear and needs to be interpretable by one of ordinary skill in the field.
Claim 4 recites the limitation “partitioning a molecular system of the target molecule into a core zone or a background zone”. Substance appears to require partitioning into both should an “or” be replaced since partitioning divides into two things.
Claims 7-8 recite the limitation " the molecular force field database" (line 4), “the fingerprints” (line 5), “said each element” (line 5), “either molecule” (line 6), “said each pair” (line 7), “said each atomic ensemble” (line 9-10). There is insufficient antecedent basis for these limitations in the claims.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”).
Claims 1, 7 and 8 have the following abstract idea analysis.
Step 1: The claims are directed to “a method, crm and system”. The claims are directed to the statutory categories accordingly.
Step 2A Prong 1: claims recite the abstract idea limitations of "calculating atomic partial charges by means of quantum mechanics methods" and " clustering the multitude of the fingerprints of the multitude of the atoms". The limitations include mathematical concept see MPEP § 2106.04(a)(2)) where it cites "managing a stable value protected life insurance policy via performing calculations” as an example of mathematical calculations. The specification also provides example computing using RESP (See USPGPUB ¶77). See USPTO 2024 example 47 claim 3 where clustering data could fall within the mathematical-concept grouping. Thus, the limitation is an abstract idea in the “mathematical concept”. Other sections of the claims such as "executing a first attention unit" and "performing a task" are advanced processes, too generic or high level to be listed as a judicial exception given the available descriptions and MPEP comparisons.
Step 2A Prong 2: The judicial exceptions recited in these claims are not integrated into a practical application. Merely invoking "a molecular force field", "computer", "molecules", "software", "storage device", or "processor" does not yield eligibility. Claims are still in line with mathematical concepts such as claim 1, 7 and 8 are not specific to a practical application. The additional elements as such are processors and instructions which do not include specialized hardware. See MPEP § 2106.05(a). The math is just being used to produce a result. Claim 1, 7 and 8 do not include a more specific field but even doing so may not be sufficient to overcome the abstract idea rejection. Merely applying an model to a field or data without an advancement in the new field or new hardware is ineligible. See MPEP § 2106.05(h).
Step 2B: The claims do not contain significantly more than their judicial exceptions. Processors, memory and other hardware are in their standard forms in the field. These additional elements are well-understood, routine, and conventional activity, see MPEP 2106.05(d)(II). Claims lacks any particular "how" or algorithm for a solution in a field in a novel way. Claims require more specificity on processes that would be incapable of simple mathematics, mental processes or use more substantial structure than conventional devices such as non-textbook implementations.
Regarding claims 2-6, they merely narrow the previously recited abstract idea limitations with more abstract concepts and/or routine fundamental processes. For the reasons described above with respect to claim 1 this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. Abstract idea steps 1, 2A prong 1 and 2 remain the same as independent analysis above. See specification for more practical application concepts as none are seen in claims 2-6.
With respect to step 2B These claims disclose similar limitations described for the dependent claims above and do not provide anything significantly more than organizing human activity concepts. Claims 2-6 recite the additional elements of "extracting a structural feature and an energetic feature from each complex molecule in the molecular force field database, wherein the structural feature denoting a set of three-dimensional spatial coordinates of an entirety of the atoms of the complex molecule, the energetic feature denoting a charge distribution of the entirety of the atoms of the complex molecule, the charge distribution comprising partial charge and proton charge; projecting a GAP (gross atom population) of each atom in each complex molecule onto a Fibonacci lattice point constructed for said each atom based on spatial distances, simulating a charge density distribution on a surface of said each atom; and performing dimensionality reduction and sorting on an energy projection value of a multitude of the Fibonacci lattice points to obtain a fingerprint for said each atom in the complex molecule. retrieving a complex molecule as a target molecule from the molecular force field database, the target molecule being a protein-ligand molecule of a combination of two molecules, with a first molecule in the complex molecule being a protein molecule, and a second molecule being a ligand small molecule. modeling the target molecule employing BFT, obtaining a Boltzmann probability between each pair of the atoms in each atomic ensemble, wherein said each atomic ensemble consisting of either a pair of atoms or a group of two or more atoms, with two atomic ensembles distinguishable if and only if the atomic type of either one of an atom of one atomic ensemble is different from the atomic type of either one of an atom of the other atomic ensemble, and wherein the Boltzmann probability is a conditional probability determined solely by an interaction energy between the pair of the atoms as determined by mutual interaction thereof; and fitting the Boltzmann probability distribution between said each pair of the atoms in an entirety of the atomic ensembles to obtain the potential energy function between said each pair of the atoms in an entirety of the atomic ensembles. for each atomic ensemble of the target molecule, partitioning a molecular system of the target molecule into a core zone or a background zone, wherein the core zone being a region of the molecular system said each atomic ensemble being situated with, while the background zone being a region of the molecular system minus the core zone; and iteratively removing influence of the atoms in the background zone on the probability density distribution of the atoms of the core zone atoms to obtain a Boltzmann probability between said each pair of atoms in said each atomic ensemble of the target molecule. wherein in said clustering, incorporating into the force field database atomic types unfound in the force field database. wherein the projection employs a Gaussian basis set as a projection function model". These elements are more abstract concepts, generic applications to a field of use or well-understood, routine, conventional activity (see MPEP § 2106.05(d) and can't be simply appended to qualify as significantly more or being a practical application. What type of application, or structure of components beyond generic machine learning is still unknown for these claims. Therefore claims 2-6 also recites abstract ideas that do not integrate into a practical application or amount to significantly more than the judicial exception, and are rejected under U.S.C. 101.
Claim 7 is rejected under 35 U.S.C. 101 because they are directed to non-statutory subject matter.
Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. As per claim 7 the claim limitation recites “computer-readable hardware storage device”. However, the usage of the phrase “computer-readable hardware storage device” is broad enough to include both “non-transitory” and “transitory” media. The specification further explicitly does not limit the utilization of a non-transitory computer-readable medium (See specification, ¶ [0116-117] where “storage medium" transitory and non-transitory mediums are discussed, however, readable medium is not defined). When the specification is silent, the BRI of a CRM and a computer readable storage media (CRSM) in view of the state of the art covers a signal per se. See Ex parte Mewherter, 2012-007962 (PTAB, 2013). Therefore, the claim must be rejected under 35 U.S.C. § 101 as covering non-statutory subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007) (transitory embodiments are not directed to statutory subject matter).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis 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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over PEREYASLAVETS et al. (US 20230129485 A1 hereinafter Pereyaslavets) in view of Zanghellini et al. (US 10025900 B2 hereinafter Zanghellini), Miller. et al. (US 20200294630 A1 hereinafter Miller) and Zheng "Generation of Pairwise Potentials Using Multidimensional Data Mining" 2018 <https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.8b00516?ref=article_openPDF>
As to independent claim 1, Pereyaslavets teaches a computer-implemented method for constructing a molecular force field, comprising the steps of: [fits (constructs) force field models ¶50 "force field models 117 can be stored in, for example, flat files, in suitable markup language files, in memory, in a database format"]
establishing a molecular force field database, comprising [force field model database ¶50 " force field models 117 can be stored in, for example, flat files, in suitable markup language files, in memory, in a database format"] a multitude of complex molecules, with each complex molecule being a protein molecule or a ligand molecule, or a combination of one or multiple protein molecules with one or multiple ligand molecules; [protein-ligand molecule ¶66 " signal can include information related to the protein-protein interactions, protein-ligand interactions, protein folding and various protein properties"]
calculating atomic partial charges by means of quantum mechanics methods, and employing computational molecular dynamics software to achieve dynamic equilibrium states; [atomic charge and QM calculation ¶69 " QM calculated molecular electrostatic potential (MEP) can be fitted at molecular surfaces using an atom-centered point charge model (e.g., as in the Restrained ElectroStatic Potential (RESP) procedure)."], [molecular dynamics (MD) simulations (software) ¶20, ¶194]
classifying atomic types for atoms in the molecular force field database: [typification ¶51]
classifying the atomic types of the multitude of the atoms belonging to said each element via a one-to-one correspondence between each fingerprint and a corresponding atom; and [typification classifies atoms ¶51]
fitting a molecular force field potential function [fits model/field (includes function) ¶54 " QM-parameterized force field model when fitting the model to QM"]
Pereyaslavets does not specifically teach conducting homology modeling for each protein molecule with missing amino acid residues.
However, Zanghellini teaches conducting homology modeling for each protein molecule with missing amino acid residues; [homology search algorithm Col. 2 ln. 11-23, sequences not available (amino missing) Col. 13 ln. 26-39, Col. 17 ln. 56-67 "identify target sequences whose pre-existing three-dimensional structures are not available"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the molecular simulation by Pereyaslavets by incorporating the conducting homology modeling for each protein molecule with missing amino acid residues disclosed by Zanghellini because both techniques address the same field of data modeling and by incorporating Zanghellini into Pereyaslavets automate designing of proteins with more desired characteristics [Zanghellini Col. 8 ln. 61-67]
Pereyaslavets and Zanghellini do not specifically teach creating a fingerprint for each atom in each of the multitude of the complex molecules; aggregating a multitude of the atoms belonging to each element from either molecule in the molecular force field database; and clustering the multitude of the fingerprints of the multitude of the atoms belonging to said each element from either molecule in the molecular force field database.
However, Miller teaches creating a fingerprint for each atom in each of the multitude of the complex molecules; [creates features/MOB (fingerprints) of molecules ¶6 "predict molecular system properties based on molecular orbital based features using molecular-orbital-based machine learning (MOB-ML) processes. Examples of molecular system properties in accordance with various embodiments of the invention include (but are not limited to): solubility, binding affinity for molecules, binding affinity for protein, redox potential, pKa, electrical conductivity, ionic conductivity, thermal conductivity, and light emission efficiency."]
aggregating a multitude of the atoms belonging to each element from either molecule in the molecular force field database; [classifying (aggregate) into clusters of atoms ¶208 "classification of the feature vectors into clusters"
clustering the multitude of the fingerprints of the multitude of the atoms belonging to said each element from either molecule in the molecular force field database; [clustering the MOBs ¶130-132 "identify linear clusters and take advantage of the local linearity of pair correlation energies as a function of MOB features"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the molecular simulation by Pereyaslavets and Zanghellini by incorporating the creating a fingerprint for each atom in each of the multitude of the complex molecules; aggregating a multitude of the atoms belonging to each element from either molecule in the molecular force field database; and clustering the multitude of the fingerprints of the multitude of the atoms belonging to said each element from either molecule in the molecular force field database disclosed by Miller because all techniques address the same field of data modeling and by incorporating Miller into Pereyaslavets and Zanghellini broaden its applications in the industrial innovation and development process [Miller ¶4]
Pereyaslavets, Zanghellini and Miller do not specifically teach BFT (Bayesian field theory) in combination with Boltzmann probability distribution.
However, Zheng teaches BFT (Bayesian field theory) in combination with Boltzmann probability distribution. [Uses BFT and Boltzmann distribution Page 5055 "In this study, we introduced the BFT approach to generate the pairwise Boltzmann probability using available structural data bases"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the molecular simulation by Pereyaslavets, Zanghellini and Miller by incorporating the BFT (Bayesian field theory) in combination with Boltzmann probability distribution disclosed by Zheng because all techniques address the same field of data modeling and by incorporating Zheng into Pereyaslavets, Zanghellini and Miller more accurately estimates the energies using biological systems [Zheng Page 5045]
As to dependent claim 5, the rejection of claim 1 is incorporated, Pereyaslavets, Zanghellini, Miller and Zheng further teach wherein in said clustering, incorporating into the force field database atomic types unfound in the force field database. [Miller clustering the MOBs ¶130-132], [Pereyaslavets un-benchmarked (unfound) ¶4, ¶6]
Claims 7 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Pereyaslavets in view of Miller and Zheng et al. (US 10332616 B2 hereinafter Zheng2).
As to independent claim 7, Pereyaslavets teaches one or more computer-readable hardware storage device having embedded therein a set of instructions which, when executed by one or more processors of a computer, causes the computer to execute operations comprising: [memory and processor with simulations ¶48-50]
classifying the atoms in the molecular force field database [database ¶50 typification (classification) ¶51]
establishing the molecular force field based on the potential energy equation between said each pair of the atoms in said each atomic ensemble. [creates a force field model ¶5, ¶54 " QM-parameterized force field model when fitting the model to QM"]
Pereyaslavets does not specifically teach by means of clustering the multitude of the fingerprints of the multitude of the atoms belonging to said each element from either molecule in the molecular force field database;
However, Miller teaches by means of clustering the multitude of the fingerprints of the multitude of the atoms belonging to said each element from either molecule in the molecular force field database; [creates features/MOB (fingerprints) of molecules ¶6 "predict molecular system properties based on molecular orbital based features using molecular-orbital-based machine learning (MOB-ML) processes. Examples of molecular system properties in accordance with various embodiments of the invention include (but are not limited to): solubility, binding affinity for molecules, binding affinity for protein, redox potential, pKa, electrical conductivity, ionic conductivity, thermal conductivity, and light emission efficiency."], [clustering the MOBs ¶130-132 "identify linear clusters and take advantage of the local linearity of pair correlation energies as a function of MOB features"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the molecular simulation by Pereyaslavets by incorporating the by means of clustering the multitude of the fingerprints of the multitude of the atoms belonging to said each element from either molecule in the molecular force field database disclosed by Miller because both techniques address the same field of data modeling and by incorporating Miller into Pereyaslavets broaden its applications in the industrial innovation and development process [Miller ¶4]
Pereyaslavets and Miller do not specifically teach obtaining the potential energy equation between said each pair of the atoms in said each atomic ensemble.
However, Zheng2 teaches obtaining the potential energy equation between said each pair of the atoms in said each atomic ensemble; [pairwise energies for atoms Col. 1-2 ln. 54-09 "first database comprises associated pairwise distant dependent energies"…"identifying all possible atom pairs of protein-ligand complexes in a given configuration space for a system that comprises proteins; creating a first database and a second database; where the first database comprises associated pairwise distant dependent energies and where the second database comprises all probabilities that include how the atom pairs can combine"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the molecular simulation by Pereyaslavets and Zanghellini by incorporating the obtaining the potential energy equation between said each pair of the atoms in said each atomic ensemble disclosed by Zheng2 because all techniques address the same field of data modeling and by incorporating Zheng2 into Pereyaslavets and Zanghellini more efficiently and accurately estimates energies of ligand [Zheng2 Col. 1 ln. 40-47].
As to independent claim 8, Pereyaslavets teaches a system [force-field device ¶46] comprising one or more computer processors configured for: [processor ¶48-50]
classifying the atoms in the molecular force field database [database ¶50 typification (classification) ¶51]
establishing the molecular force field based on the potential energy equation between said each pair of the atoms in said each atomic ensemble. [creates a force field model ¶5, ¶54 " QM-parameterized force field model when fitting the model to QM"]
Pereyaslavets does not specifically teach by means of clustering the multitude of the fingerprints of the multitude of the atoms belonging to said each element from either molecule in the molecular force field database;
However, Miller teaches by means of clustering the multitude of the fingerprints of the multitude of the atoms belonging to said each element from either molecule in the molecular force field database; [creates features/MOB (fingerprints) of molecules ¶6 "predict molecular system properties based on molecular orbital based features using molecular-orbital-based machine learning (MOB-ML) processes. Examples of molecular system properties in accordance with various embodiments of the invention include (but are not limited to): solubility, binding affinity for molecules, binding affinity for protein, redox potential, pKa, electrical conductivity, ionic conductivity, thermal conductivity, and light emission efficiency."], [clustering the MOBs ¶130-132 "identify linear clusters and take advantage of the local linearity of pair correlation energies as a function of MOB features"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the molecular simulation by Pereyaslavets by incorporating the by means of clustering the multitude of the fingerprints of the multitude of the atoms belonging to said each element from either molecule in the molecular force field database disclosed by Miller because both techniques address the same field of data modeling and by incorporating Miller into Pereyaslavets broaden its applications in the industrial innovation and development process [Miller ¶4]
Pereyaslavets and Miller do not specifically teach obtaining the potential energy equation between said each pair of the atoms in said each atomic ensemble.
However, Zheng2 teaches obtaining the potential energy equation between said each pair of the atoms in said each atomic ensemble; [pairwise energies for atoms Col. 1-2 ln. 54-09 "first database comprises associated pairwise distant dependent energies"…"identifying all possible atom pairs of protein-ligand complexes in a given configuration space for a system that comprises proteins; creating a first database and a second database; where the first database comprises associated pairwise distant dependent energies and where the second database comprises all probabilities that include how the atom pairs can combine"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the molecular simulation by Pereyaslavets and Zanghellini by incorporating the obtaining the potential energy equation between said each pair of the atoms in said each atomic ensemble disclosed by Zheng2 because all techniques address the same field of data modeling and by incorporating Zheng2 into Pereyaslavets and Zanghellini more efficiently and accurately estimates energies of ligand [Zheng2 Col. 1 ln. 40-47].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
CHONG et al. (US 20230273153 A1) teaches molecular biology and Boltzmann networks (see ¶85).
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Beau Spratt whose telephone number is 571 272 9919. The examiner can normally be reached 8:30am to 5:00pm (PST).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Welch can be reached at 571 272 7212. The fax phone number for the organization where this application or proceeding is assigned is 571 483 7388.
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/BEAU D SPRATT/ Primary Examiner, Art Unit 2143
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