DETAILED ACTION
Applicant's response, filed 10/10/2025, 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
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. As such, the effective filing date of claims 1, 3-8, 11, 13-18, and 20is 5/18/2021.
Claim Status
Claims 1, 3-8, 11, 13-18, and 20 are pending.
Claims 2, 9, 10, 12, and 19 are cancelled.
Claims 1, 3-8, 11, 13-18, and 20 are rejected.
Drawings
Response to Amendment
In view of applicant’s amendments to the drawings, previous objections over minor informalities to the drawings is withdrawn.
Claim Objections
Response to Amendment
In view of applicant’s amendments to claim 20, previous objections over minor informalities to claim 20 is withdrawn.
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.
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, 3-8, 11, 13-18, and 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, system and CRM for training a prediction to determine molecular binding force. The judicial exception is not integrated into a practical application because while claims 1, 3-8, 11, 13-18, and 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, 3-8), a system (Claims 11, 13-18), and a CRM (Claim 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 1: Constructing a virtual complex molecule, and determining a predicted binding force are processes of visualizing and representing a three-dimensional structure, as well as calculating a prediction, which are processes that can be done either with a pen and paper and/or in a human mind, and are therefore abstract ideas, specifically mental processes. Training the prediction model by minimizing a target loss function based on a difference between the predicted and real binding force is merely a verbal articulation of mathematical steps, and is therefore, an abstract idea, specifically a mathematical concept. Determining a distance between a target atom in two different molecules is merely a verbal articulation of mathematical steps, and is therefore, an abstract idea, specifically a mathematical concept. Combining the target atom with the atom in the first molecule and determining them as atoms of the virtual complex molecule is a process of visualizing and representing a three-dimensional structure, which is a process that can be done either with a pen and paper and/or in a human mind, and is therefore an abstract idea, specifically a mental process.
Claim 3: Constructing an edge between atoms having a distance less than a threshold is a process of visualizing and representing a two-dimensional network structure based upon a criterion, which is a process that can be done either with a pen and paper and/or in a human mind, and is therefore an abstract idea, specifically a mental process. Determining a representation of an atom in the virtual complex and a representation of the edge based upon a three-dimensional structure information is a process of visualizing and representing a two-dimensional network structure based upon a three-dimensional figure, which is a process that can be done either with a pen and paper and/or in a human mind, and is therefore an abstract idea, specifically a mental process.
Claim 4: Determining a feature representation for characterizing the virtual complex is a process of selecting and classifying based upon a specified criterion, which is a process that can be done either with a pen and paper and/or in a human mind, and is therefore an abstract idea, specifically a mental process. Determining the predicted binding force based upon the feature representation is merely a verbal articulation of mathematical steps, and is therefore, an abstract idea, specifically a mathematical concept.
Claim 5: Determining an atomic pair composed of an atom in the first group of atoms and an atom in the second group of atoms is a process of selecting and ordering information, which is a process that can be done either with a pen and paper and/or in a human mind, and is therefore an abstract idea, specifically a mental process. Determining an element value indexed by the first element type and the second element type based upon a weighted sum of the representations of edges is merely a verbal articulation of mathematical steps, and is therefore, an abstract idea, specifically a mathematical concept.
Claim 6: Determining a number of one or more atomic pairs having a distance less than a third threshold, and determining an element value of a matrix indexed by the first element type and second element type in the real interaction matrix based on the number of pairs are processes of selecting, ordering, and classifying based upon a specified criteria, which are processes that can be done either with a pen and paper and/or in a human mind, and are therefore abstract ideas, specifically mental processes.
Claim 7: Determining a first loss function based on the difference between the binding force and the real binding force, determining a second loss function based on the difference between the predicted interaction matrix and the real interaction matrix, and determining the target loss function based on a weighted sum of the first and second loss functions are merely verbal articulations of mathematical steps, and are therefore, abstract ideas, specifically mathematical concepts.
Claim 8: The first molecule being a ligand and the second molecule being a protein is merely reciting further limitations on something that is already abstract, and is therefore, an abstract idea, specifically a mental process.
Claim 11: Constructing a virtual complex molecule, and determining a predicted binding force are processes of visualizing and representing a three-dimensional structure, as well as calculating a prediction, which are processes that can be done either with a pen and paper and/or in a human mind, and are therefore abstract ideas, specifically a mental process. Training the prediction model by minimizing a target loss function based on a difference between the predicted and real binding force is merely a verbal articulation of mathematical steps, and is therefore, an abstract idea, specifically a mathematical concept. Determining a distance between a target atom in two different molecules is merely a verbal articulation of mathematical steps, and is therefore, an abstract idea, specifically a mathematical concept. Combining the target atom with the atom in the first molecule and determining them as atoms of the virtual complex molecule is a process of visualizing and representing a three-dimensional structure, which is a process that can be done either with a pen and paper and/or in a human mind, and is therefore an abstract idea, specifically a mental process.
Claim 13: Constructing an edge between atoms having a distance less than a threshold is a process of visualizing and representing a two-dimensional network structure based upon a criterion, which is a process that can be done either with a pen and paper and/or in a human mind, and is therefore an abstract idea, specifically a mental process. Determining a representation of an atom in the virtual complex and a representation of the edge based upon a three-dimensional structure information is a process of visualizing and representing a two-dimensional network structure based upon a three-dimensional figure, which is a process that can be done either with a pen and paper and/or in a human mind, and is therefore an abstract idea, specifically a mental process.
Claim 14: Determining a feature representation for characterizing the virtual complex is a process of selecting and classifying based upon a specified criterion, which is a process that can be done either with a pen and paper and/or in a human mind, and is therefore an abstract idea, specifically a mental process. Determining the predicted binding force based upon the feature representation is merely a verbal articulation of mathematical steps, and is therefore, an abstract idea, specifically a mathematical concept.
Claim 15: Determining an atomic pair composed of an atom in the first group of atoms and an atom in the second group of atoms is a process of selecting and ordering information, which is a process that can be done either with a pen and paper and/or in a human mind, and is therefore an abstract idea, specifically a mental process. Determining an element value indexed by the first element type and the second element type based upon a weighted sum of the representations of edges is merely a verbal articulation of mathematical steps, and is therefore, an abstract idea, specifically a mathematical concept.
Claim 16: Determining a number of one or more atomic pairs having a distance less than a third threshold, and determining an element value of a matrix indexed by the first element type and second element type in the real interaction matrix based on the number of pairs are processes of selecting, ordering, and classifying based upon a specified criteria, which are processes that can be done either with a pen and paper and/or in a human mind, and are therefore abstract ideas, specifically mental processes.
Claim 17: Determining a first loss function based on the difference between the binding force and the real binding force, determining a second loss function based on the difference between the predicted interaction matrix and the real interaction matrix, and determining the target loss function based on a weighted sum of the first and second loss functions are merely verbal articulations of mathematical steps, and are therefore, abstract ideas, specifically mathematical concepts.
Claim 18: The first molecule being a ligand and the second molecule being a protein is merely reciting further limitations on something that is already abstract, and is therefore, an abstract idea, specifically a mental process.
Claim 20: Constructing a virtual complex molecule, and determining a predicted binding force are processes of visualizing and representing a three-dimensional structure, as well as calculating a prediction, which are processes that can be done either with a pen and paper and/or in a human mind, and are therefore abstract ideas, specifically a mental process. Training the prediction model by minimizing a target loss function based on a difference between the predicted and real binding force is merely a verbal articulation of mathematical steps, and is therefore, an abstract idea, specifically a mathematical concept. Determining a distance between a target atom in two different molecules is merely a verbal articulation of mathematical steps, and is therefore, an abstract idea, specifically a mathematical concept. Combining the target atom with the atom in the first molecule and determining them as atoms of the virtual complex molecule is a process of visualizing and representing a three-dimensional structure, which is a process that can be done either with a pen and paper and/or in a human mind, and is therefore an abstract idea, specifically a mental process.
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: An electronic device, processor, memory and instructions are generic and nonspecific elements of a computer that do not improve the functioning of any computer or technology described therein [See MPEP § 2106.04(d)(1) and MPEP § 2106.05(d)].
Claim 20: A non-transitory computer-readable storage medium, a computer and computer instructions are generic and nonspecific elements of a computer that do not improve the functioning of any computer or technology described therein [See MPEP § 2106.04(d)(1) and MPEP § 2106.05(d)].
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 elements of an electronic device, processor, memory, a non-transitory computer-readable storage medium, a computer, and computer instructions are generic and nonspecific elements of a computer that are well-understood, routine, and conventional within the art and therefore does not improve the functioning of any computer or technology described therein (Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values), and 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)) [See § MPEP 2106.05(d)(II)]. Therefore, taken both individually and as a whole, the additional elements do not amount to significantly more than the judicial exception by providing an inventive concept.
Therefore, claims 1, 3-8, 11, 13-18, and 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 filed 10/10/2025 have been fully considered but they are not persuasive. Applicant asserts on page 12 of the Remarks filed 10/10/2025 that the claimed invention is not directed to abstract ideas, but rather “a method of training a neural network”. However, examiner reminds applicant that the steps of training are not additional elements when those elements are describing abstract ideas. Specifically, the additional elements are elements that are non-abstract, however the claim as recited focuses the training on the mathematics behind the training which is an abstract idea.
Applicant asserts on pages 12 and 13 of the Remarks filed 10/10/2025 that the claimed invention is directed to an improvement to a technological process, specifically the process of determining binding force to screen new drugs and accelerate drug development. However, examiner reminds applicant that the improvement must be to either the additional elements of the claims or the additional elements in combination with the judicial exception as described in MPEP 2106.05(a) It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements - Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)). Here the elements asserted as being improved, determining binding force to screen new drugs and accelerate drug development, are part of the judicial exception as described above and are not additional elements.
Finally, applicant asserts that the additional elements recited are significantly more than the judicial exception. However, applicant has provided no argument as to how an electronic device, processor, memory, instructions, a non-transitory computer-readable storage medium, a computer, and computer instructions are not well-understood, routine, and conventional within the art as was provided in the recited rejection above.
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 have 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 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, 3-8, 11, 13-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (Cell systems (2020) 308-322).
Claim 1 is directed to a method of training a prediction model for determining molecular binding force in which a three-dimensional virtual complex molecule is constructed and distance and element type matrixes are constructed to train said prediction model so as to minimize a target loss function.
Claim 11 is directed to a device for training a prediction model for determining molecular binding force in which a three-dimensional virtual complex molecule is constructed and distance and element type matrixes are constructed to train said prediction model so as to minimize a target loss function.
Claim 20 is directed to a CRM for training a prediction model for determining molecular binding force in which a three-dimensional virtual complex molecule is constructed and distance and element type matrixes are constructed to train said prediction model so as to minimize a target loss function.
Li et al. teaches in the abstract “We also developed a multi-objective neural network, called MONN, to predict both non-covalent interactions and binding affinities between compounds and proteins”, page 312, column 1, paragraph 2 “Given a graph representation of a compound and a string representation of a protein sequence, our model is expected to output a predicted pairwise non-covalent interaction matrix”, reading on determining a predicted binding force between the first molecule and the second molecule and a predicted interaction matrix between the first molecule and the second molecule based on the virtual complex molecule by using the prediction model, wherein the predicted interaction matrix indicates an element-type-based and distance-based interaction between an atom in the first molecule and an atom in the second molecule. Li et al. teaches on page 310, column 2, paragraph 2 “Comprehensive cross-validation tests on our constructed benchmark dataset demonstrated that MONN can successfully learn the pairwise non-covalent interactions derived from high quality structural data, even using the 3D structure-free information as input”, reading on constructing a virtual complex molecule based on a three-dimensional structure information of a first molecule and a second molecule, wherein the virtual complex molecule comprises a virtual representation of the first molecule and a virtual representation of at least a part of the second molecule. Li et al. teaches on page e5, paragraph 3 “For a training dataset with N samples (i.e., compound-protein pairs), we minimize the cross-entropy loss for pairwise non-covalent interaction prediction”, reading on training the prediction model by minimizing a target loss function based on a difference between the predicted binding force and a real binding force and a difference between the predicted interaction matrix and a real interaction matrix. Li et al. teaches on page e9, paragraph 1 “we use the single-linkage clustering algorithm (Gower and Ross, 1969), which ensures that the minimal distance between any two clusters is above a given clustering threshold” and in equations 54 and 55, gives the calculations for a distance metric, which reads on determining a distance between a target atom in the second molecule and an atom in the first molecule based on the three-dimensional structure information. Li et al. teaches on page e2, paragraph 2 “The graph warp unit further improves the performance of graph convolution networks by introducing a super node s, which captures the global feature for the compound of interest. The extracted global feature will also be used in the affinity prediction module, as the properties of the whole compounds can generally contribute to their binding affinities”, on page 313 “We use hierarchical clustering for splitting all the compounds (proteins) into groups (i.e., clusters) based on their similarities. A clustering threshold determines the minimal distance between clusters”, reads on combining the target atom with the atom in the first molecule and determining the target atom and the atom in the first molecule as atoms of the virtual complex molecule, in response to determining that the distance between the target atom in the second molecule and the atom in the first molecule is less than a first threshold. Li et al. teaches in the abstract “we compiled a benchmark dataset containing the inter-molecular non-covalent interactions for more than 10,000 compound-protein pairs and systematically evaluated the interpretability of neural attentions in existing models. We also developed a multi-objective neural network, called MONN, to predict both non-covalent interactions and binding affinities between compounds and proteins”, and on page 310, column 2, paragraph 1 “MONN uses a graph warp module (Ishiguro et al., 2019) in addition to a traditional graph convolution module (Lei et al., 2017) to learn both a global feature for the whole compound and local features for individual atoms of the compound to better capture the molecular features of compounds; (2) MONN contains a pairwise interaction prediction module, which can capture the non-covalent interactions between atoms of a compound and residues of a protein with extra supervision from the labels extracted from available high-quality 3D compound-protein complex structures; and (3) in MONN, the pairwise non-covalent interaction prediction results are further utilized to benefit the prediction of binding affinities, by effectively incorporating the shared information between compound and protein features into the down stream affinity prediction module”, reading on wherein the trained prediction model is configured to determine a binding force between a new first molecule and a new second molecule by using a three-dimensional structure information of the new first molecule and the new second molecule, to perform screening on corresponding drugs.
It would have been obvious at the time of first filing to have modified the teachings of Li et al. for the method of claims 1, 11 and 20 with the limitation of using the prediction to determine a binding a force between a new combination of molecules based upon the training/information learned in the previous steps, as this is what machine learning, particularly prediction models are designed for as a prediction is merely the use of existing data to forecast/predict similar things. Additionally in the abstract of Li et al. it says “We also developed a multi-objective neural network, called MONN, to predict both non-covalent interactions and binding affinities between compounds and proteins. Comprehensive evaluation demonstrated that MONN can successfully predict the non-covalent interactions between compounds and proteins that cannot be effectively captured by neural attentions in previous prediction methods”, reading on the concept of predicting binding information. Therefore, it would have been obvious at the time of first of filing to have modified the teachings and to be successful.
Claim 3 is directed to the method of claim 1 but further specifies that a virtual complex be constructed via the construction of an edge between atoms having a distance less than a particular threshold and said nodes/edges be used to construct the three-dimensional structure.
Claim 13 is directed to the device of claim 11 but further specifies that a virtual complex be constructed via the construction of an edge between atoms having a distance less than a particular threshold and said nodes/edges be used to construct the three-dimensional structure.
Li et al. teaches on page 312, column 1, paragraph 2 “An input chemical compound with Na atoms can be represented by a graph G = {V,E}, where each node vi = V, i = 1,2,…,Na, corresponds to the i-th non-hydrogen atom in the compound, and each edge ei1,i2 = E, i1, i2 = {1,2,…,Na}, corresponds to a chemical bond between the i1-th and the i2-th atoms”, reading on constructing an edge between atoms and determining a representation of an atom in the virtual complex molecule and a representation of the edge based on a three-dimensional structure information of the virtual complex molecule. It would be obvious to a person skilled in the art that an edge representing a bond between atoms in a complex would be in part based on a distance threshold, as a bond is in part due to the distance between atoms.
Claim 4 is directed to the method of claim 3 and thus claim 1, but further specifies the determining of a feature representation of the complex molecule, and determining the predicted binding force based on said feature representation.
Claim 14 is directed to the device of claim 13 and thus claim 11, but further specifies the determining of a feature representation of the complex molecule, and determining the predicted binding force based on said feature representation.
Li et al. teaches on page 312, column 1, paragraph 3 “MONN consists of four modules: (1) a graph convolution module for extracting the features of both individual atoms and the whole compound from a given molecular graph, (2) a CNN module for extracting the features of individual residues from a given protein sequence, (3) a pairwise interaction prediction module for predicting the probability of the noncovalent interaction between any atom-residue pair from the previously learned atom and residue features, and (4) an affinity prediction module for predicting the binding affinity between the given pair of compound and protein, using the previously extracted molecular features, as well as the derived pairwise interaction matrix”, which reads on determining a feature representation for characterizing the virtual complex molecule based on the representation of the atom in the virtual complex molecule and determining the predicted binding force based on the feature representation using a fully connected layer in the prediction model.
Claim 5 is directed to the method of claim 3 but further specifies determining an atomic pair from an atom in the first molecule and an atom in the second molecule and from them determining an element value indexed by the first element type and the second element type in the predicted interaction matrix based on a weighted sum of representations of edges of atomic pairs.
Claim 15 is directed to the device of claim 11 but further specifies determining an atomic pair from an atom in the first molecule and an atom in the second molecule and from them determining an element value indexed by the first element type and the second element type in the predicted interaction matrix based on a weighted sum of representations of edges of atomic pairs.
Li et al. teaches on page 315, in Figure 3, section (A) “The pairwise interaction prediction module. Here, Watom and Wresidue stand for the weight parameters of two single-layer neural networks that need to be learned”, reading on determining, for a first group of atoms of a first element type in the first molecule and a second group of atoms of a second element type in at least a part of the second molecule, an atomic pair composed of an atom in the first group of atoms and an atom in the second group of atoms; and determining an element value indexed by the first element type and the second element type in the predicted interaction matrix based on a weighted sum of representations of edges of atomic pairs.
Claim 6 is directed to the method of claim 1, but further specifies determining an atomic pair from an atom in the first molecule and an atom in the second molecule that have a distance less than a particular threshold, and from them determining an element value of a matrix element indexed by the first element type and the second element type in the real interaction matrix based on the number of the one or more atomic pairs.
Claim 16 is directed to the device of claim 1, but further specifies determining an atomic pair from an atom in the first molecule and an atom in the second molecule that have a distance less than a particular threshold, and from them determining an element value of a matrix element indexed by the first element type and the second element type in the real interaction matrix based on the number of the one or more atomic pairs.
Li et al. teaches on page 315, in Figure 3, section (A) “The pairwise interaction prediction module”, and on page 319, column 1, paragraph 1 “clustering threshold 0.3 was used for hyper-parameter calibration”, and while Li et al. does not explicitly teach the use of a “real interaction matrix”, the use of metrics such as AUC for model performance in terms of prediction, would inherently require, or at least render obvious, the need for a comparison with real, or confirmed true, data. Therefore, it would be obvious to a person skilled in the art that Li et al. would therefore read on determining, for a first group of atoms of a first element type in the first molecule and a second group of atoms of a second element type in the second molecule, a number of one or more atomic pairs composed of an atom in the first group of atoms and an atom in the second group of atoms having a distance less than a third threshold; and determining an element value of a matrix element indexed by the first element type and the second element type in the real interaction matrix based on the number of the one or more atomic pairs.
Claim 8 is directed to the method of claim 1 but further specifies that the first molecule be a ligand and the second molecule a protein.
Claim 18 is directed to the method of claim 11 but further specifies that the first molecule be a ligand and the second molecule a protein.
Li et al. teaches on page e5, paragraph 6 “Our benchmark dataset was constructed mainly based on PDBbind, which contains a high-quality set of protein-ligand complexes”, reading on wherein the first molecule is a ligand and the second molecule is a protein.
Response to Arguments
Applicant's arguments filed 10/10/2025 have been fully considered but they are not persuasive.
Applicant has amended claims to contain limitations from claims 2 and 12 as well additional non-new matter. Examiner has amended the rejections accordingly.
Applicant asserts on page 10 of the Remarks filed 10/10/2025 that in anticipation of an obviousness rejection using Li et al., “there would have been no reason to have modified Li et al. because there would be not reason or motivation to explain why providing such an arrangement would have been beneficial”. However examiner reminds applicant Li et al. does in fact use structural information of the 3-dimensional structure as previously cited above as well as the prediction of binding of binding affinities as cited above.
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