DETAILED ACTION Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Status Claim s 1-20 are currently pending and examined on the merits . Claim s 1-20 are rejected. Claims 3, 4, 13, and 19 are objected to. Priority The instant application claims priority to U.S. Provisional Application 63/181,772 filed on 29 April 2021 and European Application EP 21315150.9 filed on 27 August 2021. At this point in examination, the effective filing date of c laims 1-20 is 29 April 2021 . Information Disclosure Statement The information disclosure statement s (IDS) submitted on 3 May 2022, 19 August 2022, and 28 November 2023 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement s ha ve been considered by the examiner. Drawings The drawings are objected to because FILLIN "Enter appropriate reason" \* MERGEFORMAT "Surface Feature CGeneratorr " 104 in Fig. 1 is misspelled and should read "Surface Feature Generator" . Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance. Specification The disclosure is objected to because of the following informalities: In paragraph 33, line 4, “a pre-trained neural networks” should read “pre-trained neural networks” In paragraph 33, line 10, “based on based on” should read “based on” In paragraph 39, line 5, “has a respective surfaces” should read “have respective surfaces” In paragraph 39, lines 8-9, “can be a mapped” should read “can be mapped” In paragraph 40, line 5, “geometrical or chemical feature” should read “geometrical or chemical features” In paragraph 41, line 3, “on target molecule” should read “on the target molecule” Appropriate correction is required. 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 appl icant regards as his invention. Claim s 3-4, 13 and 19 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. The term “ within a threshold score ” in claim 3 , last line, claim 13, last line, and claim 19, last line is a relative term which renders the claim indefinite. It is unclear how the spatial similarity score can be "within" a threshold score as it could mean being within a range of threshold scores or above/below/equal to a threshold score, rather than inside a threshold score. Being "within" a threshold score could be where the similarity score is close to a threshold score by some amount, i.e. % below or above the threshold score. The specification is also silent as to which definition the term "within" takes. For examination purposes, the term "within" has been construed to be "below" or "above" a threshold score . The term “ method ” in claim 4 , line 4, is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is unclear what "method" claim 4 is referring to when it states "performing the method for each of the clusters." The specification is also silent as to what "method" the limitation says to perform. For examination purposes, the term "method" has been construed to be referring to the method of claim 2 . 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. Claim s 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more . The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion). Subject matter eligibility evaluation in accordance with MPEP 2106: Eligibility Step 1 : Claims 1-10 are directed to a method (process) for providing the identification or classification of the target molecule for presentation to a user . Claims 11-16 are directed to a non-transitory computer-readable storage medium (machine) . Claims 17-20 are directed to a system (machine) . Therefore, these claims are encompassed by the categories of statutory subject matter, and thus satisfy the subject matter eligibility requirements under Step 1. [Step 1: YES] Eligibility S tep 2A : First, it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception. Eligibility S tep 2A , Prong One : In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. C laims 1 -6, 9, and 11- 20 recite the following steps which fall within the mental processes and/or mathematical concepts groups of abstract ideas , as noted below. Independent claims 1, 11, and 17 further recite: identifying a surface mesh that defines a surface of the target molecule, the surface mesh comprising a plurality of vertices (i.e., mental processes) ; identifying a plurality of surface patches by associating each vertex of the surface mesh with a respective patch (i.e., mental processes) ; using the latent space IDs and the real space IDs to identify at least one candidate item that includes a surface resembling a surface region of the target molecule, wherein the surface region comprises multiple patches in the plurality of surface patches of the target molecule (i.e., mental processes) ; and using the at least one candidate item to determine an identification or a classification of the target molecule (i.e., mental processes). Dependent claims 2, 12, and 18 further recite: wherein identifying a first candidate item that includes a first surface resembling the surface region of the target molecule (i.e., mental processes); mapping vertices of the target molecule to vertices of the first candidate item, wherein a first vertex on the target molecule is mapped to a second vertex on the first candidate item when a difference between at least one feature at the first vertex and at the second vertex is within a predetermined threshold (i.e., mental processes and mathematical concepts) ; identifying a cluster of vertices on the target molecule that are each within a predetermined threshold distance from at least one of the mapped vertices on the target molecule (i.e., mental processes) ; aligning the cluster on the target molecule with multiple vertices of the first candidate item by using gradient descent, the multiple vertices being within the first surface on the first candidate item (i.e., mathematical concepts); and identifying, as the surface region, surface patches associated with the vertices of the cluster on the target molecule (i.e., mental processes). Dependent claims 3, 13, and 19 further recite: determining a spatial similarity score for the cluster based on a 3D distance between the vertices of the cluster and vertices on the first surface of the first candidate item (i.e., mental process es , mathematical concepts) ; wherein the first candidate item is provided in response to determining that the spatial similarity score is within a threshold score (i.e., mental process). Dependent claims 4, 14, and 20 further recit e : identifying multiple clusters of vertices on the target molecule with vertices mapped to vertices of one or more candidate items (i.e., mental processes) ; and performing the method for each of the clusters to identify one or more surfaces on the one or more candidate items as resembling respective surface regions of the target mol e cul e (i.e., mental processes) . Dependent c laims 5 and 15 further recite : filtering out, from the multiple clusters, clusters that have less than a predetermined number of vertices (i.e., mental processes) . Dependent c laims 6 and 16 further recite : ranking each cluster in the multiple clusters based on one or more of (i) number of mapped vertices in the cluster, (ii) a ratio of the number of mapped vertices in the cluster to a total number of vertices in the cluster, (iii) the number of vertices on the at least one candidate item mapped to one or more vertices of the cluster, and (iv) a ratio of the number of vertices on the at least one candidate item mapped to one or more vertices of the cluster, to a total number of vertices on the candidate item (i.e., mental processes, mathematical concepts) ; and filtering out from multiple clusters, clusters that are ranked lower than a specific threshold rank (i.e., mental processes) . D ependent c laim 9 further recites : using the identification or the classification of the antigen to design or identify an antibody for the antigen based on the epitope (i.e., mental processes) . The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pencil and paper, and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind. Therefore, claims 1 -6, 9, and 11-20 recite an abstract idea. [Step 2a, Prong One: YES] Eligibility Step 2A, Prong Two : In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that , when examined as a whole, integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)). The judicial exceptions identified in Eligibility Step 2A, Prong One are not integrated into a practical application because of the reasons noted below. C laims 5 and 6 do not recite any elements in addition to the judicial exception, and thus are part of the judicial exception. Claims 1, 11, and 17 recite generating a respective latent space ID for each of the surface patches by using a neural network. Also, further recites generating a respective real space ID for each patch in the surface patches by using a radial distribution of one or more geometric or chemical features of the patch. The limitations recite “using a neural network” and “using a radial distribution”, which provide nothing more than mere instructions to implement an abstract idea on a generic computer . See MPEP 2106.05(f). Therefore, the claimed additional elements do not integrate the abstract ideas into a practical application. Claims 1-4, 11-14, and 17-20 recite the additional non-abstract elements of data gathering : receiving a target molecule to be identified or classified (claims 1, 11, and 17 ); obtaining one or more candidate items with known surfaces (claims 1, 11, and 17); providing the identification or the classification of the target molecule for presentation to a user (claims 1, 11, and 17); providing the first candidate item as an item that includes the first surface resembling the surface region of the target molecule (claims 2, 12, and 18); wherein the first candidate item is provided in response to determining that the spatial similarity score is within a threshold score (claims 3, 13, and 19); performing the method for each of the clusters to identify one or more surfaces on the one or more candidate items as resembling respective surface regions of the target molecule (claims 4, 14, and 20). which are each a data gathering step, or a description of the data gathered. Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect the data needed to carry out the JE. The data gathering does not impose any meaningful limitation on the JE, or how the JE is performed. The additional limitation (data gathering) must have more than a nominal or insignificant relationship to the identified judicial exception. (MPEP 2106.04/.05, citing Intellectual Ventures LLC v. Symantee Corp, McRO , TLI communications, OIP Techs. Inc. v. Amason.com Inc., Electric Power Group LLC v. Alstrom S.A. ). Claims 11 and 17 recite the additional non-abstract element s (EIA) of a general-purpose computer system or parts thereof : a non-transitory, computer-readable medium storing one or more instructions executable by a computer system (claims 11 and 17); a system, comprising one or more processors (claim 17) ; The EIA do not provide any details of how specific structures of the computer elements are used to implement the JE. The claims require nothing more than a general-purpose computer to perform the functions that constitute the judicial exceptions. The computer elements of the claims do not provide improvements to the functioning of the computer itself (as in DDR Holdings, LLC v. Hotels.com LP ); they do not provide improvements to any other technology or technical field (as in Diamond v. Diehr ); nor do they utilize a particular machine (as in Eibel Process Co. v. Minn. & Ont. Paper Co. ). Hence, these are mere instructions to apply the JE using a computer, and therefore the claim does not recite integrate that JE into a practical application. Thus, the additionally recited elements merely invoke a computer as a tool, and/or amount to insignificant extra-solution data gathering activity, and as such, when all limitations in claims 1-20 have been considered as a whole, the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application . Claims 1-4, 11-14, and 17-20 contain additional elements that would not integrate a judicial exception into a practical application and are further probed for inventive concept in Step 2B. [Step 2A Prong Two: NO] Eligibility Step 2B : Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05 ). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi). The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below. With respect to claims 1-4, 11-14, and 17-20 : The limitations identified above as non-abstract elements (EIA) related to data gathering do not rise to the level of significantly more than the judicial exception. Activities such as data gathering do not improve the functioning of a computer, or comprise an improvement to any other technical field. The limitations do not require or set forth a particular machine, they do not affect a transformation of matter, nor do they provide an unconventional step ( citing McRO and Trading Technologies Int’l v. IBG ). Data gathering steps constitute a general link to a technological environment. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp., ). With respect to claims 11 and 17 : The limitations identified above as non-abstract elements (EIA) related to general-purpose computer systems do not rise to the level of significantly more than the judicial exception. These elements do not improve the functioning of the computer itself, or comprise an improvement to any other technical field ( Trading Technologies Int’l v. IBG, TLI Communications ). They do not require or set forth a particular machine ( Ultramercial v. Hulu, LLC., Alice Corp. Pty. Ltd v. CLS Bank Int’l ), they do not affect a transformation of matter, nor do they provide an unconventional step. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp., CyberSource v. Retail Decisions, Parker v. Flook , Versata Development Group v. SAP America ). The additional element of generating a respective latent space ID for each of the surface patches by using a neural network ( claims 1, 11, and 17 ) is conventional. Evidence for conventionality is shown by Khan et al. ( Applied Artificial Intelligence , 20 18 , 33(1) , 87-100 ). Khan et al. reviews “ a patch-based technique for segmentation of latent fingerprint images, which uses Convolutional Neural Network (CNN) to classify patches” (Abstract , lines 6-8 ). This shows that latent spaces per surface patch are being classified using neural networks, which makes it a conventional practice in the art. The additional element of generating a respective real space ID for each patch in the surface patches by using a radial distribution of one or more geometric or chemical features of the patch ( claims 1, 11, and 17 ) is conventional. Evidence for conventionality is shown by Gainza et al. (Nature Methods, 2019, 17(2), 184-192) , as provided in the IDS filed 9/19/2022. Gainza et al. reviews “ MaSIF uses a geodesic polar coordinate system to map the position of vertices in radial (that is, geodesic distance from the center) and angular coordinates (that is, angle with respect to a random directions) with respect to the center of the patch (Fig. 1c). These coordinates add information on the spatial relationship between features to the learning method.” (Methods, Section “Computation of geodesic polar coordinates” , lines 2-6 ). This shows that radial distribution is used to generate real space IDs by mapping radial coordinates associated with the features on the geodesic polar coordinate system , which makes it a conventional practice in the art . [Step 2B: NO] Therefore, claims 1-20 are patent ineligible under 35 U.S.C. § 101. 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 ( 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, 8-11, 17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over FILLIN "Insert the prior art relied upon." \d "[ 2 ]" Gainza et al. (Nature Methods, 2019, 17(2), 184-192) , as provided in the IDS filed 9/19/2022 . Wi th respect to claims 1, 11, and 17 : With respect to the recited receiving a target molecule to be identified or classified, Gainza et al. discloses a “Protein molecular surface” (Fig. 1a). This figure suggests a surface of a target protein molecule to be identified or classified . With respect to the recited identifying a surface mesh that defines a surface of the target molecule, the surface mesh comprising a plurality of vertices, Gainza et al. discloses “Briefly, from a protein structure we compute a discretized molecular surface (solvent excluded surface) and assign geometric and chemical features to every point (vertex) in the mesh.” (Pages 184-185, col. 1, lines 1-3, Fig. 1 a, b). This describes a discretized molecular surface, which is also the surface mesh of a target protein molecule. This also suggests that there is more than one vertex in the surface mesh, which is confirmed through observation of Fig. 1b. With respect to the recited identifying a plurality of surface patches by associating each vertex of the surface mesh with a respective path, Gainza et al. discloses “Around each vertex of the mesh, we extract a patch with geodesic radius of r = 9 Å or r = 12 Å.” (Page 185, col. 1, lines 3-5, Fig. 1 a, b). This suggests that with the plurality of vertices in the surface mesh, surface patches are identified by associating each vertex with a respective path or radius , as specified above. With respect to the recited generating a respective latent space ID for each of the surface patches by using a neural network, Gainza et al. discloses “A convolutional layer with a set of filters is then applied to the output of the soft polar grid layer.” (Page 186, col. 1, lines 6-7). Also, further discloses “The procedure is repeated for different patch locations similar to a sliding window operation on images, producing the surface fingerprint descriptor at each point in the form of a vector that embeds information about the surface patterns of the center point and its neighborhood.” (Page 186, col. 2, lines 4-8). Gainza et al. discloses “With this framework we created descriptors for surface patches that can be further processed in neural network architectures.” (Page 187, col. 1, lines 3-4). This suggests that convolutional layers are being used to compute latent space IDs for each surface patch. Fingerprint descriptors are latent space IDs because they are manually optimized vectors that describe the protein surface features. Furthermore, this is shown in Fig. 1d, where fingerprint descriptors are generated for each patch using application-specific neural network architectures. With respect to the recited generating a respective real space ID for each patch in the surface patches by using a radial distribution of one or more geometric or chemical features of the patch, Gainza et al. discloses “For each vertex within the patch, we compute two geometric features (shape index and distance-dependent curvature) and three chemical features (hydropathy index, continuum electrostatics and the location of free electrons and proton donors). The vertices within a patch are assigned geodesic polar coordinates.” (Page 185, col. 1, lines 7-13, Fig. 1c). This suggests that geodesic polar coordinates are real space IDs because each vertex in each patch are computed with one or more geometric or chemical features. Gainza et al. further discloses “ MaSIF uses a geodesic polar coordinate system to map the position of vertices in radial (that is, geodesic distance from the center) and angular coordinates (that is, angle with respect to a random directions) with respect to the center of the patch (Fig. 1c). These coordinates add information on the spatial relationship between features to the learning method.” (Methods, Section “Computation of geodesic polar coordinates”). This describes the use of radial distribution of the features by mapping radial coordinates associated with the features on the geodesic polar coordinate system. With respect to the recited obtaining one or more candidate items with known surfaces, Gainza et al. discloses “scanning a large database of descriptors of potential binders” (Page 190, col. 2, lines 18-20, Fig. 5d). Th is suggests selecting from a large database, which contains one or more candidate items or descriptors. These descriptors are of potential binders, which implies known protein molecular surfaces. With respect to the recited using the latent space IDs and the real space IDs to identify at least one candidate item that includes a surface resembling a surface region of the target molecule, wherein the surface region comprises multiple patches in the plurality of surface patches of the target molecule, Gainza et al. discloses “Each point within a patch is assigned an array of geometric and chemical input features. The input features (chemistry and geometry) are not learned, they are precomputed properties from the molecular surface. MaSIF then learns to embed the surface patch’s input features into a numerical vector descriptor” (Page 184, col. 2, lines 13-14, Fig. 1 b, d). Also, further discloses “Specifically, the MaSIF -search workflow entails two stages: (1) scanning a large database of descriptors of potential binders and selecting the top decoys by descriptor similarity and (2) three-dimensional alignment of the complexes exploiting fingerprint descriptors of multiple points within the patch, coupled to a reranking of the predictions with a separate neural network.” (Page 190, col. 2, lines 17-23, Fig. 5d). The descriptor s , which correspond to latent space ID s , are generated using the map of the geometric and chemical features, which are the polar geodesic coordinates that correspond to real space ID s . Both IDs are used to identify the matching patches in the database using the subsequent MaSIF -search workflow. With respect to the recited using the at least one candidate item to determine an identification or a classification of the target molecule, Gainza et al. discloses “ scanning a large database of descriptors of potential binders and selecting the top decoys by descriptor similarity” (Page 190, col. 2, lines 1 8 -20, Fig. 5d). This suggests that candidate items, descriptors referred to as decoys, are selected by descriptor similarity. This implies that a potential binder or target molecule can be identified or classified based on this descriptor similarity. With respect to the recited providing the identification or the classification of the target molecule for presentation to a user, Gainza et al. discloses Figure 4, which depicts how the identification or classification of the target molecule is presented to the user, in the form of three-dimensional models. Claim 11 recites a non-transitory, computer-readable medium storing one or more instructions . Claim 17 recites a system comprising of one or more processors and a computer-readable storage device coupled to the one or more processors . Broadly claiming an automated means to replace a manual function to accomplish the same result does not distinguish over the prior art. See Leapfrog Enters., Inc. v. Fisher-Price, Inc. , 485 F .3d 1157, 1161, 82 USPQ2d 1687, 1691 (Fed. Cir. 2007) (“Accommodating a prior art mechanical device that accomplishes [a desired] goal to modern electronics would have been reasonably obvious to one of ordinary skill in designing children’s learning devices. Applying modern electronics to older mechanical devices has been commonplace in recent years.”); In re Venner , 262 F .2d 91, 95, 120 USPQ 193, 194 (CCPA 1958); see also MPEP § 2144.04. Furthermore, implementing a known function on a computer has been deemed obvious to one of ordinary skill in the art if the automation of the known function on a general purpose computer is nothing more than the predictable use of prior art elements according to their established functions. KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 417, 82 USPQ2d 1385, 1396 (2007); see also MPEP § 2143, Exemplary Rationales D and F. Likewise, it has been found to be obvious to adapt an existing process to incorporate Internet and Web browser technologies for communicating and displaying information because these technologies had become commonplace for those functions. Muniauction , Inc. v. Thomson Corp., 532 F.3d 1318, 1326-27, 87 USPQ2d 1350, 1357 (Fed. Cir. 2008). With respect to claim 8 : With respect to the recited wherein the target molecule is a protein molecule, and a candidate item is a portion of a known protein molecule, Gainza et al. discloses “To benchmark MaSIF -search we simulated a scenario where the binding site of a target protein is known, and one attempts to recapitulate the true binder of a protein among many other binders. Specifically, we benchmarked MaSIF -search in 100 bound protein complexes randomly selected from our testing set (disjoint from the training set). For each complex, we first selected the center of the interface in the target protein (see Methods), and then attempted to recover the bound complex within the 100 binder proteins comprising the test set (Fig. 5d). ” (Page 190, col. 2, lines 29-3 7 , Fig. 5). Th is suggests that the target protein is the target molecule to be identified among 100 bound protein complexes . To do this, the candidate item is implied to be a binder, where the portion of this protein molecule is docked to the target as seen in Fig. 5d. With respect to claim 9 : With respect to the recited wherein the target molecule is an antigen, and a candidate item is an epitope, Gainza et al. discloses “We used MaSIF -site to predict three such designed interfaces that have been experimentally validated: an influenza inhibitor (Fig. 4a), a homo-oligomeric cage protein (Fig. 4b), and an epitope-scaffold used as an immunogen (Fig. 4c). The designs were based on wild-type scaffold proteins with no binding activity, and in each case, we compared their interface score with that of the noninteracting wild type.” (Page 189, col 2, lines 9-15, Fig. 4). Candidate items such as homo-oligomeric cage proteins can serve as epitopes as they contain regions that can be recognized by the immune system. Therefore, the wild-type scaffold proteins and their respective designs serve as antigens and thus target molecules because Fig. 4 depicts the prediction of protein-protein interaction sites from candidate items on a set of target molecules. With respect to the recited using the identification or the classification of the antigen to design or identify an antibody for the antigen based on the epitope, Gainza et al. discloses “Overall, MaSIF -site may help to identify the sites of interactions with other proteins for PPI validation, paratope/epitope prediction or small molecule binding sites, for cases where evolutionary or experimental information may not be available.” (Page 189, col. 2, lines 17-20). This implies that protein-protein interaction site validation and epitope prediction can lead to the identification of the antigen when identifying sites of interactions with other proteins. This can assist in designing or identifying an antibody for the antigen, which implies a case where evolutionary or experimental information may not be available. With respect to claim 10 : With respect to the recited wherein the neural network comprises multiple layers and each of the latent space IDs is generated using the same layers, Gainza et al. discloses “ MaSIF applies a geometric deep neural network to these input features using the polar coordinates to spatially localize features. The neural network consists of one or more layers applied sequentially; a key component of the architecture is the geodesic convolution, generalizing the classical convolution to surfaces and implemented as an op eration on local patches.” (Page 185, col. 2, lines 7-12, Fig. 1d). Also, further discloses “Fingerprint descriptors are computed for each patch using application-specific neural network architectures, which contain reusable building blocks (geodesic convolutional layers).” (Fig. 1d). This suggests that the neural network contains several layers and the fingerprint descriptors (latent space IDs) are generated using reusable convolutional layers. With respect to claim 20 : With respect to the recited identifying multiple clusters of vertices on the target molecule with vertices mapped to vertices of one or more candidate items; and performing the method for each of the clusters to identify one or more surfaces on the one or more candidate items as resembling respective surface regions of the target molecule, Gainza et al. discloses “RANSAC selects three random points from the binder patch and uses the computed descriptors to find the closest points in the target patch by descriptor distance. Using these three newly found correspondences, RANSAC attempts to align the source patch to the target patch. RANSAC iterates 2,000 times and selects the transformation with the highest number of points within 1.0 Å between binder and target.” (Methods, Section “Structural alignment and rescoring”, lines 6-11). This describes the method of mapping, where the attempt to align the source patch (candidate item) to the target patch (target molecule) is performed multiple times to identify the transformation with the highest number of points, which indicates the candidate item with a surface resembling a surface region of the target molecule. The closest points in the target patch implies the multiple clusters of vertices on the target molecule to be identified with the vertices mapped to vertices of the candidate item, which is further described in the three random points from the binder patch used along with the descriptors. Therefore, the differences in the prior art were encompassed in known variations or in principle known in the prior art. The rationale would have been the predictable use of prior art elements according to their established functions. KSR 550 U.S. at 417. For these reasons, the instant claims do not recite any new element or new function or unpredictable result, and the examiner invites the applicant to provide evidence demonstrating the novel or unobvious difference between the claimed limitations and those used in the prior art, as mere argument cannot take the place of evidence lacking in the record. Estee Lauder Inc. v. L’Oreal , S.A., 129 F .3d 588, 595 (Fed. Cir. 1997). Claims FILLIN "Pluralize claim, if necessary, and then insert the claim number(s) which is/are under rejection." \d "[ 1 ]" 2-7, 12-16, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over FILLIN "Insert the prior art reference(s) relied upon for the obviousness rejection." \d "[ 2 ]" Gainza et al. (Nature Methods, 2019, 17(2), 184-192) , as applied to claims FILLIN "Insert the claim numbers which are under rejection." \d "[ 1 ]" 1, 8-11, 17, and 20 above, in view of FILLIN "Insert the additional prior art reference(s) relied upon for the obviousness rejection." \d "[ 4 ]" Jain , A.N. (Journal of Computer-Aided Molecular Design, 1996, 10, 427-440) . Gainza et al. is applied to claim s FILLIN "Insert the claim numbers which are under rejection." \d "[ 1 ]" 1, 8-11, 17, and 20 above. With respect to claims 2, 12, and 18 : With respect to the recited wherein identifying a first candidate item that includes a first surface resembling the surface region of the target molecule , Gainza et al. discloses “scanning a large database of descriptors of potential binders and selecting the top decoys by descriptor similarity” (Page 190, col. 2, lines 18-20, Fig. 5d). This suggests that candidate items, descriptors referred to as decoys, are selected by descriptor similarity, which means they will have surfaces similar to that of the target molecule. With respect to the recited mapping vertices of the target molecule to vertices of the first candidate item, wherein a first vertex on the target molecule is mapped to a second vertex on the first candidate item when a difference between at least one feature at the first vertex and at the second vertex is within a predetermined threshold, Gainza et al. discloses “RANSAC selects three random points from the binder patch and uses the computed descriptors to find the closest points in the target patch by descriptor distance . Using these three newly found correspondences, RANSAC attempts to align the source patch to the target patch. RANSAC iterates 2,000 times and selects the transformation with the highest number of points within 1.0 Å between binder and target.” (Methods, Section “Structural alignment and rescoring”, lines 6-11). This suggests that the correspondences are the vertices being mapped between the target molecule and the binder (first candidate item), where the distance between their points are within 1.0 Å. This implies a difference between at least one feature because the points are fingerprint descriptors that have feature information associated to them. With respect to the recited identifying a cluster of vertices on the target molecule that are each within a predetermined threshold distance from at least one of the mapped vertices on the target molecule, Gainza et al. discloses “RANSAC selects three random points from the binder patch and uses the computed descriptors to find the closest points in the target patch by descriptor distance.” (Methods, Section “Structural alignment and rescoring”, lines 6- 8 ). This suggests that the closest points in the target molecule is the cluster of vertices that are within a predetermined threshold distance, which is implied in the points being the closest to each other. With respect to the identifying, as the surface region, surface patches associated with the vertices of the cluster on the target molecule, Gainza et al. discloses “ MaSIF -search will produce similar descriptors for pairs of interacting patches (low Euclidean distances between fingerprint descriptors), and dissimilar descriptors for noninteracting patches (larger Euclidean distance between fingerprint descriptors). Thus, identifying potential binding partners is reduced to a comparison of numerical vectors.” (Page 190, col. 1, lines 12-18, Fig. 5a). This implies that surface patches can be identified through the comparison of their associated fingerprint descriptors. With respect to the providing the first candidate item as an item that includes the first surface resembling the surface region of the target molecule, Gainza et al. discloses “The top decoy patches with the shortest fingerprint descriptor distance to the target patch are selected as a shortlist of potential binding partners.” (Methods, Section “Structural alignment and rescoring”, lines 2-4). The shortlist of potential binding partners contains a first candidate item. Gainza et al. does not disclose aligning the cluster on the target molecule with multiple vertices of the first candidate item by using gradient descent, the multiple vertices being within the first surface on the first candidate item. However, Jain discloses “The three critical requirements on a scoring function for a molecular docking system are accuracy, speed, and tolerance to inaccurate poses of putative ligands in protein binding sites. The function F defined here satisfies these requirements. The expected mean error of predicted affinity, estimated by cross-validation across a diverse set of binding sites and ligands, is 1.0 log unit. Using a simple optimization to speed up the identification of protein atoms near the ligand, the time to compute the affinity of benzamidine to trypsin is 0.03 s, which is fast enough for use in a docking search engine. F is a continuous and differentiable function whose maxima correspond closely to crystallographically determined structures. So, imprecise putative ligand poses can be efficiently optimized by gradient descent.” (Conclusions, lines 1-15). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention if some motivation in the prior art would have led that person to substitute in the prior art teachings for the instant claim limitations. Gainza et al. teaches alignment of the patches of the target molecule to that of the candidate item using the RANSAC algorithm in Open 3D (Methods, Section “Structural alignment and rescoring”, Supplementary Fig. 6). However, Jain teaches that alignment, or docking, can also be done using gradient descent. This technique is more advantageous because it is a simple optimization method that significantly speeds up the identification of protein atoms near the ligand, and ultimately the affinity between two molecules. One of ordinary skill in the art would recognize that substituting the RANSAC algorithm for gradient descent would have a predictable result as both function to optimize the alignment between target and candidate molecules. With respect to claims 3, 13, and 19 : With respect to the recited determining a spatial similarity score for the cluster based on a 3D distance between the vertices of the cluster and vertices on the first surface of the first candidate item, Gainza et al. discloses “To discriminate true alignments we trained a separate neural network to score binder patches after the alignment step (Supplementary Fig. 6). Once a patch alignment has been made, the nearest neighbor on the binder in 3D space to each point in the target is searched, establishing correspondences (Supplementary Fig. 6b). Then, the input to the neural network is the 3D Euclidean distance, the MaSIF -search fingerprint distance and the product of the normal between correspondences. The output is a predicted score on the alignments.” (Methods, Section “Neural network for scoring aligned patches”, lines 1-8). Gainza et al. further discloses “For each point in an aligned patch we found its nearest neighbor (in 3D space, after alignment) on the target patch; for each pair of (binder, target) points we measured MaSIF -search fingerprint descriptor distance; the Euclidean distance in 3D space and dot products between their normals .” (Methods, Section “Neural network for scoring aligned patches”, lines 14-19). This suggests that a neural network is used to score vertices of the target patch (cluster) based on its 3D Euclidean distance to the vertices of the binder patch (first candidate item), where the predicted score is a spatial similarity score. With respect to the recited wherein the first candidate item is provided in response to determining that the spatial similarity score is within a threshold score, Gainza et al. discloses “A second-stage alignment and scoring method generates the complexes based on the identified fingerprints. The top decoy patches with the shortest fingerprint descriptor distance to the target patch are selected as a shortlist of potential binding partners.” (Methods, Section “Structural alignment and rescoring”, lines 1-4). The top decoy patches with the shortest fingerprint descriptor distance indicates that there is a threshold score the spatial similarity score must be within, and the shortlist of potential binding partners contains a first candidate item. With respect to claims 4 and 14 : With respect to the recited identifying multiple clusters of vertices on the target molecule with vertices mapped to vertices of one or more candidate items; and performing the method for each of the clusters to identify one or more surfaces on the one or more candidate items as resembling respective surface regions of the target molecule, Gainza et al. discloses “RANSAC selects three random points from the binder patch and uses the computed descriptors to find the closest points in the target patch by descriptor distance. Using these three newly found correspondences, RANSAC attempts to align the source patch to the target patch. RANSAC iterates 2,000 times and selects the transformation with the highest number of points within 1.0 Å between binder and target.” (Methods, Section “Structural alignment and rescoring”, lines 6-11). This describes the method of mapping, where the attempt to align the source patch (candidate item) to the target patch (target molecule) is performed multiple times to identify the transformation with the highest number of points, which indicates the candidate item with a surface resembling a surface region of the target molecule. The closest points in the target patch implies the multiple clusters of vertices on the target molecule to be identified with the vertices mapped to vertices of the candidate item, which is further described in the three random points from the binder patch used along with the descriptors. With respect to claims 5 and 15 : With respect to the recited further comprising filtering out, from the multiple clusters, clusters that have less than a predetermined number of vertices, Gainza et al. discloses “RANSAC iterates 2,000 times and selects the transformation with the highest number of points within 1.0 Å between binder and target.” (Methods, Section “Structural alignment and rescoring”, lines 10-11). This suggests that clusters that have the highest number of points within a specified distance between the candidate item and target molecule are selected, which further implies excluding clusters with the lowest number of vertices. With respect to claims 6 and 16 : With respect to the recited ranking each cluster in the multiple clusters based on one or more of (i) number of mapped vertices in the cluster, (ii) a ratio of the number of mapped vertices in the cluster to a total number of vertices in the cluster, (iii) the number of vertices on the at least one candidate item mapped to one or more vertices of the cluster, and (iv) a ratio of the number of vertices on the at least one candidate item mapped to one or more vertices of the cluster, to a total number of vertices on the candidate item, Gainza et al. discloses a “three-dimensional alignment of the complexes exploiting fingerprint descriptors of multiple points within the patch, coupled to a reranking of the predictions with a separate neural network (see Methods and Supplementary Fig. 6).” (Page 190, col. 2, lines 20-23, Fig. 5d). This indicates that while identifying multiple clusters of vertices through the alignment of complexes, a ranking process follows. With respect to the recited filtering out, from the multiple clusters, clusters that are ranked lower than a specific threshold rank, Gainza et al. discloses “Each aligned patch was limited to 200 points, if the size of the aligned patch was greater than 200 points it was randomly sampled and if it was lower than 200 points it was zero-padded. Thus, the input to the network is a matrix of size 200,3 (200 point pairs with three features per pair).” (Methods, Section “Neural network for scoring aligned patches”, lines 20-23). This suggests that clusters lower than 200 points are excluded or filtered out, making 200 points the threshold rank in this case. With respect to claim 7 : With respect to the recited wherein the at least one feature at a vertex includes one or more of a shape index, a distance-dependent curvature, a hydropath, a continuum electrostatics, and