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
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in the instant Application No. 18/206,873, filed on 8/7/2023. As such the effective filing date of claims 1-20 is 6/7/2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 9/5/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Status
Claims 1-20 are pending.
Claims 1-20 are rejected.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: Figure 1a, Item II, Figure 1a,b, Item III, Figure 14, Item 1432. 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. 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 use of the term Rosetta, AstraZeneca, AlphaFold, Novolumab, Twist Bioscience, Zymo Research, Beckman Coulter, Sony, Qiagen, Illumina, MiSeq, GE Healthcare, Thermo Fisher Scientific, Biacore 8K, Gator BLI System, miniDAWN TREOS, Wyatt, AppliedPhotophysics, HKL Research, HKL2000, Denzo, Vitrobot Mark IV, Titan Krios G4 Microscope, Falcon4, SelectrisX, which is a trade name or a mark used in commerce, has been noted in this application. The term should be accompanied by the generic terminology; furthermore the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
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.
Claim 5 is 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 “preferably” in claim 5 is a relative term which renders the claim indefinite. The term “preferably” 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. The term preferably is used to modify the radial distance of the overlapping patches created from the decomposition of the protein molecular surface, which renders unclear the calculations necessary for reproducing the accurate radial distances necessary for the correct performance of the method.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more. The claims recite a method, system and CRM for protein interaction design using surface fingerprints. The judicial exception is not integrated into a practical application because while claims 1-20 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 simply 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 statutory subject matter (a process, machine, manufacture, or composition of matter)? [see MPEP § 2106.03]
Claims are directed to statutory subject matter, specifically methods (claims 1-10), a system (claim 11-19), 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, 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.
Claims 1, 11, and 20: Predicting at least one target interface site, identifying at least one binding seed, and performing a binding seed transplantation are processes of comparing/contrasting, selecting, and calculating information that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes.
Claims 2 and 12: The target interface site comprising a buried interface site is merely further limiting the data itself which is an abstract idea, specifically a mental process. Generating at least one surface fingerprint associated with a protein interaction is a process of calculating information that can be done via pen and paper or within the human mind and is therefore an abstract idea, specifically a mental process.
Claims 3 and 13: The surface fingerprint embedding geometric and/or chemical features of molecular structures is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claims 4 and 14: Performing a surface fingerprint-based search is a process of comparing/contrasting and selecting information that can be done via pen and paper or within the human mind and is therefore an abstract idea, specifically a mental process.
Claims 5 and 15: Decomposing a protein molecular surface into overlapping radial patches is a process of calculating information that can be done via pen and paper or within the human mind and is therefore an abstract idea, specifically a mental process.
Claims 6 and 16: Computing a coordinate system in geodesic space is a process of calculating information that can be performed via pen and paper or within the human mind and is therefore an abstract idea, specifically a mental process. Computing a coordinate system in geodesic space is a verbal articulation of a mathematical process which is an abstract idea, specifically a mathematical concept.
Claims 7 and 17: Molecular fingerprints that are complementary between interacting protein pairs and dissimilar between non-interacting pairs is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claims 8 and 18: Scoring matching surface patches is a process of calculating information that can be done via pen and paper or within the human mind and is therefore an abstract idea, specifically a mental process.
Claims 9 and 19: The interface site being a prediction output is a process of calculating information that can be done via pen and paper or within the human mind and is therefore an abstract idea, specifically a mental process. The interface site being a prediction output, and the machine learning model having been trained on the specified data is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claim 10: Using the binding seed for the specified processes is merely further limiting the data itself which is 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:
Claims 1, 11, and 20: A computer, system, computer-readable medium, processors, memory, instructions, and computer program are generic and nonspecific elements of computers that do not improve the functioning of any computer or technology described herein [See MPEP § 2106.04(d)(1) and MPEP § 2106.05(d)].
Claims 7 and 17: Outputting a vector fingerprint is an insignificant extra solution activity, specifically necessary data outputting (See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering), Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839- 40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989) and Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis)) [See MPEP § 2106.05(g)].
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept? [see MPEP § 2106.05]
Because the additional claim elements do not integrate the abstract idea into a practical application, the claims are further examined under Step 2B, which evaluates whether the additional elements, individually and in combination, amount to significantly more than the judicial exception itself by providing an inventive concept.
The claims do not recite additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that are generic, conventional, nonspecific, or insignificant extra solution activity. These additional elements include:
The additional elements of a computer, system, computer-readable medium, processors, memory, instructions, and computer program are generic and nonspecific elements of a computer that are well-understood, routine and conventional within the art and therefore do not improve the functioning of any computer or technology described therein (See 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 whole, the additional elements do not amount to significantly more than the judicial exception by providing an inventive concept.
The additional element of outputting a vector fingerprint is an insignificant extra solution activity, specifically necessary data outputting (See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering), Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839- 40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989) and Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis)) [See MPEP § 2106.05(g)]. Therefore, taken both individually and as whole, the additional elements do not amount to significantly more than the judicial exception by providing an inventive concept.
Therefore, claims 1-20, when the limitations are considered individually and as a whole, are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gainza et al. (Nature Methods (2020) 184-192) and Azoitei et al. (Science (2011) 373-376).
Claim 1 is directed to a method for protein design using fingerprints to predict binding sites, ligands, and then finally performing transplantation.
Claim 11 is directed to a system for protein design using fingerprints to predict binding sites, ligands, and then finally performing transplantation.
Claim 20 is directed to a CRM for protein design using fingerprints to predict binding sites, ligands, and then finally performing transplantation.
Gainza et al. teaches in the abstract “We present MaSIF (molecular surface interaction fingerprinting), a conceptual framework based on a geometric deep learning method to capture fingerprints that are important for specific biomolecular interactions. We showcase MaSIF with three prediction challenges: protein pocket-ligand prediction, protein–protein interaction site prediction and ultrafast scanning of protein surfaces for prediction of protein–protein complexes”, reading on a computer-implemented method for protein interaction design using surface fingerprints, the method comprising: predicting at least one target interface site with high binding propensity; and identifying at least one binding seed that displays required features to engage the target site.
Gainza et al. does not teach performing a binding seed transplantation.
Azoitei et al. teaches in the abstract “We integrated computational design with experimental selection for grafting the backbone and side chains of a two-segment HIV gp120 epitope, targeted by the cross-neutralizing antibody b12, onto an unrelated scaffold protein. The final scaffolds bound b12 with high specificity and with affinity similar to that of gp120, and crystallographic analysis of a scaffold bound to b12 revealed high structural mimicry of the gp120-b12 complex structure”, reading on performing a binding seed transplantation to protein scaffolds to confer stability and additional contacts on the designed interface.
It would have been obvious at the time of first filing to have modified the teachings of Gainza et al. for predicting binding sites and identifying binding ligands with the teachings from Azoitei et al. for motif transplantation as the latter teaches on page 376, column 2, paragraph 1 “These results indicate that b12 epitope-scaffolds are promising tools for HIV vaccine research and encourage the application of backbone grafting to engineer antigens, enzymes, and inhibitors”, and the former teaches in the abstract “We showcase MaSIF with three prediction challenges: protein pocket-ligand prediction, protein–protein interaction site prediction and ultrafast scanning of protein surfaces for prediction of protein–protein complexes. We anticipate that our conceptual framework will lead to improvements in our understanding of protein function and design”. One would have had a reasonable expectation of success given that the former is directed to designing the protein and the latter is directed to making the protein, and it would merely be the addition of a known method of production to a known method of design. Therefore, it would have been obvious at the time of first filing to have modified the teachings of both and to have been successful.
Claim 2 is directed to the method of claim 1 but further specifies the interface site comprises a target buried site, with the generation of a surface fingerprint associated with a protein interaction at that site.
Claim 12 is directed to the system of claim 1 but further specifies the interface site comprises a target buried site, with the generation of a surface fingerprint associated with a protein interaction at that site.
Gainza et al. teaches on page 193, column 1, paragraph 2 “The choice of radius was empirical, mainly driven by performance and memory constraints. For MaSIF-search we chose 12 Å because we found this to be a good value to cover the buried surface area of many PPIs”, reading on wherein the at least one target interface site comprises at least one target buried interface site, and wherein predicting the target buried interface site comprises generating at least one surface fingerprint associated with a protein interaction based on at least one protein interface.
Claim 3 is directed to the method of claim 2 and thus claim 1, but further specifies that the fingerprint embeds geometric and/or chemical features of the molecular surfaces.
Claim 13 is directed to the system of claim 12 and thus claim 11, but further specifies that the fingerprint embeds geometric and/or chemical features of the molecular surfaces.
Gainza et al. teaches in Figure 5 “MaSIF-search was trained and tested on both geometric and chemical features”, reading on wherein the at least one surface fingerprint embeds geometric and/or chemical features of molecular surfaces.
Claim 4 is directed to the method of claim 1 but further specifies performing a surface fingerprint-based search.
Claim 14 is directed to the system of claim 11 but further specifies performing a surface fingerprint-based search.
Gainza et al. teaches on page 189, column 2, paragraph 2 “Thus, we introduce MaSIFsearch, a method to quickly search protein binding partners based on surface fingerprints”, reading on further comprising performing a surface fingerprint-based search.
Claim 5 is directed to the method of claim 1 but further specifies decomposing the protein surface into overlapping radial patches.
Claim 15 is directed to the system of claim 11 but further specifies decomposing the protein surface into overlapping radial patches.
Gainza et al. teaches on page 193, column 1, paragraph 2 “For each point in the discretized protein surface mesh, a radial patch of geodesic radius of 9 or 12 Å (application-dependent) was extracted to perform an analysis of the surface features of the patch”, reading on further comprising decomposing a protein molecular surface into overlapping radial patches, preferably with a radius of 12 Å.
Claim 6 is directed to the method of claim 1 but further specifies computing a coordinate system is geodesic space.
Claim 16 is directed to the system of claim 11 but further specifies computing a coordinate system is geodesic space.
Gainza et al. teaches on page 184, column 2, paragraph 1 “The molecular surface data is described in geodesic space, meaning that the distance between two points corresponds to the distance of ‘walking’ between the points along the surface”, reading on further comprising computing a coordinate system in geodesic space.
Claim 7 is directed to the method of claim 1 but further specifies using a first model to output vector fingerprints of complementary interacting protein pairs.
Claim 17 is directed to the system of claim 11 but further specifies using a first model to output vector fingerprints of complementary interacting protein pairs.
Gainza et al. teaches on page 184, column 2, paragraph 1 “First, MaSIF decomposes a surface into overlapping radial patches with a fixed geodesic radius (Fig. 1a,b). Each point within a patch is assigned an array of geometric and chemical input features (Fig. 1b). 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 (Fig. 1d). Each descriptor is further processed with application-dependent neural network layers. The networks are trained end-to-end, meaning that the intermediate patch descriptors are not universal but rather optimized toward particular tasks”, and in paragraph 2 of the same page and column “We showcase MaSIF with three proof-of-concept applications (Fig. 1e): (1) ligand pocket similarity comparison (MaSIF-ligand); (2) protein–protein interaction (PPI) site prediction in protein surfaces (MaSIF-site) and (3) ultrafast scanning of surfaces, where we exploit surface fingerprints to predict the structural configuration of protein–protein complexes”, reading on further comprising using a first machine-learning model to output vector fingerprint descriptors that are complementary between interacting protein pairs and dissimilar between non-interacting pairs.
Claim 8 is directed to the method of claim 1 but further specifies using a second machine learning model to score matching surface patches to compute a post-alignment score.
Claim 18 is directed to the system of claim 11 but further specifies using a second machine learning model to score matching surface patches to compute a post-alignment score.
Gainza et al. teaches on page 184, column 2, paragraph 1 “First, MaSIF decomposes a surface into overlapping radial patches with a fixed geodesic radius (Fig. 1a,b). Each point within a patch is assigned an array of geometric and chemical input features (Fig. 1b). 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 (Fig. 1d). Each descriptor is further processed with application-dependent neural network layers. The networks are trained end-to-end, meaning that the intermediate patch descriptors are not universal but rather optimized toward particular tasks”, and in Figure 3 “The MaSIF-site receives as input a protein surface with a descriptor vector and outputs a surface score that reflects the predicted interface propensity”, reading on further comprising using a second machine-learning model to score matching surface patches, in particular for computing an interface post-alignment score.
Claim 9 is directed to the method of claim 1 but further specifies that at least one target site is a prediction output based on the training.
Claim 19 is directed to the system of claim 11 but further specifies that at least one target site is a prediction output based on the training.
Gainza et al. teaches in Figure 3 “The MaSIF-site receives as input a protein surface with a descriptor vector and outputs a surface score that reflects the predicted interface propensity”, on page 189, column 2, paragraph 2 “Thus, we introduce MaSIFsearch, a method to quickly search protein binding partners based on surface fingerprints”, in Figure 5 “MaSIF-search was trained and tested on both geometric and chemical features”, and on page 187, column 2, paragraph 2 “We trained MaSIF-ligand on a large set of cofactor-binding proteins using their holo structures, where sequences and structures were clustered to remove redundancy from the training and test sets”, reading on wherein the at least one target interface site with high binding propensity is a prediction output by a machine-learning model trained on data comprising surface fingerprints and/or geometric or chemical features of molecular surfaces.
Claim 10 is directed to the method of claim 1 but further specifies that the binding seed is used for the specified methods.
Azoitei et al. teaches in Figure 1 “Combined in silico–in vitro strategy for the transplantation of complex structural motifs to heterologous scaffold proteins. The diagrams illustrate the stages in the design of a non-HIV scaffold presenting two loops from the b12 epitope on HIV gp120”, and on page 373, column 1, paragraph 1 “Computational protein design tests our understanding of protein structure and folding and provides valuable reagents for biomedical and biochemical research; long term goals include the design of field- or clinic ready biosensors (1), enzymes (2), therapeutics (3), and vaccines (4, 5).”, reading on wherein the binding seed and/or the binding seed transplantation is used for one or more of: obtaining a protein-based therapeutic; obtaining an antibody; obtaining an inhibitor; or obtaining a vaccine.
Conclusion
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.
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/K.N.A./Examiner, Art Unit 1687
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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