DETAILED ACTION
Notice of 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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Status of the Claims
Claims 3-4, 14-23 and 25-26 are canceled.
Claims 1-2, 5-13, 24 and 27-30 are pending.
Claims 1-2, 7-8, 12-13, 23 and 29-30 are objected to.
Claims 1-2, 5-13, 24 and 27-30 are rejected.
Priority
This application US 18/027,571 (03/21/2023) is a 371 of PCT/EP2021082618 (11/23/2021) which claims benefit of US Application 63/118,914 (11/28/2020) as reflected in the filing receipt mailed on 01/23/2024. The claims to the benefit of priority are acknowledged and the effective filing date of claims 1-2, 5-13, 24 and 27-30 is 11/28/2020.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 06/26/2023, 11/10/2023, 09/03/2025, 02/27/2026 and 06/17/2026 were considered.
Claim objections
Claim 1 is objected to because of the following informality: the recited "in a structure of the protein" (i.e. last line of the first claim element) should read "in the structure of the protein" because "a structure of the protein" has been recited previously in the claim. Claims 12-13 repeat the issue above.
Claim 2 is objected to because of the following informality: the recited "a 3D spatial location and orientation" should read "the 3D spatial location and orientation" because "a 3D spatial location and orientation" has been recited previously in parent claim 1. Claim 23 repeat the issue above.
Claim 7 is objected to because of the following informality: the recited "based on updated" (last claim element) should read "based on the updated" for proper grammar. Claim 29 repeat the issue above.
Claim 7 is objected to under 37 CFR 1.75 as being in improper. Colons should begin lists in which list elements are separated by newlines, e.g. claim 7 "each update block further comprise" should be followed by a colon and each listed sub step should be recited in a new indented line after the colon. As set forth in 37 CPR 1.75, each element or step of the claim should be separated by a line indentation (608.01(m) Form of Claims). Sub-steps / elements should be indented from their parent step / element. This rule should be applied throughout the claims as needed. Claim 29 repeat the issue above.
Claim 8 is objected to because of the following informality: "a respective current amino acid embedding" should read "the respective current amino acid embedding" for proper agreement to its parent claim 7. Claim 30 repeats the issue above.
Claim Rejections - 35 USC § 112(b)
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.
Claims 9-10 are rejected under 35 U.S.C. 112(b)as being indefinite for failing to particularly point out and distinctly claim the subject matter the invention. Dependent claims are rejected similarly, unless otherwise noted below. The following issues cause the respective claims to be rejected under 112(b) as indefinite:
Claims 9-10 recite “the attention weights” which is indefinite because it lacks antecedent basis. A recitation for "attention weights" has not been previously recited. The previously recited "a respective attention weight between the current global embedding for the first amino acid chain and the current amino acid embedding" does not provide the proper antecedent basis for “the attention weights.” To overcome this rejection, the claims may be amended to clarify the antecedent basis for “the attention weights” pointing out to the exact “attention weights” the claims are referring to.
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 1-2, 5-13, 24 and 27-30 are rejected under 35 USC § 101 because the claimed inventions are directed to one or more Judicial Exceptions (JEs) without significantly more. Regarding JEs, "Claims directed to nothing more than abstract ideas..., natural phenomena, and laws of nature are not eligible for patent protection" (MPEP 2106.04 §I). Abstract ideas include mathematical concepts and procedures for evaluating, analyzing or organizing information, which are a type of mental process (MPEP 2106.04(a)(2)).
101 background
MPEP 2106 organizes JE analysis into Steps 1, 2A (Prong One & Prong Two), and 2B as analyzed below. MPEP 2106 and the following USPTO website provide further explanation and case law citations: uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials.
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter (MPEP 2106.03)?
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
Analysis of instant claims
Step 1: Are the claims directed to a 101 process, machine, manufacture, or composition of matter (MPEP 2106.03)?
The instant claims are directed to a method (claims 1-2 and 5-11) and a system (claim 12) and a CRM (claims 13, 24 and 27-30); each of which falls within one of the categories of statutory subject matter.
[Step 1: claims 1-2, 5-13, 24 and 27-30: Yes]
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
Background
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. MPEP § 2106.04(a)(2) further explains that abstract ideas are defined as:
• mathematical concepts (mathematical formulas or equations, mathematical relationships
and mathematical calculations) (MPEP 2106.04(a)(2)(I));
• certain methods of organizing human activity (fundamental economic principles or practices, managing personal behavior or relationships or interactions between people) (MPEP 2106.04(a)(2)(II)); and/or
• mental processes (concepts practically performed in the human mind, including observations, evaluations, judgments, and opinions) (MPEP 2106.04(a)(2)(III)).
Analysis of instant claims
With respect to the instant claims, under the Step 2A, Prong One evaluation, the claims are found to recite abstract ideas that fall into the grouping of mathematical concepts (in particular mathematical relationships and formulas) and mental processes (in particular procedures for observing, analyzing and organizing information) are as follows.
Mathematical concepts (in particular mathematical relationships and formulas) include:
• "processing an input comprising the initial structure parameters for the first amino acid chain and the data identifying the symmetry group …to generate an output that defines a final predicted structure of the protein to generate an output that defines a final predicted structure of the protein that is symmetrical with respect to the symmetry group" (independent claims 1 and 12-13);
• "wherein each update block in the sequence of update blocks has a plurality of update block parameters and performs operations" (independent claims 1 and 12-13);
• "applying a symmetrical expansion transformation to the current structure parameters for the first amino acid chain to generate respective current structure parameters for each other amino acid chain in the protein to define a current predicted structure of the protein that is symmetrical with respect to the symmetry group" (independent claims 1 and 12-13);
• "processing the current structure parameters for the amino acid chains in the protein, in accordance with values of the update block parameters of the update block, to update the current structure parameters for the first amino acid chain; wherein the structure parameters for the first amino acid chain in the protein include global structure parameters that define a 3D spatial location and orientation of the first amino acid chain in a frame of reference of the protein" (independent claims 1 and 12-13);
• "wherein applying the symmetrical expansion transformation to the current structure parameters for the first amino acid chain to generate respective current structure parameters for each other amino acid chain in the protein comprises for each other amino acid chain in the protein: generating the global structure parameters for the other amino acid chain by applying a predefined transformation to the global structure parameters for the first amino acid chain wherein the predefined transformation depends on: (i) a number of amino acid chains in the protein, and (ii) the symmetry group; and determining amino acid structure parameters for the amino acids in the other amino acid chain that match the amino acid structure parameters for the amino acids in the first amino acid chain" (independent claims 1 and 12-13);
"wherein processing the current structure parameters for the amino acid chains in the protein to update the current structure parameters for the first amino acid chain comprises: updating the current amino acid embeddings and the current global embedding for the first amino acid chain based on the current structure parameters for the amino acid chains in the protein; and updating the current structure parameters for the first amino acid chain based on updated amino acid embeddings and the updated global embedding for the first amino acid chain" (claims 7 and 29);
• "determining, for each other amino acid chain in the protein, a respective current amino acid embedding for each amino acid in the other amino acid chain based on the current amino acid embedding of a corresponding amino acid in the first amino acid chain; and updating the current amino acid embeddings and the current global embedding for the first amino acid chain using attention over the current amino acid embeddings for the amino acid chains, wherein the attention over the current amino acid embeddings for the amino acid chains is conditioned on the current structure parameters for the amino acid chains" (claims 8 and 30);
• "determining, for each amino acid in each amino acid chain, a respective attention weight between the current global embedding for the first amino acid chain and the current amino acid embedding for the amino acid based at least in part on: (i) the global structure parameters for the first amino acid chain, and (ii) the amino acid structure parameters for the amino acid and the global structure parameters for the amino acid chain of the amino acid; and updating the current global embedding for the first amino acid chain based on: (i) the attention weights, and (ii) the current amino acid embeddings for the amino acid chains" (claim 9);
• "wherein for each amino acid in each amino acid chain, determining the attention weight between the current global embedding for the first amino acid chain and the current amino acid embedding for the amino acid comprises: generating a geometric query embedding corresponding to the current global embedding for the first amino acid chain, comprising: processing the current global embedding for the first amino acid chain using one or more neural network layers to generate a 3D embedding; rotating and translating the 3D embedding into a frame of reference of the protein using the global structure parameters for the first amino acid chain; generating a geometric key embedding corresponding to the amino acid, comprising: processing the current amino acid embedding of the amino acid using one or more neural network layers to generate a 3D embedding; and rotating and translating the 3D embedding into the frame of reference of the protein using the amino acid structure parameters for the amino acid and the global structure parameters for the amino acid chain of the amino acid; and determining the attention weight based on a spatial distance between: (i) the geometric query embedding corresponding to the current global embedding for the first amino acid chain, and (ii) the geometric key embedding corresponding to the amino acid" (claim 10); and
• "for each amino acid in the first amino acid chain, updating the amino acid structure parameters for the amino acid based on the updated amino acid embedding for the amino acid; and updating the global structure parameters for the first amino acid chain based on the updated global embedding for the first amino acid chain" (claim 11).
The claims identified above read on math. The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation and determined each element performed by mathematical operation. The step directed to “executing an algorithm to apply symmetrical expansion transformation to generate acid structure parameters for the amino acids in the other amino acid chain that match the amino acid structure parameters for the amino acids in the first amino acid chain” requires mathematical techniques as the only supported embodiments because it describes a mathematical technique (MPEP 2106.04(a)(2) pertains). Further support for the mathematical techniques used in the claims is provided in the specification at [0062, 0075 and 0190], which discloses an algorithm to perform transformations operations symmetry group to the protein structure. Thus, the recited terms correspond to verbal equivalents of mathematical concepts because they constitute actions executed by a group of mathematical steps in a form of a mathematical algorithm; thus mathematical concepts (MPEP 2106.04(a)(2)). A mathematical concept need not be expressed in mathematical symbols, because "words used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). MPEP 2106.04(a)(2) pertains.
Mental processes, defined as concepts or steps practically performed in the human mind such as steps of observations, evaluations, judgments, analysis, opinions or organizing information include:
• "obtaining initial structure parameters for a first amino acid chain in the protein, wherein the structure parameters for the first amino acid chain in the protein define predicted three-dimensional (3D) spatial locations of amino acids in the first amino acid chain in a structure of the protein" (independent claims 1 and 12-13) and
• "obtaining data identifying a symmetry group, wherein the protein is predicted to fold into a structure that is symmetrical with respect to the symmetry group" (independent claims 1 and 12-13).
The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation (BRI) and determined to each cover performance either in the mind (i.e. concepts practically performed in the human mind, including observations, evaluations, judgments, and opinions) or because the method only requires a user to manually determine action based on an added number. Under the BRI, the recited limitations are mental processes because a human mind is also sufficiently capable of evaluating data parameters to choose one that defines predicted three-dimensional (3D) spatial locations of amino acids in the first amino acid chain in a structure of the protein and evaluating data to identify a symmetry group.
Dependent claims 2, 5-6, 24 and 27-28 recite further steps that limit the judicial exceptions in independent claims 1 and 12-13 and, as such, also are directed to those abstract ideas. For example, claims 2 and 24 recite further details about the structural parameters; claims 5 and 27 recite further details about the symmetry group; claims 6 and 28 recite further details about the input being processed.
[Step 2A Prong One: claims 1-2, 5-13, 24 and 27-30: Yes ]
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Background
MPEP 2106.04(d).I lists the following example considerations for evaluating whether a judicial exception is integrated into a practical application:
An improvement in the functioning of a computer or an improvement to other technology or another technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
Analysis of instant claims
Instant claims 1-2, 5-13, 24 and 27-30 recite additional elements that are not abstract ideas:
• "one or more data processing apparatus" (independent claim 1);
• "folding neural network" (claims 1, 6, 10, 12-13 and 28);
• "receiving current structure parameters for the first amino acid chain and the data identifying the symmetry group" (independent claim 1);
• "receiving a respective current amino acid embedding for each amino acid in the first amino acid chain and a current global embedding of the first amino acid chain" (claim 7);
• "one or more computers" (independent claim 12);
• "one or more storage devices communicatively coupled to the one or more computers" (independent claim 12); and
• "one or more non-transitory computer storage media" (independent claim 13).
Considerations under Step 2A, Prong Two
The recited limitations in claims 1-2, 5-13, 24 and 27-30 are interpreted as requiring the use of a computer. Hence, the claims explicitly recite steps executed by computers and therefore can be described as computer functions or instructions to implement on a generic computer.
Further steps directed to additional non-abstract elements of a computing device/computer do not describe any specific computational steps by which the "computer parts" perform or carry out the judicial exceptions, nor do they provide any details of how specific structures of the computer are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions.
The judicial exceptions in the claims are considered to perform the claimed abstract idea with a computer, which is not sufficient to integrate an abstract idea into a practical application (see MPEP 2106.05(f)); since steps that can be performed mentally and merely performing the mental process in a computer environment do not negate the fact that something that can be carried out in the human mind. See MPEP 2106.04(a)(2).III.C.
Claims directed to "receiving" read on receiving or transmitting data over a network -Symantec, 838 F.3d at 1321 - MPEP 2106.05(a) pertains; which constitutes just necessary data gathering and therefore correspond to insignificant extra-solution activity.
With respect to claims 1, 6, 10, 12-13 and 28, the computer-related elements or the general purpose computer and the recited neural network model does not rise to the level of significantly more than the judicial exception. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, which the courts have found to not provide significantly more when recited in a claim with a judicial exception (Alice Corp., 573 U.S. at225-26, 110 USPQ2d at 1984; see MPEP 2106.05(A)). The specification as published also notes that computer processors and systems, as example, are known and widely used examples of neural networks that may be used without limitation [0206]. The additional elements are set forth at such a high level of generality that they can be met by a general purpose computer. Therefore, the computer components constitute no more than a general link to a technological environment, which is insufficient to constitute an inventive concept that would render the claims significantly more than the judicial exceptions (see MPEP 2106.05(b)I-III).
The specification discloses that Sharing parameter values between the update blocks 220 reduces the number of trainable parameters of the folding neural network and may therefore facilitate effective training of the folding neural network, e.g., by stabilizing the training and reducing the likelihood of overfitting, at [0115] but does not provide a clear explanation for how the additional elements provide these improvements.
Hence, these are mere instructions to apply the abstract idea using a computer and insignificant extra-solution activity and therefore the claims do not integrate that abstract idea into a practical application (see MPEP 2106.04(d) § I; 2106.05(f); and 2106.05(g)).
In Step 2A, Prong One above, claim steps and/or elements were identified as part of one or more judicial exceptions (JEs).
In this Step 2A, Prong Two immediately above claim steps and/or elements were identified as part of one or more additional elements. Additional elements are further discussed in Step 2B below.
Here in Step 2A, Prong Two, no additional step or element clearly demonstrates integration of the JE(s) into a practical application.
[Step 2A Prong Two: claims 1-2, 5-13, 24 and 27-30: No]
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
According to analysis so far, the additional elements described above do not provide significantly more than the judicial exception. A determination of whether additional elements provide significantly more also rests on whether the additional elements or a combination of elements represents other than what is well-understood, routine, and conventional. Conventionality is a question of fact and may be evidenced as: a citation to an express statement in the specification or to a statement made by an applicant during examination that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
Claims 1-2, 5-13, 24 and 27-30 recite a computer or computer functions, interpreted as instructions to apply the abstract idea using a computer, where the computer does not impose meaningful limitations on the judicial exceptions; which can be performed without the use of a computer (MPEP 2106.04(d) § I; and MPEP 2106.05(f)).
The computer-related elements or the general purpose computer and the neural network model do not rise to the level of significantly more than the judicial exception. The claims state a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, which the courts have found to not provide significantly more when recited in a claim with a judicial exception (Alice Corp., 573 U.S. at225-26, 110 USPQ2d at 1984; see MPEP 2106.05(A)).
Further, the courts have found that receiving data is a well-understood, routine, and conventional function of a computer when claimed in a generic manner or as insignificant extra-solution activity (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network), Versa ta Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, as discussed in MPEP 2106.05(d)(Il)(i)).
The specification as published also notes that any suitable machine learning model may be used without limitation at [0206], which provides evidence that any suitable neural network could be used to model the recited “folding neural network.” The additional elements are set forth at such a high level of generality that they can be met by a general purpose computer. Therefore, the computer components constitute no more than a general link to a technological environment, which is insufficient to constitute an inventive concept that would render the claims significantly more than the judicial exceptions (see MPEP 2106.05(b) I-III).
When the claims are considered as a whole, they do not integrate the abstract idea into a practical application; they do not confine the use of the abstract idea to a particular technology; they do not solve a problem rooted in or arising from the use of a particular technology; they do not improve a technology by allowing the technology to perform a function that it previously was not capable of performing; and they do not provide any limitations beyond generally linking the use of the abstract idea to a broad technological environment. See MPEP 2106.05(a) and 2106.05(h).
The instant claims constitute insignificant extra solution activity, and when considered individually, are insufficient to constitute inventive concepts that would render the claims significantly more than an abstract idea (see MPEP 2106.05(g)). Hence, these elements, when considered individually, are insufficient to constitute inventive concepts that would render the claims significantly more than an abstract idea (see MPEP 2106.05(d)).
[Step 2B: claims 1-2, 5-13, 24 and 27-30: No]
Conclusion: Instant claims are directed to non-statutory subject matter
For the reasons above, the claims in this instant application, when the limitations are considered individually and as a whole, are directed to an abstract idea and lack an inventive concept not clearly anything significantly more.
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter 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 pre-AIA 35 U.S.C. 103(a) 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.
A. Claims 1-2, 5, 12-13, 24 and 27 are rejected under 35 U.S.C. 103(a) as being unpatentable over Andre ("Prediction of the structure of symmetrical protein assemblies." Proceedings of the National Academy of Sciences 104.45:17656-17661 (2007)) in view of Senior ("Improved protein structure prediction using potentials from deep learning." Nature 577.7792:706-710 (2020) – Published 01/15/2020), as cited on the attached Form PTO-892.
Claim 1 recites a method performed by one or more data processing apparatus for predicting a structure of a protein that comprises a plurality of amino acid chains comprising steps. Claim 12 recites a system comprising :one or more computers; and one or more storage devices communicatively coupled to the one or more computers, wherein the one or more storage devices store instructions that, when executed by the one or more computers, cause the one or more computers to perform operations for predicting a structure of a protein that comprises a plurality of amino acid chains, the method comprising said steps. Claim 13 recites one or more non-transitory computer storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations for predicting a structure of a protein that comprises a plurality of amino acid chains, the method comprising said steps
The prior art to Andre discloses a computational protocol and system to predict the structure of symmetrical protein assemblies based on the structure of a single protein subunit (pg. 17656 col. 1 para. 1); wherein a non-transitory computer storage media is provided via software for code sourcing Rosetta and plotting gnuplot and pymol (pg. 17661 col. 2 para. 3).
The steps performed by the method of claim 1, a system of claim 12, and a non-transitory computer storage media of claim 13 comprise:
obtaining initial structure parameters for a first amino acid chain in the protein, wherein the structure parameters for the first amino acid chain in the protein define predicted three- dimensional (3D) spatial locations of amino acids in the first amino acid chain in a structure of the protein; obtaining data identifying a symmetry group, wherein the protein is predicted to fold into a structure that is symmetrical with respect to the symmetry group
• Andre teaches a computational protocol to predict the structure of symmetrical protein assemblies based on the structure of a single protein subunit (pg. 17656 col. 1 para. 1); wherein the protocol starts with a first subunit (i.e. reading on first amino acid chain) placed in its coordinates (pg. 17660 col. 2 para. 1); and while bond lengths and bond angles are kept fixed, assuming perfect symmetry of the subunits, the degrees of freedom are the backbone and side chain torsion angles (pg. 17656 col. 2 para. 3) (i.e. reading on obtaining initial structure parameters for a first amino acid chain in the protein, wherein the structure parameters for the first amino acid chain in the protein define predicted three-dimensional (3D) spatial locations of amino acids in the first amino acid chain in a structure of the protein); wherein the proposed symmetrical assembly protocol is tested on a range of randomly selected symmetrical oligomers from the Protein Data Bank containing symmetry data (i.e. reading on data identifying a symmetry group) (pg. 17657 col. 2 para. 2); wherein, given the coordinates of a single subunit together with a set of symmetry transformations consistent with the desired symmetry, the position of all subunits in an oligomer can be computed (i.e. reading on wherein the protein is predicted to fold into a structure that is symmetrical with respect to the symmetry group) (pg. 17657 col. 1 para. 2) and the exact details of this process depend on the symmetry of the system (pg. 17660 col. 2 para. 1).
processing an input comprising the initial structure parameters for the first amino acid chain and the data identifying the symmetry group using a folding neural network to generate an output that defines a final predicted structure of the protein to generate an output that defines a final predicted structure of the protein that is symmetrical with respect to the symmetry group
• Andre teaches a computational protocol to predict the structure of symmetrical protein assemblies based on the structure of a single protein subunit (pg. 17656 col. 1 para. 1); wherein, given the coordinates of a single subunit together with a set of symmetry transformations consistent with the desired symmetry (i.e. reading on processing an input comprising the initial structure parameters for the first amino acid chain and the data identifying the symmetry group), the position of all subunits in an oligomer can be computed (i.e. reading on output that defines a final predicted structure of the protein that is symmetrical with respect to the symmetry group) (pg. 17657 col. 1 para. 2) and the exact details of this process depend on the symmetry of the system (pg. 17660 col. 2 para. 1).
using a folding neural network to generate an output that defines a final predicted structure of the protein … wherein the folding neural network comprises a sequence of update blocks, wherein each update block in the sequence of update blocks has a plurality of update block parameters and performs operations comprising
• Andre does not teach the recitation above. However, Senior teaches a deep residual convolutional network is made-up of blocks configured in layers (Extended Data Fig. 1b) trained to make accurate predictions of the distances between pairs of residues to construct a potential of mean force that can accurately describe the shape of a protein optimized by a simple gradient descent algorithm (i.e. reading on iteratively updating the model’s parameters) (pg. 706 Abstract); wherein the network uses structural predictions by modifying the statistical potential (i.e. reading on updating parameters) to guide the folding process (i.e. reading on using a folding neural network output that defines a final predicted structure of the protein) (pg. 707 col. 1 para. 2).
receiving current structure parameters for the first amino acid chain and the data identifying the symmetry group;
applying a symmetrical expansion transformation to the current structure parameters for the first amino acid chain to generate respective current structure parameters for each other amino acid chain in the protein to define a current predicted structure of the protein that is symmetrical with respect to the symmetry group
• Andre teaches the proposed symmetrical assembly protocol tested on a range of randomly selected symmetrical oligomers from the Protein Data Bank containing symmetry data (i.e. reading on data identifying a symmetry group) (pg. 17657 col. 2 para. 2); wherein the protocol starts with a first subunit (i.e. reading on first amino acid chain) placed in its coordinates (pg. 17660 col. 2 para. 1); wherein, given the coordinates of a single subunit together with a set of symmetry transformations consistent with the desired symmetry the position of all subunits in an oligomer can be computed (pg. 17657 col. 1 para. 2) and the exact details of this process depend on the symmetry of the system (i.e. reading on applying a symmetrical expansion transformation to the current structure parameters for the first amino acid chain to generate respective current structure parameters for each other amino acid chain in the protein to define a current predicted structure of the protein that is symmetrical with respect to the symmetry group) (pg. 17660 col. 2 para. 1).
processing the current structure parameters for the amino acid chains in the protein, in accordance with values of the update block parameters of the update block, to update the current structure parameters for the first amino acid chain
• Andre does not teach the recitation above. However, Senior teaches a deep residual convolutional network is made-up of blocks configured in layers (Extended Data Fig. 1b) trained to make accurate predictions of the distances between pairs of residues to construct a potential of mean force that can accurately describe the shape of a protein optimized by a simple gradient descent algorithm (i.e. reading on iteratively updating the model’s parameters) (pg. 706 Abstract); wherein in fragment assembly (i.e. reading on assembly of protein chains), a structure hypothesis is repeatedly modified, typically by changing the shape of a short section (i.e. processing the current structure parameters for the amino acid chains in the protein, in accordance with values of the update block parameters of the update block, to update the current structure parameters for the first amino acid chain) while retaining changes that lower the potential, ultimately leading to low potential structures (pg. 707 col. 1 para. 1).
wherein the structure parameters for the first amino acid chain in the protein include global structure parameters that define a 3D spatial location and orientation of the first amino acid chain in a frame of reference of the protein
• Andre teaches that, for symmetry implementation in a cyclic system (i.e. reading on cyclic symmetry group) each reference coordinate system can be chosen with the z axis along the rotation axis, the x axis pointing toward the rotation parallel to the axis, and with y perpendicular to the plane spanned by x and z while a translation along x in one reference system will preserve symmetry if an identical translation is applied to the other subunits (i.e. reading on global structure parameters that define a 3D spatial location and orientation of the first amino acid chain in a frame of reference of the protein) (pg. 17657 col. 1 para. 3); wherein, given the coordinates of a single subunit together with a set of symmetry transformations (i.e. reading on symmetry group and a global parameter) consistent with the desired symmetry, the position of all subunits in an oligomer can be computed (pg. 17657 col. 1 para. 2). Here, a symmetry group is interpreted as a global parameter as supported by this instant specification [0037].
wherein applying the symmetrical expansion transformation to the current structure parameters for the first amino acid chain to generate respective current structure parameters for each other amino acid chain in the protein comprises
• Andre teaches a computational protocol to predict the structure of symmetrical protein assemblies based on the structure of a single protein subunit (pg. 17656 col. 1 para. 1); wherein the protocol starts with a first subunit (i.e. reading on first amino acid chain) placed in its coordinates (pg. 17660 col. 2 para. 1); wherein, given the coordinates of a single subunit together with a set of symmetry transformations consistent with the desired symmetry, the position of all subunits in an oligomer can be computed (i.e. reading on applying the symmetrical expansion transformation to the current structure parameters for the first amino acid chain to generate respective current structure parameters for each other amino acid chain) (pg. 17657 col. 1 para. 2) and the exact details of this process depend on the symmetry of the system (i.e. reading on the use of the symmetry group as a structural parameter) (pg. 17660 col. 2 para. 1).
for each other amino acid chain in the protein: generating the global structure parameters for the other amino acid chain by applying a predefined transformation to the global structure parameters for the first amino acid chain
• Andre teaches a computational protocol to predict the structure of symmetrical protein assemblies based on the structure of a single protein subunit (pg. 17656 col. 1 para. 1); wherein, given the coordinates of a single subunit together with a set of symmetry transformations (i.e. reading on symmetry group and a global parameter) consistent with the desired symmetry, all subunits in an oligomer can be computed (pg. 17657 col. 1 para. 2). Here, it is interpreted that, in the taught "computing all subunits" step, all subunits of the computed protein would inherit the same symmetry group – which reads on generating the global structure parameters for the other amino acid chain by applying a predefined transformation to the global structure parameters for the first amino acid chain); wherein a symmetrical system is unchanged under a symmetry transformation (i.e. reading on the same symmetry group/global parameter being applied to all chains) (pg. 17657 col. 1 para. 2).
wherein the predefined transformation depends on: (i) a number of amino acid chains in the protein, and (ii) the symmetry group
• Andre teaches that during the implementation of symmetry, the energy function with respect to a symmetric degree of freedom (rigid-body or torsional) (i.e. reading on the transformation depending on (ii) the symmetry group) can be calculated by multiplying the corresponding energy function partial derivative for a single subunit by the number of subunits in the system (i.e. reading on the transformation depending on (i) a number of amino acid chains in the protein) (pg. 17657 col. 1 para. 3).
determining amino acid structure parameters for the amino acids in the other amino acid chain that match the amino acid structure parameters for the amino acids in the first amino acid chain
• Andre teaches that the transforms are identical for all subunits; equivalently, each subunit has identical coordinates when viewed in its associated reference (i.e. reading on amino acid structure parameters for the amino acids in the other amino acid chain that match the amino acid structure parameters for the amino acids in the first amino acid chain) (pg. 17657 col. 1 para. 3).
Claims 2 and 24 recite:
wherein the structure parameters for the first amino acid chain in the protein include respective amino acid structure parameters for each amino acid in the first amino acid chain, wherein the amino acid structure parameters for each amino acid define a 3D spatial location and orientation of the amino acid in a frame of reference of the first amino acid chain
• Andre teaches that, in the computational protocol for modeling of symmetrical protein assemblies, each amino acid in the protein is described by the position of the four backbone heavy atoms and a single ‘‘pseudo atom’’ representing the side chain (referred to as a centroid) (i.e. reading on wherein the structure parameters for the first amino acid chain in the protein include respective amino acid structure parameters for each amino acid in the first amino acid chain) (pg. 17656 col. 2 para. 3); wherein the model performs a global search followed by a local search where the free energy landscape is further explored in the vicinity of the conformational space (i.e. wherein the amino acid structure parameters for each amino acid define a 3D spatial location and orientation of the amino acid in a frame of reference of the first amino acid chain) of the lowest energy models (pg. 17657 col. 1 para. 1).
Claims 5 and 27 recite:
wherein the symmetry group is a cyclic symmetry group, a dihedral symmetry group, or a cubic symmetry group
• Andre teaches that, for symmetry implementation in a cyclic system (i.e. reading on cyclic symmetry group) each reference coordinate system can be chosen with the z axis along the rotation axis, the x axis pointing toward the rotation parallel to the axis, and with y perpendicular to the plane spanned by x and z while a translation along x in one reference system will preserve symmetry if an identical translation is applied to the other subunits (pg. 17657 col. 1 para. 3).
Rationale for combining (MPEP §2142-2143)
Regarding claims 1-2, 5, 12-13, 24 and 27, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Andre in view of Senior because all references disclose methods for prediction of the structure of symmetrical protein assemblies. The motivation would have been to obtain a lower computational cost associated with high resolution modeling (pg. 17659 col. 2 para. 4 Andre) and to implement a neural network to make accurate predictions of the distances between pairs of residues that conveys more information about the structure (pg. 706 Abstract Senior).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the prediction of the structure of symmetrical protein assemblies of Andre to the methods by Senior because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for prediction of the structure of symmetrical protein assemblies.
B. Claims 6-7, 11 and 28-29 are rejected under 35 U.S.C. 103(a) as being unpatentable over Andre and Senior as applied to claims 1 and 12-13 above further in view of Nambiar "Transforming the language of life: transformer neural networks for protein prediction tasks." Proceedings of the 11th ACM international conference on bioinformatics, computational biology and health informatics. (2020) Published 06/16/2020) in view of Fuchs ("Se (3)-transformers: 3d roto-translation equivariant attention networks." Advances in neural information processing systems 33:1970-1981 (2020) Published 12/10/2020), as cited on the attached Form PTO-892.
Claims 6 and 28 recite:
wherein the input processed by the folding neural network further comprises: (i) a respective initial amino acid embedding for each amino acid in the first amino acid chain, and (ii) an initial global embedding of the first amino acid chain
• Neither Andre or Senior teach "(i) a respective initial amino acid embedding for each amino acid in the first amino acid chain." However, Nambiar teaches tools developed to create amino acid sequence embeddings for protein prediction tasks (pg. 2 col. 1 para. 3); wherein the embedding layer prepares the amino acid input sequence for the Transformer encoder layers by converting each amino acid token into a vector (i.e. reading on (i) a respective initial amino acid embedding for each amino acid in the first amino acid chain) (pg. 3 col. 2 para. 4).
• Neither Andre or Senior teach "(ii) an initial global embedding of the first amino acid chain." However, Fuchs teaches a 3D roto-translation equivariant attention network (pg. 2 Title) applicable to 3D molecular structures (i.e. comprising proteins with multiple amino acid chains) (pg. 1 - Introduction - para. 2); wherein the neural network uses explicit imposition of equivariance constraints on the self-attention mechanism while respecting and leveraging the symmetries of the task at hand (pg. 1 - Introduction - para. 2); wherein self-attention consist of two components: input-dependent attention weights invariant to global pose and an embedding of the input, called a value embedding which is equivariant to global pose (i.e. reading on global embedding) while preserving relative positional information between features in the input (pg. 2 para. 1); wherein the query, key, and value vectors are embeddings of the input features (pg. 3 para. 1). Global embedding is interpreted as a vector representation based on a global parameter as supported by this instant specification [0043] – therefore an embedding based on the symmetry of the system reads on a global embedding.
Claims 7 and 29 recite:
wherein the operations performed by each update block further comprise
receiving a respective current amino acid embedding for each amino acid in the first amino acid chain and a current global embedding of the first amino acid chain; and
wherein processing the current structure parameters for the amino acid chains in the protein to update the current structure parameters for the first amino acid chain comprises:
updating the current amino acid embeddings and the current global embedding for the first amino acid chain based on the current structure parameters for the amino acid chains in the protein; and
updating the current structure parameters for the first amino acid chain based on updated amino acid embeddings and the updated global embedding for the first amino acid chain
• Neither Andre or Senior teach "receiving a respective current amino acid embedding for each amino acid in the first amino acid chain … updating the current amino acid embeddings for the first amino acid chain based on the current structure parameters for the amino acid chains in the protein … updating the current structure parameters for the first amino acid chain based on updated amino acid embeddings for the first amino acid chain." However, Nambiar teaches tools developed to create amino acid sequence embeddings for protein prediction tasks (pg. 2 col. 1 para. 3); wherein the embedding layer prepares the amino acid input sequence for the Transformer encoder layers by converting each amino acid token into a vector (i.e. reading on receiving a respective current amino acid embedding for each amino acid in the first amino acid chain) (pg. 3 col. 2 para. 4); wherein the proposed model employs fine-tuning stages for hyperparameters and optimization steps (pg. 5 col. 2 para. 5) leading to fine-tuned embeddings (pg. 5 col. 2 para. 2) (i.e. reading on updating the current amino acid embeddings for the first amino acid chain based on the current structure parameters for the amino acid chains in the protein … updating the current structure parameters for the first amino acid chain based on updated amino acid embeddings for the first amino acid chain).
• Neither Andre or Senior teach "receiving a current global embedding of the first amino acid chain … updating the current global embedding for the first amino acid chain based on the current structure parameters for the amino acid chains in the protein … updating the current structure parameters for the first amino acid chain based on the updated global embedding for the first amino acid chain." However, However, Fuchs teaches a 3D roto-translation equivariant attention network (pg. 2 Title) applicable to 3D molecular structures (i.e. comprising proteins with multiple amino acid chains) (pg. 1 - Introduction - para. 2); wherein self-attention consist of two components: input-dependent attention weights invariant to global pose and an embedding of the input, called a value embedding which is equivariant to global pose (i.e. reading on receiving a current global embedding of the first amino acid chain) while preserving relative positional information between features in the input (pg. 2 para. 1); wherein network node features are updated using the proposed equivariant attention mechanism in four steps (pg. 5 Fig. 2) (i.e. reading on updating the current global embedding for the first amino acid chain based on the current structure parameters for the amino acid chains in the protein … updating the current structure parameters for the first amino acid chain based on the updated global embedding for the first amino acid chain). Global embedding is interpreted as a vector representation based on a global parameter as supported by this instant specification [0043] – therefore an embedding based on the symmetry of the system reads on a global embedding.
Claim 11 recites:
wherein updating the current structure parameters for the first amino acid chain based on the updated amino acid embeddings and the updated global embedding for the first amino acid chain comprises:
for each amino acid in the first amino acid chain, updating the amino acid structure parameters for the amino acid based on the updated amino acid embedding for the amino acid; and updating the global structure parameters for the first amino acid chain based on the updated global embedding for the first amino acid chain
• Neither Andre or Senior teach "updating the amino acid structure parameters for the amino acid based on the updated amino acid embedding for the amino acid." However, Nambiar teaches tools developed to create amino acid sequence embeddings for protein prediction tasks (pg. 2 col. 1 para. 3); wherein the embedding layer prepares the amino acid input sequence for the Transformer encoder layers by converting each amino acid token into a vector (i.e. reading on amino acid embedding for each amino acid) (pg. 3 col. 2 para. 4); wherein the proposed model employs fine-tuning stages for hyperparameters and optimization steps (pg. 5 col. 2 para. 5) leading to fine-tuned embeddings (pg. 5 col. 2 para. 2) (i.e. reading on updating the amino acid structure parameters for the amino acid based on the updated amino acid embedding for the amino acid).
• Neither Andre or Senior teach " updating the global structure parameters for the first amino acid chain based on the updated global embedding for the first amino acid chain." However, However, Fuchs teaches a 3D roto-translation equivariant attention network (pg. 2 Title) applicable to 3D molecular structures (i.e. comprising proteins with multiple amino acid chains) (pg. 1 - Introduction - para. 2); wherein self-attention consist of two components: input-dependent attention weights invariant to global pose and an embedding of the input, called a value embedding which is equivariant to global pose (i.e. reading on global embedding of an amino acid chain) while preserving relative positional information between features in the input (pg. 2 para. 1); wherein network node features are updated using the proposed equivariant attention mechanism in four steps (pg. 5 Fig. 2) (i.e. reading on updating the global structure parameters for the first amino acid chain based on the updated global embedding for the first amino acid chain). Global embedding is interpreted as a vector representation based on a global parameter as supported by this instant specification [0043] – therefore an embedding based on the symmetry of the system reads on a global embedding.
Rationale for combining (MPEP §2142-2143)
Regarding claims 6-7, 11 and 28-29, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Andre and Senior in view of Nambiar and Fuchs because all references disclose methods for prediction of the structure of symmetrical molecular assemblies. The motivation would have been to:
• incorporate a promising framework for fine-tuning sequence representations for other protein prediction tasks (pg. 1 Abstract Nambiar) and
• incorporate a variant of the self-attention module equivariant under continuous 3D roto-translations for competitive performance on real-world datasets (pg. 1 Abstract Fuchs).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the prediction of the structure of symmetrical molecular assemblies of Andre and Senior to the methods by Nambiar and Fuchs because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for prediction of the structure of symmetrical molecular assemblies.
No prior art has been applied to the following claims
Claims 8-10 and 30 are free of the analogous art at least because close art, e.g. Andre, Senior, Nambiar and Fuchs as cited in the attached PTO-892 Form, either individually or in obvious combination, does not teach the recited combination of:
• "determining, for each other amino acid chain in the protein, a respective current amino acid embedding for each amino acid in the other amino acid chain based on the current amino acid embedding of a corresponding amino acid in the first amino acid chain; and updating the current amino acid embeddings and the current global embedding for the first amino acid chain using attention over the current amino acid embeddings for the amino acid chains, wherein the attention over the current amino acid embeddings for the amino acid chains is conditioned on the current structure parameters for the amino acid chains" (claim 8) and
• "wherein updating the current amino acid embeddings for the first amino acid chain based on the current structure parameters for the amino acid chains in the protein comprises: determining, for each other amino acid chain in the protein, a respective current amino acid embedding for each amino acid in the other amino acid chain based on the current amino acid embedding of a corresponding amino acid in the first amino acid chain; and updating the current amino acid embeddings and the current global embedding for the first amino acid chain using attention over the current amino acid embeddings for the amino acid chains, wherein the attention over the current amino acid embeddings for the amino acid chains is conditioned on the current structure parameters for the amino acid chains" (claim 30).
Conclusion
No claims are allowed.
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/F.F.L./Examiner, Art Unit 1685
/JANNA NICOLE SCHULTZHAUS/Examiner, Art Unit 1685
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