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 priority based on provisional application 63/229,897 filed on 8/5/2021. As such the effective filing date of claims 1-30 is 8/5/2021.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/16/2022 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 1-30 are pending.
Claims 1-30 are rejected.
Drawings
It was noted that a petition for color drawings was accepted on 02/08/2023.
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 17 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. Within the claim the dimensionality is given by the calculation L by L by K, however K is not defined within the claim, nor the proceeding/following claims. After a review of the specification no reference to the definition of K was found, and as such the calculation essentially functions as a “black box” with variables not described in what they are or how they are calculated. There is reference to L by L by n or L by L by 2n within the specification and this is used for the interpretation under 35 U.S.C. 103 below, however applicant must clarify whether this is the correct understanding and amend the specification/claims accordingly.
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 non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because claims 1-20 are directed to mere memory and runtime logic both of which are data per se and software per se as MPEP 2106.03 states - Non-limiting examples of claims that are not directed to any of the statutory categories include: Products that do not have a physical or tangible form, such as information (often referred to as "data per se") or a computer program per se (often referred to as "software per se") when claimed as a product without any structural recitations. Within the claims no physical or structural recitations are provided and therefore the claim as a whole is directed to non-statutory subjected matter. However, directing the claims to a system and including a change in the use of “runtime logic” to “a processor configured to…” would make the claim subject matter eligible. As such, prosecution under 35 U.S.C. 101 is continued below for claims 1-30 including claims that are not currently subject matter eligible.
Claims 1-6, 16-18, 20-25, and 28-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more. The claims recite a classifier, method and CRM for classifying variant pathogenicity using protein contact maps. The judicial exception is not integrated into a practical application because while claims 1-6, 16-18, 20-25, and 28-30 attempt to integrated the exception into a practical application, said application is either generically recited computer elements that do not add a meaningful limitation to the abstract idea or it is insignificant extra solution activity and merely implementing the abstract idea on a computer. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer elements only store and retrieve information in memory as well as perform basic calculations that are known to be well-understood, routine and conventional computer functions as recognized by the decisions listed in MPEP § 2106.05(d).
Framework with which to Analyze Subject Matter Eligibility:
Step 1: Are the claims directed to a category of stator subject matter (a process, machine, manufacture, or composition of matter)? [see MPEP § 2106.03]
Claims are directed to statutory subject matter, specifically a classifier (Claims 1-20), a method (Claims 21-27) and a CRM (Claims 28-30)
Step 2A Prong One: Do the claims recite a judicially recognized exception, i.e., an abstract idea, a law of nature, or a natural phenomenon? [see MPEP § 2106.04(a)]
The claims herein recite abstract ideas, specifically mental processes and mathematical concepts.
With respect to the Step 2A Prong One evaluation, the instant claims are found herein to recite abstract ideas that fall into the grouping of mental processes and mathematical concepts.
Claim 1: Generating a pathogenicity indication is a process of comparing/contrasting and 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.
Claim 2: Generating a pathogenicity indication is a process of comparing/contrasting and 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.
Claim 3: The reference sequence having L amino acids and the alternative having L amino acids is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claim 4: The sequences being characterized by a one-hot encoded matrix of size L by C is a verbal articulation of a mathematical process and is therefore an abstract idea, specifically a mental concept. The sequences being characterized by a one-hot encoded matrix of size L by C 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.
Claim 5: The conservation profiles being size L by C is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claim 16: Generating the protein contact map by processing the information via the neural network 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.
Claim 17: The protein contact map having total dimensionality of L by L by K is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claim 21: Generating a pathogenicity indication is a process of comparing/contrasting and 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.
Claim 22: Generating a pathogenicity indication is a process of comparing/contrasting and 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.
Claim 23: The reference sequence having L amino acids and the alternative having L amino acids is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claim 24: The sequences being characterized by a one-hot encoded matrix of size L by C is a verbal articulation of a mathematical process and is therefore an abstract idea, specifically a mental concept. The sequences being characterized by a one-hot encoded matrix of size L by C 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.
Claim 28: Generating a pathogenicity indication is a process of comparing/contrasting and 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.
Claim 29: Generating a pathogenicity indication is a process of comparing/contrasting and 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.
Claim 30: The sequences having L amino acids 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:
Claim 1: Memory storing information and a neural network 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)].
Claim 2: Memory storing information and a neural network 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)].
Claim 6: The neural network being a convolutional neural network is an insignificant extra solution activity, specifically mere data gathering (See 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), 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), Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) (non-precedential), and Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55) [See MPEP § 2106.05(g)].
Claim 18: The second neural network being a convolutional neural network is an insignificant extra solution activity, specifically mere data gathering (See 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), 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), Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) (non-precedential), and Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55) [See MPEP § 2106.05(g)].
Claim 21: A computer and neural network 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)]. Storing a reference, alternative, and protein contact map, and providing the same as input to a neural network are insignificant extra solution activities, specifically mere data gathering (See 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), 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), Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) (non-precedential), and Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55) [See MPEP § 2106.05(g)].
Claim 22: Storing a protein profile, and conservation profile, and providing the specified information as input to the neural network are insignificant extra solution activities, specifically mere data gathering (See 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), 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), Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) (non-precedential), and Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55) [See MPEP § 2106.05(g)].
Claim 25: The neural network being a convolutional neural network is an insignificant extra solution activity, specifically mere data gathering (See 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), 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), Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) (non-precedential), and Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55) [See MPEP § 2106.05(g)].
Claim 28: A non-transitory computer readable storage medium, computer program instructions, neural network, and processor 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)]. Storing a reference, alternative, and protein contact map, and providing the same as input to a neural network are insignificant extra solution activities, specifically mere data gathering (See 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), 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), Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) (non-precedential), and Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55) [See MPEP § 2106.05(g)].
Claim 29: A non-transitory computer readable storage medium and neural network 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)]. Storing a reference, alternative, and protein contact map, and providing the same as input to a neural network are insignificant extra solution activities, specifically mere data gathering (See 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), 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), Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) (non-precedential), and Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55) [See MPEP § 2106.05(g)].
Claim 30: A non-transitory computer readable storage medium is a generic and nonspecific element of computers that does not improve the functioning of any computer or technology described herein [See MPEP § 2106.04(d)(1) and MPEP § 2106.05(d)].
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept? [see MPEP § 2106.05]
Because the additional claim elements do not integrate the abstract idea into a practical application, the claims are further examined under Step 2B, which evaluates whether the additional elements, individually and in combination, amount to significantly more than the judicial exception itself by providing an inventive concept.
The claims do not recite additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that are generic, conventional or nonspecific. These additional elements include:
The additional elements of a computer, non-transitory computer readable storage medium, computer program instructions, neural network, and processor, are all generic and nonspecific elements of a computer that do not improve the functioning of any computer or technology described herein (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 § 2106.04(d)(1) and MPEP § 2106.05(d)]. Therefore, taken both individually and as a whole, the additional elements do not amount to significantly more than the judicial exception by providing an inventive concept.
The additional elements of storing a reference, alternative, and protein contact map (Conventional: MPEP 2106.05(d) - 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); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93), providing the same as input to a neural network (Conventional: MPEP 2106.05(g) - 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)), and the use of a convolutional neural network (Conventional:) are insignificant extra solution activities, specifically mere data gathering and necessary data outputting (See 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), 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), Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) (non-precedential), and Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55) [See MPEP § 2106.05(g)]. Therefore, taken both individually and as a whole, the additional elements do not amount to significantly more than the judicial exception by providing an inventive concept.
Therefore, claims 1-25, and 28-30, when the limitations are considered individually and as a whole, are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Claims 7, 19, and 26 recite sufficient structure of the convolutional neural network so as to integrate the judicial exception into limitations that are significantly more according to MPEP 2106.05 - Limitations that the courts have found to qualify as "significantly more" when recited in a claim with a judicial exception include: Applying the judicial exception with, or by use of, a particular machine, e.g., a Fourdrinier machine (which is understood in the art to have a specific structure comprising a headbox, a paper-making wire, and a series of rolls) that is arranged in a particular way to optimize the speed of the machine while maintaining quality of the formed paper web, as discussed in Eibel Process Co. v. Minn. & Ont. Paper Co., 261 U.S. 45, 64-65 (1923). As such dependent claims 8-15 and 27 also are significantly more according to MPEP 2106.05.
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 1-3, 16, 18-23, 25-26, and 28-30 are rejected under 35 U.S.C. 103 as being unpatentable over Zaucha et al. (NAR Genomics and Bioinformatics (2020) 1-8) and Adhikari et al. (Bioinformatics (2018) 1466-1472).
Claim 1 is directed to a classifier using memory and runtime logic to take sequences and contact maps to produce pathogenic indications of variants.
Claim 21 is directed to a method that takes sequences and contact maps to produce pathogenic indications of variants.
Claim 28 is directed to a CRM that takes sequences and contact maps to produce pathogenic indications of variants.
Zaucha et al. teaches in the abstract “Using the presently available datasets of annotated missense variants, we ran a protein family-specific benchmarking of tools for predicting the pathogenicity of single amino acid variants”, on page 2, column 2, paragraph 4 “For each protein family, the corresponding alignment based on representative proteomes (clustered at sequence identity of 75%) was extracted from the Pfam database”, on page 3, column 1, paragraph 2 “Apart from the sequence-based characteristics, we considered structure-specific data, including fraction of sequence annotated with secondary structure elements, fraction of residues forming helices and strands, and features related to specific inter-residue contacts extracted from the PDB files. First, contact density was taken as the total number of contacts normalized by domain length. Second, the maximum inter-residue contact connectivity was calculated from the residue connectivity graph… Relative contact order is calculated as…where L is the protein length, N is the total number of contacts…”.
Zaucha et al. does not teach the actual construction of contact maps and use of multiple neural networks.
Adhikari et al. teaches in the abstract “In this paper we discuss DNCON2, an improved protein contact map predictor based on two-level deep convolutional neural networks. It consists of six convolutional neural networks—the first five predict contacts at 6, 7.5, 8, 8.5 and 10A° distance thresholds, and the last one uses these five predictions as additional features to predict final contact maps”, on page 1467, column 1, paragraph 3 “in this paper, we present our improved contact prediction method—DNCON2. The primary enhancements of DNCON2 are (i) inclusion of coevolution-based features, (ii) new deep convolutional neural networks to predict full contact maps and (iii) addition of new features at multiple distance thresholds, which further improves the performance. In DNCON2, we transform all 27 input features, e.g. scalar features like protein length, one-dimensional (1D) features like secondary structure prediction and two-dimensional (2D) features like coevolution-based predictions, into 56 two-dimensional features”, in column 2, paragraph 4 of the same page “In addition to the existing features used in the original DNCON, we used new features derived from multiple sequence alignments, coevolution-based predictions and three-state secondary structure predictions from PSIPRED”, which in view of Zaucha et al. reads on memory storing (i) a reference amino acid sequence of a protein, (ii) an alternative amino acid sequence of the protein that contains a variant amino acid caused by a variant nucleotide, and (iii) a protein contact map of the protein; and runtime logic, having access to the memory, configured to provide (i) the reference amino acid sequence, (ii) the alternative amino acid sequence, and (iii) the protein contact map as input to a first neural network, and to cause the first neural network to generate a pathogenicity indication of the variant amino acid as output in response to processing (i) the reference amino acid sequence, (ii) the alternative amino acid sequence, and (iii) the protein contact map.
It would have been obvious at the time of first filing to have modified the teachings of Zaucha et al. for the predicting of pathogenicity using sequence and contact information, with the teachings of Adhikari et al. for the construction of contact maps using multiple neural networks as the former derives their contact information from contact maps and according to the latter their contact maps “achieves mean precisions of 35, 50 and 53.4%, respectively, higher than 30.6% by MetaPSICOV on CASP10 dataset, 34% by MetaPSICOV on CASP11 dataset and 46.3% by Raptor-X on CASP12 dataset, when top L/5 long-range contacts are evaluated”. One would have had a reasonable expectation of success given that the latter is geared towards the generation of data that is used in the second, and the method of ensemble CNNs are usable across a wide range of applications. Therefore, it would have been obvious at the time of first filing to have modified the teachings of each and to be successful.
Claim 2 is directed to the classifier of claim 1 but further specifies taking into account conservation profiles in order to predict the pathogenicity.
Claim 22 is directed to the method of claim 21 but further specifies taking into account conservation profiles in order to predict the pathogenicity.
Claim 29 is directed to the CRM of claim 28 but further specifies taking into account conservation profiles in order to predict the pathogenicity.
Zaucha et al. teaches on page 2, column 1, paragraph 2 “methods capturing the evolutionary conservation at specific genomic positions [such as CADD (6)] should be expected to yield more accurate predictions”, it therefore would have been obvious under the teachings of Zaucha et al. to include such information, reading on wherein the memory stores an amino acid-wise primate conservation profile of the protein, an amino acid-wise mammal conservation profile of the protein, and an amino acid-wise vertebrate conservation profile of the protein, and wherein the runtime logic further configured to provide (i) the reference amino acid sequence, (ii) the alternative amino acid sequence, (iii) the protein contact map, (iv) the amino acid-wise primate conservation profile, (v) the amino acid-wise mammal conservation profile, and (vi) the amino acid-wise vertebrate conservation profile as input to the first neural network, and to cause the first neural network to generate the pathogenicity indication of the variant amino acid as output in response to processing (i) the reference amino acid sequence, (ii) the alternative amino acid sequence, (iii) the protein contact map, (iv) the amino acid-wise primate conservation profile, (v) the amino acid-wise mammal conservation profile, and (vi) the amino acid-wise vertebrate conservation profile.
Claim 3 is directed to the classifier of claim 2 and thus claim 1, but further specifies that the sequences have the same number of amino acids.
Claim 23 is directed to the method of claim 21 but further specifies that the sequences have the same number of amino acids.
Claim 30 is directed to the CRM of claim 28 but further specifies that the sequences have the same number of amino acids.
It would have been obvious at the time of first filing to have both the reference sequence and the alternative sequence have the same length as the method is specifically looking at the differences between the chosen amino acids within the sequence, how those changes affect contact mapping, and how those differences and contact maps influence pathogenicity. Therefore, it would have been obvious to have the sequences match each other during alignment, reading on wherein the reference amino acid sequence has L amino acids, wherein the alternative amino acid sequence has L amino acids.
Claim 16 is directed to the classifier of claim 1 but further specifies the protein contact map being generated by a second neural network using the sequence and one of the specified data types.
Adhikari et al. teaches in the abstract “In this paper we discuss DNCON2, an improved protein contact map predictor based on two-level deep convolutional neural networks. It consists of six convolutional neural networks—the first five predict contacts at 6, 7.5, 8, 8.5 and 10A° distance thresholds, and the last one uses these five predictions as additional features to predict final contact maps”, and on page 1467, column 1, paragraph 3 “in this paper, we present our improved contact prediction method—DNCON2. The primary enhancements of DNCON2 are (i) inclusion of coevolution-based features, (ii) new deep convolutional neural networks to predict full contact maps and (iii) addition of new features at multiple distance thresholds, which further improves the performance. In DNCON2, we transform all 27 input features, e.g. scalar features like protein length, one-dimensional (1D) features like secondary structure prediction and two-dimensional (2D) features like coevolution-based predictions, into 56 two-dimensional features”, reading on wherein the protein contact map is generated by a second neural network in response to processing (i) the reference amino acid sequence and at least one of (ii) an amino acid- wise protein secondary structure profile, (iii) an amino acid-wise solvent accessibility profile, (iv) an amino acid- wise position-specific scoring matrix, and (v) an amino acid-wise position-specific frequency matrix.
Claim 18 is directed to the classifier of claim 16 and thus claim 1, but further specifies the second neural network as a CNN.
Adhikari et al. teaches in the abstract “In this paper we discuss DNCON2, an improved protein contact map predictor based on two-level deep convolutional neural networks. It consists of six convolutional neural networks—the first five predict contacts at 6, 7.5, 8, 8.5 and 10A° distance thresholds, and the last one uses these five predictions as additional features to predict final contact maps”, reading on wherein the second neural network is a second convolutional neural network.
Claim 19 is directed to the classifier of claim 18 and thus claim 1, but further specifies the architecture of the neural network.
It would have been obvious at the time of first filing to have optimized the neural network architecture to obtain a best performance particularly since all of the elements of the neural networks recited are inherent layers within any convolutional network, i.e. convolution, residual, and dimension augmentation layers. Therefore, it would have been obvious to optimize the architecture to arrive at wherein the second convolutional neural network comprises (i) one or more 1D convolution layers, followed by (ii) one or more residual blocks with 1D convolutions, followed by (iii) a spatial dimensionality augmentation layer, followed by (iv) one or more residual blocks with 2D convolutions, and followed by (v) one or more 2D convolution layers.
Claim 20 is directed to the classifier of claim 1 but further specifies the use of multiple neural networks as an ensemble for the prediction.
Adhikari et al. teaches in the abstract “In this paper we discuss DNCON2, an improved protein contact map predictor based on two-level deep convolutional neural networks. It consists of six convolutional neural networks—the first five predict contacts at 6, 7.5, 8, 8.5 and 10A° distance thresholds, and the last one uses these five predictions as additional features to predict final contact maps”, reading on wherein multiple trained instances of the first neural network are used as an ensemble for variant pathogenicity prediction during inference.
Claim 25 is directed to the method of claim 21 but further specifies that the neural network be a convolutional neural network.
Adhikari et al. teaches in the abstract “In this paper we discuss DNCON2, an improved protein contact map predictor based on two-level deep convolutional neural networks. It consists of six convolutional neural networks—the first five predict contacts at 6, 7.5, 8, 8.5 and 10A° distance thresholds, and the last one uses these five predictions as additional features to predict final contact maps”, reading on wherein the first neural network is a first convolutional neural network.
Claim 26 is directed to the method of claim 25 but further specifies the architecture of the neural network.
It would have been obvious at the time of first filing to have optimized the neural network architecture to obtain a best performance particularly since all of the elements of the neural networks recited are inherent layers within any convolutional network, i.e. convolution, residual, and dimension augmentation layers. Therefore, it would have been obvious to optimize the architecture to arrive at wherein the first convolutional neural network comprises (i) one or more one-dimensional (1D) convolution layers, followed by (ii) a first set of residual blocks with 1D convolutions, followed by (iii) a second set of residual blocks with 1D convolutions, followed by (iv) a spatial dimensionality augmentation layer, followed by (v) a first set of residual blocks with two-dimensional (2D) convolutions, followed by (vi) one or more 2D convolution layers, followed by (vii) one or more fully connected layers, and followed by (viii) a pathogenicity indication generation layer.
Claims 4-8, and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Zaucha et al. (NAR Genomics and Bioinformatics (2020) 1-8) and Adhikari et al. (Bioinformatics (2018) 1466-1472) as applied to claims 1-3, 16, 18-23, 25-26, and 28-30 above, and further in view of Jing et al. (IEEE/ACM transactions on computational biology and bioinformatics (2019) 1918-1931).
Claim 4 is directed to the classifier of claim 3 and thus claim 1, but further specifies using one hot encoding for the sequences.
Claim 24 is directed to the classifier of claim 23 and thus claim 21, but further specifies using one hot encoding for the sequences.
Zaucha et al. and Adhikari et al. teach the classifier and method of claims 1 and 21 respectively.
Zaucha et al. and Adhikari et al. do not teach using one hot encoding for the sequences.
Jing et al. teaches in the abstract “In this article, we make a systematic classification and propose a comprehensive review and assessment for various amino acid encoding methods”, and on page 1919, column 1, paragraph 1 “The most widely used encodings are the one-hot encoding, the position specific scoring matrix (PSSM) encoding, and some physic-chemical properties encoding”, and as there are only 20 amino acids this reads on wherein the reference amino acid sequence is characterized as a reference one-hot encoded matrix of size L by C, where C denotes twenty amino acid categories, wherein the alternative amino acid sequence is characterized as an alternative one-hot encoded matrix of size L by C.
Claim 5 is directed to the classifier of claim 4 and thus claim 1, but further specifies the conservation profiles having the same structure as the sequences.
Zaucha et al. and Adhikari et al. teach the classifier and method of claims 1 and 21 respectively.
It would be inherent that an examination of conservation, amino-acid wise using a sequence would take the dimension of L by C as the length of the sequence is L and the total number of amino acids is 20, thereby reading on wherein the amino acid-wise primate conservation profile is of size L by C, wherein the amino acid-wise mammal conservation profile is of size L by C, and wherein the amino acid-wise vertebrate conservation profile is of size L by C.
Claim 6 is directed to the classifier of claim 5 and thus claim 1, but further specifies the neural network being a convolutional neural network.
Zaucha et al. and Adhikari et al. teach the classifier and method of claims 1 and 21 respectively.
Adhikari et al. teaches in the abstract “In this paper we discuss DNCON2, an improved protein contact map predictor based on two-level deep convolutional neural networks. It consists of six convolutional neural networks—the first five predict contacts at 6, 7.5, 8, 8.5 and 10A° distance thresholds, and the last one uses these five predictions as additional features to predict final contact maps”, reading on wherein the first neural network is a first convolutional neural network.
Claim 7 is directed to the classifier of claim 6 and thus claim 1, but further specifies the architecture of the neural network.
Zaucha et al. and Adhikari et al. teach the classifier and method of claims 1 and 21 respectively.
It would have been obvious at the time of first filing to have optimized the neural network architecture to obtain a best performance particularly since all of the elements of the neural networks recited are inherent layers within any convolutional network, i.e. convolution, residual, and dimension augmentation layers. Therefore, it would have been obvious to optimize the architecture to arrive at wherein the first convolutional neural network comprises (i) one or more one-dimensional (1D) convolution layers, followed by (ii) a first set of residual blocks with 1D convolutions, followed by (iii) a second set of residual blocks with ID convolutions, followed by (iv) a spatial dimensionality augmentation layer, followed by (v) a first set of residual blocks with two-dimensional (2D) convolutions, followed by (vi) one or more 2D convolution layers, followed by (vii) one or more fully connected layers, and followed by (viii) a pathogenicity indication generation layer.
Claim 8 is directed to the classifier of claim 6 and thus claim 1, but further specifies the dimensionality of the input as L by 1.
The input is recited as being sequence information, which is a sequence of length L, therefore it would be inherent that the input would have a dimensionality of L by 1 as a sequence is a vector with dimension L by 1, thereby reading on wherein a spatial dimensionality of an input processed by a first 1D convolution layer in the one or more ID convolution layers is L by 1.
Subject Matter Potentially Free from the Prior Art
Claims 9-15 and 17 are potentially free from the prior art as claim 9 recites a depth dimensionality which is equal to the 5 times the number of amino acid categories. This could not be found within the prior art, particularly as it relates to pathogenicity and the use of contact maps in predicting it. As such the dependent claims are also potentially free from the prior art. In regards claim 17, K is not described within the claim or within the specification and as such a dimension of L by L by K is interpreted to mean the length of the sequence by the length of the sequence by the number of features. This specific representation of dimension for spatial maps could not be found within the literature, particularly as it relates to predicting pathogenicity and is as such potentially free from the prior art.
Conclusion
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/K.N.A./Examiner, Art Unit 1687
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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