DETAILED ACTION
It appears the inventor(s) filed the current application pro se (i.e., without the benefit of representation by a registered patent practitioner). While inventors named as applicants in a patent application may prosecute the application pro se, lack of familiarity with patent examination practice and procedure may result in missed opportunities in obtaining optimal protection for the invention disclosed. The inventor(s) may wish to secure the services of a registered patent practitioner to prosecute the application, because the value of a patent is largely dependent upon skilled preparation and prosecution. The Office cannot aid in selecting a patent practitioner.
A listing of registered patent practitioners is available at https://oedci.uspto.gov/OEDCI/. Applicants may also obtain a list of registered patent practitioners located in their area by writing to Mail Stop OED, Director of the U.S. Patent and Trademark Office, P.O. Box 1450, Alexandria, VA 22313-1450.
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 .
Status of Claims
Claims 1-20 are pending and examined on the merits.
Priority
The instant application claims no benefit of priority. Thus, the effective filing date of the claims is 6/1/2022.
The applicant is reminded that amendments to the claims and specification must comply with 35 U.S.C. § 120 and 37 C.F.R. § 1.121 to maintain priority to an earlier-filed application. Claim amendments may impact the effective filing date if new subject matter is introduced that lacks support in the originally filed disclosure. If an amendment adds limitations that were not adequately described in the parent application, the claim may no longer be entitled to the priority date of the earlier filing.
Information Disclosure Statement
There is no information disclosure statement (IDS) filed.
The listing of references in the specification is not a proper information disclosure statement.
37 CFR 1.98(b) requires a list of all patents, publications, or other information submitted for
consideration by the Office, and MPEP § 609.04(a) states, "the list may not be incorporated into the
specification but must be submitted in a separate paper." Therefore, unless the references have been
cited by the examiner on form PTO-892, they have not been considered.
Specification
The disclosure is objected to because of the following informalities:
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
Appropriate correction is required.
Nucleotide and/or Amino Acid Sequence Disclosures
REQUIREMENTS FOR PATENT APPLICATIONS CONTAINING NUCLEOTIDE AND/OR AMINO ACID SEQUENCE DISCLOSURES
Items 1) and 2) provide general guidance related to requirements for sequence disclosures.
37 CFR 1.821(c) requires that patent applications which contain disclosures of nucleotide and/or amino acid sequences that fall within the definitions of 37 CFR 1.821(a) must contain a "Sequence Listing," as a separate part of the disclosure, which presents the nucleotide and/or amino acid sequences and associated information using the symbols and format in accordance with the requirements of 37 CFR 1.821 - 1.825. This "Sequence Listing" part of the disclosure may be submitted:
In accordance with 37 CFR 1.821(c)(1) via the USPTO patent electronic filing system (see Section I.1 of the Legal Framework for Patent Electronic System (https://www.uspto.gov/PatentLegalFramework), hereinafter "Legal Framework") as an ASCII text file, together with an incorporation-by-reference of the material in the ASCII text file in a separate paragraph of the specification as required by 37 CFR 1.823(b)(1) identifying:
the name of the ASCII text file;
ii) the date of creation; and
iii) the size of the ASCII text file in bytes;
In accordance with 37 CFR 1.821(c)(1) on read-only optical disc(s) as permitted by 37 CFR 1.52(e)(1)(ii), labeled according to 37 CFR 1.52(e)(5), with an incorporation-by-reference of the material in the ASCII text file according to 37 CFR 1.52(e)(8) and 37 CFR 1.823(b)(1) in a separate paragraph of the specification identifying:
the name of the ASCII text file;
the date of creation; and
the size of the ASCII text file in bytes;
In accordance with 37 CFR 1.821(c)(2) via the USPTO patent electronic filing system as a PDF file (not recommended); or
In accordance with 37 CFR 1.821(c)(3) on physical sheets of paper (not recommended).
When a “Sequence Listing” has been submitted as a PDF file as in 1(c) above (37 CFR 1.821(c)(2)) or on physical sheets of paper as in 1(d) above (37 CFR 1.821(c)(3)), 37 CFR 1.821(e)(1) requires a computer readable form (CRF) of the “Sequence Listing” in accordance with the requirements of 37 CFR 1.824.
If the "Sequence Listing" required by 37 CFR 1.821(c) is filed via the USPTO patent electronic filing system as a PDF, then 37 CFR 1.821(e)(1)(ii) or 1.821(e)(2)(ii) requires submission of a statement that the "Sequence Listing" content of the PDF copy and the CRF copy (the ASCII text file copy) are identical.
If the "Sequence Listing" required by 37 CFR 1.821(c) is filed on paper or read-only optical disc, then 37 CFR 1.821(e)(1)(ii) or 1.821(e)(2)(ii) requires submission of a statement that the "Sequence Listing" content of the paper or read-only optical disc copy and the CRF are identical.
Specific deficiencies and the required response to this Office Action are as follows:
Specific deficiency - This application fails to comply with the requirements of 37 CFR 1.821 - 1.825. This application contains a “Sequence Listing” as a PDF file (37 CFR 1.821(c)(2)) or as physical sheets of paper (37 CFR 1.821(c)(3)). A copy of the "Sequence Listing" in computer readable form (CRF) has been submitted; however, the content of the CRF does not comply with one or more of the requirements of 37 CFR 1.822 through 1.824, as indicated in the "Error Report" that indicates the "Sequence Listing" could not be accepted. Refer to attachment or document "Computer Readable Form (CRF) for Sequence Listing – Defective" dated 11/8/2022.
Required response – Applicant must provide:
A replacement "Sequence Listing" part of the disclosure, as described above in item 1); together with
An amendment specifically directing its entry into the application in accordance with 37 CFR 1.825(b)(2);
A statement that the "Sequence Listing" includes no new matter as required by 37 CFR 1.825(b)(5); and
A statement that indicates support for the amendment in the application, as filed, as required by 37 CFR 1.825(b)(4).
If the replacement "Sequence Listing" part of the disclosure is submitted according to item 1) a) or b) above, Applicant must also provide:
A substitute specification in compliance with 37 CFR 1.52, 1.121(b)(3), and 1.125 inserting the required incorporation-by-reference paragraph, consisting of:
A copy of the previously-submitted specification, with deletions shown with strikethrough or brackets and insertions shown with underlining (marked-up version);
A copy of the amended specification without markings (clean version); and
A statement that the substitute specification contains no new matter and
An amendment to the specification to remove the “Sequence Listing previously submitted as a PDF file (37 CFR 1.821(c)(2)) or as physical sheets of paper (37 CFR 1.821(c)(3))
If the replacement "Sequence Listing" part of the disclosure is submitted according to item 1) c) or d) above, Applicant must also provide:
A CRF in accordance with 1.821(e)(1) or 1.821(e)(2) as required by 37 CFR 1.825(b)(6)(ii); and
Statement according to item 2) a) or b) above.
Specific deficiency – Nucleotide and/or amino acid sequences appearing in the specification are not identified by sequence identifiers in accordance with 37 CFR 1.821(d).
Required response – Applicant must provide:
A substitute specification in compliance with 37 CFR 1.52, 1.121(b)(3) and 1.125 inserting the required sequence identifiers, consisting of:
A copy of the previously-submitted specification, with deletions shown with strikethrough or brackets and insertions shown with underlining (marked-up version);
A copy of the amended specification without markings (clean version); and
A statement that the substitute specification contains no new matter.
Claim Objections
Claims 1, 2, 6, 15, and 18 objected to because of the following informalities:
Acronyms throughout the claims should be defined upon first use:
Claim 1; SV, CNV, TAD
Claim 2; GAM, HMM
Claim 6; ICE
Claim 18; CTCF
Claim 1: line 4, "inferring copy number from Hi-C map" should read "inferring a copy number from a Hi-C map"; line 5, "balancing separate matrix and correcting CNV effect" should read "balancing separate matrices and correcting for CNV effects"; and line 11, "iisualizing reconstructed Hi-C map and genome browser tracks" should read "visualizing a reconstructed Hi-C map using genome browser tracks".
Claim 15: line 1, "wherein candidates are removed if the whole path or part of the path form a circular assembly are removed, or if they are redundant" should read "wherein candidate complex SVs are removed if the whole path or part of the path form a circular assembly, or if they are redundant".
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 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.
Claim 1 recites "which can be used to identify critical oncogenic regulatory elements for tumorigenesis and potentially reveal therapeutic targets". It is unclear what the metes and bounds, and the definition of "critical" is intended to encompass with respect to identifying oncogenic regulatory elements for tumorigenesis. Additionally, the metes and bounds of permissive claim language (e.g. "can" and "potentially") is not clear with respect to revealing therapeutic targets. To further prosecution, this quoted part of the preamble is interpreted as not further limiting claim 1.
Claim 1 attempts to claim a process without setting forth any steps involved in the process. Specifically, these limitations include "inferring copy number from Hi-C map; balancing separate matrix and correcting CNV effect; simulating CNV effects on a normal Hi-C map; detecting rearranged fragment, filtering SV, and assembling complex SV; normalizing allele; performing machine-learning based loop detection; identifying Neo-TAD". Therefore, the claim is indefinite because it merely recites a use without any active, positive steps delimiting how this use is actually practiced similar to the findings in Ex parte Erlich, 3 USPQ2d 1011 (Bd. Pat. App. & Inter. 1986):
Claim 1 recites "inferring copy number from Hi-C map". It is not clear how copy number is to be inferred from a Hi-C map, as there are no steps expounded upon in the claim. To further prosecution, the limitation is interpreted as generally encompassing page 6 of the instant specification under "a) Copy number inference from Hi-C map", which includes the limitations of claim 2.
Claim 1 recites "balancing separate matrix and correcting CNV effect; simulating CNV effects on a normal Hi-C map". It is not clear how "a normal Hi-C map" is obtained for this step of simulation. While not lacking antecedent basis, it is still not explicit that the normal Hi-C map is derived from the prior "correcting" step. To further prosecution, the limitation is interpreted as generally encompassing pages 7-8 of the instant specification under "b) Separate matrix balancing and CNV effect correction" and "c) Simulation of CNV effects on a normal Hi-C map", and specifically as "balancing separate matrices and correcting for CNV effects to produce a normal Hi-C map; simulating CNV effects on the normal Hi-C map"
Claim 1 recites "detecting rearranged fragment, filtering SV, and assembling complex SV". It is now clear that "detecting rearranged fragments" would result in SVs to be filtered, and the instant specification does not explicitly detail this step either. To further prosecution, the limitation is interpreted as generally encompassing page 9 of the instant specification under "d) Rearranged fragment detection, SV filtering and complex SV assembling", and specifically as "detecting rearranged fragments and calling structural variants (SV), filtering SVs, and assembling complex SVs"
Claim 1 recites "normalizing allele" on line 9. It is not clear how "allele" (or alleles) are to be normalized, or if the Hi-C map is being normalized to alleles, as there are no steps expounded upon in the claim. To further prosecution, the limitation is further interpreted as generally encompassing pages 10-11 of the instant specification under "e) Allele normalization", which includes "a linear regression-based method is applied to minimize the differences between local distance decay curves and the whole-genome distance decay curve".
Claim 1 recites "performing machine-learning based loop detection". It is not clear what specific machine-learning algorithm and associated parameters is used to achieve loop detection. To further prosecution, the limitation is further interpreted as generally encompassing pages 11 of the instant specification under "f) Allele normalization", which includes the limitations of claim 17, and specifically interpreted as "performing chromatin loop detection by any method available to one having ordinary skill in the art".
Claim 1 recites "identifying Neo-TAD" on line 11. It is not clear how "Neo-TAD" (or Neo-TADs) are to be identified, as there are no steps expounded upon in the claim. To further prosecution, the limitation is further interpreted as generally encompassing page 12 of the instant specification under "g) Neo-TAD identification", which includes training HMM models using DIs (directionality index) of a reference genome and the normalized Hi-C map to predict a "start" or "end" state of each bin defined by genomic intervals (coordinates).
Claim 3 recites "the HMM is built using the pomegranate Python package". It is not clear what version or particular parameters (e.g. distributions, edges, starts, ends, etc.) are used for building the HMM with this Python package. The instant specification does not illuminate the matter further. To further prosecution, the limitation is interpreted as the HMM being built by any method available to one having ordinary skill in the art.
Claim 4 recites "the parameters of transition and emission". There is insufficient antecedent basis for “the parameters of transition and emission". To further prosecution, the limitation is interpreted as “probabilities of transition and emission".
Claim 6 attempts to claim a process without setting forth any steps involved in the process. Specifically, these limitations include "a modified ICE procedure is used by applying ICE to regions with different copy numbers separately". Therefore, the claim is indefinite because it merely recites a use without any active, positive steps delimiting how this use is actually practiced similar to the findings in Ex parte Erlich, 3 USPQ2d 1011 (Bd. Pat. App. & Inter. 1986). To further prosecution, the limitation is interpreted as generally encompassing page 7 of the instant specification under "b) Separate matrix balancing and CNV effect correction", which includes calculating marginal sums and bias vectors after extracting each separate data set based on copy number count. And interpreted specifically as "wherein an iterative correction and eigenvector decomposition procedure is applied to separate genomic regions, comprising calculating marginal sums and bias vectors after extracting a data set for each separate genomic region based on copy number count".
Claim 8 recites "the stretch of the rearranged fragments is recognized by locating the corner square block on the correlation matrix". There is insufficient antecedent basis for “the stretch" and "the correlation matrix". Additionally, it is not clear what is meant by the term "stretch", and the specification does not further explicitly illuminate the matter, however from the description on page 9 and its reference to figure 2D, it is likely intended to mean a contiguous linear interval or region of the genome. Finally, it is not clear which matrix is being referred to in this claim as there are multiple matrices (or Hi-C maps which can also be considered a matrix) in claim 1. However, as this step is referring to the rearrangement of fragments, "the correlation matrix" will be interpreted as concerning the normal Hi-C map produced from the step of balancing separate matrices. Furthermore, the specification does not further define how to locate or identify one or more "corner square blocks" on a Hi-C map (correlation matrix), and therefore the claim limitations are difficult to interpret even speculatively. To further prosecution, claim 8 is interpreted as not further limiting claim 1.
Claim 9 recites "the principal component analysis is performed". There is insufficient antecedent basis for “the principal component analysis", and it is not clear what data the PCA is being performed on and the specification (specifically page 9) does not further illuminate the matter in detail. To further prosecution, due to the challenges of the interpretation of claim 8, from which claim 9 depends, claim 9 is also interpreted as not further limiting claim 8, and by extension claim 1.
Claim 14 recites "the linear regression-based method is applied to determine the assembly continuity". There is insufficient antecedent basis for “the linear regression-based method" or "the assembly". To further prosecution, the limitation is interpreted as "a linear regression-based method is applied to determine the continuity of each candidate complex SV".
Claim 17 recites "a machine-learning based framework referred to as Peakachu is applied for loop detection". It is not clear what version or particular parameters (e.g. window sizes, resolutions, etc.) are used for training the ML-based framework used for loop detection. The instant specification does not illuminate the matter further. To further prosecution, the limitations are interpreted as performing chromatin loop detection by any method available to one having ordinary skill in the art.
Claim 18 recites "the higher probability score computed by the CTCF or the H3K27ac model". There is insufficient antecedent basis for “the higher probability score" and "the CTCF or the H3K27ac model". To further prosecution, the limitation is interpreted as "for each pixel of the reconstructed Hi-C map, one or more probabilities that the pixel contains a peak is computed and recorded".
Claim 19 recites "the same pooling algorithm used in Peakachu was applied to each SV region independently". It is not clear what version or particular parameters (e.g. window sizes, resolutions, etc.) are used for training the ML-based framework used for loop detection. The instant specification does not illuminate the matter further. Additionally, the claim recites "the best-scored loop contacts from each cluster". There is insufficient antecedent basis for “each cluster", and it is not clear what has been clustered from the other claims in this branch of the claim tree (19-18-17-1). To further prosecution, the limitations are interpreted as "the probabilities are filtered with a pre-defined probability threshold, and the remaining peaks are selected for visualizing".
Claim 20 recites "neo-TAD detection algorithm is based on the directionality index". There is insufficient antecedent basis for “the directionality index". To further prosecution, the limitation is interpreted as "neo-TAD detection algorithm is based on a directionality index".
All other claims depend from independent claim 1, and therefore are also rejected under 35 USC 112(b) as being indefinite for failing to particularly point out and distinctly claim 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(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 2 and 16-20 rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim 2 rejected as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends. Claim 2 recites "a two-step process is used to detect CNV directly from a Hi-C map at high resolution, said process comprising: a GAM being utilized to model the non-linear relationship between the one-dimensional coverage profile and different biases commonly observed in a Hi-C experiment; an HMM based segmentation algorithm being utilized to determine the boundaries of CNV segments from the initial CNV profile", which does not further limit claim 1 based on the interpretation of the "identifying Neo-TAD" limitation in the section "Claim Rejections - 35 USC 112(b)", above.
Claim 16 rejected as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends. Claim 7 recites "a linear regression-based model is utilized to minimize the differences between local distance decay curves and the whole-genome distance decay curve for allele normalization", which does not further limit claim 1 based on the interpretation of the "normalizing allele" limitation in the section "Claim Rejections - 35 USC 112(b)", above.
Claim 17 rejected as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends. Claim 17 recites "a machine-learning based framework referred to as Peakachu is applied for loop detection", which does not further limit claim 1 based on the interpretation of the "performing machine-learning based loop detection" limitation in the section "Claim Rejections - 35 USC 112(b)", above.
Claims 18-19 depend from claim 17, and therefore are also rejected under 35 USC 112(d).
Claim 20 rejected as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends. Claim 20 recites "neo-TAD detection algorithm is based on the directionality index (DI) and takes two steps comprising: calculating DI along the reference genome (hg38) and used as input to learn a global HMM model; recalculating DI on the allele normalized Hi-C map of each local assembly and use the model trained above and the Viterbi algorithm to predict the state of each bin", which does not further limit claim 1 based on the interpretation of the "identifying Neo-TAD" limitation in the section "Claim Rejections - 35 USC 112(b)", above.
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 an abstract idea of a mental process, a mathematical concept, organizing human activity, or a law of nature or natural phenomenon without significantly more. In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claim 1: “inferring copy number from Hi-C map” provides an evaluation (inferring copy number involves evaluating a Hi-C map) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
“detecting rearranged fragments and calling structural variants (SV), filtering SVs, and assembling complex SVs” (as interpreted above) provides an evaluation (detecting, filtering, and assembling SVs involves evaluating a normal Hi-C map) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
“a linear regression-based method is applied to minimize the differences between local distance decay curves and the whole-genome distance decay curve” (as interpreted above) provides a mathematical calculation (minimizing differences between local and global distance decay curves requires calculation of linear-regressions) that is considered a mathematical concept, which is an abstract idea.
“training HMM models using DIs (directionality index)” (as interpreted above) provides a mathematical calculation (identifying neo-TADs involves calculating DIs) that is considered a mathematical concept, which is an abstract idea.
Claim 5: “the consecutive bins assigned with the same state are merged together to form a CNV segment” provides an evaluation (merging bins based on state requires evaluation of the state of each bin) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 6: “calculating marginal sums and bias vectors after extracting a data set for each separate genomic region based on copy number count” (as interpreted above) provides a mathematical calculation (calculating sums and vectors involves mathematical calculations) that is considered a mathematical concept, which is an abstract idea.
Claim 7: “a mathematical framework is utilized to simulate the effect of abnormal karyotypes on a diploid Hi-C dataset” provides a mathematical calculation (simulating using a mathematical framework involves mathematical calculations) that is considered a mathematical concept, which is an abstract idea.
Claim 10: “the SVs are filtered by a linear regression model fitted between the global average contact frequencies and the local distance averaged contact frequencies for each SV” provides a mathematical calculation (filtering via a linear regression model involves mathematical calculations of the global average contact frequencies and the local distance averaged contact frequencies) that is considered a mathematical concept, which is an abstract idea.
Claim 11: “the complex SVs are assembled after being detected by checking the overlap of rearranged fragments between simple SVs” provides an evaluation (detecting complex SVs involves evaluating overlap of simple SVs) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 13: “the shortest paths between any two nodes of the graph are calculated by the Dijkstra's algorithm” provides a mathematical calculation (filtering via a linear regression model involves mathematical calculations of the global average contact frequencies and the local distance averaged contact frequencies) that is considered a mathematical concept, which is an abstract idea.
Claim 14: “a linear regression-based method is applied to determine the continuity of each candidate complex SV” (as interpreted above) provides an evaluation (determining the continuity of each complex SV involves evaluating each complex SV) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
These recitations are similar to the concepts of collecting information, analyzing it, and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind or are mathematical relationships. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas.
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). The judicial exceptions listed above are not integrated into a practical application because the claims do not recite an additional element or elements that reflects an improvement to technology. Specifically, the claims recite the following additional elements:
Claim 1: “balancing separate matrices and correcting for CNV effects to produce a normal Hi-C map” (as interpreted above) provides insignificant extra-solution activities (balancing a matrix and correcting for effects are pre-solution activities involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
“simulating CNV effects on the normal Hi-C map” provides insignificant extra-solution activities (running simulations for benchmarking is a pre-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
“performing chromatin loop detection by any method available to one having ordinary skill in the art” (as interpreted above) provides insignificant extra-solution activities (running a ML model is a pre-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
“identifying Neo-TAD” (as interpreted above) provides insignificant extra-solution activities (identifying neo-RADs involves training models and is a pre-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
“visualizing a reconstructed Hi-C map using genome browser tracks” provides insignificant extra-solution activities (visualizing genomes is a post-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claims 3 and 4: “the HMM is built using the pomegranate Python package with each state approximated by a 2-component Gaussian mixture” and “the Baum-Welch algorithm is used to estimate the parameters of transition and emission, and the Viterbi algorithm is used to predict the state (copy number) of each bin” provides insignificant extra-solution activities (using established algorithms to perform their intended function is a pre-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 12: “a directed graph is built with each node representing a simple SV, and each edge representing an overlap of rearranged fragments in consistent orientations” provides insignificant extra-solution activities (building a directed graph is a pre-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
The steps for balancing, correcting, and normalizing data, running simulations, training models, using established algorithms, and visualizing data are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application because they are pre- and post-solution activities involving data manipulation steps (see MPEP 2106.04(d)(2)). Therefore, claims 1-20 are directed to an abstract idea (Step 2A, Prong 2: NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application, or equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment.
The limitations for balancing, correcting, and normalizing data, running simulations, training models, using established algorithms, and visualizing data are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application. Furthermore, no inventive concept is claimed by these limitations as they are well-understood, routine, and conventional.
The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-20 are not patent eligible.
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.
Claims 1-4, 7-11, 16-17, and 20 rejected under 35 U.S.C. 103 as being unpatentable over Gu et al. (WO-2021119550) in view of Wang et al. (Wang, Su, et al. "HiNT: a computational method for detecting copy number variations and translocations from Hi-C data." Genome biology 21.1 (2020): 73), Shen et al. (Shen et al. "A hidden Markov model for copy number variant prediction from whole genome resequencing data." BMC bioinformatics 12.Suppl 6 (2011): S4), Servant et al. (Servant et al. "Effective normalization for copy number variation in Hi-C data." Bmc Bioinformatics 19.1 (2018): 313), Oliveira et al. (Oliveira et al. "Chromosome modeling on downsampled Hi-C maps enhances the compartmentalization signal." The Journal of Physical Chemistry B 125.31 (2021): 8757-8767), and Calandrelli et al. (Calandrelli et al. "GITAR: an open source tool for analysis and visualization of Hi-C data." Genomics, proteomics & bioinformatics 16.5 (2018): 365-372).
Regarding claims 1-4, 7-9, 16-17, and 20, Gu teaches inferring a copy number from a Hi-C map, comprising a two-step process is used to detect CNV directly from a Hi-C map at high resolution (Para.0035 "the method further comprises distinguishing between heterozygous and homozygous structural variations in samples based at least in part on the determined sequence information. In certain embodiments, the method further comprises resolving the structural variation based at least in part on the determined sequence information. In certain embodiments, the structural variation resolved is a copy number variation")
Gu also teaches balancing separate matrices and correcting for CNV effects to produce a normal Hi-C map (para.0347 "These non-uniformities were accounted for by normalizing each contact matrix using a matrix-balancing algorithm due to Knight and Ruiz").
Gu also teaches detecting rearranged fragments and calling structural variants (SV), filtering SVs, and assembling complex SVs (Para.0405 "FIG. 19 can demonstrate a procedure for reconstruction of complex genomic rearrangements").
Gu also teaches performing chromatin loop detection by any method available to one having ordinary skill in the art (same as claim 17) (Para.0017 "the method further comprises determining the sequence of a loop anchor with at least 10 base pair resolution. In certain embodiments, the method further comprises identifying a sequence motif bound by a protein within 50 base pairs outside of the loop anchor" as loop anchors part of a chromatin loop).
Gu also teaches visualizing a reconstructed Hi-C map using genome browser tracks (Para.0204 "the invention provides a method for quality control analysis of genome assemblies by visually examining a contact map derived from a DNA proximity ligation assay. In certain example embodiments, the visual examination may be facilitated by a computer implemented graphical user interface, wherein the graphical user interface facilitates annotation of the genome assembly. In certain example embodiments, the contig map may span a single contig or scaffold").
Gu does not explicitly teach: a GAM being utilized to model the non-linear relationship between the one-dimensional coverage profile and different biases commonly observed in a Hi-C experiment; an HMM based segmentation algorithm being utilized to determine the boundaries of CNV segments from the initial CNV profile; simulating CNV effects on the normal Hi-C map and a mathematical framework is utilized to simulate the effect of abnormal karyotypes on a diploid Hi-C dataset; allele-based normalization of the Hi-C map comprising; a linear regression-based model is utilized to minimize the differences between local distance decay curves and the whole-genome distance decay curve for allele normalization; nor calculating DI along the reference genome (hg38) and used as input to learn a global HMM model and recalculating DI on the allele normalized Hi-C map of each local assembly and use the model trained above and the Viterbi algorithm to predict the state of each bin.
However, Wang teaches a GAM being utilized to model the non-linear relationship between the one-dimensional coverage profile and different biases commonly observed in a Hi-C experiment (same as claim 2) (Page 2 last paragraph "To model the non-linear correlation between 1D coverage and biases observed (Additional file 1: Fig. S3), we use a generalized additive model (GAM) with the Poisson link function").
However, Shen teaches an HMM based segmentation algorithm being utilized to determine the boundaries of CNV segments from the initial CNV profile (same as claims 2 and 3) (Page 2 col 1 Results paragraph 1 "To address this issue, we augment a regular 1st order HMM with a grid of specialized deletion states that explicitly model medium-size deletions and flanking regions (F states) (Figure 1). Specifically, in the generative model going through a chromosome by each position, a hidden state represents the copy number, emitting both number of reads starting from this position and the out-distance of the mate-pair. And the emission probability of a state in the HMM is calculated jointly from the mate-pair distance and depth of coverage (Equation 1)"); additionally, the limitation of claim 4 reciting the Baum-Welch algorithm is used to estimate probabilities of transition and emission, and the Viterbi algorithm is used to predict the state (copy number) of each bin is simply a specific implementation of an expectation maximization algorithm for HMMs as evidenced by Stephen Tu (page 1 paragraph 1 “Note that Baum-Welch is simply an instantiation of the more general Expectation-Maximization (EM) algorithm.”) and Churbanov et al. (“With a sampling rate of 20 μs, processing even a modest blockade signal of 200 ms duration (10,000 sample points) becomes problematic, mostly because of the size of the dynamic programming tables required in the conventional implementations of the HMM's Baum-Welch and Viterbi decoding algorithms” suggests using Baum-Welch and Viterbi algorithms in an HMM are conventional).
However, Servant teaches simulating CNV effects on the normal Hi-C map and a mathematical framework is utilized to simulate the effect of abnormal karyotypes on a diploid Hi-C dataset (claim 7) (Page 4 Figure 1 shows results of various simulations of CNV effects, and page 12 col 2 "Simulation of cancer Hi-C data").
However, Oliveira teaches allele-based normalization of the Hi-C map comprising; a linear regression-based model is utilized to minimize the differences between local distance decay curves and the whole-genome distance decay curve for allele normalization (same as claim 16) ("After the convergence of the type A–B interactions, training is performed in the Ideal Chromosome (IC) parameters. The IC energy function is associated with polymer compaction, where the function is calibrated to match the experimental scaling decay curve").
However, Calandrelli teaches calculating DI along the reference genome (hg38) and used as input to learn a global HMM model; and recalculating DI on the allele normalized Hi-C map of each local assembly and use the model trained above and the Viterbi algorithm to predict the state of each bin (same as claim 20) (Page 3 figure 1 HiCool workflow shows "TAD analysis" where both an "observed" and "true" DI is calculated and used in an HMM model for state prediction).
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Gu as taught by Wang in order to utilize Hi-C data for SV calling, and avoid their confounding effects (page 10 Conclusion paragraph "Although not as sensitive as WGS data in general, Hi-C data can be surprisingly effective for CNV and translocation detection despite its less even coverage, and it can complement WGS data for detection of translocations in repetitive regions. As new technologies for capturing three-dimensional interactions are introduced, further computational methods will be needed to avoid the confounding effects of SVs"). One skilled in the art would have a reasonable expectation of success because both methods are concerned with utilizing Hi-C data for SV detection.
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Gu as taught by Shen in order to store CNV size once the path goes beyond one end of a pair of break points for the segment ("The primary challenge of modeling mate pairs through a HMM is that a 1st order Markov chain does not store the information of inferred CNV size once the path goes beyond one end of a pair of break points, and higher order Markov chain is computational prohibitive for processing genome-wide data"). One skilled in the art would have a reasonable expectation of success because both methods are concerned with predicting copy number variation from sequencing data.
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Gu as taught by Servant in order to explore structural variations on tumor samples (page 12 col 1 last paragraph "Taken together, the analyses covered here confirm that Hi-C can be a powerful technique to explore structural variations on tumor samples and highlight the importance of using dedicated methods for the analysis of such data"). One skilled in the art would have a reasonable expectation of success because both methods are using the same data type (Hi-C sequencing data) for analyzing SVs.
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Gu as taught by Oliveira in order to enhance the compartmentalization signal between regions that are far from each other in sequence (page 9 col 2 first paragraph "the simulations enhanced the compartmentalization signal of regions in the Hi-C map far from the main diagonal, i.e., spatial contacts between regions far from each other in sequence"). One skilled in the art would have a reasonable expectation of success because both methods are concerned with detecting chromatin interactions from Hi-C data.
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Gu as taught by Calandrelli in order to facilitate a standardized way of analyzing Hi-C genomic interaction data (page 2 col 2 last paragraph "a standardized, easy to use, and flexible solution, to manage Hi-C genomic interaction data, from processing, to storage and visualization"). One skilled in the art would have a reasonable expectation of success because both methods are performing analysis on the same data type.
Regarding claim 10, Gu in view of Wang, Shen, Servant, Oliveira, and Calandrelli teach the methods of Claims 1 on which this claim depends/these claims depend, respectively. Gu also teaches the SVs are filtered by a linear regression model fitted between the global average contact frequencies and the local distance averaged contact frequencies for each SV (Para.0404 "Genomic rearrangements on a map generated from a method described herein are easily discernible as sharp, asymmetrical peaks of contact probability. The direction of the asymmetrical peak can reveal the relative orientation of the rearranged chromosomal fragments").
Regarding claim 11, Gu in view of Wang, Shen, Servant, Oliveira, and Calandrelli teach the methods of Claims 1 on which this claim depends/these claims depend, respectively. Gu also teaches the complex SVs are assembled after being detected by checking the overlap of rearranged fragments between simple SVs (Para.0025 "the method further comprises assembling a whole genome or partial genome from the determined sequence information").
Claim 5 rejected under 35 U.S.C. 103 as being unpatentable over Gu et al. (WO-2021119550) in view of Wang et al. (Wang, Su, et al. "HiNT: a computational method for detecting copy number variations and translocations from Hi-C data." Genome biology 21.1 (2020): 73), Shen et al. (Shen et al. "A hidden Markov model for copy number variant prediction from whole genome resequencing data." BMC bioinformatics 12.Suppl 6 (2011): S4), Servant et al. (Servant et al. "Effective normalization for copy number variation in Hi-C data." Bmc Bioinformatics 19.1 (2018): 313), Oliveira et al. (Oliveira et al. "Chromosome modeling on downsampled Hi-C maps enhances the compartmentalization signal." The Journal of Physical Chemistry B 125.31 (2021): 8757-8767), and Calandrelli et al. (Calandrelli et al. "GITAR: an open source tool for analysis and visualization of Hi-C data." Genomics, proteomics & bioinformatics 16.5 (2018): 365-372) as applied to claims 1-4, 7-11, 16-17, and 20 above, and further in view of Das et al. (Das et al. Next-generation genotype imputation service and methods. Nat Genet. 2016 Oct;48(10):1284-1287. doi: 10.1038/ng.3656. Epub 2016 Aug 29. PMID: 27571263; PMCID: PMC5157836).
Gu et al. in view of Wang et al., Shen et al., Servant et al., Oliveira et al., and Calandrelli et al. are applied to claims 1-4, 7-11, 16-17, and 20.
Regarding claim 5, Gu in view of Wang, Shen, Servant, Oliveira, and Calandrelli teach the method of Claim 4 on which this claim depends/these claims depend.
Gu, Wang, Shen, Servant, Oliveira, nor Calandrelli explicitly teach the consecutive bins assigned with the same state are merged together to form a CNV segment.
However, Das teaches merging segments based on similarity of state (Page 3 paragraph 5 "This algorithm is based on a ‘state space reduction’ of the hidden Markov models (HMMs) describing haplotype sharing; it exploits similarities among haplotypes in small genomic segments to reduce the effective number of states over which the HMM iterates (Fig. 1 and Online Methods)" suggests the idea of merging segments based on similarity of state).
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Gu, Wang, Shen, Servant, Oliveira, and Calandrelli as taught by Das in order to to improve computational efficiency of running the algorithm (page 2 abstract "Here we describe improvements to imputation machinery that reduce computational requirements by more than an order of magnitude with no loss of accuracy in comparison to standard imputation tools"). One skilled in the art would have a reasonable expectation of success because, while Das is concerned with GWAS data sets, HMMs are commonly applied to all manner of sequencing data, of which the instant application is concerned with.
Claims 12-15 rejected under 35 U.S.C. 103 as being unpatentable over Gu et al. (WO-2021119550) in view of Wang et al. (Wang, Su, et al. "HiNT: a computational method for detecting copy number variations and translocations from Hi-C data." Genome biology 21.1 (2020): 73), Shen et al. (Shen et al. "A hidden Markov model for copy number variant prediction from whole genome resequencing data." BMC bioinformatics 12.Suppl 6 (2011): S4), Servant et al. (Servant et al. "Effective normalization for copy number variation in Hi-C data." Bmc Bioinformatics 19.1 (2018): 313), Oliveira et al. (Oliveira et al. "Chromosome modeling on downsampled Hi-C maps enhances the compartmentalization signal." The Journal of Physical Chemistry B 125.31 (2021): 8757-8767), and Calandrelli et al. (Calandrelli et al. "GITAR: an open source tool for analysis and visualization of Hi-C data." Genomics, proteomics & bioinformatics 16.5 (2018): 365-372) as applied to claims 1-4, 7-11, 16-17, and 20 above, and further in view of Shi et al. (Shi et al. "Gene Sequence Assembly Algorithm Model Based on the DBG Strategy and Its Application." Journal of Healthcare Engineering 2021.1 (2021): 6676194).
Gu et al. in view of Wang et al., Shen et al., Servant et al., Oliveira et al., and Calandrelli et al. are applied to claims 1-4, 7-11, 16-17, and 20.
Regarding claim 12, Gu in view of Wang, Shen, Servant, Oliveira, and Calandrelli teach the method of Claim 11 on which this claim depends/these claims depend.
Gu, Wang, Shen, Servant, Oliveira, nor Calandrelli explicitly teach a directed graph is built with each node representing a simple SV, and each edge representing an overlap of rearranged fragments in consistent orientations.
However, Shi teaches a directed graph for contig assembly, which is the ultimate output of the limitation (Page 5 col 2 first paragraph "digraph is a directed graph type predefined in the PAR platform").
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Gu, Wang, Shen, Servant, Oliveira, and Calandrelli as taught by ZZZ in order to assemble a whole or partial genome for the analysis for structural variants (Gu Para.0025 "the method further comprises assembling a whole genome or partial genome from the determined sequence information"). One skilled in the art would have a reasonable expectation of success because both methods are concerned with assembling genomic sequence data in order to identify variable features.
Regarding claim 13, Gu in view of Wang, Shen, Servant, Oliveira, and Calandrelli teach the method of Claim 12 on which this claim depends/these claims depend. Gu, Wang, Shen, Servant, Oliveira, nor Calandrelli explicitly teach the shortest paths between any two nodes of the graph are calculated by the Dijkstra's algorithm and each such path is defined as a candidate complex SV.
However, Shi also teaches using the Dijkstra algorithm for resolving bubbles, which finds the shortest path between any two nodes (Page 4 col 2 step (5) "Remove bubbles: use the Dijkstra algorithm to search for bubbles, and then merge the bubble paths").
Regarding claim 14, Gu in view of Wang, Shen, Servant, Oliveira, and Calandrelli teach the method of Claim 12 on which this claim depends/these claims depend. Gu, Wang, Shen, Servant, Oliveira, nor Calandrelli explicitly teach a linear regression-based method is applied to determine the continuity of each candidate complex SV.
However, Shi also teaches continuously assembling contigs into larger structures (Page 7 col 2 paragraph 2 "Among them, the ADT type is named Bassemble and has a type parameter elem; the function contigs means to find a path from De Bruijn graph that each edge has and only passes once to obtain contigs; the function scaffolds indicates that the assembled contigs will continue to be assembled to form the 9nal output genome sequence" suggests continuously assembling contigs into larger structures).
Regarding claim 15, Gu in view of Wang, Shen, Servant, Oliveira, and Calandrelli teach the method of Claim 12 on which this claim depends/these claims depend. Gu, Wang, Shen, Servant, Oliveira, nor Calandrelli explicitly teach candidate complex SVs are removed if the whole path or part of the path form a circular assembly, or if they are redundant.
However, Shi also teaches removal of repeats in a De Bruijn graph (Page 5 col 2 paragraph 3 "Repeat removal is to remove the tiny repeats in the De Bruijn graph").
Claims 18-19 rejected under 35 U.S.C. 103 as being unpatentable over Gu et al. (WO-2021119550) in view of Wang et al. (Wang, Su, et al. "HiNT: a computational method for detecting copy number variations and translocations from Hi-C data." Genome biology 21.1 (2020): 73), Shen et al. (Shen et al. "A hidden Markov model for copy number variant prediction from whole genome resequencing data." BMC bioinformatics 12.Suppl 6 (2011): S4), Servant et al. (Servant et al. "Effective normalization for copy number variation in Hi-C data." Bmc Bioinformatics 19.1 (2018): 313), Oliveira et al. (Oliveira et al. "Chromosome modeling on downsampled Hi-C maps enhances the compartmentalization signal." The Journal of Physical Chemistry B 125.31 (2021): 8757-8767), and Calandrelli et al. (Calandrelli et al. "GITAR: an open source tool for analysis and visualization of Hi-C data." Genomics, proteomics & bioinformatics 16.5 (2018): 365-372) as applied to claims 1-4, 7-11, 16-17, and 20 above, and further in view of Sullivan et al. (WO-2020198704).
Gu et al. in view of Wang et al., Shen et al., Servant et al., Oliveira et al., and Calandrelli et al. are applied to claims 1-4, 7-11, 16-17, and 20.
Regarding claims 18-19, Gu in view of Wang, Shen, Servant, Oliveira, and Calandrelli teach the method of Claim 17 on which this claim depends/these claims depend.
Gu, Wang, Shen, Servant, Oliveira, nor Calandrelli explicitly teach for each pixel of the reconstructed Hi-C map, a probability that the pixel contains a peak is computed and recorded; or the probabilities are filtered with a pre-defined probability threshold, and the remaining peaks are selected for visualizing.
However, Sullivan teaches calculating a balanced interaction density for each pixel, calculating a global threshold using these values, then applying this threshold to all pixels in the Hi-C map (Para.163 "In some embodiments, the methods further comprise calculating a balanced interaction density for each pixel. A balanced interaction density is calculated by normalizing and correcting the interaction density for sequencing coverage, sequence features such as restriction enzyme or other specific motifs, abundance, background signal, noise, or variation. In some embodiments, the global threshold is calculated using the balanced density interaction for each pixel" and para.164 "the first threshold comprises a global threshold. A global threshold is a threshold that is applied over the entire image").
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Gu, Wang, Shen, Servant, Oliveira, and Calandrelli as taught by Sullivan in order to identify structural variation or genomic abnormalities (para.001 "For decades clinicians have used genetic tests to identify chromosomal structural variants, or genomic abnormalities, responsible for Mendelian diseases, cancers, autism and other human diseases. Similar tests are also employed for agricultural, veterinary, research and other purposes"). One skilled in the art would have a reasonable expectation of success because both methods are utilizing the same data type for SV detection.
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Fortin et al. "Reconstructing A/B compartments as revealed by Hi-C using long-range correlations in epigenetic data." Genome biology 16.1 (2015): 180
Conclusion
No claims are allowed. However, the following claim drafted by the examiner and considered to distinguish patentably over the art of record in this application, is presented to applicant for consideration:
Claim 6: The method of claim 1, wherein an iterative correction and eigenvector decomposition procedure is applied to separate genomic regions, comprising calculating marginal sums and bias vectors after extracting a data set for each separate genomic region based on copy number count.
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/R.A.P./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686