DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-20 are currently pending and under exam herein.
Claims 1-20 are rejected.
Priority
Applicant's claim for the benefit of prior-filed application, U.S. Provisional Application No. 63/362,667 filed on April 8, 2022, under 35 U.S.C. 119(e) is acknowledged. At this point in examination, the effective filing date of claims 1-20 is April 8, 2022.
Information Disclosure Statement
No information disclosure statement (IDS) has been filed herein.
Claim Interpretation
The scope of claims is determined by giving claim language its’ broadest reasonable interpretation in light of the specification as it would be interpreted by one of ordinary skill in the art (MPEP 2111).
For the purposes of examination, the term “low coverage sequencing data” will be interpreted as having 20x coverage or less as taught in Martin et al. (The American Journal of Human Genetics, vol. 108, no. 4, pp. 656-68). Martin et al. simulated low coverage data by down sampling reads data between 0.5x to 20x coverage (p. 658-659, An in silico framework for evaluating data generation strategies with high-coverage WGS data, paragraph 2; Figure 1C).
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 a judicial exception without significantly more.
Step 1:
The first part of the eligibility analysis evaluates whether a claim falls within any statutory category (MPEP 2106.03). Claims 1-10 recite a series of steps to smooth low coverage sequencing data, normalize it, and input the data into a disease classifier. The claims are directed to a method and fall within one of the statutory categories of invention. Claims 11-19 recite a computer system configured to smooth, normalize, and input low coverage sequencing data into a disease classifier. The claims are directed to a machine and fall within one of the statutory categories of invention. Claim 20 recites a non-transitory computer-readable medium with software which causes a processor to smooth low coverage sequencing data. The claim is directed to a computer and falls within one of the statutory categories of invention (Step 1: YES).
Step 2A, prong 1:
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 those concepts:
Claim 1 recites: ...reducing noise for the analysis of low coverage sequencing data from a nucleic acid sample using a system; allocating... the set of sequence reads into a plurality of genomic bins; and introducing, subsequent to the allocating, a pseudocount number to bincount values to produce a smoothed dataset, wherein each of the bincount values is associated with one of the plurality of genomic bins.
Claim 4 recites: wherein the introducing comprises... introducing the pseudocount number responsive to identifying that a predetermined characteristic associated with the set of sequence reads is below a predetermined threshold.
Claim 5 recites: wherein the introducing comprises... determining the pseudocount number based on at least one factor.
Claim 6 recites: wherein the at least one factor is selected from the group consisting of: a bincount value metric, a median absolute pairwise deviation (mapd) metric, and a cross validation metric.
Claim 7 recites: wherein the pseudocount number is 10.
Claim 8 recites: ... performing, using the smoothed dataset, at least one normalization step on the set of sequence reads for the nucleic acid sample.
Claim 9 recites: applying, to a classification model associated with a disease state, the normalized set of sequence reads; and determining, from results derived from the applying, whether an indication of the disease state is detected from the normalized set of sequence reads.
Claim 10 recites: wherein the disease state is cancer.
Claim 11 recites: ...allocating the set of sequence reads into a plurality of genomic bins; and introducing, subsequent to the allocating, a pseudocount number to bincount values to produce a smoothed dataset, wherein each of the bincount values is associated with one of the plurality of genomic bins.
Claim 14 recites: wherein the operations to introduce further comprise: ... introducing the pseudocount number responsive to identifying that a predetermined characteristic associated with the set of sequence reads is below a predetermined threshold.
Claim 15 recites: wherein the operations to introduce further comprise: ... determining the pseudocount number based on at least one factor.
Claim 16 recites: wherein the at least one factor is selected from the group consisting of: a bincount value metric, a median absolute pairwise deviation (mapd) metric, and a cross validation metric.
Claim 17 recites: wherein the operations further comprise: performing, using the smoothed dataset, at least one normalization step on the set of sequence reads for the nucleic acid sample.
Claim 18 recites: applying, to a classification model associated with a disease state, the normalized set of sequence reads; and determining, from results derived from the applying, whether an indication of the disease state is detected from the normalized set of sequence reads.
Claim 19 recites: wherein the disease state is cancer.
Claim 20 recites: ... allocating the set of sequence reads into a plurality of genomic bins; and introducing, subsequent to the allocating, a pseudocount number to bincount values to produce a smoothed dataset, wherein each of the bincount values is associated with one of the plurality of genomic bins.
The limitations of reducing noise of low coverage sequence data, allocating sequences reads into genomics bins, and introducing a pseudocount number to bincounts recited in claims 1, 11, and 20 encompass mathematical calculations performed to reduce noise in a sequence dataset and the mental process of evaluating sequences and choosing which bin they are placed in.
The limitations of introducing the pseudocount number responsive to identifying when a characteristic of the sequence data falls below a threshold recited in claims 4 and 14 encompass the mental process of observing data to compare values. The limitations of determining the pseudocount number based on at least one factor recited in claims 5 and 15 encompasses the mental process of observation and evaluation to choose a pseudocount number and the limitations of the factor being a bincount, median absolute pairwise deviation, or cross validation metric recites in claims 6 and 16 only further limit the mental process to evaluate certain factors. The claim 7 limitation of the pseudocount being 10 further limits the mathematical calculations of introducing the pseudocount number.
The limitations of performing a normalization step recited in claims 8 and 17 encompass mathematical calculations performed on the sequence data. The limitations of applying sequence reads to a classification model and determining whether there is an indication of a disease state recited in claims 9 and 18 encompass the mental process of evaluating data and making a judgement as to the presence of a disease and the limitations of the disease state being cancer recited in claims 10 and 19 only further limit the mental process of determining a disease to cancer.
Therefore, these limitations fall under the “Mathematical concepts” and “Mental processes” groupings of abstract ideas (Step 2A, prong 1: YES).
Step 2A, prong 2:
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 (Step 2A, prong 2). The claims recite the following additional elements:
Claim 1 recites: receiving, at an input component of the system, a set of sequence reads associated with the nucleic acid sample; ... using a processor component of the system...
Claim 2 recites: wherein the low coverage sequencing data is derived at least partially from off-target data associated with a cell free DNA (cfDNA) assay.
Claim 3 recites: wherein the cfDNA assay is a targeted DNA methylation assay, and wherein the low coverage sequencing data corresponds to somatic copy number alteration (SCNA) sequencing data.
Claim 4 recites: ... automatically introducing...
Claim 5 recites: ... automatically determining...
Claim 11 recites: A system... comprising: a database; and at least one processing component configured to perform operations including: receiving a set of sequence reads associated with the nucleic acid sample.
Claim 12 recites: wherein the low coverage sequencing data is at least partially derived from off-target data associated with a cell free DNA (cfDNA) assay.
Claim 13 recite: wherein the cfDNA assay is a targeted DNA methylation assay, and wherein the low coverage sequencing data corresponds to somatic copy number alteration (SCNA) sequencing data.
Claim 14 recites: ... automatically introducing...
Claim 15 recites: ... automatically determining...
Claim 20 recites: A non-transitory computer-readable medium storing instructions ..., the instructions, when executed by one or more processors, causing the one or more processors to perform operations comprising: receiving a set of sequence reads associated with the nucleic acid sample...
The additional elements of receiving a set of sequence reads recited in claims 1, 11, and 20 are examples of pre-solution activity of gathering data for use in a process and amount to insignificant extra-solution activity (MPEP 2106.05g). The additional elements of the sequence data being derived from off-target data in a cell free DNA assay and corresponding to somatic copy number alterations recited in claims 2-3 and 12-13 only further limit the receiving a set of sequence reads by specifying particular types of reads. The additional elements of an input component and a processor component recited in claim 1, a system comprising a database and at least one processing component recited in claim 11, and a non-transitory computer-readable medium strong instructions, when executed, cause one or more processors to perform operations recited in claim 20 are recitations of computers at a high level of generality invoking them to perform the judicial exceptions. These additional elements amount to instructions to apply the judicial exception in a general computer environment (MPEP 2106.05f). Likewise, the additional elements of automatically introducing and automatically determining recited in claims 4-5 and 14-15 are invocations of using computers to perform the judicial exception and are mere instruction to apply the exception using computers.
Therefore, the judicial exception is not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology or applies/uses the recited judicial exception in some other meaningful way and the claims are directed to the judicial exception (Step 2A, prong 2: NO).
Step 2B:
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). Claims 1, 4-5, 11, 14-15, and 20 recite additional elements that equate to mere instructions to apply the recited judicial exception in a generic computing environment. Claims that amount to nothing more than instructions to apply the judicial exception using a generic computer do not render an abstract idea eligible. Alice Corp., 576 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. The additional elements recited in claims 1-3, 11-13, and 20 amount to well-understood, routine and conventional (WURC) activity because they recite computer functions that the courts have ruled to be WURC such as 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); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
As such, the combination of additional elements recited in the claims is well-understood, routine, and conventional. The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transform 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) and claims 1-20 are not patent eligible.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 4, 8-11, 14, and 17-20 are rejected under 35 U.S.C. 102(a)(1) & (a)(2) as being anticipated by Burke et al. (US20180300451A1). The italicized text corresponds to the instant claim limitations.
Regarding claim 1, Burke et al. teach a method and system to correct count bias in in sequencing data such as in Figure 2 which shows coverage below 20x in certain regions (paragraphs 0016, 0033, 0035) thus disclosing a method of reducing noise for the analysis of low coverage sequencing data from a nucleic acid sample using a system… Burke et al. teach the system receives sequencing reads (paragraphs 0040-0042; Figure 1D), divides the reads into bins (paragraph 0045), and adds a pseudocount to generate adjusted bin counts (paragraph 0070) which altogether discloses …receiving, at an input component of the system, a set of sequence reads associated with the nucleic acid sample; allocating, using a processor component of the system, the set of sequence reads into a plurality of genomic bins; and introducing, subsequent to the allocating, a pseudocount number to bincount values to produce a smoothed dataset, wherein each of the bincount values is associated with one of the plurality of genomic bins.
Regarding claim 11, Burke et al. teach their system can have a database (paragraph 0042) which, in combination with the teachings referenced above for claim 1 of correcting count bias in sequencing data below 20x (paragraphs 0016, 0033, 0035; Figure 2), receiving sequencing reads at a computer (paragraphs 0040-0042; Figure 1D), binning sequencing read (paragraph 0045), and adding a pseudocount (paragraph 0070) discloses a system for reducing noise for the analysis of low coverage sequencing data from a nucleic acid sample, the system comprising: a database; and at least one processing component configured to perform operations including: receiving a set of sequence reads associated with the nucleic acid sample; allocating the set of sequence reads into a plurality of genomic bins; and introducing, subsequent to the allocating, a pseudocount number to bincount values to produce a smoothed dataset, wherein each of the bincount values is associated with one of the plurality of genomic bins.
Regarding claim 4, Burke et al teach that their invention provides automated determination of and correction for count or bias based on nucleic acid content in a bin (paragraph 0009) and can automatically detect and correct count bias based on percent GC content or other characteristics of the sequence data (paragraph 0048, 0050; Figure 4) which discloses wherein the introducing comprises automatically introducing the pseudocount number responsive to identifying that a predetermined characteristic associated with the set of sequence reads is below a predetermined threshold.
Regarding claim 14, the teachings of Burke et al. of automatically detecting and correcting count bias (paragraphs 0009, 0048, 0050; Figure 4) disclose wherein the operations to introduce further comprise: automatically introducing the pseudocount number responsive to identifying that a predetermined characteristic associated with the set of sequence reads is below a predetermined threshold.
Regarding claim 8, Burke et al. teach normalizing the observed counts (paragraph 0066, 0108) which discloses further comprising performing, using the smoothed dataset, at least one normalization step on the set of sequence reads for the nucleic acid sample.
Regarding claim 17, the teachings of Burke et al. of normalizing observed counts (paragraphs 0066, 0108) disclose wherein the operations further comprise: performing, using the smoothed dataset, at least one normalization step on the set of sequence reads for the nucleic acid sample.
Regarding claim 9, data obtained from the analyses can be used to train deep learning and statistical models to predict disease presence (paragraph 0119) which discloses applying, to a classification model associated with a disease state, the normalized set of sequence reads; and determining, from results derived from the applying, whether an indication of the disease state is detected from the normalized set of sequence reads.
Regarding claim 18, the teachings of Burke et al. of training a machine learning model to predict disease presence (paragraph 0119) disclose wherein the operations further comprise: applying, to a classification model associated with a disease state, the normalized set of sequence reads; and determining, from results derived from the applying, whether an indication of the disease state is detected from the normalized set of sequence reads.
Regarding claim 10, Burke et al. teach that sequence data generated can be used to diagnose or prognose cancer (paragraph 0116) which discloses wherein the disease state is cancer.
Regarding claim 19, the teaching of Burke et al. that their data can be used to diagnose or prognose cancer (paragraph 0116) discloses wherein the disease state is cancer.
Regarding claim 20, Burke et al. teach their computer system has static storage devices for storing instructions such as a non-transitory computer-readable medium (paragraph 0124, 0128-0129) which, in combination with the teachings in combination with the teachings referenced above for claims 1 and 11 of correcting count bias in sequencing data below 20x (paragraphs 0016, 0033, 0035; Figure 2), receiving sequencing reads at a computer (paragraphs 0040-0042; Figure 1D), binning sequencing read (paragraph 0045), and adding a pseudocount (paragraph 0070) discloses a non-transitory computer-readable medium storing instructions for reducing noise for the analysis of low coverage sequencing data from a nucleic acid sample, the instructions, when executed by one or more processors, causing the one or more processors to perform operations comprising: receiving a set of sequence reads associated with the nucleic acid sample; allocating the set of sequence reads into a plurality of genomic bins; and introducing, subsequent to the allocating, a pseudocount number to bincount values to produce a smoothed dataset, wherein each of the bincount values is associated with one of the plurality of genomic bins.
Claim Rejections - 35 USC § 103
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 2-3 and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Burke et al. (US20180300451A1) as applied to claims 1 and 11 above, and further in view of Valouev et al. (US20210102262A1). The italicized text corresponds to the instant claim limitations.
The limitations of claims 1 and 11 were taught by Burke et al.
Burke et al. appears to be silent on the claims 2 and 12 limitations of wherein the low coverage sequencing data is derived at least partially from off-target data associated with a cell free DNA (cfDNA) assay and the claims 3 and 13 limitations of wherein the cfDNA assay is a targeted DNA methylation assay, and wherein the low coverage sequencing data corresponds to somatic copy number alteration (SCNA) sequencing data. However, these limitations were known in the art at the effective filing date of the invention, as taught by Valouev et al.
Regarding claims 2 and 12, Valouev et al. teach improvements in targeted sequencing technologies using off-target data to determine if a subject has a disease such as cancer (paragraph 0003) and alterations in cell free DNA (cfDNA) found in patient samples can be used as diagnostic biomarkers for cancers (paragraph 0008) which discloses wherein the low coverage sequencing data is derived at least partially from off-target data associated with a cell free DNA (cfDNA) assay.
Regarding claims 3 and 13, Valouev et al. teach that copy number variations are found in cfDNA released by cancer cells (paragraph 0009). Valouev et al. teach that methylation status is known to correlate with the presence of cancer (paragraph 0013) and in some embodiments the data used in their methods is targeted DNA methylation sequencing (paragraph 0027) which discloses wherein the cfDNA assay is a targeted DNA methylation assay, and wherein the low coverage sequencing data corresponds to somatic copy number alteration (SCNA) sequencing data.
An invention would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date of the invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Burke et al. teach correcting errors and bias in read data improves discovery and diagnosis of medical conditions (paragraphs 0008-0009). Valouev et al. teach using off-target data improves the precision of diagnosing a disease condition (paragraphs 0014-0016). Therefore, one of ordinary skill in the art would have been motivated to use the methods and system taught by Burke et al. on off-target data from cfDNA and/or targeted DNA methylation assays taught by Valouev et al. in order to accurately predict and determine the presence of disease from sequence data. Furthermore, one of ordinary skill in the art would predict the techniques taught by Burke et al. could be readily added to data generated from Valouev et al. with a reasonable expectation of success because Burke et al. and Valouev et al. successfully demonstrated their analysis techniques on sequencing data. The inventions of claims 2-3 and 12-13 are therefore prima facie obvious.
Claims 5, 7, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Burke et al. (US20180300451A1) as applied to claims 1 and 11 above, and further in view of Nishida et al. (Nucleic Acids Research, vol. 37, no. 3, pp. 939-44). The italicized text corresponds to the instant claim limitations.
The limitations of claims 1 and 11 were taught by Burke et al.
Burke et al. appears to be silent on the claims 5 and 15 limitations of wherein the introducing comprises automatically determining the pseudocount number based on at least one factor and wherein the operations to introduce further comprise: automatically determining the pseudocount number based on at least one factor and the claim 7 limitations of wherein the pseudocount number is 10. However, these limitations were known in the art before the effective filing date of the invention as taught by Nishida et al.
Regarding claims 5 and 15, Nishida et al. teach finding an optimal pseudocount value by comparing original probability position matrices (PPM) with sampled PPMs using various metrics (p. 940-941, Materials and Methods) which discloses the claim 5 limitation of wherein the introducing comprises automatically determining the pseudocount number based on at least one factor and the claim 15 limitation of wherein the operations to introduce further comprise: automatically determining the pseudocount number based on at least one factor.
Regarding claim 7, Nishida et al. teach larger pseudocounts are optimal for motifs with higher entropy (p. 942, Dependence of optimal pseudocount on sample size and entropy). Figure 3A-B and Figure 4A-B show motifs with higher entropy having an optimal pseudocount approaching 10 (p.943). These teachings disclose wherein the pseudocount number is 10 because the particular claimed pseudocount value is a result-effective variable that could be arrived at by one of ordinary skill in the art through routine experimentation. Nishida et al. teach that although on average an optimal pseudocount is close to 1, the optimal value depends on the comparison method (p. 943-944, Conclusion). Nishida et al. teach that motifs with higher entropy were removed from due to increased calculation time and that such removal introduces bias (p. 940-941, Comparison procedures) and higher entropy motifs demonstrate larger optimal pseudocounts that positively correlate with the entropy (p. 943-944, Conclusion). Indicating that had motifs with higher entropy been analyzed, the optimal pseudocount would have been ten or greater.
Nishida et al. teach pseudocounts are allocated to avoid bias due to small sample size when calculating position weight matrices for transcription binding sites (Abstract). Often times, nucleotides will have a frequency of zero in positions of a binding motif, but this is likely due to chance from having a small sample size and assigning a probability of zero is too harsh and causes issues with data analysis on computers due to negative infinity values (p. 939, Introduction, paragraphs 1-4). Nishida et al. teach although the use of pseudocounts is ubiquitous there is no standardized method to choose them and their effect on the data has not been studied systematically (p. 939-940, Introduction, paragraphs 5-6).
Burke et al. teach correcting errors and bias in read data improves discovery and diagnosis of medical conditions (paragraphs 0008-0009) and pseudocounts are used in some embodiments to control variance for small value (paragraph 0070). A person of ordinary skill in the art would be motivated to find the optimal pseudocount value and incorporate it into the invention of Burke et al. to reduce bias in the data and better diagnose medical conditions. There would be a reasonable expectation of success because Nishida et al. demonstrated determining optimized pseudocount values on sequence data with small sample size using various metrics. The invention of claims 5, 7, and 15 are therefore prima facie obvious.
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Burke et al. (US20180300451A1) and Nishida et al. (Nucleic Acids Research, vol. 37, no. 3, pp. 939-44) as applied to claims 1, 5, 11, and 15 above, and further in view of Ahsendorf et al. (PLOS ONE, edited by Tae-Young Roh, vol. 12, no. 12, p. e0186324). The italicized text corresponds to the instant claim limitations.
The limitations of claims 1 and 11 were taught by Burke et al. and the limitations of claims 5 and 15 were taught by Nishida et al.
Burke et al. and Nishida et al. appear to be silent on the claims 6 and 16 limitation of wherein the at least one factor is selected from the group consisting of: … a cross validation metric. However, this limitation was known in the art at the effective filing date of the invention, as taught by Ahsendorf et al.
Regarding claims 6 and 16, Ahsendorf et al. teach optimizing pseudo counts by 10-fold CV (cross-validation) in binned epigenetic marker data to predict gene expression (p. 8-10, Prediction of gene expression; p. 17, Predicting (CAGE) gene expression) which discloses wherein the at least one factor is selected from the group consisting of: … a cross validation metric.
An invention would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date of the invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Ahsendorf et al. teach that pseudocounts of the best models varied when evaluated with tenfold cross validation (p. 8-10, Prediction of gene expression, paragraphs 1-2). Burke et al. teach accumulated data can be used to train deep learning or other statistical methods to predict disease presence, prognostic outcome, and expected response to treatment (paragraph 0119). Therefore, one of ordinary skill in the art would have been motivated to use cross validation on models trained in Burke et al. in order to obtain the optimal pseudocount value and best performing statistical model. Furthermore, one of ordinary skill in the art would predict cross validation could be readily used to evaluate models with a reasonable expectation of success because Ahsendorf et al. demonstrated such utility and Burke et al. teach using a pseudocount in their calculations (paragraph 0070). The inventions of claims 6 and 16 are therefore prima facie obvious.
Conclusion
No claims are allowed.
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/T.Y.O./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
