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 .
Priority
Acknowledgement is made of provisional application 63/125,171 filed December 14th, 2020 and 371 filed October 1st, 2021. The effective filing date is December 14th, 2020.
Status of Claims
Claims 1-20 are currently pending and examined on the merits.
Information Disclosure Statement
The information disclosure statements filed 10/18/2023 and 18/265,118 are acknowledged. A signed copy of the corresponding 1449 form has been included with this Office action.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION. —The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 12 is rejected under 35 U.S.C. 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.
Claim 12 recites “classifying the sample data comprises applying a state vector machine.”, on lines 1-2. The specification is silent regarding a clear and precise definition of “state vector machine” and one skilled in the art would be unclear in its meaning. Using the broadest reasonable interpretation of the claims, “state vector machine” is interpreted as “vector quantization”, such that the claim reads “…classifying the sample data comprises applying vector quantization.” More broadly, this is interpreted as applying a clustering technique to classify high dimensionality data.
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 mental steps, mathematic concepts, organizing human activity, or a natural law without significantly more.
Step 2A, Prong 1
In accordance with MPEP § 2106, claims found to recite statutory subject matter (claim 1-16 are drawn to a method; claims 17-20 is drawn to a system) (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 (listed numerically) recite the following limitations that equate to an abstract idea (reasonings in parentheses):
Recited:
…receiving a population of sample data, wherein the population includes amplicon counts per sample data; (which further limits claim 1)
generating a first matrix of the amplicon counts per sample data; (mental step, i.e. can be performed with pen and paper)
dividing the first matrix into a product of a second matrix and a third matrix, the second matrix being signatures of short and long DNA fragments and the third matrix being intensities of each signature of the short and long DNA fragments; (mathematical concept of a mathematical calculation, i.e. counting and numerical division)
in the second matrix, determining whether each signature is a long or short fragment per each amplicon count; (mental step)
in the third matrix, determining intensities of each signature per the sample data; and (mental step)
classifying the sample data based on the intensities of each signature (mental step).
Recited: normalizing the amplicon counts. (mental step)
Recited: filtering the amplicon counts. (mental step)
Recited: the signatures include a first signature indicative of the short fragment size and a second signature indicative of the long fragment size. (which further limits claim 1)
Recited: the short fragment size is indicative of cancer. (which further limits claim 4)
Recited: the long fragment size is indicative of normal. (which further limits claim 1).
Recited: assigning a classifier value of 1 to sample data having a greater intensity of the first signature. (mental step)
Recited: applying linear regression analysis to the intensities of each signature per each sample data. (mathematical calculation)
Recited: applying a non-negative least square function to the intensities of each signature per each sample data. (mathematical calculation)
Recited: applying linear regression analysis to the intensities of each signature per each sample data. (mathematical calculation)
Recited: applying a deep learning model. (which further limits claim 1)
Recited: classifying the sample data comprises applying a state vector machine. (which further limits claim 1)
Recited: each sample data is a chromosomal arm. (which further limits claim 1)
Recited: each sample data is a sequenced DNA sample. (which further limits claim 1)
Recited: iteratively improving one or more algorithms applied in the method. (mental step)
Recited: the short fragment size is indicative of at least one of adenomatous polyps or advanced adenomas in an organ or tumor. (which further limits claim 1)
Recited:
…receiving a population of sample data, wherein the population includes amplicon counts per sample data; (which further limits claim 17)
generating a first matrix of the amplicon counts per sample data; (mental step, i.e. can be performed with pen and paper)
dividing the first matrix into a product of a second matrix and a third matrix, the second matrix being signatures of short and long DNA fragments and the third matrix being intensities of each signature of the short and long DNA fragments; (mathematical concept of a mathematical calculation, i.e. counting and numerical division)
in the second matrix, determining whether each signature is a long or short fragment per each amplicon count; (mental step)
in the third matrix, determining intensities of each signature per the sample data; and (mental step)
classifying the sample data based on the intensities of each signature (mental step).
Recited: the signatures include a first signature indicative of the short fragment size and a second signature indicative of the long fragment size. (which further limits claim 17)
Recited: wherein the short fragment size is indicative of cancer. (which further limits claim 18)
Recited: the short fragment size is indicative of at least one of an adenomatous polyp or advanced adenoma in an organ or tumor. (which further limits claim 18).
The claims recite an abstract idea of analyzing DNA (See MPEP 2106.07(a)).
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 mathematical relationships. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas.
While claim 1 recites performing some aspects of the analysis using a “system”, there are no additional limitations that indicate that this system requires anything other than carrying out the recited mental process or mathematical concept in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then if falls within the “Mental processes” grouping of abstract ideas. As such, claim(s) 1-16 recite(s) an abstract idea/law of nature/natural phenomenon (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 or not (Step 2A, Prong 2). This 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 or uses the recited judicial exception to affect a particular treatment for a condition. Rather, the instant claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment or mere instructions to apply the recited judicial exception via a generic treatment. Specifically, the claims recite the following additional elements:
Claims 1 and 17 recite: …receiving a population of sample data, wherein the population includes amplicon counts per sample data; (which further limits claim 1 and 17)
Claims 4 and 18 recite: the signatures include a first signature indicative of the short fragment size and a second signature indicative of the long fragment size. (which further limits claims 1 and 17)
Claims 5 and 19 recite: the short fragment size is indicative of cancer. (which further limits claims 4 and 18)
Claim 11 recites: applying a deep learning model. (which further limits claim 1)
Claim 12 recites: classifying the sample data comprises applying a state vector machine. (which further limits claim 1)
Claim 13 recites: each sample data is a chromosomal arm. (which further limits claim 1)
Claim 14 recites: each sample data is a sequenced DNA sample. (which further limits claim 1)
Claims 16 and 20 recite: the short fragment size is indicative of at least one of adenomatous polyps or advanced adenomas in an organ or tumor. (which further limits claims 1 and 17)
There are no limitations that indicate that the claimed analysis engine or the formats of the provided data require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. As such, claims 1-20 are directed to an abstract idea (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).
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 equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment and/or mere data gathering (see MPEP 2106.05(g)).
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 is/are not patent eligible.
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.
Claim(s) 1-10, 12-15, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Oliver Claude Venn (WO2018085862A2) as evidenced by Daniel Lee et al (NIPS'00: Proceedings of the 14th International Conference on Neural Information Processing Systems. Pages 535 – 541), Haixuan Yang et al. (PLoS ONE 11(10): e0164880.) and Zhenfeng Zhu et al. (Neurocomputing Volume 73, Issues 10–12, June 2010, Pages 1783-1793).
Claim references are italicized.
Regarding claims 1 and 17, Venn teaches a computer-implemented method for detecting the presence of a cancer in a patient comprising receiving a data set in a computer comprising a processor and a computer-readable medium, wherein the data set comprises a plurality of sequence reads obtained by sequencing a plurality of nucleic acids in a biological test sample from the patient, inclusive of amplicon counts as disclosed in paragraph 0054 of the specification (Abstract, clm. 1, Spec, re: clm. 1, 17…A method/system… for classifying data using non-negative matrix factorization, the method comprising: …receiving a population of sample data, wherein the population includes amplicon counts per sample data…).
Venn further teaches applying non-negative matrix factorization (NMF) to an amplicon counts matrix (Spec, para. 0004, re: clm. 1, 17 … generating a first matrix of the amplicon counts per sample data…).
Venn further teaches the application of NMF algebra to generate a signature matrix on paragraph 0003 of the specification.
Finally, Venn teaches in claims 4, 5 and 37 (nonexclusively) classification of sample data as cancer or non-cancer related based upon intensities (clm. 1, clm. 4, Spec para. 0061, 0065, 00205, re: clm. 1, 17 … generating a first matrix of the amplicon counts per sample data; dividing the first matrix into a product of a second matrix and a third matrix, the second matrix being signatures of short and long DNA fragments and the third matrix being intensities of each signature of the short and long DNA fragments; in the second matrix, determining whether each signature is a long or short fragment per each amplicon count; in the third matrix, determining intensities of each signature per the sample data; and classifying the sample data based on the intensities of each signature.).
Venn does not explicitly disclose dividing a first matrix of amplicon counts into a second and third, nor does Venn explicitly disclose that the second matrix is composed of short and long DNA fragments, or intensities as stated in the instant claims (re: clm. 1, … dividing the first matrix into a product of a second matrix and a third matrix, the second matrix being signatures of short and long DNA fragments and the third matrix being intensities of each signature of the short and long DNA fragments; in the second matrix, determining whether each signature is a long or short fragment per each amplicon count; in the third matrix determining intensities of each signature per the sample data; and classifying the sample data based on the intensities of each signature.).
In KSR Int 'l v. Teleflex, the Supreme Court, in rejecting the rigid application of the teaching, suggestion, and motivation test by the Federal Circuit, indicated that “The principles underlying [earlier] cases are instructive when the question is whether a patent claiming the combination of elements of prior art is obvious. When a work is available in one field of endeavor, design incentives and other market forces can prompt variations of it, either in the same field or a different one. If a person of ordinary skill can implement a predictable variation, § 103 likely bars its patentability.” KSR Int'l v. Teleflex lnc., 127 S. Ct. 1727, 1740 (2007).
Applying the KSR standard of obviousness to Venn, the examiner concludes that the teaching of NMF applied to a signature matrix derived from amplicon counts as disclosed by Venn represents a combination of known elements which yields the predictable result of a second and third matrix. The use of NMF as taught by Venn in this combination would have further served to achieve the predictable result of the generation of a second and third matrix because of NMF algebra, as evidenced by Lee et al.
The resultant division of the first matrix into a second and third matrices in such a combination of NMF with a signature matrix is merely a "predictable use of prior art elements according to their established functions,” that would have been obvious to one of ordinary skill in the art of bioinformatics (re: clm. 1, …generating a first matrix of the amplicon counts per sample data; dividing the first matrix into a product of a second matrix and a third matrix…).
Furthermore, Venn further teaches on para. 00205 of the specification detection of a short DNA fragment, stating: “Signature 6 is associated with high numbers of small (shorter than 3 base pairs) insertions and deletions at mono- or polynucleotide repeats.”
The length of a DNA fragment is definitively tied to the number of nucleotides in said fragment, Venn teaches in paragraph 0004, Fig 31 (fragment length distribution), para. 0065 (“A polynucleotide, for example, can be broken up, or fragmented into, a plurality of segments…”) and in para. 0061 (“a read segment can refer to an aligned sequence read, a collapsed sequence read, or a stitched read… a read segment can refer to an individual nucleotide base, such as a single nucleotide variant…”).
A count matrix can contain fragments of DNA of varying sizes (i.e. both short and long relative to each other). Venn therefore teaches on para. 0061, 0065, and 00205 generating signatures of short and long DNA fragments, which are associated with features and described as “exposure weights”, which are equivalent to intensities of each signature.
Therefore, NMF applied to a signature matrix derived from amplicon counts would have further served to achieve the predictable result of a second matrix of short and long DNA fragments, and a third matrix of intensities which may be used for classification purposes as disclosed in paragraph 0003 of Venn. Determining signatures via amplicon count and classifying sample data based upon intensities in such a combination of NMF with a signature matrix is merely a "predictable use of prior art elements according to their established functions." (re: clm. 1, … the second matrix being signatures of short and long DNA fragments and the third matrix being intensities of each signature of the short and long DNA fragments; in the second matrix, determining whether each signature is a long or short fragment per each amplicon count; in the third matrix, determining intensities of each signature per the sample data; and classifying the sample data based on the intensities of each signature.). Therefore, claims 1 and 17 of the applicant’s invention would have been prima facie obvious to one of skill in the art at the time of filing of the application, absent evidence to the contrary.
Regarding claims 13 and 14, Venn teaches DNA sequencing of a biological test sample (abstract, clm. 1).
Applying the KSR standard of obviousness to Venn, the examiner concludes that the teaching of sequencing applied to a biological sample disclosed by Venn represents a combination of known elements which yields the predictable result of a sequenced DNA sample, inclusive of a chromosome arm. The use of sequencing by Venn in this combination would have further served to achieve the predictable result of a sequenced DNA sample because sequencing said sample inherently includes a chromosomal arm. The resultant sequencing of a sample is merely a "predictable use of prior art elements according to their established functions,” that would have been obvious to one of ordinary skill in the art of bioinformatics (re: clm. 13, each sample data is a chromosomal arm…, clm. 14, … each sample data is a sequenced DNA sample.). Therefore, claims 13 and 14 of the applicant’s invention would have been prima facie obvious to one of skill in the art at the time of filing of the application, absent evidence to the contrary.
Regarding claim 2, Venn states that a signature matrix can embody probabilities on para. 0004 of the specification: “The construction of a signature matrix using non-negative matrix factorization can be generalized to multiple features relevant to cancer detection and/or classification. In some embodiments, a signature matrix comprises a plurality of signatures where the probability of the occurrence for each of a plurality of features are represented.”
Applying the KSR standard of obviousness to Venn, the Examiner concludes that the combination of a NMF-derived signature matrix with probability calculations is applying a known technique to a known method with no more than a predictable outcome of a normalized signature matrix. One of skill would have had a reasonable expectation of success at applying a normalization technique to the method of NMF and probability calculations as NMF provides all the necessary instructions or elements through standard data preparation protocols standard in the art as evidenced by Yang et al. and Zhu et al. Therefore, claim 2 of the applicant’s invention would have been prima facie obvious to one of skill in the art at the time of filing of the application, absent evidence to the contrary.
Regarding claim 3, Venn teaches filtering amplicon counts on Fig. 21, and in paragraph 00120 of the specification, stating: “In one use case, in order to filter out data of a directed graph having lower levels of importance, the sequence processor 1605 removes (e.g., "trims" or "prunes") nodes or edges having a count less than a threshold value, and maintains nodes or edges having counts greater than or equal to the threshold value.” (re: clm. 3, … filtering the amplicon counts.). Therefore, claim 3 of the applicant’s invention would have been prima facie obvious to one of skill in the art at the time of filing of the application, absent evidence to the contrary.
Regarding claims 4-6, 18, and 19, Venn teaches DNA sequencing and the generation of a signature matrix, but does not explicitly disclose signatures indicative of short or long fragments, nor explicitly state their size association with cancer/normal (Abstract, clm. 1, re: clm. 4, 18, … wherein the signatures include a first signature indicative of the short fragment size and a second signature indicative of the long fragment size…clm. 5, 19, … wherein the short fragment size is indicative of cancer…, clm. 6, … wherein the long fragment size is indicative of normal.)
Applying the KSR standard of obviousness to Venn, the Examiner concludes that the combination of NMF with DNA sequencing data is applying a known technique to a known method with no more than a predictable outcome of a signature matrix derived from amplicon data. One of skill would have had a reasonable expectation of success at applying NMF to the DNA sequencing data as Venn provides all the necessary instructions or elements in paragraphs 0061, 0065, 0091-0094 and 00205 of the specification, which details the application of NMF to sequencing data, and associated data annotation in Figures.
Venn further teaches on para. 0061, 0065, and 00205 generating signatures of short and long DNA fragments, which are associated with features and described as “exposure weights”. which are equivalent to intensities of each signature. It would be obvious to one of ordinary skill in the art that exposure weights are equivalent to intensities of each signature, and the weights inherently may be applied to counts of fragments of varying sizes (re: clm. 4, … the signatures include a first signature indicative of the short fragment size and a second signature indicative of the long fragment size.).
Venn further teaches on Fig. 9 a scatter plot of showing the count of mutations in cfDNA from cancer patients and healthy subjects as a function of age, as tied to a “signature 1”, which reads on a demonstration of nucleotide counts being associated with cancer and health dependent on count size (re: clm. 5, … wherein the short fragment size is indicative of cancer., clm. 6, … wherein the long fragment size is indicative of normal.)
Therefore, Venn provides all the necessary instructions or elements for one of skill in the art to apply NMF to sequencing data. Claims 4-6, 18, and 19 of the applicant’s invention would have been prima facie obvious to one of skill in the art at the time of filing of the application, absent evidence to the contrary.
Regarding claims 7 and 8, Venn teaches in specification paragraphs 0092-0094 the application of NMF to DNA sequencing data. Venn explicitly states on paragraph 0091 that:
“… sample matrix M can be decomposed, or deconvoluted, using non-negative matrix factorization into two nonnegative matrices: a matrix “P” of r number of mutational signatures by n contexts (or features) (where elements of P take values in [0, 1]) and a matrix “E” of exposure weights that each patient has to the r mutational signatures. The product of signature matrix P and exposure matrix E (P×E) for a patient sample is an approximate reconstruction of the observed mutations for a given patient test sample.”, showing value assignment within the range 0 and 1 within signature matrices. Venn further discloses in claim 137 that the presence of cancer in the patient is detected when the one or more exposure weights for the one or more mutational signatures exceeds a threshold value.
Venn does not explicitly disclose a value of 1 and 0 as associated with a first or second signature (re: clm. 7, … assigning a classifier value of 1 to sample data having a greater intensity of the first signature…, clm. 8, … assigning a classifier value of 0 to sample data having a greater intensity of the second signature.).
Applying the KSR standard of obviousness to Venn, the Examiner concludes that the combination of NMF with DNA sequencing data is applying a known technique to a known method with no more than a predictable outcome of a signature matrix derived from amplicon data with a classifier value of 1 assigned to sample data having a greater intensity in a first signature, and a classifier value of 0 to sample data having a greater intensity of the second signature.
One of skill would have had a reasonable expectation of success at applying NMF to the DNA sequencing data as Venn provides all the necessary instructions or elements in specification paragraphs 0092-0094, which detail the application of non-negative matrix factorization to infer latent mutational signatures for cancer detection, diagnosis and classification, and inherently could include (given signature presence) signatures associated with cancer with a classifier value of 1, and signatures associated with a normal or healthy state with a classifier value of 0. Therefore, claims 7 and 8 of the applicant’s invention would have been prima facie obvious to one of skill in the art at the time of filing of the application, absent evidence to the contrary.
Regarding claim 9, Venn teaches applying a non-negative last squares function to data signatures stating in paragraph 0191 of the specification that: “Additionally, since mutational signatures can share sequence contexts, analysis also involves “regularizing” the coefficient estimates to shrink estimates towards zero. In other words, the analyses described herein seek to perform variable selection and shrinkage to isolate the important mutational processes out of the set of specified mutational signatures. Two techniques known for this include ridge regression and the lasso. In this example, elastic net non-negative least squares regression is used…” (re: clm. 9, …comprising applying a non-negative least square function to the intensities of each signature per each sample data...). Therefore, claim 9 of the applicant’s invention would have been prima facie obvious to one of skill in the art at the time of filing of the application, absent evidence to the contrary.
Regarding claim 10, Venn discloses linear regression in paragraph 102 of the specification stating: “For example, in one embodiment, non-negative linear regression can be used to determine, or infer, cancer status from the patient's unique mutational profile and a matrix of mutational signatures.”, which reads on applying linear regression to signatures from sample data (re: clm. 10, …comprising applying linear regression analysis to the intensities of each signature per each sample data.). Therefore, claim 10 of the applicant’s invention would have been prima facie obvious to one of skill in the art at the time of filing of the application, absent evidence to the contrary.
Regarding claim 11, Venn does not explicitly disclose applying deep learning to classify sample data.
Applying the KSR standard of obviousness to Venn, the examiner concludes that there was a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings to predictably lead to the application of deep learning to classify sample data as derived within the methods of identifying somatic mutational signatures for early cancer detection as taught by Venn.
One of ordinary skill in the art of bioinformatics would be motivated to apply deep learning because the application would lead to a stronger method of classifying sample data. In support of this motivation, Venn describes on paragraph 0090 that “…a machine learning approach can be utilized to infer underlying mutational signatures identified in a patient test sample (e.g., a cell-free nucleic acid sample). In general, any known machine learning approach can be utilized in practicing the present invention.”
There would have been a reasonable expectation of success because machine learning methods are already applied in the current invention. Therefore, claim 11 of the applicant’s invention would have been prima facie obvious to one of skill in the art at the time of filing of the application, absent evidence to the contrary.
Regarding claim 12, “state vector machine” is interpreted as “vector quantization”. Venn teaches in paragraph 0078 of the specification: “In other embodiments, the methods of the invention may use principal components analysis (PCA) or vector quantization (VQ) approaches to construct a signature matrix.” (re: clm. 12, …classifying the sample data comprises applying a state vector machine.).
Regarding claim 15, Venn teaches that:
“In one embodiment, as additional sequencing data is obtained for new patient test samples (e.g., from cfDNA), sample matrix M can be updated with the new data and the performance of signature matrix P can be re-evaluated, or a new P can be generated. The process can be repeated any number of times to construct a matrix for optimal (robust) performance. It is believed that signature matrix P improves as sample size increases as subsampling analysis of a patient cohort has demonstrated that the performance of non- negative matrix factorization decreases with sample size…” updating and improving signature matrix reads on improving an algorithm applied to amplicons (re: clm. 15, … iteratively improving one or more algorithms applied in the method.). Repeating a process with updated data reads on improving an algorithm.
Claim(s) 16 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Oliver Claude Venn as evidenced by Daniel Lee et al. , Haixuan Yang et al. and Zhenfeng Zhu et al. as applied to claims 1-15, and 17-19 in view of Bert Vogelstein et al (US20190256924A1).
Venn is applied to claims 1-6, 9-10, 12-15, and 17-19 above.
Venn teaches methods and systems for identifying somatic mutational signatures for detecting, diagnosing, monitoring and/or classifying cancer in a patient known to have, or suspected of having cancer using a non-negative matrix factorization (NMF) approach to construct a signature matrix that can be used to identify latent signatures (inclusive of fragment size) in a patient sample for detection and classification of cancer (Abstract, Spec, para. 0003).
Venn does not teach indicating adenomatous polyp or advanced adenoma in an organ or tumor in a short fragment size (re: clm. 16, 20 … the short fragment size is indicative of at least one of an adenomatous polyp or advanced adenoma in an organ or tumor.)
Vogelstein et al. teaches method of evaluating a subject for the presence of any of a plurality of cancers in a subject, comprising detecting in a biological sample obtained from the subject the presence of one or more driver gene mutations in one or more driver genes via sequencing one or more regions of interest or amplicons comprising the driver gene mutation, inclusive of those associated with adenomas (clm. 1, 2, Spec, para. 0752-0753; re: clm. 16, 20, … the short fragment size is indicative of at least one of an adenomatous polyp or advanced adenoma in an organ or tumor…)
Applying the KSR standard of obviousness to Venn and Volgelstein et al., the examiner concludes that there was a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings to predictably lead to a method to detect cancer in a subject using NMF to generate a signature matrix containing features wherein a short fragment size is indicative of an adenoma.
One of ordinary skill in the art of bioinformatics would be motivated to apply Volgelstein et al.’s detection method because the application would lead to a stronger method of classifying sample data.
There would have been a reasonable expectation of success because both Venn and Volgelstein et al. teach methods within the same field of invention. In support of this expectatoin, Volgelstein et al. states in paragraphs 0751-0753 of the specification: “In some embodiments, methods provided herein to detect aneuploidy can be combined with methods to detect the presence of one or more genetic biomarkers (e.g., mutations)… “ and “…Methods provided herein can be used to [detect] any type of cancer.” Therefore, claims 16 and 20 of the applicant’s invention would have been prima facie obvious to one of skill in the art at the time of filing of the application, absent evidence to the contrary.
Conclusion
No claims are allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN T STUBBS whose telephone number is (571)272-0340. The examiner can normally be reached M-F 8-5 EST.
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/J.T.S./Examiner, Art Unit 1686 /LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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