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/Action Summary
This action is in response to the papers filed on March 18, 2026.
Claim 9 was canceled in the response. Claims 1-7 and 11-12 are under examination. No other claims are currently pending in the present application.
Any objections and rejections not reiterated below are hereby withdrawn.
The objections to the specification have been withdrawn in view of the amendments to the specification.
The rejections of record under 35 U.S.C. 112(b) have been withdrawn in view of the amendments to the claims.
Priority/Effective Filing Date
This application, filed on July 13, 2023 is a 371 of PCT/CN2021/071822, filed on January 14, 2021.
Drawings
The drawings filed on July 13, 2023 are acceptable.
Claim Interpretation
In the interest of compact prosecution, the indefinite claim terms identified above have been interpreted as follows.
In claim 1, “the coverage” has been broadly interpreted as encompassing methods wherein obtaining sequencing data comprises either: a) obtaining “raw” sequencing data (e.g. fastq files), processing and aligning the reads to a reference genome to obtain read counts per arbitrary intervals (i.e. coverage values per interval) OR obtaining sequencing data that consists of coverage values per arbitrary intervals (i.e. the data has already been processed/aligned) (e.g. sam, bam, bed, bedgraph files).
In claim 4 “relative coverage” has been interpreted as described in the examples in the specification wherein, for each sample, the number of reads counted per arbitrary genomic interval are normalized to the total number of aligned reads for that sample (e.g. reads per million mapped reads).
In claim 1, “obtaining sequencing data of a gene set… wherein the gene set… is the gene set having differences in the coverage at the transcription start site regions in constructing the disease prediction model” has been broadly interpreted as encompassing obtaining sequencing data comprising the gene set having differences in the coverage at the transcription start site regions recited by claim 1. It is noted that the open “comprising” language in the claims does not limit the obtaining step to embodiments wherein sequencing data is only obtained for a selected or determined gene set.
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 1-7, and 11-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
This rejection has been updated as necessitated by the amendments to the claims.
35 U.S.C. 101 requires that to be patent-eligible, an invention (1) must be directed to one of the four statutory categories, and (2) must not be wholly directed to subject matter encompassing a judicially recognized exception. MPEP 2106. Regarding judicial exceptions, “[p]henomena of nature, though just discovered, mental processes, and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work.” Gottschalk v. Benson, 409 U.S. 63, 67 (1972); see also MPEP 2106, part II.
Based upon consideration of the claims as a whole, as well as consideration of elements/steps recited in addition to the judicial exception, the present claims fail to meet the elements required for patent eligibility.
Question 1
The claimed invention is directed to processes that involve natural principles and judicial exceptions.
Question 2A Prong I
The claims are taken to be directed to a natural phenomenon and abstract ideas. Claim 1 as amended is directed to “A cell-free DNA-based disease prediction method, which uses a disease prediction model” comprising steps of:
“for a cell-free DNA sample of an individual to be tested, obtaining sequencing data of a gene set… obtaining coverage of the sequencing data at the transcription start site regions; and inputting the coverage… into the disease prediction model”
wherein the disease prediction model is constructed by a method comprising steps of:
“obtaining sequencing data of cell-free DNA samples of… diseased… and control individuals”, “selecting a gene set having differences in coverage at the transcription start regions between the diseased… and control individuals” and “training a prediction model by inputting the coverage… to construct a disease prediction model”
AND wherein the gene set for which sequencing data is obtained from the cell-free DNA of the subject to be tested is the gene set having differences [between known diseased individuals and known control individuals] (i.e. positive and negative controls).
Claims 2-7 and 11-12 recite further limits on the particular diseases (a cancer; claim 2) (lung cancer, liver cancer, or colorectal cancer; claim 11) , the particular sample type (a body fluid; claim 3) (blood; claim 12), particular data characteristics/analysis steps (the coverage is relative coverage… normalize[ed] according to the overall aligned read count of each sample; claim 4) (the prediction model is a logistic regression model or a random forest model; claim 7).
Claims 1-7 and 11-12 are directed to a process that involves the judicial exception of a law of nature/natural phenomenon (i.e. the natural correlation between the presence/abundance of particular DNA fragments in body fluids (claims 3 and 12, blood) and the presence of a disease state (claims 2 and 4, cancer), and an abstract idea (i.e. a mental step) (i.e. “obtaining sequencing data”, “selecting a gene set having differences in coverage”, and a comparison to control). As amended, claim 1 now includes the method recited in now canceled claim 9 for constructing the disease prediction model comprising steps of obtaining sequencing data from diseased (positive control) and “control” (negative control) individuals; selecting genes having different coverage at the transcription start sites (TSS) in the sequencing data between the two control groups, and training a prediction model by inputting the coverage data for the genes with coverage differences at TSS based upon the comparison between the two control groups.
A correlation that preexists in the human is an unpatentable phenomenon. The association between the presence/abundance of DNA fragments in a cell free DNA sample from a body fluid (e.g. blood) and the presence of a disease (e.g. cancer) is a law of nature/natural phenomenon.
Steps requiring “obtaining sequencing data” and “selecting a gene set having differences in coverage” encompass mental steps comprising reading reports and comparing to a control (e.g. inspecting tables of coverage values such as bedgraph files or other tabular data and selecting intervals with different coverage values from a control sample).
These “obtaining” and “selecting” steps are each mental steps that could be performed by a human using mental steps or basic critical thinking which are abstract ideas.
The steps requiring predicting whether the individual to be tested suffers from a disease (i.e. diagnosing) based upon the presence/abundance (i.e. coverage) of the DNA fragments in a cell free DNA sample taken from the individual amount to no more than an “instruction to apply the natural law” through mental steps. Even if the step requires something more, such as to verbalize the discovery of the natural law, this mere verbalization is not an application of the natural law to a new and useful end. These steps fail to provide the “practical assurance” sought by the Prometheus Court that the “process is more than a drafting effort designed to monopolize the law of nature itself.”
Claim 1 requires comparing the abundance of the DNA fragments in a cell free DNA sample to control samples. A comparison to a control is an abstract idea. (See MPEP 2106.04(a)(2)(III)(A); claims to “comparing BRCA sequences such that the comparison steps can practically be performed in the human mind, University of Utah Research Foundation v. Ambry Genetics, 774 F.3d 755, 763, 113 USPQ2d 1241, 1246 (Fed. Cir. 2014).
Question 2A Prong II
The exception is not integrated into a practical application of the exception. The claims do not recite any additional elements that integrate the exception into a practical application of the exception. While the claims recite “training a prediction model by inputting the… data… to construct a disease prediction model” and “inputting the… data… into the disease prediction model to predict whether the individual… suffers from the disease”. These steps are not an integration of the exception into a practical application. Rather, the “training a prediction model” step constitutes mere data analysis required to perform the claimed method. Similarly, the step of “inputting the… data…to predict” merely constitutes data analysis required to observe/identify the presence of the natural law/correlation.
Question 2B
The second step of Alice involves determining whether the remaining elements, either in isolation or combination with the other non-patent eligible elements, are sufficient to “transform the nature of the claims into a patent-eligible application” Alice, 134 S. Ct. at 2355 (quoting Mayo, 132 S. Ct. at 1297).
The claims are not sufficiently defined to provide a method which is significantly more than a statement of a natural principle for at least these reasons:
The claims do not add a specific limitation other than what is well-understood, routine, and conventional in the field.
Steps directed to obtaining sequencing data from cell-free DNA samples from negative and positive control individuals (e.g. those without and with a particular disease) and from individuals to be tested are mere data gathering steps that amount to extra solution activity to the judicial exception. These steps tell users to collect the sequencing data by any method known in the art. These steps are recited at an extremely high level of generality.
Steps directed to inputting sequencing data to train or test or use disease prediction models encompassing logistic regression or random forest models are similarly recited at an extremely high level of generality and are well-understood, routine, and conventional in the field.
The prior art, for example, Raman et al., “Shallow whole-genome sequencing of plasma cell-free DNA accurately differentiates small from non-small cell lung carcinoma” Genome Medicine (2020) 12:35, published April 21, 2020 and Ulz et al., “Inferring expressed genes by whole-genome sequencing of plasma DNA” Nature Genetics (2016) 48:10, published August 29, 2016 each teach collecting whole-genome sequencing data (i.e. comprising coverage at transcription start sites) for disease and control samples, selecting genes with differential coverage comprising coverage at the TSS, and training prediction models using support vector machines (Ulz et al., page 1279, column 2, paragraph 7), “prediction by SVMs” or random forest, support vector machine, logistic regression, elastic net, and lasso regularization (Raman et al., page 4, column 1, paragraph 4- page 4, column 2, paragraph 1).
Similarly, Snyder et al., US 2020/0199685 A1 (published June 25, 2020) teach sequencing cell free DNA from healthy and diseased humans, calculating coverage at cfDNA fragment endpoints at transcription start sites, and training/using prediction models based upon said coverage data to identify individuals with the given disease (Snyder et al., paragraphs 0050-0066). Snyder further teaches that the prediction models comprise logistic regression models (Snyder et al., paragraph 0269).
The claims do not require the use of any particular non-conventional reagents.
When recited at this extremely high level of generality, there is no meaningful limitation that distinguishes this step from well understood, routine, and conventional activities prior to applicant’s invention and at the time the application was filed.
For these reasons, the claims are rejected under section 101 as being directed to non-statutory subject matter.
Response to arguments
The arguments and assertions in the response have been thoroughly reviewed in view of the amended claims and are not persuasive for the following reasons.
The response asserts the following steps “describe a human-designed analytical and computational workflow… not a natural phenomenon” and “the predictive model and the selected gene set are artificial constructs” demonstrate that “the claim does not seek to patent naturally occurring DNA or a natural correlation”:
Obtaining sequencing data of a defined set of genes from a cell-free DNA sample.
As described above, the claims do not require sequencing only the defined gene set, but encompass embodiments wherein the data are simply downloaded, or filtered from pre-existing or whole-genome sequencing data.
As described above, “obtaining data” necessary to observe a natural correlation (the abundance of particular DNA fragments in blood) is a generically recited extra-solution activity that encompasses any method of “obtaining data” including inspecting data from a database and does not require any particular “laboratory processing” steps as asserted by the response.
Calculating coverage at transcription start site regions.
As described above, this generically recited “calculating” step by any method amounts to a mental step at least because it encompasses simple counting of reads assigned to known genome intervals by any generic alignment method.
Inputting coverage into a trained prediction model.
As described above, steps of obtaining sequencing data from controls and selecting genes with differences in the sequencing data between the controls (i.e. comparing controls) are observations of a natural correlation that are so broadly recited as to be performed in the human mind and by any method (as in the cited Univ. Utah v. Ambry Genetics case).
Further, as described above, the generically recited “training a prediction model” for “predicting a disease” is neither integrated into any particular application (i.e. predicting any disease, or any cancer) nor is more than a statement of that which is routine and conventional in the relevant art (see prior art references discussed above which train prediction models on DNA sequencing coverage data from positive and/or negative cancer controls for diagnosing/differentiating (i.e. predicting) the presence of various cancers (i.e. a disease).
The response asserts “claim 1 recites a specific technological implementation of disease prediction using sequencing data, rather than a generalized concept of analyzing information” and “the claim is narrowly focused on sequencing data derived from cell-free DNA, coverage measurements specifically at [TSS] and a prediction model trained using [coverage at TSS]”
This assertion is likewise not persuasive for the reasons described above and in the updated 101 rejection. Briefly, the claims precisely preempt all forms of comparing the presence/abundance of cell-free TSS fragments (i.e. a subset of naturally occurring DNA in blood) between controls with and without any possible disease based upon the presence/abundance of said naturally occurring DNA fragments. The recitation of generic “training/trained prediction models” likewise appears to encompass any method of application of the identified natural correlation (the presence/abundance of a subset of DNA fragments in blood) and the presence of any disease.
The response further asserts the claims recite significantly more than the recited judicial exceptions. This assertion is not persuasive. As described above, the identified limitations: “generating sequencing data” is not a practical application, but is a generically recited data gathering step necessary to observe the natural correlation; “calculating coverage” is a generically recited data analysis/mental step necessary to observe the natural correlation; “selecting a gene set based upon differential coverage (i.e. difference in abundance) between (control groups)” is a mental process, and “training and applying a predictive model” is not a particular application but rather a generic instruction to “apply” an observation of the natural correlation in a generic way to identify individuals in which the natural correlation is present.
Finally the response asserts “the claim is directed to a practical technological application”. This assertion is likewise not persuasive for all of the reasons already discussed above regarding the particularity of the application (observe the presence/abundance of a subset of DNA fragments in blood that are naturally correlated with the presence of any disease; any cancer; or any of a subset of cancers for which the recited natural correlation and process steps are well understood and conventional in the art).
For all of the reasons above and those already of record, the claims are/remain rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
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 1-7, and 11-12 are rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C. 102(a)(2) as being anticipated by Snyder et al., US 2020/0199685 A1 (published June 25, 2020).
This rejection has been updated as necessitated by the amendments to the claims.
Regarding claim 1, Snyder et al. teach methods for constructing and using disease prediction models (Snyder er al., Abstract), comprising sequencing cell free DNA fragments from healthy subjects and subjects having a disease encompassing cancer (or a targeted subset of fragments comprising transcription start sites) (Snyder et al., paragraph 0064-0065). Snyder et al. further teach sequencing cell free DNA from healthy and diseased humans, calculating coverage at cfDNA fragment endpoints at transcription start sites, and training/using prediction models based upon said coverage data to identify individuals with the given disease (Snyder et al., paragraphs 0050-0066). Snyder further teaches that the prediction models comprise logistic regression models (Snyder et al., paragraph 0269). Snyder et al. further teach obtaining sequencing data from an individual to be tested comprising sequencing data at transcription start sites, and inputting the sequencing data into the prediction model to diagnose the individual (Snyder et al., paragraph 0249-0262).
Regarding claims 2 and 11, Snyder et al. teach the disease is cancer (Snyder et al., paragraph 0064), the cancer is lung adenocarcinoma or colorectal cancer (i.e. lung or colorectal cancer), and the prediction includes diagnosis of cancer (i.e. early screening of tumors or detection of tumor recurrence) (Snyder et al., paragraph 0002).
Regarding claims 3 and 12, Snyder et al. teach the cell free DNA samples are derived from circulating plasma (i.e. blood), urine, and other bodily fluids (Snyder et al., paragraph 0003).
Regarding claim 4, Snyder et al. teach the coverage of the cell-free DNA on the genome is normalized to correct for differences in sequencing depth or coverage (i.e. the coverage is the relative coverage) (Snyder et al., paragraph 0156).
Regarding claim 5, Snyder et al. teaches isolated cell free DNA is targeted to transcription start sites (i.e. the fragments comprise the TSS) (Snyder et al., paragraph 0168) and the cfDNA fragments are shorter than about 200 base pairs or less than 120 bp (Snyder et al., paragraph 0166-0167) (i.e. the fragments comprising the TSS are within 100 bp upstream and downstream of the TSS).
Regarding claim 6, Snyder et al. teaches 10 non-overlapping genomic regions (overlapping ~40 genes) were selected to predict patient plasma samples from patients with lung adenocarcinoma (i.e. lung cancer), healthy controls, and breast ductal carcinoma (Snyder et al., paragraphs 0233-0237).
Regarding claim 7, Snyder et al. teaches the prediction model is a logistic regression model (Snyder et al., paragraph 0269).
Response to arguments
The response asserts that Snyder et al. do not teach all of the limitations of amended claim 1. These assertions have been thoroughly reviewed and are not persuasive as described for each alleged non-anticipated limitation below.
Selecting a gene set based on differential transcription start site coverage between control groups.
Using the same differential gene set for disease prediction in a test individual
As discussed above, the claim language “comprises” obtaining coverage of the sequencing data at transcription start sites for the genes in the gene set… wherein the gene set is the gene set having differences in coverage at TSS (observed between the control groups). This claim language does not require that sequencing data is only obtained for the fragments corresponding to the transcription start sites for the selected gene set.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., the sequencing data obtained for the individual to be tested is restricted to only the transcription start sites of the genes having differences in TSS coverage in cfDNA compared between two control groups) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Claims 1-4, 7 and 11-12 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Raman et al., “Shallow whole-genome sequencing of plasma cell-free DNA accurately differentiates small from non-small cell lung carcinoma” Genome Medicine (2020) 12:35, published April 21, 2020.
This rejection has been updated as necessitated by the amendments to the claims.
Regarding claim 1, Raman et al. teach collecting whole-genome sequencing data (i.e. comprising coverage at transcription start sites) for disease and control samples, (Raman et al., page 2-3 bridging paragraph), selecting genes with differential coverage comprising coverage at the TSS (Raman et al., page 3, column 2-page 4, column 1), and training/diagnosis using predictive modeling comprising random forest, support vector machine, logistic regression, elastic net, and lasso regularization (Raman et al., page 4, column 1, paragraph 4- page 4, column 2, paragraph 1).
Furthermore, Raman et al. teach using a disease prediction model constructed by the methods described above to differentially diagnose subtypes of lung cancer based upon sequencing data comprising TSS coverage data (Raman et al., page 7, paragraph 1).
Regarding claims 2 and 11, Raman et al. teach diagnosing lung cancer (Raman et al., page 9, column 1-2 bridging paragraph).
Regarding claims 3 and 12, Raman et al. teach sequencing cell-free DNA from blood (Raman et al., page 3, column 1, paragraph 3-column 2, paragraph 2).
Regarding claim 4, Raman et al. teach the cfDNA sequencing coverage data is normalized (i.e. is relative coverage) (Raman et al., page 3, column 2, paragraph 3).
Regarding claim 7, Raman et al. teach predictive modeling using logistic regression and random forest models (Raman et al., page 4, column 1-2 bridging paragraph).
Response to arguments
The response asserts that Raman does not meet the claim limitations of amended claim 1 because Raman “analyzes genome-wide copy number alterations derived from shallow whole-genome sequencing of plasma cfDNA… uses genomic bins across the genome… rather than focusing on transcription start site regions.
This assertion has been thoroughly reviewed and is likewise not persuasive at least because the claim language “comprises” obtaining coverage of the sequencing data at transcription start sites for the genes in the gene set… wherein the gene set is the gene set having differences in coverage at TSS (observed between the control groups). This claim language does not require that sequencing data is only obtained for the fragments corresponding to the transcription start sites for the selected gene set.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., the sequencing data obtained for the individual to be tested is restricted to only the transcription start sites of the genes having differences in TSS coverage in cfDNA compared between two control groups) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Claims 1-5 and 11-12 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ulz et al., “Inferring expressed genes by whole-genome sequencing of plasma DNA” Nature Genetics (2016) 48:10, published August 29, 2016.
Regarding claim 1, Ulz et al. teach obtaining cfDNA sequencing data from blood of individuals with various cancers and healthy control individuals (Ulz et al., page 1273, column 2, paragraph 2, and page 1279, column 1, paragraph 2), identifying transcription start sites having different coverage in cancer relative to control (page 1279, column 2, paragraph 5 “TSS Profiles”), and training a prediction model (Ulz et al., page 1279, column 2, paragraph 7).
Furthermore, Ulz et al. teach diagnosis of cancer (Ulz et al., page 1276-1277 bridging paragraph), including colon, prostate, breast, and lung cancer (Ulz et al., page 1279, column 1, paragraph 2) using a trained predictive model based upon cfDNA coverage at transcription start site regions.
Regarding claims 2 and 11, Ulz et al. teach the disease prediction includes diagnosis of cancer (Ulz et al., page 1276-1277 bridging paragraph), including colon, prostate, breast, and lung cancer (Ulz et al., page 1279, column 1, paragraph 2).
Regarding claims 3 and 12, Ulz et al. teach collecting cfDNA from blood (Ulz et al., page 1279, column 1, paragraph 3).
Regarding claim 4, Ulz et al. teach the coverage is relative coverage (Ulz et al., figure 5C and page 1279, column 2, paragraph 5 “TSS Profiles”).
Regarding claim 5, Ulz et al. teach the data is analyzed in windows/bins surrounding TSS spanning TSS+/- 1kb (Ulz et al., figure 5C, see below).
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Ulz Figure 5C, B13: cancer cfDNA
Response to arguments
The response asserts that Ulz fails to disclose each and every element arranged as required by amended claim 1. The response specifically asserts “Ulz does not disclose… selecting a gene set based on differential transcription start site coverage between diseased and control individuals; constructing a disease prediction model; training a model using a gene set selected based on differential TSS coverage; using the same differential gene set during disease prediction”
These assertions have been thoroughly reviewed and are not persuasive. As described in the updated 102 rejection above, Ulz et al., explicitly teach “identifying regions at TSS where nucleosome occupancy results in different read depth coverage for expressed and silent genes, and by machine learning for gene classification… plasma DNA (cell free DNA) read depth patterns from healthy donors reflected the signature of hematopoietic cells… whereas… in plasma DNA from patients with cancer having metastatic disease, expressed cancer driver genes in regions with somatic copy number gains were classified with high accuracy (Ulz et al., abstract). Furthermore, the assertion that Ulz et al. does not identify genes whose TSS coverage differs between healthy and diseased individuals and does not select gene sets based on such differences is not persuasive. Ulz et al., Figure 5 (reproduced in full below with highlighted caption) demonstrates identifying a subset of TSS having different coverage in cancer cfDNA relative to healthy controls.
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Conclusion
No claim is allowed.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/Z.M.T./Examiner, Art Unit 1682
/WU CHENG W SHEN/Supervisory Patent Examiner, Art Unit 1682
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