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 .
Election/Restriction
Applicant’s election without traverse of Group I, claims 1-2, 9-10, 12-13, 17, 27-28, and 35-39 in the reply filed on September 30, 2025 is acknowledged.
Claim Status and Action Summary
This action is in response to the papers filed on January 29, 2026.
Claims 1, 9-10, 12, 17, 27-28, 35-39, 56-61, and newly added claim 109 are currently pending in the present application. Claims 56-61 are withdrawn as directed to a non-elected invention. Claims 1-, 9-10, 12, 17, 27-28, 35-39, and 109 are under examination.
Any objections and rejections not reiterated below are hereby withdrawn.
The rejections of record under 35 U.S.C. 112(b) have been withdrawn in view of the amendments to the claims including: adding method steps that refer back to the preamble of the claims, removing indefinite relative terms, and arguing persuasively that the claim term “predicting” should be interpreted based upon the common usage in the “machine learning and statistical arts [wherein] “prediction” refers to the output of a classification model that determines which class or category a sample belongs to based on observed features”; and “The “prediction” is the classification of whether the subject has cancer based on the detected difference in fragmentation profiles.”
The rejections of record of claims 1, 9, 10, 12 and 17 under 35 U.S.C. 102 over Cristiano, Underhill, Mouliere, Abdueva, or Velculescu have been withdrawn in view of the substantial claim amendments to independent claim 1.
The rejection of record of claims 28 and 35 under 35 U.S.C. 102 over Cristiano have been withdrawn in view of applicant’s persuasive argument that Cristiano’s method comprising “the machine learning classification model (stochastic gradient boosting)” is not the same as “fitting a finite mixture of distributions”.
The rejection of record of claims 1, 10, and 12 under 35 U.S.C. 103 over Abdueva alone has been withdrawn in view of the substantial claim amendments to independent claim 1.
Priority
The present application, filed on February 2, 2023, is a 371 of PCT/US2021/046272, filed on August 17, 2021, and claims priority to U.S. Provisional patent application Nos: 63/163434, filed on March 19, 2021 and 63067244, filed on August 18, 2020.
Drawings
The drawings filed on February 2, 2023 are acceptable.
Claim Interpretation
The examiner has accepted the definition of the claim term “predicting” asserted in the response in view of persuasive arguments against the previous 112(b) rejection of record on the grounds that the term should be interpreted according to its usage in the machine learning and statistical arts. The term “predicting” has therefore been interpreted as being defined as “the classification of whether the subject has cancer based on the detected difference in fragmentation profiles.” As such, the claims explicitly do not encompass embodiments wherein “prediction” means making a determination that a subject will, in the future, develop cancer or that a subject is at a particular risk of developing cancer in the future.
Claim Objections
Claim 35 is objected to because of the following informalities: The word “or” is omitted from the recited alternative upper bounds of selected cfDNA fragment size: “greater than 200, 250, 300, 350 bp.” Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following 112(b) rejections are new grounds of rejection necessitated by the amendments to the claims.
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.
Claims 1, 9, 10, 12, 17, and 109 are 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 1 recites a step of “processing a sample… into sequencing libraries”. It is unclear whether the claim requires preparing multiple sequencing libraries from a single sample (e.g. technical replicates, subsamples of particular ranges of fragment sizes, etc.) or whether the claim requires processing multiple samples into sequencing libraries where each sample is processed into a single library.
Claims 9, 10, 12, 17, and 109 are indefinite because they depend from, and thus include the indefinite limitation(s) of the base claim.
Claim Rejections - 35 USC § 101
This rejection has been updated as necessitated by the amendments to the claims.
Claims 1-2, 9-10, 12-13, 17, 27-28, 35-39, and 109 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
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. M.P.E.P.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 M.P.E.P. 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.
Step 1
The claimed invention is directed to a process that involves a natural principle and judicial exceptions.
Step 2A prong I
The claims are taken to be directed to a natural phenomenon and mental processes.
Claim 1 is directed to a method for determining the cancer status of a subject comprising (a) processing a sample… comprising cfDNA… into sequencing libraries, (b) sequencing the libraries, (c) mapping the sequences to a windowed genome, (d) analyzing the length of the mapped sequences in each of the windows, (e) analyzing a shape of a curve of cfDNA fragment density… comprising fitting a finite mixture of truncated normal distributions to the counts of fragment sizes between 105 bp and 220 bp…(f) comparing the shape of the curve… to a reference curve shape from a healthy subject, (g) determining the subject has cancer when the shape of the curve differs from the reference curve shape, and administering a cancer treatment to the subject.
Claims 9-10, 12, and 17 further require specific analysis steps comprising size selection and methods of data processing/analysis.
Claim 27 is directed to a method of “predicting” cancer status comprising (a) analyzing a shape of a curve of cell free DNA (cfDNA) fragment size density in a sample from a subject, (b) comparing said shape to a reference shape, and (c) detecting cancer when the shapes are different.
Claims 28, 35, 38, and 39 further require specific analysis steps comprising size selection and methods of data processing/analysis.
Claims 36 and 37 further require that the reference shape is obtained from a healthy subject and the cancer is selected from a particular group of cancers, respectively.
Claim 109 depends from claim 1 and provides a list of alternative cancer treatments that may be administered to the subject.
Claims 1 and 27 (and their respective dependent claims) are directed to a process that involves the judicial exception of a law of nature (i.e. the natural correlation between the distribution of fragment lengths of cfDNA in a particular tissue/fluid (for example, human blood plasma) and the presence of cancer cells in the organism from in the cfDNA is observed), abstract ideas (i.e. comparing the shapes of curves of observed fragment length density between a subject and a healthy control), and mental processes (i.e. excluding fragment lengths, quantifying the shape, fitting distributions).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the reasons that follow.
A correlation that preexists in the human is an unpatentable phenomenon. The association between the relative abundances of nucleic acids of particular lengths in a human (i.e. the shape of the curve of cfDNA fragment size density) and the presence of cancer cells in the human is a law of nature/natural phenomenon. The “analyzing” and “determining” steps recited by claims 1-2, 9-10, 12-13, 17, 27-28 and 35 amount to no more than an “instruction to apply the natural law”. Furthermore, these steps amount to no more than a mental step. 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 law of nature to a new and useful end. The “analyzing” and “detecting” steps recited by claims 1 and 27, respectively, do not require the process user to do anything in light of the correlation. 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.”
Claims 1 , 27, 36, and 38-39 recite the abstract idea of comparing a shape of a curve of cfDNA fragment size density to a control. A comparison to control is an abstract idea (See MPEP 2106.04(a)(2)(III)(A); claims to “comparing BRCA sequences and determining the existence of alterations,” where the claims cover any way of 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).
Claim 1 as amended recites abstract ideas (mathematical calculations implemented on a generic computer): “analyzing, using a computer, the windows of mapped sequences to determine cfDNA fragment lengths and “analyzing a shape of a curve of cfDNA fragment size density… compris[ing] fitting a finite mixture of truncated normal distributions to the counts of fragment sizes…”
Step 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.
Claim 1 recites “administering a cancer treatment to the subject”. This step does not recite any particular treatment that integrates the exception into a new and useful end. Indeed, the specification describes at paragraphs 0056-0059 that the terms “a cancer” and “a cancer treatment” are effectively unlimited in scope encompassing any type of cancer at any stage in any subject and any “appropriate” cancer treatment. Therefore, the claims as informed by the specification do not recite any particular integration of the judicial exception (i.e. a statement of the natural correlation between the presence of circulating tumor DNA in cfDNA and the presence of cancer cells in a subject) into any particular treatment based on the judicial exception. Similarly, claim 109, which depends from claim 1, lists several broad categories of potential cancer treatments that do not integrate into any particular application of the judicial exceptions to any particular cancer. Rather, these limitations refer a skilled artisan to a general field of use for the observed natural correlation (See MPEP 2106.04(d)(2)(a).
Claims 2, 9-10, 12, 13, 17, 28, 35, and 38-39 recite various steps directed to particular methods of analyzing or collecting the data (i.e. the fragment lengths of cfDNA) from the subject and/or the control. These steps are not an integration into a particular application. Rather these steps amount to mere data gathering and analysis necessary to perform the claimed methods of “determining” or “predicting” cancer status.
Regarding mathematical analysis steps recited in addition to another judicial exception:
Because a judicial exception is not eligible subject matter, Bilski, 561 U.S. at 601, 95 USPQ2d at 1005-06 (quoting Chakrabarty, 447 U.S. at 309, 206 USPQ at 197 (1980)), if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application. See, e.g., RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) ("Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract"); Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) (eligibility "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself."). For a claim reciting a judicial exception to be eligible, the additional elements (if any) in the claim must "transform the nature of the claim" into a patent-eligible application of the judicial exception, Alice Corp., 573 U.S. at 217, 110 USPQ2d at 1981).
Step 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 the following reasons.
The claims do not add a specific limitation other than what is well-understood, routine, and conventional in the field. Steps directed to “processing [samples comprising cfDNA] into sequencing libraries… sequencing… mapping… fragments to a genome… and… determining cfDNA fragment lengths”, excluding particular ranges of fragment sizes, and comparing/analyzing the shape(s) of curves (i.e. statistical frequency distributions) of cfDNA fragment size density are mere data gathering steps that amount to extra solution activity to the judicial exception.
Claims 1-2, 9-10, 12, 27-28, and 35-39 do not require the use of a particular method to obtain the cfDNA fragment size density curves. Rather, these claims encompass any method comprising DNA sequencing, gel electrophoresis, microfluidics, etc. by which naturally-occurring DNA fragments may be separated by length or by which the lengths of individual DNA molecules may be inferred or directly observed (i.e. sequencing/mapping to a reference genome).
The prior art, for example, Underhill et al., “Fragment Length of Circulating Tumor DNA” PLoS Genetics 12(7):e1006162, published July 18, 2016 and Cristiano et al., “Genome-wide cell-free DNA fragmentation in patients with cancer”, Nature Vol 570, p 385-404, published May 29, 2019, each teach methods comprising observing differences in the fragment length of cfDNA in humans with cancer compared to healthy controls (Underhill et al., abstract and figure 3) (Cristiano et al., abstract and figure 1).
As amended, claim 1 requires that “analyzing the shape of the curve of cfDNA fragment size density comprises fitting a finite mixture of truncated normal distributions to the counts of fragment sizes…” This mathematical calculation is likewise well-understood, routine and conventional as evidenced by the prior art using similar mathematical steps to analyze biological distributions. For example, Yang et al., “Continuous-Trait Probabilistic Model for Comparing Multi-species Functional Genomic Data”, Cell Systems 7, 208-218 (supplement, page e6, paragraph 6), Feng et al., “mRIN for direct assessment of genome-wide and gene-specific mRNA integrity from large-scale RNA-sequencing data”, published August 3, 2015 and MacDonald, “Letter to the editor: Fitting truncated normal distributions” Statistical Methods in Medical Research 2018, vol. 27(12) 3835-3838 teach methods for analyzing various univariate distributions comprising fitting a finite mixture of truncated normal distributions in: biological data (MacDonald et al., page 3787), functional genomic data (Yang et al., supplement, page e6, paragraph 6), and distributions of degraded nucleic acids relative to control samples (Feng et al., page 8-9 and Figure 2).
Furthermore, the claims do not require the use of any particular non-conventional reagents. When recited at this very high level of generality, there is no meaningful limitation that distinguishes these steps 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.
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.
Claims 27, and 36-39 are rejected under 35 U.S.C. 102(a)(1) as being clearly anticipated by Cristiano et al., “Genome-wide cell-free DNA fragmentation in patients with cancer”, Nature Vol 570, p 385-404, published May 29, 2019.
This rejection has been updated as necessitated by the amendments to the claims.
Regarding claim 27, Cristiano et al. teach methods of “screening, early detection, and monitoring of human cancer” (i.e. determining/predicting cancer status) comprising “evaluat[ing] fragmentation patterns (i.e. analyzing a shape of a curve) of cell-free DNA across the genome” in subjects with cancer compared to controls (Cristiano et al., abstract and any of figures 1-3).
Regarding claim 36, Cristiano et al. teach the reference is cfDNA fragment size density in a sample from a healthy subject (Cristiano et al., figure 3)
Regarding claim 37, Cristiano et al. teach applying the methods discussed above to colorectal cancer, lung cancer, breast cancer, gastric cancer, pancreatic cancer, bile duct cancer, and ovarian cancer (Cristiano et al., figure 3; see below)
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Regarding claim 38, Cristiano et al. teach comparing the cfDNA fragment size density curve to a reference over the whole genome (Cristiano et al., figure 3 a and b).
Regarding claim 39, Cristiano et al. teach comparing the cfDNA fragment size density curve to a reference over subgenomic intervals (Cristiano et al., figure 3c).
Response to arguments
The response primarily asserts that Cristiano does not teach the limitation “…wherein analyzing the shape of the curve of cfDNA fragment size density comprises fitting a finite mixture of truncated normal distributions to the counts of fragment sizes between 105 bp and 220 bp…” as recited in claim 1 as amended, and that the Cristiano’s method comprising “the machine learning classification model (stochastic gradient boosting)” is not the same as “fitting a finite mixture of distributions”.
The arguments regarding newly added limitations to claim 1 are not persuasive regarding the rejections of record over claim 27 (which is an independent claim) and its dependents.
The argument that Cristiano’s statistical method is not the same as the “fitting” step recited in claim 28 (and thus its dependent claim 35) are persuasive and the corresponding 102 rejections over these claims have been withdrawn.
Claims 27 and 36-39 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Underhill et al., “Fragment Length of Circulating Tumor DNA” PLoS Genetics 12(7):e1006162, published July 18, 2016.
This rejection has been updated as necessitated by the amendments to the claims.
Regarding claim 27, Underhill et al. teach determining/predicting cancer status comprising analyzing a shape of a curve of cfDNA fragment size density compared to that of a healthy control (Underhill et al., figure 3, see below).
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Regarding claim 36, Underhill et al. teach the reference curve is a distribution of cfDNA fragment size density measured in a sample obtained from a healthy subject (Underhill et al., figure 3).
Regarding claim 37, Underhill et al. teach various cancers are characterized by differences in cfDNA fragment size density compared to healthy controls including: colorectal cancer (Underhill et al., page 13, paragraph 1) and lung cancer (Underhill et al., figure 4).
Regarding claim 38, Underhill et al. teach comparing cfDNA fragment size densities over the whole genome (Underhill et al. figure 3).
Regarding claim 39, Underhill et al. teach comparing cfDNA fragment size densities for fragments comprising a cancer-associated mutation (BRAF V600E) (i.e. a subgenomic interval) (Underhill et al., figure 3C).
Response to arguments
The response argues that Underhill does not disclose fitting a finite mixture of truncated normal distributions to fragment size counts or analyzing curve shape via statistical distributions.
The rejection of record of claim 1 over Underhill has been withdrawn in view of the amendments.
The arguments in the response against the rejections of claims 27 and 36-39 appear to rely on the limitations added to claim 1 and on “analyzing curve shape via statistical distributions”. These arguments have been thoroughly reviewed and are not persuasive because these features are not recited in claims 27 and 36-39. 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 27, and 36-39 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Mouliere et al., “Enhanced detection of circulating tumor DNA by fragment size analysis” Science Translational Medicine. 10, eaat4921 (published November 7, 2018).
This rejection has been updated as necessitated by the amendments to the claims.
Regarding claim 27, Mouliere et al. teach methods of determining/predicting cancer status in a subject comprising analyzing the shapes of cfDNA fragment size density curves in samples from a subject and healthy controls (Mouliere et al., Abstract and figure 1, see below).
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Regarding claim 36, Mouliere et al. teach the reference curve is measured in a sample obtained from a healthy subject (Mouliere et al., Abstract and figure 1).
Regarding claim 37, Mouliere et al. teach various cancers are characterized by differences in cfDNA fragment size density compared to healthy controls including: colorectal cancer, lung cancer, breast cancer, ovarian cancer (Mouliere et al., figure 1) and pancreatic cancer (Mouliere et al., page 3, column 1, paragraph 2).
Regarding claim 38, Mouliere et al. teach comparing the shape of the curves (i.e. the distribution of fragment sizes) over the whole genome (Mouliere et al., figure 1)
Regarding claim 39, Mouliere et al. teach processing cfDNA samples into sequencing libraries, performing “low-pass whole genome sequencing (0.4x)” (i.e. “low-coverage whole genome sequencing”), mapping the fragments to a reference genome, and analyzing cfDNA fragment lengths of the mapped sequences (Mouliere et al., Abstract) Mouliere et al. further teach mapping and analyzing fragment length distributions in windows across the genome and comparing the shape of the curves (i.e. the distribution of fragment sizes) over windows spanning the whole genome (i.e. subgenomic intervals) (Mouliere et al., figure 3).
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Response to arguments
The response argues that Mouliere does not anticipate claim 1 as amended because “Mouliere do not disclose fitting finite mixture of distributions and analyzing “shape of curve” via mixture model fitting”.
The rejection of record of claims 1, 13, and 17 have been withdrawn in view of the amendments to the claims.
These arguments have been thoroughly reviewed and are not persuasive regarding claims 27 (which is an independent claim) and its dependent claims 36-39, because limitations regarding “analyzing a shape of a curve via mixture model fitting… and finite mixture models” are not recited by the claims.
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 27-28, and 36-39 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Abdueva et al., US 2019/0287645 A1, published September 19, 2019.
This rejection has been updated as necessitated by the amendments to the claims.
Regarding claim 27, Abdueva et al. teach determining/predicting cancer status comprising analyzing/comparing cell-free DNA fragment size distributions from a subject and a healthy control (Abdueva et al., paragraph 0050).
Regarding claim 28, Abdueva et al. teach analyzing the shape of the cfDNA curves comprises estimating a multimodal density of the fragment density profiles (i.e. fitting a finite mixture of distributions to counts of fragment sizes) (Abdueva et al., paragraphs 0011 and 0156).
Regarding claim 36, Abdueva et al. teach the reference curve shape is obtained from a sample of a healthy subject (Abdueva et al., paragraph 0050).
Regarding claim 37, Abdueva et al. teach the cancer to be determined/predicted includes: colorectal cancer, lung cancer, breast cancer, and pancreatic cancer (Abdueva et al., paragraph 0235).
Regarding claims 38 and 39, Abdueva et al. teach analyzing cfDNA fragment size density over the whole genome, applying a multi-parametric analysis, and identifying localized genomic regions that contain patterns/clusters of significant variation (i.e. subgenomic intervals) (Abdueva et al., paragraphs 0258-0260).
Response to arguments
The response asserts that Abdueva does not anticipate claim 1 as amended because “Abdueva discloses mixture models, but NOT in the context of fitting truncated normal distributions to fragment size counts between 105 bp and 220 bp”. The response further argues that the bivariate mixture models analyze fragment start position and fragment length together as a two-dimensional distribution to identify anomalous fragments distributions to assert that the method of Abdueva is fundamentally different from the claimed univariate mixture of truncated normal distributions fit to counts of fragment sizes within the specific range of 105-220 bp.
The 102 rejection of record over independent claim 1 and its dependents have been withdrawn in view of the amendments to claim 1.
Regarding independent claim 27 and dependent claims therefrom, these arguments have been reviewed and are not persuasive. Claims 27, 28, and 35-39 do not recite the features emphasized in the arguments (i.e. “fitting truncated normal distributions to fragment size counts between 105 bp and 220 bp…”).
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).
Furthermore, the assertion that the methods taught by Abdueva are “fundamentally different” from the claimed methods because they encompass bivariate mixture models that take into account fragment start position (i.e. location in a genome window) and fragment length are not persuasive. Abdueva explicitly teaches “the use of a uni-parametric or multi-parametric analysis to determine a plasma deregulation score… [that] varies across the genome… based on, e.g., the distribution of lengths of fragments that overlap with each position of a portion or all of the genome” (Abdueva et al., paragraph 0174), wherein a “plasma deregulation score” can be indicative of a tumor burden or a disease burden (i.e. “indicative of” or “predicting” cancer status.
Claims 27, and 36-39 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Velculescu et al., WO 2019/222657 A1, published November 21, 2019.
This rejection has been updated as necessitated by the amendments to the claims.
Regarding claim 27, Velculescu et al. teach “DNA Evaluation of Fragments for early Interception (DELFI)”, a method of identifying the presence of cancer in a subject comprising analyzing differences in cfDNA fragmentation profiles between the subject and a healthy control (Velculescu et al., page 2, line 9- page 3, line 10). Velculescu et al. further teach administering a cancer treatment to the subject (Velculescu et al., page 4, lines 25-31).
Regarding claim 36, Velculescu et al. teach the reference curve is obtained from a healthy subject (Velculescu et al., page 3, line 28-page 4 line 5).
Regarding claim 37, Velculescu et al. teach “the cancer can be colorectal cancer, lung cancer, breast cancer, bile duct cancer, pancreatic cancer, gastric cancer, or ovarian cancer.” (Velculescu et al., page 4 lines 19-20).
Regarding claim 38, Velculescu et al. teach “the step of comparing can include comparing the cfDNA fragmentation profile to a reference cfDNA fragmentation profile in windows across the whole genome” (Velculescu et al., page 4 line 20-22).
Regarding claim 39, Velculescu et al. teach “The step of comparing can include comparing the cfDNA fragmentation profile to a reference cfDNA fragmentation profile over a subgenomic interval” (Velculescu et al., page 4, line 22-24).
Response to arguments
The arguments presented against the 102 rejections of record over Velculescu et al. rely on the feature “fitting a finite mixture of truncated normal distributions to the counts of fragment sizes between 105 bp and 220 bp…” and “finite mixture models”.
These arguments have been reviewed and are not persuasive because claims 27 and 36-39 do not recite the features emphasized in the arguments (i.e. “fitting truncated normal distributions to fragment size counts between 105 bp and 220 bp…”).
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).
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.
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 1, 9, 10, 12, 17, 27, 28, 35-39, and (newly added claim) 109 are rejected under 35 U.S.C. 103 as being unpatentable over Cristiano et al., “Genome-wide cell-free DNA fragmentation in patients with cancer”, Nature Vol 570, p 385-404, published May 29, 2019 in view of Abdueva et al., US 2019/0287645 A1, published September 19, 2019, Feng et al., “mRIN for direct assessment of genome-wide and gene-specific mRNA integrity from large-scale RNA-sequencing data”, published August 3, 2015, and MacDonald, “Letter to the editor: Fitting truncated normal distributions” Statistical Methods in Medical Research 2018, Vol 27(12) 3835-3838.
This is a new grounds of rejection necessitated by the amendments to the claims in the response filed January 29, 2026.
Regarding claim 1, Cristiano et al. teach methods of “screening, early detection, and monitoring of human cancer” (i.e. determining/predicting cancer status) comprising “evaluat[ing] fragmentation patterns (i.e. analyzing a shape of a curve) of cell-free DNA across the genome” in subjects with cancer compared to controls (Cristiano et al., abstract and figures 1-3).
Additionally, Cristiano et al. teach collecting cell-free DNA at the time of diagnosis, before tumor resection or therapy, after which a subset of patients were sampled again across several timepoints while they were undergoing anti-EGFR or anti-ERBB2 therapy (i.e. administering a cancer treatment to the subject) (Cristiano et al., page 390, column 1, paragraph 2).
Cristiano et al. further teach processing samples from subjects comprising cfDNA fragments into sequencing libraries, performing whole genome sequencing, mapping the sequenced fragments to a windowed genome, and analyzing mapped sequences having lengths between 100 bp and 220 bp to determine cfDNA fragment length distributions in each genome window (Cristiano et al., page 390, column 1, paragraph 4 and page 390, column 2, paragraphs 2-3).
Cristiano et al. do not teach that the analyzing the shape of a curve step comprises “fitting finite mixtures of truncated normal distributions to the counts of fragment sizes between 105 bp and 220 bp, wherein the finite mixture comprises components… characterized by a mean, variance, and contribution to an overall mixture”.
However, Abdueva et al. teach methods for determining/predicting cancer status comprising analyzing and comparing cell-free DNA fragment size distributions from a subject suspected of having cancer and a healthy control (Abdueva et al., paragraph 0050).
Abdueva et al. teach steps comprising analyzing the shape of cfDNA curves obtained from a subject and a healthy control comprise estimating a multimodal density of the fragment density profiles (i.e. fitting a finite mixture of distributions to counts of fragment sizes) (Abdueva et al., paragraphs 0011 and 0156) “the use of a uni-parametric or multi-parametric analysis to determine a plasma deregulation score… [that] varies across the genome… based on, e.g., the distribution of lengths of fragments that overlap with each position of a portion or all of the genome” (Abdueva et al., paragraph 0174), wherein a “plasma deregulation score” can be indicative of a tumor burden or a disease burden (i.e. “indicative of” or “predicting” cancer status.
Abdueva et al. teach: “ “Fragmentome profiles” can be analyzed by an anomaly detection algorithm to identify abnormal conditions (e.g., malignant cancer in a subject). Anomaly detection is widely used in data mining and may be performed with the use of mixture models and the expectation-maximization (EM) algorithm.” (Abdueva et al., paragraph 0335).
Abdueva et al. do not teach that the mixture models fit to counts of fragment sizes comprise a mixture of truncated normal distributions.
However, Feng et al. teach methods of quantifying and comparing distributions of nucleic acid fragment lengths produced by degradation processes wherein detection of anomalous fragment length distribution patterns are associated with an “integrity score” that is calculated from a mixture of truncated normal distributions comprising means, variances, and contributions to an overall mixture (Feng et al., page 8, column 2).
Similarly, MacDonald teaches methods for analyzing biological data (providing an example of a histogram of “Density” of observed values vs. an independent variable “ferritin concentration” or “blood fat content” comprising fitting univariate truncated normal distributions to the observed distribution using EM-type algorithms or using a Newton-based optimizer.
Therefore, it would have been prima facie obvious prior to the effective filing date of the claimed invention for one of ordinary skill in the art to have modified the methods taught by Cristiano et al. comprising comparing the frequency (i.e. density) of long fragments to short fragments in cfDNA from a subject and a healthy control to determine a cancer status of the subject with the methods taught by Abdueva et al., Feng et al., and MacDonald comprising methods for determining/predicting cancer status comprising analyzing and comparing cell-free DNA fragment size distributions from a subject suspected of having cancer and a healthy control using mixture models fit to counts of fragment sizes (Abdueva et al.), analyzing distributions of partially degraded nucleic acids comprising fitting a mixture of truncated normal distributions to observed data (Feng et al.), and the methods for analyzing distributions of observed densities in biological data comprising fitting univariate truncated normal distributions to the observed distribution using EM-type algorithms or using a Newton-based optimizer (MacDonald).
The ordinary artisan would have been motivated to utilize the fragment-size density analysis steps taught by Abdueva et al., Feng et al., and the methods for fitting truncated normal distributions taught by MacDonald because of the teaching of MacDonald that analyses of distributions of densities of observed biological data can be accomplished by fitting mixtures of truncated normal distributions to the observed data comprising EM algorithms or the “conceptually simple route of direct numerical maximization of likelihood… [that is] computationally simple and apparently very fast”. MacDonald further teaches such methods advantageously permit implementation of the statistical analysis without need of a specialized research statistician (MacDonald, page 3, paragraph 5).
Regarding claim 9, Cristiano et al. teach analyzing “short fragments” between 100 and 150 bp and “long fragments” between 151 and 220 bp (i.e. within the recited range of selected fragment sizes) (Cristiano et al., page 390, column 2, paragraph 3).
Regarding claim 10, Cristiano et al. teach fitting multinomial probability distributions to the observed distributions (i.e. the shapes of the curves) (Cristiano et al., page 391, column 1-2 bridging paragraph).
Regarding claim 12, Cristiano et al. teach analyzing the shape of the curve of cfDNA fragment size density comprises analyzing “short fragments… having lengths between 100 and 150 bp” (Cristiano et al., page 390, column 2, paragraph 3) (i.e. excluding fragment sizes greater than 170 bp).
Cristiano et al. do not appear to teach “excluding fragment sizes less than 105 bp” because the “short fragment” cutoff is defined at 100 bp.
However, in the absence of unexpected results due to the exclusion of fragments with lengths between 100 and 105 bp, it would have been prima facie obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention that a cutoff difference of 5 bp is a simple and obvious variant of the method disclosed by Cristiano et al. The ordinary artisan would have had a reasonable expectation that including (or excluding) fragments in this size range (100-105) in a method for assessing the distribution of fragment sizes of near-mononucleosome protected size fragments (i.e. an expected “normal” distribution centered around 147 (core nucleosome particle) + ~20bp (linker DNA)) would have produced reasonably equivalent results.
Regarding claim 17, Cristiano et al. teach processing cfDNA samples from subjects into sequencing libraries, performing whole genome sequencing, mapping the sequences to a genome in windows, and analyzing the fragment lengths in each window (Cristiano et al., page 390, column 1 paragraphs 4-5 and column 2, paragraphs 1-5) (also see Cristiano et al., figure 3).
Regarding newly added claim 109, which depends from claim 1, Cristiano et al. teach collecting cell-free DNA at the time of diagnosis, before tumor resection or therapy, after which a subset of patients were sampled again across several timepoints while they were undergoing anti-EGFR or anti-ERBB2 therapy (i.e. a targeted therapy, a signal transduction inhibitor, monoclonal antibodies, etc.) (Cristiano et al., page 390, column 1, paragraph 2). Cristiano et al. further teach a different subset of patients diagnosed by cfDNA analysis were treated with neoadjuvant therapy (Cristiano et al., page 390, column 1, paragraph 2).
Regarding claim 27, Cristiano et al. teach methods of “screening, early detection, and monitoring of human cancer” (i.e. determining/predicting cancer status) comprising “evaluat[ing] fragmentation patterns (i.e. analyzing a shape of a curve) of cell-free DNA across the genome” in subjects with cancer compared to controls (Cristiano et al., abstract and any of figures 1-3).
Regarding claim 28, Cristiano et al. teach fitting distributions to counts of fragment sizes (Cristiano et al., page 391, column 1-2 bridging paragraph).
Regarding claim 35, Cristiano et al. teach analyzing “short fragments” between 100 and 150 bp and “long fragments” between 151 and 220 bp (i.e. within the recited range of selected fragment sizes) (Cristiano et al., page 390, column 2, paragraph 3).
Regarding claim 36, Cristiano et al. teach the reference is cfDNA fragment size density in a sample from a healthy subject (Cristiano et al., figure 3)
Regarding claim 37, Cristiano et al. teach applying the methods discussed above to colorectal cancer, lung cancer, breast cancer, gastric cancer, pancreatic cancer, bile duct cancer, and ovarian cancer (Cristiano et al., figure 3; see below)
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Regarding claim 38, Cristiano et al. teach comparing the cfDNA fragment size density curve to a reference over the whole genome (Cristiano et al., figure 3 a and b).
Regarding claim 39, Cristiano et al. teach comparing the cfDNA fragment size density curve to a reference over subgenomic intervals (Cristiano et al., figure 3c).
Response to arguments
The arguments presented in the response address the 103 rejection of record over Cristiano alone. The new grounds of rejection under U.S.C. 103 necessitated by the amendments to the claims address the amended claims.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claim 1 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 18 of copending Application No. 18/286,081 (herein referred to as ‘081) in view of Abdueva et al., US 2019/0287645 A1, published September 19, 2019, Feng et al., “mRIN for direct assessment of genome-wide and gene-specific mRNA integrity from large-scale RNA-sequencing data”, published August 3, 2015, and MacDonald, “Letter to the editor: Fitting truncated normal distributions” Statistical Methods in Medical Research 2018, Vol 27(12) 3835-3838.
This is a new grounds of rejection necessitated by the amendments to the claims.
Regarding claim 1, the claims of ‘081 recite a method of detecting cancer (i.e. determining cancer status) in a subject comprising: a) determining a cfDNA fragmentation profile of a sample from the subject by obtaining and isolating cfDNA fragments, sequencing the fragments, mapping the fragments to a genome to obtain windows of mapped sequences, analyzing the windows to determine fragment lengths, and classifying the subject as having cancer or not having cancer (‘081, claim 1) by comparison to a reference cfDNA fragmentation profile (‘081, claim 18).
The claims of ‘081 do not recite “fitting finite mixtures of truncated normal distributions to the counts of fragment sizes between 105 bp and 220 bp, wherein the finite mixture comprises components… characterized by a mean, variance, and contribution to an overall mixture”.
However, Abdueva et al. teach methods for determining/predicting cancer status comprising analyzing and comparing cell-free DNA fragment size distributions from a subject suspected of having cancer and a healthy control (Abdueva et al., paragraph 0050).
Abdueva et al. teach steps comprising analyzing the shape of cfDNA curves obtained from a subject and a healthy control comprise estimating a multimodal density of the fragment density profiles (i.e. fitting a finite mixture of distributions to counts of fragment sizes) (Abdueva et al., paragraphs 0011 and 0156) “the use of a uni-parametric or multi-parametric analysis to determine a plasma deregulation score… [that] varies across the genome… based on, e.g., the distribution of lengths of fragments that overlap with each position of a portion or all of the genome” (Abdueva et al., paragraph 0174), wherein a “plasma deregulation score” can be indicative of a tumor burden or a disease burden (i.e. “indicative of” or “predicting” cancer status.
Abdueva et al. teach: “ “Fragmentome profiles” can be analyzed by an anomaly detection algorithm to identify abnormal conditions (e.g., malignant cancer in a subject). Anomaly detection is widely used in data mining and may be performed with the use of mixture models and the expectation-maximization (EM) algorithm.” (Abdueva et al., paragraph 0335).
Abdueva et al. do not teach that the mixture models fit to counts of fragment sizes comprise a mixture of truncated normal distributions.
However, Feng et al. teach methods of quantifying and comparing distributions of nucleic acid fragment lengths produced by degradation processes wherein detection of anomalous fragment length distribution patterns are associated with an “integrity score” that is calculated from a mixture of truncated normal distributions comprising means, variances, and contributions to an overall mixture (Feng et al., page 8, column 2).
Similarly, MacDonald teaches methods for analyzing biological data (providing an example of a histogram of “Density” of observed values vs. an independent variable “ferritin concentration” or “blood fat content” comprising fitting univariate truncated normal distributions to the observed distribution using EM-type algorithms or using a Newton-based optimizer.
Therefore, it would have been prima facie obvious prior to the effective filing date of the claimed invention for one of ordinary skill in the art to have modified the methods claimed by ‘081 with the methods taught by Abdueva et al., Feng et al., and MacDonald comprising methods for determining/predicting cancer status comprising analyzing and comparing cell-free DNA fragment size distributions from a subject suspected of having cancer and a healthy control using mixture models fit to counts of fragment sizes (Abdueva et al.), analyzing distributions of partially degraded nucleic acids comprising fitting a mixture of truncated normal distributions to observed data (Feng et al.), and the methods for analyzing distributions of observed densities in biological data comprising fitting univariate truncated normal distributions to the observed distribution using EM-type algorithms or using a Newton-based optimizer (MacDonald).
The ordinary artisan would have been motivated to utilize the fragment-size density analysis steps taught by Abdueva et al., Feng et al., and the methods for fitting truncated normal distributions taught by MacDonald because of the teaching of MacDonald that analyses of distributions of densities of observed biological data can be accomplished by fitting mixtures of truncated normal distributions to the observed data comprising EM algorithms or the “conceptually simple route of direct numerical maximization of likelihood… [that is] computationally simple and apparently very fast”. MacDonald further teaches such methods advantageously permit implementation of the statistical analysis without need of a specialized research statistician (MacDonald, page 3, paragraph 5).
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim 1 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of copending Application No. 18/844,348 (herein referred to as ‘348) in view of Abdueva et al., US 2019/0287645 A1, published September 19, 2019, Feng et al., “mRIN for direct assessment of genome-wide and gene-specific mRNA integrity from large-scale RNA-sequencing data”, published August 3, 2015, and MacDonald, “Letter to the editor: Fitting truncated normal distributions” Statistical Methods in Medical Research 2018, Vol 27(12) 3835-3838.
This is a new grounds of rejection necessitated by the amendments to the claims.
Regarding claim 1, the claims of ‘348 recite a method for monitoring cancer (i.e. determining the cancer status) comprising determining a cfDNA fragmentation profile (i.e. analyzing a shape of a curve of cfDNA fragment size density) and “calculating a divergence score based on the ratio of short to long fragments in the sample as correlated to a ratio from a sample from a healthy subject” (i.e. a difference between the subject and a reference sample from a healthy subject that is indicative of a tumor)(‘348, claim 1).
The claims of ‘348 do not recite “fitting finite mixtures of truncated normal distributions to the counts of fragment sizes between 105 bp and 220 bp, wherein the finite mixture comprises components… characterized by a mean, variance, and contribution to an overall mixture”.
However, Abdueva et al. teach methods for determining/predicting cancer status comprising analyzing and comparing cell-free DNA fragment size distributions from a subject suspected of having cancer and a healthy control (Abdueva et al., paragraph 0050).
Abdueva et al. teach steps comprising analyzing the shape of cfDNA curves obtained from a subject and a healthy control comprise estimating a multimodal density of the fragment density profiles (i.e. fitting a finite mixture of distributions to counts of fragment sizes) (Abdueva et al., paragraphs 0011 and 0156) “the use of a uni-parametric or multi-parametric analysis to determine a plasma deregulation score… [that] varies across the genome… based on, e.g., the distribution of lengths of fragments that overlap with each position of a portion or all of the genome” (Abdueva et al., paragraph 0174), wherein a “plasma deregulation score” can be indicative of a tumor burden or a disease burden (i.e. “indicative of” or “predicting” cancer status.
Abdueva et al. teach: “ “Fragmentome profiles” can be analyzed by an anomaly detection algorithm to identify abnormal conditions (e.g., malignant cancer in a subject). Anomaly detection is widely used in data mining and may be performed with the use of mixture models and the expectation-maximization (EM) algorithm.” (Abdueva et al., paragraph 0335).
Abdueva et al. do not teach that the mixture models fit to counts of fragment sizes comprise a mixture of truncated normal distributions.
However, Feng et al. teach methods of quantifying and comparing distributions of nucleic acid fragment lengths produced by degradation processes wherein detection of anomalous fragment length distribution patterns are associated with an “integrity score” that is calculated from a mixture of truncated normal distributions comprising means, variances, and contributions to an overall mixture (Feng et al., page 8, column 2).
Similarly, MacDonald teaches methods for analyzing biological data (providing an example of a histogram of “Density” of observed values vs. an independent variable “ferritin concentration” or “blood fat content” comprising fitting univariate truncated normal distributions to the observed distribution using EM-type algorithms or using a Newton-based optimizer.
Therefore, it would have been prima facie obvious prior to the effective filing date of the claimed invention for one of ordinary skill in the art to have modified the methods claimed by ‘348 with the methods taught by Abdueva et al., Feng et al., and MacDonald comprising methods for determining/predicting cancer status comprising analyzing and comparing cell-free DNA fragment size distributions from a subject suspected of having cancer and a healthy control using mixture models fit to counts of fragment sizes (Abdueva et al.), analyzing distributions of partially degraded nucleic acids comprising fitting a mixture of truncated normal distributions to observed data (Feng et al.), and the methods for analyzing distributions of observed densities in biological data comprising fitting univariate truncated normal distributions to the observed distribution using EM-type algorithms or using a Newton-based optimizer (MacDonald).
The ordinary artisan would have been motivated to utilize the fragment-size density analysis steps taught by Abdueva et al., Feng et al., and the methods for fitting truncated normal distributions taught by MacDonald because of the teaching of MacDonald that analyses of distributions of densities of observed biological data can be accomplished by fitting mixtures of truncated normal distributions to the observed data comprising EM algorithms or the “conceptually simple route of direct numerical maximization of likelihood… [that is] computationally simple and apparently very fast”. MacDonald further teaches such methods advantageously permit implementation of the statistical analysis without need of a specialized research statistician (MacDonald, page 3, paragraph 5).
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Conclusion
No claim is allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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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