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 .
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10/15/2025 has been entered.
Claims 1-10, 12-23, 45-49 are pending in this application. Claims 1-7 stand withdrawn from consideration as being drawn to a non-elected invention. The election was made without traverse 1/17/2023. Claims 47-49 are newly added. Claims 8-10, 12-23, and 45-49 are under examination.
The examiner attempted to negotiate allowable subject matter prior to writing this action, however as of the date of writing, no agreement was reached. The proposals will be included at the end of this action.
Claim Interpretation
The claims in this application are given their broadest reasonable interpretation (BRI) using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art.
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 8-10, 12-23, 45-49 is/ are/ remain 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.
Applicant is directed to MPEP 2106 and the Federal Register notice (FR89, no 137 (7/17/2024) p 58128-58138) for the most current and complete guidelines in the analysis of patent- eligible subject matter. The current MPEP is the primary source for the USPTO’s patent eligibility guidance.
With respect to step (1): YES. The claims are drawn to statutory categories: processes.
With respect to step (2A) (1): YES, the claims recite an abstract idea, and a law of nature. The claims explicitly recite elements that, individually and in combination, constitute one or more judicial exceptions (JE).
Mathematic concepts, Mental Processes or Elements in Addition (EIA) in the claim(s) include:
8. (Currently Amended) A method of determining whether a subject has or does not have cancer, the method comprising:
obtaining cell-free deoxyribonucleic acid (DNA) fragments derived from the subject;
sequencing at least a portion of the cell-free DNA fragments, whereby genetic sequence information is produced;
(EIA- elements in addition to the abstract idea, comprising the preamble, with the goal of the method, and steps of necessary data gathering, related to obtaining a sample, and performing routine sequencing on cfDNA from the sample.)
mapping, based on the genetic sequence information, sequence reads to a reference genome and computing subject fragmentome data;
(Mental process of observation, comparison and judgement between sequence reads and reference genome sequences, and “computing” is a mathematic concept of performing calculations to obtain undefined “fragmentome data.”)
constructing, from the subject fragmentome data, a subject multi-parametric fragmentome model representative of the subject's cell-free DNA fragments;
(Mental process of combining or compiling a profile, or structure of data from the subject fragmentome data. Specification denotes as a profile, not a mathematic model.)
classifying the subject by applying a trained variant-free fragmentome machine learning classifier to the subject multi-parametric fragmentome model,
wherein the trained variant-free fragmentome machine learning classifier operates without using base identity as an input feature,
(Mathematic concept of applying subject specific data/ parameters to a trained ML classifier. The ML classifier has the restriction of not using base identity, but no other structure or specific operation.)
wherein the trained variant-free fragmentome machine learning classifier is trained by:
obtaining, from a plurality of different classes representing a set of subjects with a tumor-related shared characteristic, fragmentome training data comprising multi-parametric fragmentome models computed from cell-free DNA fragments of subjects in the class, and
(EIA- step of necessary data gathering, from sets of subjects having a shared characteristic, which were “computed” as a mathematic concept, to generate training profiles.)
training, by a computer, a learning algorithm on the fragmentome training data to generate the trained variant-free fragmentome machine learning classifier; and
(Mathematic concept of applying training data values to a machine learning algorithm to create a classifier. No details of the algorithm, or how it acts to generate classifications.)
outputting, by the trained variant-free fragmentome machine learning classifier, a determination that the subject has or does not have cancer based on the subject multi-parametric fragmentome model.
(EIA- routine output of results, in any form.)
9. EIA- describing data acquired.
10. mathematic concept of calculating distributions and matching loci.
12. mathematic concept modification.
13. mental step of comparison.
14. EIA- describing data gathered.
15. Mathematic concept of data transformation
16. mathematic concept of joint distribution modeling.
17. mental concept of recognition of peaks in data.
18. mental concept of recognition of differences between profiles.
19. mathematic concept of counting, determining breadth of a peak by measurement, shifts in peaks by measurement, mental concepts of recognizing data, or changes in data.
20. mental concept of recognizing the source of a portion of the profile.
21. mathematic concept of performing multi-parametric analysis.
22. mathematic concept of calculating a distribution score.
23. mathematic concept of estimation of a value.
45. modifying the mathematic concept by enumerating classes of cancer.
46. modifying the mathematic concept by enumerating tumor-related characteristics.
47. EIA- relating to data gathered from the subject.
48. EIA specifying the sources of the reference data.
49. mathematic concepts of multi-parametric modeling, using joint distribution modeling.
Natural law embraced by claim(s) 8-10, 12-23, 45-49:
The claims also involve a naturally occurring correlation, i.e., the naturally occurring correlation between certain genetic changes in a subject, and whether they naturally exhibit a phenotype, cancer. The sequence information of the sample is a natural phenomenon and the relationship of that information to a phenotypic trait is simply put a natural law. The naturally occurring relationship between genotypes and other characteristics of an organism is a recognized, naturally occurring correlation which exists whether or not it is measured. Nothing more than the observation is required by the claims.
In Mayo, the discovery underlying the claims was that when blood levels were above a certain level harmful effects were more likely and when they were below another level the drug's beneficial effects were lost. Mayo, 566 U.S. at 74--76. The claims provided that particular levels of measured metabolite indicated a need to increase or decrease the amount of drug subsequently administered to the subject. Id. at 75. However, the claims did not require any actual action be taken based on the measured level of metabolite. Id. at 75-76. Thus, the claims which required only the observation of a natural law were deemed patent-ineligible.
Similarly, here, the claim requires only the observation of the natural law, and for this reason too are properly deemed patent-ineligible.
These correlate to at least the following examples provided in MPEP 2106.04b:
“iii. a correlation between variations in non-coding regions of DNA and allele presence in coding regions of DNA, Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1375, 118 USPQ2d 1541, 1545 (Fed. Cir. 2016);
iv. a correlation that is the consequence of how a certain compound is metabolized by the body, Mayo Collaborative Servs. v. Prometheus Labs., 566 U.S. 66, 75-77, 101 USPQ2d 1961, 1967-68 (2012);
v. a correlation between the presence of myeloperoxidase in a bodily sample (such as blood or plasma) and cardiovascular disease risk, Cleveland Clinic Foundation v. True Health Diagnostics, LLC, 859 F.3d 1352, 1361, 123 USPQ2d 1081, 1087 (Fed. Cir. 2017);
vii. qualities of bacteria such as their ability to create a state of inhibition or non-inhibition in other bacteria, Funk Bros., 333 U.S. at 130, 76 USPQ at 281; and
xi. the natural relationship between a patient’s CYP2D6 metabolizer genotype and the risk that the patient will suffer QTc prolongation after administration of a medication called iloperidone, Vanda Pharmaceuticals Inc. v. West-Ward Pharmaceuticals, 887 F.3d 1117, 1135-36, 126 USPQ2d 1266, 1281 (Fed. Cir. 2018).”
With respect to step 2A (2): NO, the claims do not integrate the JE(s) into a practical application. The claims were examined further to determine whether they integrated any JE into a practical application (MPEP 2106.04(d)). The claimed additional elements are analyzed alone, or in combination to determine if the JE is integrated into a practical application (MPEP 2106.05(a-c, e, f and h)).
Claim(s) 8, 9, 14 recite the additional non-abstract element(s) of data gathering, or a description of the data gathered.
Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect the data needed to carry out the JE. The data gathering does not impose any meaningful limitation on the JE, or how the JE is performed. The additional limitation (data gathering) must have more than a nominal or insignificant relationship to the identified judicial exception. (MPEP 2106.04/.05, citing Intellectual Ventures LLC v. Symantec Corp, McRO, TLI communications, OIP Techs. Inc. v. Amason.com Inc., Electric Power Group LLC v. Alstrom S.A.).
Claim(s) 8, 10, 15 recite the additional non-abstract element (EIA) of a general-purpose computer system or parts thereof.
The EIA do not provide any details of how specific structures of the computer elements are used to implement the JE. The claims require nothing more than a general-purpose computer to perform the functions that constitute the judicial exceptions. The computer elements of the claims do not provide improvements to the functioning of the computer itself (as in DDR Holdings, LLC v. Hotels.com LP); they do not provide improvements to any other technology or technical field (as in Diamond v. Diehr); nor do they utilize a particular machine (as in Eibel Process Co. v. Minn. & Ont. Paper Co.). Hence, these are mere instructions to apply the JE using a computer, and therefore the claim does not recite integrate that JE into a practical application.
Dependent claim(s) 10, 12-23, 45-49 recite(s) an abstract limitation to the JE reciting additional mathematic concepts, or mental processes. Additional abstract limitations cannot provide a practical application of the JE as they are a part of that JE.
In combination, the limitations of data gathering, for the purpose of carrying out the JE, using a general-purpose computer merely provide extra-solution activity, and fail to integrate the JE into a practical application.
With respect to step 2B: NO the claims do not provide a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). The additional elements were considered individually and in combination to determine if they provide significantly more than the judicial exception. (MPEP 2106.05.A i-vi).
With respect to claim(s) 8, 9: The limitation(s) identified above as non-abstract elements (EIA) related to data gathering do not rise to the level of significantly more than the judicial exception.
Chandrananda et al ((2015) BMC Medical Genomics 8:29; of record) is cited in the specification as providing techniques of obtaining samples, obtaining cfDNA fragments from the sample, sequencing cfDNA from the sample and analyzing the genetic sequence information produced, by steps such as mapping to reference genomes at [00184]. These elements meet the BRI of the identified data gathering limitations. As such, the prior art recognizes that this data gathering element is routine, well understood and conventional in the art (as in Alice Corp., CyberSource v. Retail Decisions, Parker v. Flook).
This meets the BRI of the data gathering limitations, and shows the prior art considered the steps identified as data gathering were routine, well-understood and conventional in the art.
In the specification at [0143, 0180-0183] it is disclosed that the steps identified as data gathering can be met using well-known, routine and conventional processes for obtaining a sample, obtaining cell-free DNA or RNA fragments from a sample, sequencing the cfDNA or cfRNA from the sample to generate sequence information data and analyzing the resulting “fragmentome” polynucleotide representations in a dataset or from a database. Commercially available equipment and processes such as HiSeq™ or Ion Torrent™ are cited in [00180-1] as being suitable for carrying out the same added data gathering steps.
Activities such as data gathering do not improve the functioning of a computer, or comprise an improvement to any other technical field. The limitations do not require or set forth a particular machine, they do not effect a transformation of matter, nor do they provide an unconventional step (citing McRO and Trading Technologies Int’l v. IBG). Data gathering steps constitute a general link to a technological environment. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp.,).
With respect to independent claim 8, 15: the limitations identified above as non-abstract elements (EIA) related to general-purpose computer systems do not rise to the level of significantly more than the judicial exception.
The specification, at [0314-0325] discloses the use of routine general-purpose computers for carrying out the invention, and/or the use of commercially available computer system elements. These general-purpose computers can carry out the training, and the ML algorithms as required.
These elements do not improve the functioning of the computer itself, or comprise an improvement to any other technical field (Trading Technologies Int’l v IBG, TLI Communications). They do not require or set forth a particular machine (Ultramercial v. Hulu, LLC., Alice Corp. Pty. Ltd v. CLS Bank Int’l), they do not effect a transformation of matter, nor do they provide an unconventional step. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp., CyberSource v. Retail Decisions, Parker v. Flook, Versata Development Group v. SAP America).
Dependent claim(s) 10, 12-23, 45-49 each recite a limitation requiring additional mathematic concepts or mental processes. Additional abstract limitations cannot provide significantly more than the JE as they are a part of that JE (MPEP 2106.05).
In combination, the data gathering steps providing the information required to be acted upon by the JE, performed in a generic computer or generic computing environment fail to rise to the level of significantly more than that JE. The data gathering steps provide the data for the JE, which is carried out by the general-purpose computers. No non-routine step or element has clearly been identified.
The claims have all been examined to identify the presence of one or more judicial exceptions. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether the additional limitations integrate the judicial exception into a practical application. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether those additional limitations provide an inventive concept which provides significantly more than those exceptions. For these reasons, the claims, when the limitations are considered individually and as a whole, are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Applicant’s arguments:
Applicant’s arguments have been carefully considered but are not persuasive.
With respect to the arguments regarding the asserted improvement, and the machine learning models, the claims do not set forth the requisite links between the data gathered, the training of the ML, the structure of that ML and how the training affects that structure, to obtain the desired results.
The examiner acknowledges Applicant’s arguments which set forth that the claims lead to an improvement in the determination of whether a subject does or does not have cancer. According to the guidance set forth in MPEP 2106, this is an improvement to the judicial exception itself, and is not reflected back into a specific technological environment or practically applied process.
An improvement in the judicial exception itself is not an improvement in the technology. For example, in In re Board of Trustees of Leland Stanford Junior University, 989 F.3d 1367, 1370, 1373 (Fed. Cir. 2021) (Stanford I), Applicant argued that the claimed process was an improvement over prior processes because it ‘‘yields a greater number of haplotype phase predictions,’’ but the Court found it was not ‘‘an improved technological process’’ and instead was an improved ‘‘mathematical process.’’ The court explained that such claims were directed to an abstract idea because they describe ‘‘mathematically calculating alleles’ haplotype phase,’’ like the ‘‘mathematical algorithms for performing calculations’’ in prior cases. Notably, the Federal Circuit found that the claims did not reflect an improvement to a technological process, which would render the claims eligible (FR89 no.137, p58137, 7/17/2024).
Here, Applicant has provided an improved mathematical process of training and identifying machine learning algorithms, which are then used in the improved mathematical process to determine whether a subject does or does not have cancer.
The improvement in determination of whether a subject does or does not have cancer (carried out by the judicial exception) does not provide an improvement in the technology of obtaining cfDNA samples and obtaining sequencing data. The collection of data is carried out, unchanged, whether or not the judicial exception is applied. (Cleveland Clinic Foundation: using well-known or standard laboratory techniques is not sufficient to show an improvement (MPEP2106.05(a)).
The improvement in the determination of whether a subject does or does not have cancer (achieved by the judicial exception) does not require a non-conventional interaction with a specific element of a computer as was required in Enfish. The disputed claims in Enfish were patent-eligible because they were "directed to a specific improvement to the way computers operate, embodied in [a] self-referential table." Enfish, 822 F.3d at 1336. The court found that the "plain focus of the claims" there was on an improvement to computer functionality itself-a self-referential table for a computer database, designed to improve the way a computer carries out its basic functions of storing and retrieving data- not on a task for which a computer is used in its ordinary capacity. Id. at 1335-36. The court noted that the specification identified additional benefits conferred by the self-referential table (e.g., increased flexibility, faster search times, and smaller memory requirements), which further supported the court's conclusion that the claims were directed to an improvement of an existing technology. Id. at 1337 (citation omitted).
The improvement in the determination of whether a subject does or does not have cancer (carried out by the judicial exception) does not improve the functionality of the computer itself as in Finjan, Visual Memory, or SRI Int’l. In Finjan, claims to virus scanning were found to be an improvement in computer technology. In Visual Memory, claims to an enhanced computer memory system were found to be directed to an improvement in computer capabilities. In SRI Int'l, claims to detecting suspicious activity by using network monitors and analyzing network packets were found to be an improvement in computer network technology.
The improvement in the determination of whether a subject does or does not have cancer does not provide an improvement in computer animation and use rules to automate a subjective task of humans to create a sequence of synchronized, animated characters as in McRo. In McRO, it was not the mere presence of unconventional rules that led to patent eligibility. In McRO, "[t]he claimed improvement was to how the physical display operated (to produce better quality images)." SAP Am. v. InvestPic, LLC, 898 F.3d 1161, 1167 (Fed. Cir. 2018). The claims in McRO recited a step of applying the data sets generated using the specific claimed rules to a sequence of animated characters to produce lip synchronization and facial expression control of those animated characters. McRO, 837 F.3d at 1308. Thus, the claims were directed to an improvement in computer animation and used rules to automate a subjective task of humans to create a sequence of synchronized, animated characters. Id. at 1314--15.
In the claims at issue here, there is no such application of specifically claimed rules to produce an improved technological result. The process of determination of whether a subject does or does not have cancer is not a technological process; it is information evaluation.
With respect to the identified elements in addition (EIA) to the JE, each has been addressed above.
In the claims, the EIA identified as data gathering steps do not affect how the steps of the abstract idea are performed, they provide the data which is acted upon by the limitations of the JE. These data gathering steps do not apply, rely on, or use the steps identified as making up the JE. Rather, the mathematic calculation steps avail themselves of the data gathered. The data gathering in the claims constitutes insignificant pre-solution activity. See MPEP § 2106.05(g):
MPEP2106.05(g). “The term "extra-solution activity” can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim...”
“An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent.”
See also CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372 (Fed. Cir. 2011) ("[E]ven if some physical steps are required to obtain information from the database ... such data-gathering steps cannot alone confer patentability.").
The listed claims set forth the element in addition (EIA) to the JE of a computing system. The computing system limitations are recited at such a high level of generality, they can be met by a general-purpose computer system and is not considered a particular machine or manufacture integral to the claim (MPEP 2106.05(b)). Routine computer elements acting upon the data in a manner consistent to and according to their design are not considered to be sufficient to provide eligibility. (see, for example MPEP 2106.04(d): Gottschalk v. Benson “‘held that simply implementing a mathematical principle on a physical machine, namely a computer, was not a patentable application of that principle.”)
Claims that recite performing information analysis (training a ML model, and applying experimental data to the model), as well as the collection and manipulation of information related to such analysis, have been determined by our reviewing court to be an abstract concept that is not patent eligible. See SAP, 898 F.3d, 1165, 1167, 1168 (Claims reciting "[a] method for providing statistical analysis" (id. at 1165) were determined to be "directed to an abstract idea" (id. at 1168)); see also Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat'l Ass 'n, 776 F.3d 1343, 1345, 1347 (Fed. Cir. 2014) (finding the "claims generally recite ... extracting data ... [and] recognizing specific information from the extracted data" and that the "claims are drawn to the basic concept of data recognition").
"As many cases make clear, even if a process of collecting and analyzing information is limited to particular content or a particular source, that limitation does not make the collection and analysis other than abstract." SAP, 898 F.3d at 1168 (internal quotation marks omitted)).
Step 2B requires that we look to whether the claim "adds a specific limitation beyond the judicial exception that [is] not 'well-understood, routine, conventional' in the field." Guidance 89 Fed. Reg. at 58133 (emphasis added); MPEP § 2106.05(d); see BSG TechLLCv. BuySeasons, Inc., 899 F.3d 1281, 1290 (Fed. Cir. 2018).
The EIA identified as data gathering were shown, by citation of prior art references, and by citation in the specification, to be well-understood, routine, and conventional limitations in bioinformatics
The EIA identified as computer system related, were shown, by reference to the specification, to be well-understood, routine and conventional computer elements.
In light of the foregoing, we conclude that the claims are directed to no more than judicial exceptions to Section 101 and do not recite the "significantly more" requisite to transform the nature of the claim into a patent-eligible application.
Further, with respect to the arguments regarding the alleged improvement, it is unclear that the independent claims recite all the necessary and sufficient steps required to achieve that improvement. MPEP 2106.05(a): “An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102- 03; DDR Holdings, 773F.3d at 1259, 113 USPQ2d at 1107.”
The MPEP sets forth that “if the examiner concludes the disclosed invention does not improve technology, the burden shifts to applicant to provide persuasive arguments supported by any necessary evidence to demonstrate that one of ordinary skill in the art would understand that the disclosed invention improves technology. Any such evidence submitted under 37 CFR 1.132 must establish what the specification would convey to one of ordinary skill in the art and cannot be used to supplement the specification.” Applicant’s arguments cannot take the place of evidence.
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.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 8-10, 12-23, and 45-49 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-21 of U.S. Patent No. 11,952,616. Although the claims at issue are not identical, they are not patentably distinct from each other because the patent claims determine whether a patient has, or does not have cancer, by analysis of cfDNA from the patient, and analyzing the sequence data of the cfDNA. The independent claim of the patent is generic with respect to the ML, or analysis of the sequence data. Dependent claims of the patent “determine a quantitative measure” from the sequence reads to determine the presence or absence of cancer. Mapping, using a “trained classifier”, types of cancer, types of characteristics, types of samples, tumor DNA are all claimed in dependent claims.
Claims 8-10, 12-23, and 45-49 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim1-23 of U.S. Patent No. 12,428,670. Although the claims at issue are not identical, they are not patentably distinct from each other because the patent claims determine whether a patient has, or does not have a recurrence of cancer, by analysis of cfDNA from the patient, and analyzing the sequence data of the cfDNA. The independent claim of the patent is generic with respect to the ML, or analysis of the sequence data. Dependent claims of the patent “determine a quantitative measure” from the sequence reads to determine the presence or absence of cancer. Mapping, using a “trained classifier”, types of cancer, types of characteristics, types of samples, tumor DNA are all claimed in dependent claims.
Claims 8-10, 12-23, 45-59 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 16/244,966 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the ‘966 performs the same steps and analyses, using variant-free classifiers, to detect the presence or absence of a genetic aberration in cfDNA of a subject. This application is broader in scope than the instant application as the variant is not linked to a cancer class, or tumor characteristic, but they are encompassed by the claims of the ‘966 application.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
However, this application has been allowed; once it has issued this rejection will no longer be provisional.
Summary of proposed amendments:
From the Examiner, 1/14/2026:
“8. (proposed with some mark-up) A method of determining whether a subject has or does not have cancer, the method comprising:
obtaining cell-free deoxyribonucleic acid (DNA) fragments derived from the subject;
sequencing at least a portion of the cell-free DNA fragments, whereby genetic sequence information is produced;
mapping, based on the genetic sequence information, sequence reads to a reference genome and computing subject fragmentome data, wherein the computed subject fragmentome data comprises: (i) a length of the DNA fragments that align with each of a plurality of base positions in the genome, (ii) a number of the DNA fragments that align with each of a plurality of base positions in the genome, and (iii) a number of the DNA fragments that start or end at each of a plurality of base positions;
constructing, from the computed subject fragmentome data, a subject multi-parametric fragmentome profile representative of the subject's cell-free DNA fragments,
wherein the subject multiparametric fragmentome profile comprises at least three values indicating a quantitative measure of a characteristic selected from: (i) DNA sequences mapping to a genetic locus, (ii) DNA sequences starting at a genetic locus, (iii) DNA sequences ending at a genetic locus; (iv) a dinucleosomal protection or mononucleosomal protection of a DNA sequence; (v) DNA sequences located in an intron or exon of a reference genome; (vi) a size distribution of DNA sequences having one or more characteristics; and (vii) a length distribution of DNA sequences having one or more characteristics;
classifying the subject by applying a trained variant-free fragmentome machine learning classifier to the subject multi-parametric fragmentome profile,
wherein the trained variant-free fragmentome machine learning classifier operates without using base identity as an input feature,
wherein the trained variant-free fragmentome machine learning classifier is trained by:
obtaining, from a plurality of different classes representing a set of subjects with a tumor-related shared characteristic, fragmentome training data comprising: (i) a length of the DNA fragments that align with each of the plurality of base positions in the genome, (ii) a number of the DNA fragments that align with each of the plurality of base positions in the genome, and (iii) a number of the DNA fragments that start or end at each of the plurality of base positions;
wherein the tumor-related shared characteristic is selected from the group consisting of a tumor type, tumor severity, tumor aggressiveness, tumor clonality, tumor recurrence and resistance to treatment;
generating multi-parametric fragmentome training profiles computed from cell-free DNA fragments of subjects sharing each tumor-related characteristic,
wherein each multiparametric fragmentome training profile comprises at least three values indicating a quantitative measure of a characteristic selected from: (i) DNA sequences mapping to a genetic locus, (ii) DNA sequences starting at a genetic locus, (iii) DNA sequences ending at a genetic locus; (iv) a dinucleosomal protection or mononucleosomal protection of a DNA sequence; (v) DNA sequences located in an intron or exon of a reference genome; (vi) a size distribution of DNA sequences having one or more characteristics; and (vii) a length distribution of DNA sequences having one or more characteristics;
training, by a computer, a machine learning algorithm on the fragmentome training profiles;
wherein the machine learning algorithm acts on each fragmentome training profile to determine quantitative measures associated with each shared tumor-related characteristic; and analyzes the quantitative measures to classify the presence or absence of cancer, thereby generating the trained variant-free fragmentome machine learning classifier; and
outputting, by the trained variant-free fragmentome machine learning classifier, a determination that the subject has or does not have cancer
Counter proposal of 1/23/2026, from Applicant:
(Currently Amended) A method of determining whether a subject has or does not have cancer, the method comprising:
obtaining cell-free deoxyribonucleic acid (DNA) fragments derived from the subject;
sequencing at least a portion of the cell-free DNA fragments, whereby genetic sequence information is produced;
mapping, based on the genetic sequence information, sequence reads to a reference genome and computing subject fragmentome data, wherein the computed subject fragmentome data comprises at least one of: (i) a length of the DNA fragments that align with each of a plurality of base positions in the genome, (ii) a number of the DNA fragments that align with each of a plurality of base positions in the genome, or (iii) a number of the DNA fragments that start or end at each of a plurality of base positions;
constructing, from the subject fragmentome data, a subject multi-parametric fragmentome profile, wherein the subject multiparametric fragmentome profile comprises at least three values indicating a quantitative measure of a characteristic selected from: (i) DNA sequences mapping to a genetic locus, and (ii) DNA sequences starting at a genetic locus;
classifying the subject by applying a trained variant-free fragmentome machine learning classifier to the subject multi-parametric fragmentome profilefragmentome machine learning classifier operates without using base identity as an input feature, wherein the trained variant-free fragmentome machine learning classifier is trained by:
obtaining, from a plurality of different classes representing a set of subjects with a tumor-related shared characteristic, fragmentome training data comprising at least one of (i) a length of the DNA fragments that align with each of the plurality of base positions in the genome, (ii) a number of the DNA fragments that align with each of the plurality of base positions in the genome, or (iii) a number of the DNA fragments that start or end at each of the plurality of base positions
constructing, from the fragmentome training data, multi-parametric fragmentome training profiles, wherein each multi-parametric fragmentome training profile comprises at least three values indicating a quantitative measure of a characteristic selected from: (i) DNA sequences mapping to a genetic locus, (ii) DNA sequences starting at a genetic locus, and (iii) DNA sequences ending at a genetic locus, and
training, by a computer, a machine learning algorithm on the multi-parametric fragmentome training profilesthereby generat[[e]]ing the trained variant-free fragmentome machine learning classifier configured to classify the presence or absence of cancer; and
outputting, by the trained variant-free fragmentome machine learning classifier, a determination that the subject has or does not have cancer
Comments from Examiner, 1/29/2026:
“I think we are still missing a link between the training data, the tumor/cancer characteristic, and what the ML is doing when classifying the test data.
I suggest adding the shared characteristic to the constructed training data:
“constructing, from the fragmentome training data, multi-parametric fragmentome training profiles, wherein each multi-parametric fragmentome training profile comprises the tumor-related characteristic and at least three values indicating a quantitative measure of a characteristic selected from: (i) DNA sequences mapping to a genetic locus, (ii) DNA sequences starting at a genetic locus, and (iii) DNA sequences ending at a genetic locus, and”
I’m wrangling a bit over how to describe the ML classifier, and I found 4 possible ways supported by the specification that could modify the “training step”:
1. training, by a computer, a machine learning algorithm on the multi-parametric fragmentome training profiles thereby generat[[e]]ing the trained variant-free fragmentome machine learning classifier configured to classify the presence of absence of cancer by determining a likelihood that the subject belongs to one or more classes of the tumor-related shared characteristics; and
(from paragraph 0067, 00194)
2: training, by a computer, a machine learning algorithm on the multi-parametric fragmentome training profiles thereby generat[[e]]ing the trained variant-free fragmentome machine learning classifier configured to classify the presence or absence of cancer by determining a likelihood that the subject belongs to a tumor-associated class; and
(from paragraph 0081, 00194; could also read “one or more tumor-associated class”)
3: training, by a computer, a machine learning algorithm to create a probabilistic classifier with on the multi-parametric fragmentome training profiles thereby generat[[e]]ing the trained variant-free fragmentome machine learning classifier configured to classify the presence or absence of cancer; and
(from paragraph 0085)
4. training, by a computer, a machine learning algorithm to create one or more probabilistic classifiers with on the multi-parametric fragmentome training profiles thereby generat[[e]]ing the trained variant-free fragmentome machine learning classifier configured to classify the presence or absence of cancer; and
(from paragraph 0030)”
Conclusion
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/MARY K ZEMAN/ Primary Examiner, Art Unit 1686