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 .
Applicants Amendment
Applicants’ preliminary amendment filed 5/17/2024 has been received and entered. The specification has been amended (10/23/2023 preliminary amendment). Claims 1-20 have been amended, claims 21-66 have been cancelled.
Claims 1-20 are pending.
Priority
This application filed 10/23/2023 is a 371 National stage filing of PCT/US2022/026066 filed 4/22/2022, which claim benefit to US Provisional application 63/179167 filed 4/23/2021.
Information Disclosure Statement
The four information disclosure statements (IDS) submitted on 10/23/2023 through 12/3/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim analysis
Claim 1 has been amended and is generally directed to detecting a disease by analyzing for the presence of cfDNA, in particular ‘aberrant fragments’ frequency. Given the guidance of the specification and requirements of the claims, aberrant is based on the differences seen between healthy and diseased subjects. The claims set forth that the final determination step is provided using a machine learning classifier. Claim was amended to include that the comparison is performed between subjects of the same species being diseased and healthy, implying claim 1 broadly comprises comparing disease and healthy states among any different species. Dependent claims provide that further information about the subjects be provided for training and assessment, the disease of interest is cancer, source of the sample being tested containing cfDNA, specific known machine learning algorithms such as SVM, random forest,… and numbers of fragments per sample are analyzed.
For step 1 of the 101 analysis, the claims are found to be directed to a statutory category of a method.
For step 2A of the 101 analysis, the judicial exception of the claims are the steps of observing and accessing sequence data for start and end of the fragment sequences, and/or identifying unique ‘aberrant’ sequences that are only present in either healthy or diseased subjects. The step of determining ends in light of the specification use aligning and comparing sequence to arrive at the identification of the ends of the fragments, and are instructional steps to analyze the sequence data representing the cfDNA in a sample. The claim generically requires using machine learning, while dependent claims set forth well known classifiers. The judicial exception is a set of instructions for analysis of cfDNA sequence data and appear to fall into the category of Mathematical Concepts, to the extent that a ‘frequency’ has to be calculated which requires using mathematical formulas or equations; and also Mental Processes, that is concepts performed in the human mind (including an observation, evaluation, judgment, opinion) here observing start and stop ends of the fragments and counting them based number observed. The breadth of “determining” and “inputting” encompasses non-transformative visual assessment of sequence data coupled with prior knowledge of the correlation of said disease/phenotype versus healthy source. This breadth does not impose a meaningful limit on the claim scope, such that all others are not precluded from using the natural principle relying on differences of cfDNA fragments being present in healthy and disease states. Although the claims recite using a machine learning classifier for learning and analysis, the courts have also identified limitations that did not integrate a judicial exception into a practical application; for example, merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). Computing, constructing datasets and using statistical models was well understood, conventional, and routinely performed in the art at the time the application was filed. See also MPEP § 2106.05(h) for a discussion on generally linking the use of a judicial exception to a particular technological environment or field of use. The claims appear to fall into the category of Mathematical Concepts, as it applies the use of statistics and mathematical relationships in analyzing probabilities, and also into the category of mental processes, as concepts performed in the human mind (including an observation, evaluation, judgment, opinion) because there is no apparent complexity to analysis of data that is collected and simply requires observing the ends of cfDNA fragments as presently claimed.
Recent guidance from the office requires that the judicial exception be evaluated under a second prong to determine whether the judicial exception is practically applied. In the instant case, the claims do not have an additional element to which the analysis is applied. The claims do have steps of obtaining the sequence data for analysis, and the number being analyzed, but the two together do not appear to provide a practical application, nor an improvement to purification or determining as generically claimed. This judicial exception requires steps recited at high level of generality and with respect to machine learning or producing a classifier are only stored on a non-transitory media, and is not found to be a practical application of the judicial exception as broadly set forth as a whole.
For step 2B of the 101 analysis, each of the independent claims recites additional elements and are found to be the steps of obtaining sequence data about the cfDNA in a sample to assess the ends. As such, the claims do not provide for any additional element to consider under step 2B as a practical application and only a means to provide the data for analysis. With respect to using machine learning to classify the observed data, it is noted that in explaining the Alice framework, the Court wrote that "[i]n cases involving software innovations, [the step one] inquiry often turns on whether the claims focus on the specific asserted improvement in computer capabilities or, instead, on a process that qualifies as an abstract idea for which computers are invoked merely as a tool." The Court further noted that "[s]ince Alice, we have found software inventions to be patent-eligible where they have made non-abstract improvements to existing technological processes and computer technology." Moreover, these improvements must be specific -- "[a]n improved result, without more stated in the claim, is not enough to confer eligibility to an otherwise abstract idea . . . [t]o be patent-eligible, the claims must recite a specific means or method that solves a problem in an existing technological process." As indicated in the summary of the judicial exception above and in view of the teachings of the specification, the steps are drawn to analysis of sequence data of cfDNA. While the instructions for machine learning can be stored on a medium and could be implemented on a computer, together the steps do not appear to result in significantly more than a means to compare sequences. The judicial exception of the method as claimed can be performed by hand and in light of the previous claims to a computer medium and in light of the teaching of the specification on a computer. In review of the instant specification the methods do not appear to require a special type of processor and can be performed on a general purpose computer. Dependent claims set forth additional steps which are more specifically define the considerations and steps of calculating, and comparing, and do not add additional elements which result in significantly more to the claimed method for the analysis. No additional steps are recited in the instantly claimed invention that would amount to significantly more than the judicial exception. Without additional limitations, a process that employs mathematical algorithms (aligning sequences) to manipulate existing information (identify sequence starts and ends) to generate additional information is not patent eligible. In other words, patenting abstract idea (designing probes to a target sequence) cannot be circumvented by attempting to limit the use to a particular technological environment or purpose and desired result.
One way to overcome a rejection for non-patent-eligible subject matter is to persuasively argue that the claimed subject matter is not directed to a judicial exception. Another way for the applicants to overcome the rejection is to persuasively argue that the claims contain elements in addition to the judicial exception that either individually or as an ordered combination are not well understood, routine, or conventional. Another way for the applicants to overcome the rejection is to persuasively argue that the claims as a whole result in an improvement to a technology. Persuasive evidence for an improvement to a technology could be a comparison of results of the claimed subject matter with results of the prior art, or arguments based on scientific reasoning that the claimed subject matter inherently results an improvement over the prior art. The applicants should show why the claims require the improvement in all embodiments.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Alcaide et al. (2020) and Grunt et al. (2018).
Independent claim 1 encompasses a method of detecting a disease by analyzing for the presence of cfDNA, in particular detecting ‘aberrant fragments’ frequency in healthy and diseased subjects. Given the guidance of the specification and requirements of the claims, aberrant encompasses any of observable differences seen between the healthy and diseased subjects, either in unique/novel cfDNA fragments or changes in the start/end sites of the fragments. Claim was amended to include that the comparison is performed between subjects of the same species being diseased and healthy, implying claim 1 broadly comprises comparing disease and healthy states among any different species. Dependent claims provide that further information about the subjects be provided for training and assessment, the disease of interest is cancer, source of the sample being tested containing cfDNA, specific known machine learning algorithms such as SVM or random fores, and numbers of fragments per sample are analyzed to provide for informative information correlating observed cfDNA and the state of the subject.
At the time of the invention, cfDNA changes in disease states was well known, and the change was provided relative to individuals without the disease. For example, Grunt et al. teach that circulating cell-free DNA (cfDNA) is a non-invasive and powerful detection, diagnosis, prognosis, therapy response monitoring and recovery prediction tool, which opens new possibilities in fields of prenatal care, tumor therapy and transplant medicine. Generally, it is known that cfDNA is released from cells undergoing apoptosis and necrosis or by active secretion, and depending on the source and mechanisms of release, cfDNA is shed as fragments with different genetic and epigenetic profiles and in various lengths into the bloodstream. More specifically related to the requirements of the claims, Grunt et al demonstrate that in pathological states, cfDNA size patterns have been found to vary from cfDNA released under healthy physiological conditions and provides evidence for the clinical relevance of size and implicitly different start and ends of cfDNA fragments that are present in circulating DNA. Similarly,
Alcaide et al. provide evidence and detailed analysis for evaluating the quantity, quality and size distribution of cell-free DNA using digital PCR. Alcaide et al. provide a more detailed explanation stating ‘Generally, cfDNA circulates in fragments ranging between 120–220 bp, or multiples thereof, with a maximum peak at 167 bp. This pattern agrees with the length of DNA wrapped around a single nucleosome, plus a short stretch of ~ 20 bp (linker DNA) bound to a histone H13,8. As nucleosome positioning varies between different tissues, and in malignant neoplasms, the local pattern of fragmentation has been shown to aid in determining the predominant cell-type of origin contributing to the cfDNA pool’. In the art, Alcaide summarizes that since their initial description in 1948, small DNA fragments travelling in the non-cellular component of internal bodily fluids and excretions have revolutionized numerous fields in public health and preventive medicine. Cell-free DNA is generally thought to arise from cellular breakdown mechanisms but also through active release from living cells. More specifically, the analysis of altered nucleosome fingerprints, together with outstanding advances regarding the characterization of the cfDNA methylome, one can correlate the detection and classification of even early-stage cancers using cfDNA, and that it has become a comprehensive biomarker in the fields of non-invasive cancer detection and monitoring, organ transplantation, prenatal genetic testing and pathogen detection.
Given the known correlation of cfDNA changes with disease and a variety of other conditions, it would have been prima facie obvious to one having ordinary skill in the art at the time the invention was made to analyze size or sequence data of cfDNA from a variety of subjects with a variety of conditions/diseases as compared to healthy individuals to find informative sequences and changes using classifiers such as that provided by machine learning. Machine learning tools such as SVM and random forest were well known and used to classify data, and one having ordinary skill in the art would have been motivated to use such computer analysis for a quicker means of analyzing large amounts of sequence data. Given the abundant knowledge about cfDNA and guidance of both Grutn et al. and Alcaide et al, there would have been a reasonable expectation of success given the results describing and demonstrating the versatility of various detection methods and known correlations.
Thus, the claimed invention as a whole was clearly prima facie obvious.
Conclusion
No claim is allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Joseph T Woitach whose telephone number is (571)272-0739. The examiner can normally be reached Mon-Fri; 8:00-4:00.
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/Joseph Woitach/ Primary Examiner, Art Unit 1687
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