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 .
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 10/10/2025 has been entered.
Applicant’s Amendment
Applicant’s amendment filed 10/17/2025 has been received and entered. Claims 1, 6, 12-17, 21 have been amended.
Examiner Comment: It is noted that two sets of amendments were provided, in case there are difference, for clarity the marked-up copy is being examined.
Claims 1-21 are pending.
TrackOne Request
Applicants request for Prioritized examination filed 12/27/2024 was GRANTED (see paper entered 2/7/2025).
This is Applicants’ first RCE, and did not request that the application be part of TrackOne.
Election/Restriction
Applicant’s election without traverse of Group I in the reply filed on 2/26/2025 is acknowledged, and in prosecution it was noted that upon initial search and review it did not appear that examination of both groups would be an undue burden and the restriction requirement was withdrawn.
Claims 1-21, drawn to a method and a system for detecting CNV through analyzing cfDNA.
Priority
This application filed 12/27/2024 claims benefit to US provisional 63/615773 filed 12/28/2023.
Applicants provide no comment on the summary of priority.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-21 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention is withdrawn.
The amendments to the claims have addressed the issues of record.
Claims 1-21 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Specifically, a new wherein clause is provided describing ‘quality controlled cfDNA sequence data’ to replace a specific selection step and it is unclear if the wherein clause is simply descriptive or if it is still an active requirement. As a wherein clause it appears it is relative and unclear to what or where the data is derived and specifically, it is unclear if ‘quality controlled’ is defined by the various characteristics or that some form of undefined ‘quality controlled’ source is provided and whether it represents some form of data derived from the sample. While it appears that cfDNA is provided in the blood sample, it is unclear what the relationship of quality controlled cfDNA read data is to this sample source or if somehow related to the reference genome, and more generally how it is to be used in the method as a whole.
Additionally, the claim has been amended from applying a model to generating ‘the trained model, and there is insufficient antecedent basis for the model; it is unclear what the model is, how it is generated, or how any or what features are encoded such that it provides any specific type of output from analysis. It is acknowledged that the claim provides possible relationships that may be observed however step e) and detecting is not clear. Further, step e) lists several machine learning model formats but fails to specifically indicate how they are applied, and only lists desired types of aneuploidies or alterations that might be observed. Dependent claims have been amended consistently and fail to address the issue and are included in the basis of the rejection.
More clearly setting forth limitations as to what and how the quality controlled cfDNA data is, and addressing necessary steps in how it is used in creating a model and how the model is applied to cfDNA read data would address the basis of the issues in the rejection above.
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 1-21 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 still is generally directed to a method for the analysis of sequence reads obtained from cfDNA present in a pregnant woman to assess the genotype of the fetus as reflected in the read data obtained. The amendment to claim 1 has removed an active step and has provide a wherein clause describing control cfDNA to be used in the analysis, and removed applying a model to the analysis to ‘generating and encoding input features for the trained model’ and provides relationships of what might be observed and the listing of known machine learning models generically which can be used and applied to cfDNA read data to determine the possible presences or absence of known genetic alterations. There are no specific rules for training a machine learning model and the claims provide equations/relationships of what might be observed in read data for deletions, duplications or normal genomes. The claims and the algorithms appear to provide a statistical prediction of normalized read data for the detection of alterations in the genome. There are no specific means of providing the initial read data that is analyzed and only that it is derived from the blood of a pregnant female. The basis of the invention is the analysis of read data, and is based on the observation that cfDNA provides a representation of the genome, and if within the genome there are duplications, deletions or other types of possible variations, the cfDNA would reflect these variations. For the claims, the method provides broadly and generically the use of steps for the collection and processing of a sample to obtain sequence reads which are known in the art, and after obtaining read data, it is subsequently analyzed by binning the reads and determining if a copy number variable (CNV) exists using model learning assessing maternal and fetal reads and changes above and/or below a threshold indicates a CNV.
Dependent claims have not been amended, and still the claims set forth specific relationships of expected read information and the use of known modeling tools such as Gaussian mixture model, SVM or HMM for the analysis. Dependent claims provide for limitations related to the number of reads and read depth, and considerations and requirements on training models, as well as specific target chromosomal areas known to be associated with genetic disorders (see claims 9-11 for example). In view of the teaching of the specification the formulas set forth in the claim set forth relationships or ratios which might be expected to be observed in the read data and which would be indicative of a change, for example insertion, deletion or duplication, which indicative of a CNV associated with known genetic disorders. Further, the steps are completely relative and dependent by the sequence data which obtained for binning, GC analysis, determination if shorter reads are fetal or maternal and final interpretation of the data once assessed for CNV in a bin.
For step 1 of the 101 analysis, the claims are found to be directed to a statutory category of a process and product.
For step 2A of the 101 analysis, the judicial exception of the claims are the steps of accessing sequence data for whether the number of a given sequence reads indicates a CNV. The step of aligning and comparing sequence to arrive at the identification of bins and counting the number of reads in a bin are instructional steps. In light of the specification and art of record, the claim requires computing similarity scores for alignment and binning sequences, as well as a general assessment of increase/decrease from expected number of reads associated with a normal genome. The judicial exception is a set of instructions for analysis of sequence data and appears to fall into the category of Mathematical Concepts, here mathematical relationships and/or mathematical formulas or equations which describe the expected change in reads if a CNV is present; and Mental Processes, that is concepts performed in the human mind (including an observation, evaluation, judgment, opinion) since reads can be aligned, binned and assessed for difference by observation. The breadth of detecting, analyzing, and applying models encompasses non-transformative visual assessment of an individual for given genotypes, coupled in part with prior knowledge of the correlation of said differences with genetic disorders. This breadth does not impose a meaningful limit on the claim scope, such that all others are not precluded from using the natural principle. Although the claims recite using models often implemented in a computer environment, 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). As the steps are very generally recited, the combination of together reasonably interpreted as mere data gathering and analysis. Computing, constructing datasets and using statistical models was well understood, conventional, and routinely performed in the art at the time the application was filed. 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 or amount of data that is collected and analyzed 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 steps of obtaining the read data appear to be generic and separable as generically providing data for further analysis. This judicial exception requires steps recited at high level of generality and are only stored on a non-transitory, and is not found to be a practical application of the judicial exception as broadly set forth.
For step 2B of the 101 analysis, each of the independent claims recites additional elements for obtaining a sample and sequencing cfDNA to obtain read data, and are found to be the steps of obtaining sequence data. The analysis is performed using a computer, and the system is consistent with obtaining reads with a sequencer and analyzing the data with a computer, and as such, the claims do not provide for any additional element to consider under step 2B which appears significantly more nor a technical improvement when viewed as a whole. It is noted with respect to the recitation of using a computer 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. While the instruction 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 sequence reads. 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 a deletion or insertion) to generate additional information is not patent eligible. Furthermore, if a claim is directed essentially to a method of calculating, using a mathematical formula, even if the solution is for a specific purpose, the claimed method is non-statutory. In other words, patenting abstract idea like determining CNV by changes in the read data that represents the status of the genome of a fetus cannot be circumvented by attempting to limit the use to a particular technological environment or purpose and desired result.
Response to Applicants’ arguments
Applicants submit that the claims as amended are directed to patent eligible subject matter because it provides ‘a specific technical solution to a real clinical problem’ when analyzing cfDNA or more generally NIPT. Providing an overview of the claims, Applicants argue there is real biological input which is not abstract, specific features for ‘encoding’ and are not conventional was of normalization or general statistics for the analysis, and that a structured model is provided with structured objectives noting the various attributes of the algorithm. Applicants argue that the invention is rooted in specific concrete technologies and that overall, the claims provide significantly more by adding mathematical scores to help a method to quickly search for specific information among the vast amount of data.
In response, the initial steps of providing a sample and obtaining sequence read data representing the cfDNA in the sample is not abstract has been acknowledged and considered under step 2B as a known and generic means of obtaining read data. Both the generic methods of obtaining read data and the presence of fetal DNA in the blood of the pregnant female was well known and found to be conventional means of obtaining data for further analysis. The difference as set forth in the claims is the statistical analysis of the read data, and appears to be what the claim as a whole is directed to.
While it is acknowledged that mathematical formulas are set forth in the claims, however it does not appear that this results any specific structured model as there is no specific features and means that appear to provide a unique or dynamic ‘model’ and the steps generally appears to provide the relationship of the reads that might be observed and how one would interpret ‘unbalanced’ read amounts as either normal, duplications or deletions once the data is normalized. As argued previously, given that genome of the fetus can be anything, it does not appear that one common model would allow to quickly search through data more quickly as generically argued. To the contrary, it appears that the cfDNA read data has to be completely analyzed into bins, then potentially re-assessed based on the GC content of the bins, then using the bin data a model is used to establish various parameters and values to see if there is a correlative change in a bin that represents a change in the genome associated and in the respective bin(s). There does not appear to be any steps which reduce the ability to assess or more quickly assess data as asserted by Applicants.
With respect to Applicants arguments that there is significantly more than formulas and that the computer technology and processing is improved computer technology. It is acknowledged that depending on the amount and complexity of the data, the analysis of mapping, binning and the calculations for representative number of reads and interpretation of the read numbers can be computer intensive, however there does not appear to be any evidence that the method as claimed does anything to reduce or address this issue. The steps of the claims are generic and high level, being most specific in how values are determined, but do not appear to reduce the burden of the data analysis. Further, in view of the previous claims and now claim 21, the method of claim 1 does not necessarily result in an efficient or correlative result for interpretation since it can be subject to further re-assessment to optimize the analysis and model of the data.
Given the evidence of record and the claim limitation requirements, it appears that the claims are directed to mapping and assessing the number of reads in a given region of a human genome and interpreting changes in the number of reads as an indication of a variation in that region of the genome of a fetus. The present claims appear to be directly completely to a judicial exception of analyzing read data without any application nor any evidence for improvement of computer function for the practice of the claimed invention as a computer implemented method. Accordingly for the reasons above and of record, the rejection is maintained.
As noted previously, 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.
Conclusion
No claim is allowed.
Reviewing the results of an inventor search, US Applications 17/930705 is noted as a method that analyzes cfDNA for the presence of cancer, however the claims do not provide the same analysis steps and also require specific targets than the present claims provide.
More generally, the art of record demonstrates the use of cfDNA in analyzing the fetal genome. For example, Zhao et al. provide detailed teaching for the detection of fetal sub-chromosomal abnormalities by sequencing cfDNA from maternal plasma generally consistent with the intent of the present claims. Similarly, Familiari et al. provide a systemic review of cell‐free DNA analysis of maternal blood in prenatal screening for chromosomal microdeletions and microduplications. The art provided a variety of means of analyzing the read data, including the use of artificial intelligence and machine learning methods to improve pregnancy outcomes reviewed by Davidson et al. However, while the art provides for the usefulness of analyzing cfDNA and many method of assessment for known genetic abnormalities which provide for insertion, deletion and copy number variations derived from the sequence data, it fails to provide the specific limitations required of the instant claims such as providing perchrx, RC and DP values for modeling chromosomal changes.
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