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 .
Status of the claims
Claims 71, 154, 156, 161, 163, 164, 166-170, 172, and 175 are currently pending.
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 71, 154, 156, 161, 163, 164, 166-170, 172, and 175 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Under 35 U.S.C. § 101, an invention is patent-eligible if it claims a “new and useful process, machine, manufacture, or composition of matter.” However, not every discovery is eligible for patent protection. Diamond v. Diehr, 450 U.S. 175, 185 (1981). “Excluded from such patent protection are laws of nature, natural phenomena, and abstract ideas.” Id. The Supreme
Court articulated a two-step analysis to determine whether a claim falls within an excluded category of invention. Alice Corp. v. CLS Bank Int’l, 573 U.S. 208 (2014); Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 75–77 (2012).
In the first step, it is determined “whether the claims at issue are directed to one of those patent-ineligible concepts.” Alice, 573 U.S. at 217. If it is determined that the claims are directed to an ineligible concept, then the second step of the two-part analysis is applied in which it is asked “[w]hat else is there in the claims before us?” Id. (alteration in original) (citation and
quotation marks omitted). The Court explained that this step involves a search for an “‘inventive concept’”—i.e., an element or combination of elements that is “sufficient to ensure that the patent in practice amounts to significantly more than a patent upon the [ineligible concept] itself.”
Alice, 573 U.S. at 217–18 (alteration in original) (citing Mayo, 566 U.S. at
75–77).
Alice, relying on the analysis in Mayo of a claim directed to a law of nature, stated that in the second part of the analysis, “the elements of each claim both individually and ‘as an ordered combination’” must be considered “to determine whether the additional elements ‘transform the
nature of the claim’ into a patent-eligible application.” Alice, 573 U.S. at 217 (citation omitted).
The PTO published revised additional guidance on the application of 35 U.S.C. § 101. Eligibility Guidance, 84 Fed. Reg. 50, MPEP 2016. This guidance provides additional direction on how to implement the two-part analysis of Mayo and Alice.
Step 2A, Prong One, of the Eligibility Guidance, looks at the specific limitations in the claim to determine whether the claim recites a judicial exception to patent eligibility. In Step 2A, Prong Two, the claims are examined to identify whether there are additional elements in the claims that integrate the exception in a practical application, namely, is there a “meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception.” 84 Fed. Reg. 54 (Prong Two).
If the claim recites a judicial exception that is not integrated into a practical application, then as in the Mayo/Alice framework, Step 2B of the 2019 Eligibility Guidance instructs us to determine whether there is a claimed “inventive concept” to ensure that the claims define an invention that is significantly more than the ineligible concept itself. 84 Fed. Reg. 56.
With these guiding principles in mind, the claimed subject matter is evaluated eligibility under 35 U.S.C. § 101.
Step 1: Whether the claim is to a statutory category—MPEP 2106.03
The claims are directed to a “method.” A method is also a “process,” one of the broad statutory categories of patent-eligible subject matter under 35 U.S.C. § 101. Because the claim falls into one of the statutory categories of patent-eligible subject matter, following the first step of the Mayo/Alice analysis, we proceed to Step 2A, Prong One, of the Eligibility Guidance (MPEP 2106.04).
Step 2A, Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? – MPEP 2106.04 II.A.1.
In Step 2A, Prong One, of the Eligibility Guidance, the specific limitations in the claim are examined to determine whether the claim recites a judicial exception to patent eligibility, namely, whether the claim recites an abstract idea, law of nature, or natural phenomenon. Eligibility Guidance, 84 Fed. Reg. at 54; MPEP 2106.04 II.A.
Claims 71 and 175 ’s method of determining the cellular origin of variant alleles has the steps, (A) obtaining a dataset comprising a first plurality of nucleic acid fragment sequences in electronic form … , (B) compressing the dataset by assigning … , (C) assigning each respective variant allele of a respective locus in the plurality of loci either to a first category of alleles originating from non-cancerous cells or to a second category of alleles originating from cancer cells … .
Step (B) has the language compressing the dataset by assigning … a per-allele size distribution metric … thereby obtaining a set of size-distribution metrics, wherein the per-allele size-distribution metric is computed independently for the reference allele and the variant allele at the locus, which corresponds to an abstract idea of a mathematical calculation – i.e., the Specification describes [0140]: “In some embodiments, a size-distribution metric refers to a single value that is representative of the distribution, e.g., a central tendency of length across the distribution, such as an arithmetic mean, weighted mean, midrange, midhinge, trimean, Winsorized mean, median, or mode of the distribution.”
Step (C) has the language providing the compressed dataset to a machine learning model that is trained using per-allele size-distribution metrics in combination with one or more quantitative features on a target dataset, wherein the machine learning model is configured to analyze the compressed dataset and classify each respective variant allele of a respective locus in the plurality of loci as originating from a cancer cell or a non- cancerous cell based on the per-allele size-distribution metric. The step of “providing … to a machine learning model” is described in the specification as a numerical algorithm (Specification [0283]: “In some embodiments, the parametric or non-parametric based classifier is an expectation maximization algorithm”) which is equivalent to an abstract idea of mathematical equation. In addition, the machine learning model includes the step of analyzing the compressed dataset and classifying variant alleles, which, given the plain meaning of analyzing and classifying, falls into mental processes of abstract ideas since human mind is capable of classifying data based on the result of a data analysis.
Step (D) has the language receiving, from the machine learning model, a category assignment for assigning each respective variant allele of [[a]] the respective locus in the plurality of loci that designates whether each respective variant allele originates from the cancer cell or the non-cancerous cell.
The step of designating variant allele can be particularly performed in human mind (mental process), because human mind is able to distinguish cancer cells and non-cancerous cells based on a size distribution matrix (also, a mathematical concept).
Claim 71 recites (E) generating, based on the category assignments, a report indicating the likelihood that the subject has a cancer-related variant.
The step of generating a report can be practically performed in human mind (mental process), for example by using a pen and paper, since humans are capable of generating/creating a report/document based on known data/category assignment. Additionally, the limitation “indicating a likelihood” is considered as mathematical calculation of a probability, and as such, falls into mathematical concepts groupings of abstract ideas.
Claim 175 recites (E) leveraging the category assignment for each respective variant allele of the respective locus in the plurality of loci received from the machine learning model to identify a genetic change in the subject associated with cancer.
The step of leveraging the category assignment can be practically performed in human mind (mental process), since human mind is capable of leveraging an assignment to identify a change (for example, genetic change) based on the result of an analysis.
Thus, claims 71 and 175’s claim steps of obtaining, compressing, providing, receiving and leveraging when given their broadest reasonable interpretation correspond to mental processes, mathematical calculations or associated data gathering for use in the calculation.
In addition, the claims recite a law of nature or natural phenomena that encompasses the discovery of a correlation of sequence data with cancer cells. Such a correlation is merely the natural relationship between a genomic sequence and the presence of cancer.
2A, Prong One is Yes.
Step 2A, Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? – MPEP 2106.04 II.A.2.
Prong Two of Step 2A of the Eligibility Guidance asks whether there are additional elements that integrate the exception into a practical application. As explained in the Eligibility Guidance, integration may be found when an additional element “reflects an improvement in the
functioning of a computer, or an improvement to other technology or technical field” or “applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is
more than a drafting effort designed to monopolize the exception.” Eligibility Guidance, 84 Fed. Reg. at 55.
The additional elements of claim 71 are:
The additional element of obtaining refers to “sequences in electronic form from a first biological sample” representing cell-free DNA having an allele. This step corresponds to mere data gathering from another source not encompassed in the claimed method, see Specification [0200], [0217], [0244], [0266], [0290], [0315]. Such a step does not meaningfully limit the claim because the data is collected from an unknown source and is really only sequence data that “represents all or a portion of a respective cell-free DNA molecule” encompassing a generic “locus” and “allele” for use in the mathematical calculation. Thus, the sequence data limitations are selecting a particular type of data for use in the mathematical calculation and are an insignificant extra-solution activity that does not meaningfully limit the claim. Such a step does not integrate the judicial exception into a practical application in Step 2A Prong Two and does not amount to significantly more in Step 2B of 101 analysis. MPEP 2106.05(g).
The additional element of “providing the dataset” equate to mere data gathering activity because it serves merely to provide data that is analyzed by the judicial exception. The courts have identified limitations that merely gather data as insignificant extra-solution activity that does not integrate the abstract idea into a practical application (see MPEP 2106.05(g)).
Similarly, the additional element of “receiving category assignments” equate to mere data gathering activity because it serves merely to provide data that is analyzed by the judicial exception. The courts have identified limitations that merely gather data as insignificant extra-solution activity that does not integrate the abstract idea into a practical application (see MPEP 2106.05(g)).
The dependent claims 154, 156, 161, 163, 164, 166-172 all relate to selecting of a particular type of data for use in the mathematical calculation and do not recite any additional elements.
Therefore, the additional elements amount to insignificant extra solution activities, and as such, the claims as a whole do not integrate the judicial exception into a practical application. Thus, claims 71, 154, 156, 161, 163, 164, 166-172, and 175 are directed to an abstract idea. [Step 2A, Prong 2: NO].
Step 2B Whether there is an inventive concept – MPEP 2106.05
Step 2B of the Eligibility Guidance asks whether there is an inventive concept. In making this Step 2B determination, we must consider whether there are additional limitations or elements recited in the claim “that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present,” or whether the claim “simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.” Eligibility Guidance, 84 Fed. Reg. 56.
If there are no meaningful limitations in the claim that transform the exception into a patent-eligible application, such that the claim does not amount to significantly more than the exception itself, the claim is not patent-eligible.
The claims also do not include limitations that result in the claims as a whole amounting to significantly more than the judicial exception. The claims refer to obtaining a “dataset” from another source which is then manipulated by numerical calculations to classify aspects of a sample. Details regarding the type of algorithm or specifying the particular data selected for input into the exception are not significantly more. Thus, none of the claims include limitations such that the claims amount to significantly more than the exception itself, the claim is not patent-eligible. [Step 2B: NO]
Therefore, the instantly rejected claims are not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Response to Arguments
Applicant's arguments filed 02/10/2026 have been fully considered but they are not persuasive.
Applicant states:
Under the August 2025 Memo, a claim recites a mental process only where the steps "can practically be performed in the human mind," including operations such as "observations, evaluations, judgments, and opinions."Auqust 2025 Memo at p. 2. The August 2025 Memo cautions examiners against improperly extending the "mental process" category to claims involving machine learning or large-scale biological data analysis. jd. Here, the claimed method cannot practically be performed in the human mind: it requires calculating per-allele size-distribution metrics independently for the reference and variant alleles at multiple genomic loci, compressing those metrics, and applying machine learning models trained to classify allele-level fragment profiles as being derived from tumor or non-tumor sources. Such steps involve nontrivial biological data processing, locus-specific modeling, and computational inference, all of which exceed human cognitive capability and fall outside the scope of any "mental process" exception.
It is respectfully submitted that the above statement is not persuasive. The Applicant remarks are directed to Step 2A Prong One of 101 analysis, specifically that whether the claims recite a judicial exception.
With regards to Applicant stating “improperly extending the "mental process" category to claims involving machine learning or large-scale biological data analysis.”, Examiner submits that instant claims do not require large-scale biological data analysis as it analyses a portion of cfDNA (for example, 100 bps). Therefore, calculating and compressing (abstract ideas) data from a portion of cfDNA using a machine learning (mathematical algorithm) are within human capabilities. Furthermore, Examiner stated that the steps of calculating and compressing and using a machine learning algorithm are mathematical processes not mental processes.
Applicant further states:
The Office further alleges that "the step of "providing... to a machine learning model" is described in the specification a numerical algorithm... which is equivalent to an abstract idea of mathematical equation. See Office Action at p. 6. Applicant disagrees that the cited limitations recite a judicial exception related to a mathematical concept. As emphasized in the August 2025 Memo, statistical operations and numerical values do not themselves constitute a mathematical concept unless a mathematical relationship, formula, or algorithm is expressly recited. Id. at 3. The present claims do not disclose or rely on specific equations or algorithms, but rather utilize trained machine learning models (e.g., classifiers) operating on compressed per-allele fragment metrics. This mirrors the structure discussed in example 39 discussed in the August 2025 Memo, i.e., that "even though 'training the neural network' involves a broad array of techniques and/or activities that may involve or rely upon mathematical concepts, the limitation does not set forth or describe any mathematical relationships, calculations, formulas, or equations using words or mathematical symbols. August 2025 Memo at p.3.
It is respectfully submitted that the above statement is not persuasive. The providing step classifies and analyses data using a trained machine learning model. The claims do not provide any details about how the trained ANN operates or how the classification and analysis are made, and the plain meaning of “classifying” and “analyzing” encompass mental observations or evaluations. Furthermore, Under its broadest reasonable interpretation when read in light of the specification, the “classifying” encompasses mathematical calculation, specification [0021] “a parametric or non-parametric based test classifier that evaluates the size distribution metric for the respective allele in each respective validation genotype data construct and each correlated cancer status in the set of cancer conditions” [00250] “the parametric or non-parametric based classifier is an expectation maximization algorithm” [00252] “the parametric or non-parametric based classifier is an unsupervised clustering algorithm”. Additionally, “providing the dataset” quates to mere data gathering (insignificant extra solution activity).
Further with regards to applicant stating that “The present claims do not disclose or rely on specific equations or algorithms”, Examiner states that the compressing step includes specific mathematical calculations of per-allele size distribution matric corresponding to a mean fragment length.
With regards to Applicant referring to Example 39, Examiner submits that in contrast to Example 39, which includes an active step of training using the created training dataset, instant application recites the step of providing a dataset to a machine learning model (data gathering/insignificant extra solution activity) that is trained to classify and analyze data (abstract ideas); there are no active steps pertaining to machine learning or any details of how the algorithm trains and works using the dataset to classify data.
Applicant further states:
In Desjardins, the ARP rejected an eligibility analysis that abstracted away the technical details of an Al system and reduced the claims to a high- level description of "an algorithm applied to data," The ARP explained that such over- generalization improperly ignores the specific structure of the claimed data representations, the training and operation of the model, and the technological problem being solved. The previously presented and/or currently claimed embodiments are not directed to a disembodied mathematical formula or a generic algorithm operating on abstract numbers. Rather, the claimed limitations are directed to a specific technological workflow that transforms biologically derived sequencing data into per-allele fragment-distribution metrics and applies a trained machine learning model to classify the cellular origin of those alleles. The claims recite concrete data structures (e.g.,-21- allele-specific fragment length distributions), defined data transformations (e.g., compression into per-allele metrics), and a trained classifier that operates on those domain-specific inputs to produce a clinically meaningful output.
As in Desjardins, the Office Action here characterizes the invention at an impermissibly high level of generality, reducing a biologically grounded machine- learning pipeline to merely obtaining a dataset and performing abstract classification. This characterization overlooks the claim's specific technological features, including the allele-level compression of cfDNA fragment distributions and the use of biologically informed training data to enable origin classification. Under Desjardins, eligibility cannot be evaluated by stripping away these technical details and recasting the invention as a generic algorithm.
Characterizing this sequence of steps as merely an "algorithm" improperly abstracts away the biological context and the technical specificity of the claimed method, precisely the type of high-level generalization cautioned against in Desjardins. Moreover, unlike a pure mathematical concept, the claims do not recite any formula, equation, or mathematical relationship divorced from application. Instead, they define a particular way of processing genomic data that cannot practically be performed in the human mind and that is rooted in improvements to computational analysis of cfDNA. Consistent with both the August 2025 Memo and the reasoning of Desjardins, such claims should not be treated as reciting a judicial exception under Step 2A, Prong One.
It is respectfully submitted that the above statement is not persuasive. The Applicant remarks are directed to Step 2A Prong Two of 101 analysis, specifically whether the additional elements integrate the recited judicial exception into a practical application of the exception.
With regards to applicant referring to Desjardin, Examiner submits that in Desjardin the improvement was to how the machine learning model itself operates (improvement to the machine learning architecture), and not, for example, mathematical calculations (abstract ideas).
“The independent claim in Ex parte Desjardins contained specific limitations as to how at least some aspects of the asserted improvements are achieved: (emphasis added).
"When evaluating the claim as a whole, we discern at least the following limitation of independent claim 1 that reflects the improvement: "adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task." We are persuaded that constitutes an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation." Ex parte Desjardins, p9
In contrast, instant claims do not clearly set forth the link between the data gathered, the initial training of the ML, the structure of the ML, and how training or retraining affects the structure to obtain the desired results. Emphasis added.
Further with regards to Applicant referring to August 2025 Memo, Examiner states that “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).
Applicant further states:
The claims do not merely "obtain a dataset" (1;. at p. 10) and apply unspecified numerical processing. Rather, the claimed method recites a series of domain-specific and interdependent steps that collectively operate on cfDNA methylation data at the fragment and allele level, culminating in the computation of cell source fractions, which is a clinically actionable output that informs diagnostic decisions. This multi-step workflow is neither conventional nor abstract. For instance, as explained in the specification (e.g., at [0112] - [0114]; [0208], [0352]), the classifier is trained on real biological samples with known origin labels, enabling non-binary scoring that reflects subtle methylation patterns not discernible from raw counts alone. The resulting values are not generic features, but biologically specific variables that reflect tissue origin at a high resolution.
Moreover, the Office has not established, with the factual showing required under Berkheimer v. HP Inc., 881 F.3d 1360, 1368 (Fed. Cir. 2018), that the specific combination of these steps, e.g., fragment-level methylation scoring, allele-wise compression, reference-based normalization, and statistical cell-type deconvolution, is well-understood, routine, or conventional. No cited reference, alone or in combination, discloses this full pipeline, and none teach a machine learning classifier trained on methylation state vectors aggregated per-allele to infer cell origin at the level claimed.
It is respectfully submitted that these are not persuasive. The Applicant remarks are directed to Step 2B of 101 analyses, specifically evaluating additional elements to determine whether they amount to an inventive concept by considering them both individually and in combination to ensure that they amount to significantly more than the judicial exception itself.
The additional elements of obtaining data, providing the dataset, and receiving a category assignment, and generating a report amount to necessary data gathering and outputting, as such, is insignificant extra-solution activity and does not integrate the judicial exception into a practical application. As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept. See MPEP 2106.05(g)(3).
Further with regards to Applicant stating “No cited reference, alone or in combination, discloses this full pipeline”, Examiner submits that "‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter." Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1315, 120 USPQ2d 1353, 1358 (Fed. Cir. 2016) (quoting Diamond v. Diehr, 450 U.S. at 188–89, 209 USPQ at 9). See also Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016) ("a claim for a new abstract idea is still an abstract idea. The search for a § 101 inventive concept is thus distinct from demonstrating § 102 novelty."). In addition, the search for an inventive concept is different from an obviousness analysis under 35 U.S.C. 103. See, e.g., BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1350, 119 USPQ2d 1236, 1242 (Fed. Cir. 2016). See MPEP 2106.05 I.
Therefore, the instantly rejected claims are not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 71, 154, 156, 161, 163, 164, 166-172 and 175 are rejected under 35 U.S.C. 103 as being unpatentable over Abdueva (WO2018009723A1) in view of Underhill et al. (Fragment Length of Circulating Tumor DNA, Published: July 18, 2016, PloS Genetics, 12(7): e1006162. Pages: 1-24).
Regarding claim 71, Abdueva discloses (i) providing a training set comprising: (1) a set of reference distribution scores for each of one or more populations of cell-free DNA from each of a plurality of control subjects, wherein each reference distribution score is indicative of one or more of: (i) a length of the DNA fragments that align with each of a plurality of base positions in a genome, (ii) a number of the DNA fragments that align with each of a plurality of base positions in a genome, and (iii) a number of the DNA fragments that start or end at each of a plurality of base positions in a genome; (2) a set of phenotypic distribution scores for each of one or more populations of cell-free DNA from each of a plurality of subjects having an observed phenotype, wherein each phenotypic distribution score is indicative of one or more of: (i) a length of the DNA fragments that align with each of a plurality of base positions in a genome, (ii) a number of the DNA fragments that align with each of a plurality of base positions in a genome, and (iii) a number of the DNA fragments that start or end at each of a plurality of base positions in a genome [0015].
Abdueva further discloses analyzing cell-free deoxyribonucleic acid (DNA) fragments derived from a subject [0017] and that the dataset is a collection of cfDNA from a biological sample [00153].
Abdueva further discloses that the biological sample refers to a tissue or fluid sample derived from a subject... A biological sample can comprise, for example, a bodily fluid .. Bodily fluids include, for example, blood, serum, plasma, tumor cells, saliva, urine, lymphatic fluid, prostatic fluid, seminal fluid, milk, sputum, stool, tears, and derivatives of these [00139].
Reading on limitations of (A) obtaining a dataset comprising a first plurality of nucleic acid fragment sequences in electronic form from a first biological fluid sample from a subject.
Abdueva further discloses that methods provided herein may use sequence information in a macroscale and global manner, with or without somatic variant information, to assess a fragmentome profile that can be representative of a tissue of origin, disease, progression [0004].
Abdueva discloses (i) providing a training set comprising: (1) a set of reference distribution scores for each of one or more populations of cell-free DNA from each of a plurality of control subjects, wherein each reference distribution score is indicative of one or more of: (i) a length of the DNA fragments that align with each of a plurality of base positions in a genome, (ii) a number of the DNA fragments that align with each of a plurality of base positions in a genome, and (iii) a number of the DNA fragments that start or end at each of a plurality of base positions in a genome; (2) a set of phenotypic distribution scores for each of one or more populations of cell-free DNA from each of a plurality of subjects having an observed phenotype, wherein each phenotypic distribution score is indicative of one or more of: (i) a length of the DNA fragments that align with each of a plurality of base positions in a genome, (ii) a number of the DNA fragments that align with each of a plurality of base positions in a genome, and (iii) a number of the DNA fragments that start or end at each of a plurality of base positions in a genome [0015] (instant specification para (000128] .. As used herein, the term "locus" refers to a position (e.g., a site) within a genome, i.e., on a particular chromosome. In some embodiments, a locus refers to a single nucleotide position within a genome, i.e., on a particular chromosome. In some embodiments, a locus refers to a small group of nucleotide positions within a genome, e.g., as defined by a mutation.. instant specification para [00129) .. As used herein, the term "allele" refers to a particular sequence of one or more nucleotides at a chromosomal locus).
Abdueva further discloses identifying a sequence aberration at a given locus by incorporating information about known somatic mutations detected at any other loci in a genome [00217].
Reading on limitations of “wherein each respective nucleic acid fragment sequence in the first plurality of nucleic acid fragment sequences represents all or a portion of a respective cell-free DNA molecule in a population of cell-free DNA molecules in the first biological fluid sample, the respective nucleic acid fragment sequence encompassing a corresponding locus, in a plurality of loci, represented by at least a reference allele and a variant allele within the population of cell-free DNA molecules”.
Abdueva further teaches a classifier for determining genetic aberrations in a test subject using deoxyribonucleic acid (DNA) fragments from cell-free DNA obtained from the test subject, comprising: (a) an input of a set of distribution scores for each of one or more populations of cell-free DNA obtained from each of a plurality of subjects, wherein each distribution score is generated based at least on one or more of: (i) a length of the DNA fragments that align with each of a plurality of base positions in a genome, (ii) a number of the DNA fragments that align with each of a plurality of base positions in a genome, and (iii) a number of the DNA fragments that start or end at each of a plurality of base positions in a genome; and (b) an output of classifications of one or more genetic aberrations in the test subject.
Abdueva further discloses that the classifier includes variant-free and variant-aware classifiers [00216-00217].
Abdueva further discloses ([00148] “The term ‘quantitative measure,’ as used herein … e.g., … mean”; [00220]; [00243]-[00252]; claims 3, 6, 7, 9, 10, and 28; [00153]: dataset including values indicating a quantitative measure of characteristics including size distribution; and [0078] a classifier for determining a likelihood that the subject belongs to one or more classes of clinical significance and that the quantitative measure comprises a size distribution of DNA sequences having the selected characteristics. Abdueva further discloses combining fragmentome profile existing somatic mutation panels, For example, if a certain SNV is predominantly present in shorter fragments than average (e.g., less than 155, 154, 153, 152, 151, 150, 149, or 148 bp in length), then it is more likely that the SNV is a somatic mutation (for example, allele-specific-detection)… The fragmentome profiling analysis may comprise performing a uni-parametric or multi -parametric analysis of cell-free DNA representative of a subject. From a given subject's sequence data, one or more expected distributions may be generated for each base position across the reference genome, where each expected distributions describes one or more of: the number of reads that map to the given position, the cell-free DNA fragment lengths that map to the given position, the number of cell-free DNA fragments that start at the given position, and the number of cell-free DNA fragments that end at the given position. [00239] (for example, fragmentome profiling analysis that involves analyzing the size distribution of cell-free DNA (cfDNA) fragments with a quantitative mean calculation, which allows for the differentiation between somatic mutation-carrying fragments (variant allele) and non-mutated fragments (reference allele)). Abdueva further discloses that the fragmentome profiling analysis may comprise performing a uni-parametric or multi -parametric analysis of cell-free DNA representative of a subject. From a given subject's sequence data, one or more expected distributions may be generated for each base position across the reference genome, where each expected distributions describes one or more of: the number of reads that map to the given position, the cell-free DNA fragment lengths that map to the given position, the number of cell-free DNA fragments that start at the given position, and the number of cell-free DNA fragments that end at the given position [00240]. Abdueva further discloses that by performing base pair-wise comparisons between sample and reference at a given locus of a genome, observations of any deviations from this pattern (e.g., increased or decreased number of reads than expected at a given base position, or a shift in the distribution) reveal tumor-relevant information, such as tumor burden, tumor type, tumor clonality or heterogeneity, tumor aggressiveness, etc. Such deviations are downstream consequences of nucleosomal positioning variation and of cellular processes [00241].
Reading on limitations of (B) compressing the dataset by assigning, for each respective allele represented at each locus in the plurality of loci, a size-distribution metric based on a characteristic of the distribution of the fragment lengths of the cell-free DNA molecules in the population of cell-free DNA molecules that encompass the respective allele, wherein the per-allele size-distribution metric is computed independently for the reference allele and the variant allele at the locus, and wherein the size-distribution metric corresponds to a value representative of a mean fragment length across the distribution.
Abdueva further discloses (C) providing the compressed dataset (providing as inputs into the classifier a set of distribution scores) to a machine learning model (computer-implemented classifier) that is trained using size-distribution metrics in combination with one or more quantitative features on a target dataset (training set; the multi-parametric analysis comprises mapping to each of a plurality of base positions or regions of a genome, one or more distributions selected from the group consisting of: (i) a distribution of the number of unique cell-free DNA fragments containing a sequence that covers the mappable position in the genome, (ii) a distribution of the fragment lengths for each of at least some of the cell-free DNA fragments such that the DNA fragment contains a sequence that covers the mappable position in the genome, and (iii) a distribution of the likelihoods that a mappable base-pair position will appear at a terminus of a sequenced DNA fragment [0018]) ( see also, [0013] the classifier further comprises an input of a set of genetic variants at one or more loci of the genome), wherein the machine learning model is configured to analyze the compressed dataset and classify each respective variant allele of a respective locus in the plurality of loci as originating from a cancer cell or a non- cancerous cell based on the per-allele size-distribution metric; and (E) generating, based on the category assignments, a report indicating the likelihood that the subject has a cancer-related variant (a trained classifier, comprising: (a) providing a plurality of different classes, wherein each class represents a set of subjects with a shared characteristic; (b) for each of a plurality of populations of cell-free DNA obtained from each of the classes, providing a multi-parametric model representative of cell-free deoxyribonucleic acid (DNA) fragments from the populations of cell- free DNA, thereby providing a training data set; and (c) training, by a computer, a learning algorithm on the training data set to create one or more trained classifiers (the classifier is variant-aware classifier [00216]), wherein each trained classifier is configured to classify a test population of cell-free DNA from a test subject into one or more of the plurality of different classes,… where each of the plurality of different classes is selected from the group consisting of: healthy and various cancers [0030-0031]; a method of generating a classifier for determining a likelihood that a subject belongs to one or more classes of clinical significance, where the class of clinical significance indicates a presence or absence of one or more cancers [0039]).
Further regarding claim 71, Abdueva discloses that one or more fragmentome profiles (or fragmentome data) may be incorporated into a classifier to determine the likelihood of presence or absence of one or more canonical driver mutations. Abdueva further discloses that a class of clinical significance may be a category, for example, indicating an abnormal biological state or a genetic variant. Examples of classes of clinical significance include (i) presence or absence of one or more genetic variants, (ii) presence or absence of one or more cancers, (iii) presence or absence of one or more canonical driver mutations, (iv) presence or absence of one or more disease subtypes (e.g., lung cancer molecular subtypes) [00194-00195].
Abdueva discloses fragmentome profiling determining SNV in unique molecules from a cell free DNA sample (for example, allele-specific detection) as well as fragment size of each unique molecule and adjusting the confidence score of the calling of a somatic SNV based on the size distribution of the unique molecules which include the SNV, where the size distribution is mean fragment length [00129] [00220], and wherein the fragmentome profiling includes generating the a given subject's sequence data, one or more expected distributions may be generated for each base position across the reference genome, where each expected distributions describes one or more of: the number of reads that map to the given position, the cell-free DNA fragment lengths that map to the given position, the number of cell-free DNA fragments that start at the given position, and the number of cell-free DNA fragments that end at the given position [00239-00242].
Abdueva does not expressly disclose that the size-distribution is a per-allele size-distribution metric. Underhill discloses a method of isolating specific subset of cfDNA fragment length to improve detection of ctDNA, where distinct differences in fragment length size between ctDNAs and normal cell-free DNA were defined. (abstract). Underhill further discloses calculating the allele-specific mean fragment length for healthy and tumor samples sample (figure 4, pgs. 8 and 9).
In KSR Int 'l v. Teleflex, the Supreme Court, in rejecting the rigid application of the teaching, suggestion, and motivation test by the Federal Circuit, indicated that “The principles underlying [earlier] cases are instructive when the question is whether a patent claiming the combination of elements of prior art is obvious. When a work is available in one field of endeavor, design incentives and other market forces can prompt variations of it, either in the same field or a different one. If a person of ordinary skill can implement a predictable variation, § 103 likely bars its patentability.” KSR Int'l v. Teleflex lnc., 127 S. Ct. 1727, 1740 (2007).
Applying the KSR standard to Abdueva and Underhill, the examiner concludes that the combination of Abdueva and Underhill represents the use of known techniques to improve similar methods. Both Abdueva and Underhill are directed to fragment size analysis of cfDNA. Abdueva disclosed a multi-parametric analysis of cfDNA which extracts fragmentomic features across multiple genomic locations and incorporates them into a machine learning classifier to determine a likelihood of that a subject belongs to one or more classes of clinical significance. Abdueva further discloses that the multi-parametric distribution comprises quantifying the length of the DNA fragment/ a size distribution of DNA sequences having feature selected from: (i) DNA sequences mapping to the genetic locus, (ii) DNA sequences starting at the locus, and (iii) DNA sequences ending at the genetic locus; and d) based on the dataset, determining a likelihood of the abnormal biological state (for example, per-allele size distribution; see instant specification para [00129] . As used herein, the term "allele" refers to a particular sequence of one or more nucleotides at a chromosomal locus) implicitly disclosing per-allele size distribution. Further Abdueva discloses fragmentome profiling determining SNV in unique molecules from a cell free DNA sample (for example, implicitly disclosing allele-specific detection) as well as fragment size of each unique molecule and adjusting the confidence score of the calling of a somatic SNV based on the size distribution of the unique molecules which include the SNV, where the size distribution is mean fragment length [00129] [00220], and wherein the fragmentome profiling includes generating the a given subject's sequence data, one or more expected distributions may be generated for each base position across the reference genome, where each expected distributions describes one or more of: the number of reads that map to the given position, the cell-free DNA fragment lengths that map to the given position, the number of cell-free DNA fragments that start at the given position, and the number of cell-free DNA fragments that end at the given position [00239-00242]. In the same field of research, Underhill provided the allele-specific determination of mean fragment length of cfDNA calculated for both reference and variant sample. Replacing the known per allele mean fragment length calculation of Underhill with fragment length distribution analysis of Abdueva would have provided a more robust predictive model to determine the presence or absence of the genetic aberration. One ordinary skilled in the art before he effective filing data of the claimed invention would have been capable of applying the known technique of Underhill to the base method of Abdueva and the results would have been predictable to one ordinary skilled in the art. One ordinary skilled in the art before he effective filing data of the claimed invention would have had a reasonable expectation of success at combining the method of Abdueva and Underhill. This combination would have been expected to have provided a diverse data modality model with higher sensitivity and accuracy. Therefore, the invention would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention, absent evidence to the contrary.
Regarding claim 154’s language wherein the subject has not been diagnosed as having cancer, Abdueva teaches where the subject is not diagnosed for cancer ([00140]).
Regarding claim 156’s language wherein the plurality of loci is selected from a predetermined set of loci that includes less than all loci in the genome of the subject, Abdueva teaches “The methods can focus on a particular genetic locus (or loci) of interest” ([0007]; [00248], Table 1; [00309]).
Regarding claim 161-164, Abdueva teaches high coverage ([00328]: “[00328] Distributions of cfDNA fragment length and position, and associated somatic genomic profiles of over 15 thousand patients with advanced- stage clinical cancer were determined by a highly accurate, deep-coverage (15,000x) ctDNA NGS test targeting 70 genes.”) which also corresponds to loci selected from all loci in the genome.
Regarding claim 166, Abdueva teaches a single nucleotide polymorphism locus ([00190]). Regarding claims 167-168, Abdueva teaches deletion of nucleotides, i.e., Exon 19 deletion ([00195]; claim 2; [0006]; [0026]; [00258]). Regarding claims 169-170, Abdueva teaches insertions (claim 2; [0006]; [0056]; claim 11; [00248], Table 1).
Regarding claims 170 and 172, Abdueva teaches the size distribution can be a median ([00148]); [0039]: “the quantitative measure comprises a size distribution of DNA sequences”).
Regarding claim 175, Abdueva teaches a method of determining the cellular origin of variant alleles present in a biological fluid sample (claim 1; claim 19; [0022]; [00198] “One or more fragmentome profiles (or fragmentome data) may be incorporated into a classifier to determine the likelihood of information derived from tumor microenvironment (e.g., tissue of origin corresponding to cfDNA fragments).”; [00222] “In another example, the number of reads in any given position in a genome interposed with the length of the reads at that position in the genome, and may yield insight into tumor status of a subject from which the cell-free DNA sample was acquired, such as tissue of origin, tumor burden, tumor aggressiveness, tumor druggability, tumor evolution and clonality, and tumor resistance to treatment.”), the method comprising:
Abdueva discloses (i) providing a training set comprising: (1) a set of reference distribution scores for each of one or more populations of cell-free DNA from each of a plurality of control subjects, wherein each reference distribution score is indicative of one or more of: (i) a length of the DNA fragments that align with each of a plurality of base positions in a genome, (ii) a number of the DNA fragments that align with each of a plurality of base positions in a genome, and (iii) a number of the DNA fragments that start or end at each of a plurality of base positions in a genome; (2) a set of phenotypic distribution scores for each of one or more populations of cell-free DNA from each of a plurality of subjects having an observed phenotype, wherein each phenotypic distribution score is indicative of one or more of: (i) a length of the DNA fragments that align with each of a plurality of base positions in a genome, (ii) a number of the DNA fragments that align with each of a plurality of base positions in a genome, and (iii) a number of the DNA fragments that start or end at each of a plurality of base positions in a genome [0015].
Abdueva further discloses analyzing cell-free deoxyribonucleic acid (DNA) fragments derived from a subject [0017] and that the dataset is a collection of cfDNA from a biological sample [00153].
Abdueva further discloses that the biological sample refers to a tissue or fluid sample derived from a subject... A biological sample can comprise, for example, a bodily fluid .. Bodily fluids include, for example, blood, serum, plasma, tumor cells, saliva, urine, lymphatic fluid, prostatic fluid, seminal fluid, milk, sputum, stool, tears, and derivatives of these [00139].
Reading on limitations of (A) obtaining a dataset comprising a first plurality of nucleic acid fragment sequences in electronic form from a first biological fluid sample from a subject.
Abdueva further discloses that methods provided herein may use sequence information in a macroscale and global manner, with or without somatic variant information, to assess a fragmentome profile that can be representative of a tissue of origin, disease, progression [0004].
Abdueva discloses (i) providing a training set comprising: (1) a set of reference distribution scores for each of one or more populations of cell-free DNA from each of a plurality of control subjects, wherein each reference distribution score is indicative of one or more of: (i) a length of the DNA fragments that align with each of a plurality of base positions in a genome, (ii) a number of the DNA fragments that align with each of a plurality of base positions in a genome, and (iii) a number of the DNA fragments that start or end at each of a plurality of base positions in a genome; (2) a set of phenotypic distribution scores for each of one or more populations of cell-free DNA from each of a plurality of subjects having an observed phenotype, wherein each phenotypic distribution score is indicative of one or more of: (i) a length of the DNA fragments that align with each of a plurality of base positions in a genome, (ii) a number of the DNA fragments that align with each of a plurality of base positions in a genome, and (iii) a number of the DNA fragments that start or end at each of a plurality of base positions in a genome [0015] (instant specification para (000128] .. As used herein, the term "locus" refers to a position (e.g., a site) within a genome, i.e., on a particular chromosome. In some embodiments, a locus refers to a single nucleotide position within a genome, i.e., on a particular chromosome. In some embodiments, a locus refers to a small group of nucleotide positions within a genome, e.g., as defined by a mutation.. instant specification para [00129) .. As used herein, the term "allele" refers to a particular sequence of one or more nucleotides at a chromosomal locus).
Abdueva further discloses identifying a sequence aberration at a given locus by incorporating information about known somatic mutations detected at any other loci in a genome [00217].
Reading on limitations of “wherein each respective nucleic acid fragment sequence in the first plurality of nucleic acid fragment sequences represents all or a portion of a respective cell-free DNA molecule in a population of cell-free DNA molecules in the first biological fluid sample, the respective nucleic acid fragment sequence encompassing a corresponding locus, in a plurality of loci, represented by at least a reference allele and a variant allele within the population of cell-free DNA molecules”.
Abdueva further teaches a classifier for determining genetic aberrations in a test subject using deoxyribonucleic acid (DNA) fragments from cell-free DNA obtained from the test subject, comprising: (a) an input of a set of distribution scores for each of one or more populations of cell-free DNA obtained from each of a plurality of subjects, wherein each distribution score is generated based at least on one or more of: (i) a length of the DNA fragments that align with each of a plurality of base positions in a genome, (ii) a number of the DNA fragments that align with each of a plurality of base positions in a genome, and (iii) a number of the DNA fragments that start or end at each of a plurality of base positions in a genome; and (b) an output of classifications of one or more genetic aberrations in the test subject.
Abdueva further discloses that the classifier includes variant-free and variant-aware classifiers [00216-00217].
Abdueva further discloses ([00148] “The term ‘quantitative measure,’ as used herein … e.g., … mean”; [00220]; [00243]-[00252]; claims 3, 6, 7, 9, 10, and 28; [00153]: dataset including values indicating a quantitative measure of characteristics including size distribution; and [0078] a classifier for determining a likelihood that the subject belongs to one or more classes of clinical significance and that the quantitative measure comprises a size distribution of DNA sequences having the selected characteristics.
Reading on limitations of (B) compressing the dataset by assigning, for each respective allele represented at each locus in the plurality of loci, a size-distribution metric based on a characteristic of the distribution of the fragment lengths of the cell-free DNA molecules in the population of cell-free DNA molecules that encompass the respective allele, wherein the size-distribution metric corresponds to a value representative of a mean fragment length across the distribution.
Abdueva further discloses (C) providing the compressed dataset (providing as inputs into the classifier a set of distribution scores) to a machine learning model (computer-implemented classifier) that is trained using size-distribution metrics in combination with one or more quantitative features on a target dataset (training set; the multi-parametric analysis comprises mapping to each of a plurality of base positions or regions of a genome, one or more distributions selected from the group consisting of: (i) a distribution of the number of unique cell-free DNA fragments containing a sequence that covers the mappable position in the genome, (ii) a distribution of the fragment lengths for each of at least some of the cell-free DNA fragments such that the DNA fragment contains a sequence that covers the mappable position in the genome, and (iii) a distribution of the likelihoods that a mappable base-pair position will appear at a terminus of a sequenced DNA fragment [0018]) ( see also, [0013] the classifier further comprises an input of a set of genetic variants at one or more loci of the genome), wherein the machine learning model is configured to analyze the compressed dataset and classify each respective variant allele of a respective locus in the plurality of loci as originating from a cancer cell or a non- cancerous cell based on the per-allele size-distribution metric; and (E) generating, based on the category assignments, a report indicating the likelihood that the subject has a cancer-related variant (a trained classifier, comprising: (a) providing a plurality of different classes, wherein each class represents a set of subjects with a shared characteristic; (b) for each of a plurality of populations of cell-free DNA obtained from each of the classes, providing a multi-parametric model representative of cell-free deoxyribonucleic acid (DNA) fragments from the populations of cell- free DNA, thereby providing a training data set; and (c) training, by a computer, a learning algorithm on the training data set to create one or more trained classifiers (the classifier is variant-aware classifier [00216]), wherein each trained classifier is configured to classify a test population of cell-free DNA from a test subject into one or more of the plurality of different classes,… where each of the plurality of different classes is selected from the group consisting of: healthy and various cancers [0030-0031]; a method of generating a classifier for determining a likelihood that a subject belongs to one or more classes of clinical significance, where the class of clinical significance indicates a presence or absence of one or more cancers [0039]).
Further regarding claim 175, Abdueva discloses that one or more fragmentome profiles (or fragmentome data) may be incorporated into a classifier to determine the likelihood of presence or absence of one or more canonical driver mutations. Abdueva further discloses that a class of clinical significance may be a category, for example, indicating an abnormal biological state or a genetic variant. Examples of classes of clinical significance include (i) presence or absence of one or more genetic variants, (ii) presence or absence of one or more cancers, (iii) presence or absence of one or more canonical driver mutations, (iv) presence or absence of one or more disease subtypes (e.g., lung cancer molecular subtypes) [00194-00195].
Abdueva discloses fragmentome profiling determining SNV in unique molecules from a cell free DNA sample (for example, allele-specific detection) as well as fragment size of each unique molecule and adjusting the confidence score of the calling of a somatic SNV based on the size distribution of the unique molecules which include the SNV, where the size distribution is mean fragment length [00129] [00220], and wherein the fragmentome profiling includes generating the a given subject's sequence data, one or more expected distributions may be generated for each base position across the reference genome, where each expected distributions describes one or more of: the number of reads that map to the given position, the cell-free DNA fragment lengths that map to the given position, the number of cell-free DNA fragments that start at the given position, and the number of cell-free DNA fragments that end at the given position [00239-00242].
Abdueva does not expressly disclose that the size-distribution is a per-allele size-distribution metric. Underhill discloses a method of isolating specific subset of cfDNA fragment length to improve detection of ctDNA, where distinct differences in fragment length size between ctDNAs and normal cell-free DNA were defined. (abstract). Underhill further discloses calculating the allele-specific mean fragment length for healthy and tumor samples sample (figure 4, pgs. 8 and 9).
Abdueva further discloses (E) leveraging the category assignment for each respective variant allele of the respective locus in the plurality of loci received from the machine learning model to identify a genetic change in the subject associated with cancer.([00327-00328] example 1: Cell-free DNA fragmentation patterns reveal changes associated with somatic mutations in the primary tumors and improve sensitivity and specificity of somatic variant detection … to reflect nucleosomal occupancy in hematopoietic cells… Distributions of cfDNA fragment length and position, and associated somatic genomic profiles of over 15 thousand patients with advanced- stage clinical cancer were determined by a highly accurate, deep-coverage (15,000x) ctDNA NGS test targeting 70 genes. An integrative analysis of variant-free fragmentome profiling was performed, and the fragmentome profile was tested for association with detected somatic alterations using statistical methods. Distinct classes of fragmentomic subtypes (e.g., sub-types with differential. fragmentome profiles revealed by visual observation, clustering, or other approaches) were observed to be significantly enriched in samples with well-characterized driver alterations and genomic molecular subtypes). See also claim 20, performing a multi-parametric analysis to (i) measure RNA expression of the cell-free DNA fragments, (ii) measure methylation of the cell-free DNA fragments, (iii) measure a nucleosomal mapping of the cell-free DNA fragments, or (iv) identify the presence of one or more somatic single nucleotide polymorphisms in the cell-free DNA fragments or one or more germline single nucleotide polymorphisms in the cell-free DNA fragments.
Response to Arguments
Applicant's arguments filed 02/10/2026 have been fully considered but they are not persuasive. The claim amendments necessitated a new round of art rejection. As such, the combination of Abdueva and Underhill teaches all the limitations of instant claims.
Conclusion
No claims are 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GHAZAL SABOUR whose telephone number is (703)756-1289. The examiner can normally be reached M-F 7:30-5:00.
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/G.S./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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