DETAILED ACTION
Applicant's response, filed 07/14/2025, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application. Herein, "the previous Office action" refers to the Final rejection of 03/13/2025
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 07/14/2025 has been entered.
Claim Status
Claims 1-2, 4-5, 7-9, 13-16, 18-19, 21-23, 26, 28-29, 32, 34-35, and 40 are currently pending and under exam herein.
Claims 3, 6, 10-12, 17, 20, 24-25, 27, 30-31, 33, 36-39, and 41-67 were previously cancelled.
Claims 1-2, 4-5, 7-9, 13-16, 18-19, 21-23, 26, 28-29, 32, 34-35, and 40 are rejected.
Priority
The previously discussed claim for the benefit of priority assigned an effective filing date of 14 September 2018. In future actions, the effective filing date of one or more claims may change, due to amendments to the claims, or further analysis of the disclosure(s) of the priority application(s).
Information Disclosure Statement
Information Disclosure Statement, filed 06/17/2025, has been considered and is in compliance with the provisions of 37 CFR 1.97. Signed copies of the IDS document are included with this Office Action.
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 1-2, 4-5, 7-9, 13-16, 18-19, 21-23, 26, 28-29, 32, 34-35, and 40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Any newly applied rejection/portion is necessitated by instant application amendment.
The instant rejection reflects the framework as outlined in the MPEP at 2106.04:
Framework with which to Evaluate Subject Matter Eligibility:
(1) Are the claims directed to a process, machine, manufacture, or composition of matter;
(2A) Prong One: Do the claims recite a judicially recognized exception, i.e. a law of nature, a natural phenomenon, or an abstract idea;
Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application (Prong Two); and
(2B) If the claims do not integrate the judicial exception, do the claims provide an inventive concept.
Framework Analysis as Pertains to the Instant Claims:
With respect to step (1): yes, the claims are directed to a method, a computer-readable medium, and a system for detecting copy number variation (CNV) in genetic sequences, therefore the answer is "yes".
With respect to step (2A)(1), the claims recite an abstract idea. To determine if the claims recite any concepts that equate to an abstract idea, law of nature, or natural phenomenon, MPEP at 2106.03 teaches abstract ideas include mathematical concepts (mathematical formulas or equations, mathematical relationships, and mathematical calculations), certain methods of organizing human activity, and mental processes (including procedures for collecting, observing, evaluating, and organizing information (see MPEP 2106.04(a)(2)). In the instant application, the claims recite the following limitations that equate to an abstract idea with mental steps and mathematical concepts.
With respect to the instant claims, under the step (2A)(1) evaluation, the claims are found herein to recite abstract ideas that fall into the grouping of mental processes (in particular procedures for obtaining, analyzing, and organizing information) and mathematical concepts (in particular mathematical relationships and formulas).
The claims directing to abstract ideas are as follows:
Mental processes:
Claims 1, 15, and 28 recite determining a read depth for the genetic sequence… separating the genetic sequence into a plurality of bins, each bin of the plurality of bins comprising a plurality of base pairs of the genetic sequence, wherein a read depth of a first bin of the plurality of bins is determined using the read depth determined for the genetic sequence;…determining a CNV status for the first bin of the plurality of bins… identifying two or more bins to be merged to create a merged bin and an associated merged CNV status, wherein: the merged CNV status is determined based at least in part on the CNV statuses of each of the two or more bins; merging the two or more bins to create the merged bin when the merged CNV status is a same CNV status as CNV statuses of each of the two or more bins over a threshold number of base pairs in the two or more bins; and identifying the at least one CNV in the genetic sequence based on the determined CNV statuses.
Claim 4 and 18 recite aligning the genetic sequence with a reference genome to generate genetic sequence alignment data
Claim 5, 19, and 32 recite identifying an at least one unique genetic region … within the at least one autosomal chromosome comprises: determining that each 25 k-mer of the at least one unique genetic region appears only once within the genetic sequence; and determining that the at least one unique genetic region comprises greater than 20,000 base pairs.
Claims 7, 21, and 34 recite comparing the read depth of the at least one autosomal chromosome to the read depth of the genetic sequence; and determining whether the genetic sequence comprises an aneuploidy based on the compared read depths.
Mathematical concepts:
Claim 1 recites converting the read depth of the first bin to a percentile; and converting the percentile to a CNV status by providing the percentile as input to a Hidden Markov Model with a Poisson distribution of read depth, wherein the CNV status is indicative of whether the first bin includes at least one duplicated or deleted CNV; and
Claim 4 and 18 recite wherein: dividing the genetic sequence into the plurality of bins comprises dividing the genetic sequence alignment data into the plurality of bins.
Claim 5, 19, and 32 recite calculating a read depth for the at least one unique genetic region
Claims 7, 21, and 34 recite calculating a read depth of the at least one autosomal chromosome based on a read depth of the at least one unique genetic region
Claims 8, 22, and 35 recite wherein the two or more bins comprise adjacent pluralities of base pairs associated with the two or more bins.
Claims 9 and 23 recite wherein converting the read depth to a percentile comprises: dividing the read depth of each bin of the plurality of bins by the number of base pairs in the plurality of base pairs and multiplying by the read depth of the genetic sequence.
Claims 13, 26, and 40 recite dividing the merged bin into a plurality of regions, each region comprising an equal number of base pairs; assigning a uniqueness value to each region; filtering out regions having a uniqueness value below a threshold value; and identifying the at least one CNV status based on one or more regions that remain following the filtering.
Hence, the claims explicitly recite elements that, individually and in combination, constitute abstract ideas.
With respect to step (2A), under the broadest reasonable interpretation (BRI), the instant claims recite “detecting copy number variation (CNV) in genetic sequences.” Instant claims 1, 15, and 28 are therefore directed to the judicial exceptions of abstract groupings, both mathematical (determining a read depth…a Hidden Markov Model (HMM) with a Poisson distribution… converting the read depth to a percentile…to a CNV status… by the number of base pairs… multiplying by the read depth… genetic sequences/merged bins) and mental processes (identifying the at least one CNV … aligning the genetic sequence … comparing the read depth…determining whether …identifying two or more bin…merging…over a threshold number…).
Because the claims do recite judicial exceptions, direction under step (2A)(2) provides that the claims must be examined further to determine whether they integrate the abstract ideas into a practical application (MPEP 2106.04(d). A claim can be said to integrate a judicial exception into a practical application when it applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception. This is performed by analyzing the additional elements of the claim to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d).I.; MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the judicial exception, the claim is said to fail to integrate into a practical application (MPEP 2106.04(d).III).
With respect to the instant recitations, the claims recite the following additional elements considered for practical application:
Claims 1, 15, and 28 recite CRM … system…processor(s)
Claims 2, 16, and 29 recite partial genome sequence or a whole genome sequence (WGS).
Said steps that are “in addition” to the recited judicial exception in the instant claims represent those of mere instructions or field of use limitations (partial/whole genome sequence) to implement in the recited judicial exception and do not impart meaning to said recited judicial exception, such that is applied in a practical manner. Further with respect to the additional elements in the instant claims, these steps direct to mere data gathering and handling (genetic sequence data) to carry out the abstract idea without imposing any meaningful limitation on the abstract idea. Thereby these steps are insignificant extra-solutions activity steps and are insufficient to integrate an abstract idea into a practical application. (MPEP 2106.05(g).
Further steps herein directed to additional non-abstract elements of computer components (processor, non-transitory computer-readable medium, system) do not describe any specific computational steps by which the “computer parts” perform or carry out the abstract idea, nor do they provide any details of how specific structures of the computer, such as the computer-readable recording media, are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute the abstract idea. Hence, these are mere instructions to apply the abstract idea using a computer, and therefore the claim does not integrate that abstract idea into a practical application. The courts have weighed in and consistently maintained that when, for example, a memory, display, processor, machine, etc.… are recited so generically (i.e., no details are provided) that they represent no more than mere instructions to apply the judicial exception on a computer, and these limitations may be viewed as nothing more than generally linking the use of the judicial exception to the technological environment of a computer. (see MPEP 2106.05(f)). None of the recited dependent claims recite additional elements which would integrate a judicial exception into a practical application.
As such, the claims are lastly evaluated using the step (2B) analysis, wherein it is determined that because the claims recite abstract ideas, and do not integrate that abstract ideas into a practical application, the claims also lack a specific inventive concept. The judicial exception alone cannot provide the inventive concept or the practical application and that the identification of whether the additional elements amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they provide significantly more than the judicial exception. (MPEP 2106.05.A i-vi).
With respect to the instant claims, the additional elements of data gathering, instructions, and field of use limitations described above do not rise to the level of significantly more than the judicial exception. As directed in the Berkheimer memorandum of 19 April 2018 and set forth in the MPEP, determinations of whether or not additional elements (or a combination of additional elements) may provide significantly more and/or an inventive concept rests in whether or not the additional elements (or combination of elements) represents well-understood, routine, conventional activity. Said assessment is made by a factual determination stemming from a conclusion that an element (or combination of elements) is widely prevalent or in common use in the relevant industry, which is determined by either a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
With respect to the instant recitations, the claims recite the following additional elements considered for inventive concepts:
Claims 1, 15, and 28 recite CRM … system…processor(s)
Claims 2, 16, and 29 recite partial genome sequence or a whole genome sequence (WGS).
These additional elements do not contribute significantly more to well-known and conventional steps, which are routinely performed by a biochemist with ordinary skill in the art of as of the effective filing date, and in their own heads. The courts have recognized the following laboratory techniques as well-understood, routine, conventional activity in the life science arts when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: detecting DNA or enzymes in a sample, Sequenom, 788 F.3d at 1377-78, 115 USPQ2d at 1157); Cleveland Clinic Foundation 859 F.3d at 1362, 123 USPQ2d at 1088 (Fed. Cir. 2017); analyzing DNA to provide sequence information or detect allelic variants, Genetic Techs., 818 F.3d at 1377; 118 USPQ2d at 1546; and amplifying and sequencing nucleic acid sequences, University of Utah Research Foundation v. Ambry Genetics, 774 F.3d 755, 764, 113 USPQ2d 1241, 1247 (Fed. Cir. 2014).
Furthermore, the MPEP 2106.05(a) teaches applying the judicial exception with computers or devices to perform or automate an existing process, and constitute insignificant extra-solution activity, as recited: “Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017) or speeding up a loan-application process by enabling borrowers to avoid physically going to or calling each lender and filling out a loan application, LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016) (non-precedential).
With respect to the instant claims, the steps and additional elements involving mathematical applications and automated mental steps to handle and analyze genetic data in order to detect copy number variation (CNV) do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-2, 4-5, 7-9, 13-16, 18-19, 21-23, 26, 28-29, 32, 34-35, and 40 are not patent eligible.
Response to Remarks – 101
Applicant's remarks (p.13-14), filed 07/14/2025, have been fully considered and are not persuasive for the previously stated reasons in the 03/13/2025 Office Action, with additions necessitated by claim amendments. The Applicant asserts:
[Regarding independent claims 1, 15, and 28 technical improvement over conventional CNV detection systems} …The present technology merges bins in the claimed manner to mitigate noise in the detected CNV statuses and improve the accuracy of the ultimate CNV determination (Present Application, ¶¶ 72-73). The claimed technology balances the noise consideration with the loss of data by only "merging two or more bins to create the merged bin when the merged CNV status is a same CNV status as the CNV statuses of each of the two or more bins over a threshold number of base pairs in the two or more bins" so that data is not lost when combining too many different CNV statuses to create the merged CNV status (Present Application, ¶ 72). In that way, the claimed technology improves the accuracy of the ultimate CNV identification "based on the determined merged CNV status" by minimizing the noise exhibited in the data and preventing data from being lost while doing so.
However this is not persuasive because the improvement to CNV detection analysis is an improvement to the judicial exception (JE) itself, the abstract ideas (both mental and mathematical). Instant claim amendments of identifying…merging…over thresholds… are mental and mathematical concepts for data handling optimization or an intended use of genetic data (to identify/denoise similar data—same CNV status). The JE cannot provide the practical integration nor the inventive concept. MPEP 2106.4(I) recites: “The Federal Circuit has also applied this principle, for example, when holding a concept of using advertising as an exchange or currency to be an abstract idea, despite the patentee’s arguments that the concept was "new". Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 714-15, 112 USPQ2d 1750, 1753-54 (Fed. Cir. 2014). Cf. Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016) ("a new abstract idea is still an abstract idea") (emphasis in original).” Ultimately, the guidance set forth in the MPEP teaches instant claims are an improvement to the abstract idea itself, not reflected back into a specific technological environment or integrated process. Any improvement or non-routine step or nonconventional element cannot be found in the judicial exceptions alone. “An inventive concept "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself." Genetic Techs. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016).”
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 pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Note: quotes from the instant application are italicized in the following section.
A. Claims 1-2, 4-5, 7-9, 13-16, 18-19, 21-23, 26, 28-29, 32, 34-35, and 40 are rejected under 35 U.S.C. § 103 as being unpatentable over Guan (03/13/2025 PTO 892), in view of Yoon (03/13/2025 PTO 892). Any newly applied rejection/portion is necessitated by instant application amendment.
Regarding instant claims 1, 15, and 28, the instant application recites detecting at least one copy number variation (CNV) in a genetic sequence:
a method with a CRM and a system (CRM with processor)
determining a read depth for the genetic sequence…
separating the genetic sequence into a plurality of bins, each bin of the plurality of bins comprising a plurality of base pairs of the genetic sequence
determining a CNV status for the first bin of the plurality of bins at least in part by: converting the read depth of the first bin to a percentile; and converting the percentile to a CNV status by providing the percentile as input to a Hidden Markov Model with a Poisson distribution of read depth, wherein the CNV status is indicative of whether the first bin includes at least one duplicated or deleted CNV;
identifying two or more bins to be merged to create a merged bin and an associated merged CNV status, wherein: the merged CNV status is determined based at least in part on the CNV statuses of each of the two or more bins; merging the two or more bins to create the merged bin when the merged CNV status is a same CNV status as CNV statuses of each of the two or more bins over a threshold number of base pairs in the two or more bins; and
identifying the at least one CNV in the genetic sequence based on the determined CNV status.
The prior art to Guan teaches a data processing method, a device [0029; 0121], a storage medium [0030], and processors [0031] for determining chromosomal abnormality diagnosis with CNVs corrected for interferences (Guan in Abstract). Sample sequence reads are obtained and aligned with reference sequences to determine in “the sample chromosomes with the human genome the base sequence reads of reference sequences [that have a] unique match” (at least one unique genetic region within an at least one autosomal chromosome) [Guan in claim 1]. The reads are divided and binned by predetermined window conditions for the quantity of base sequence reads and determining a gene copy number variation CNV according to the hidden Markov model/HMM (Guan in claim 1 and [0014:- 0015; 0049]). Further, Guan teaches a low value of base sequence reads in a bin correlates with CNV [claims 1 and 2: “ the state of observation area bin increases for copy number, is represented with k=1；It is equal to predetermined threshold in the number of base sequence reads. In the case of, it determines that the state of the observation area bin is neutral for copy number, is represented with k=0；In the number of base sequence reads In the case that mesh is less than predetermined threshold, determines that the state of the observation area bin is lacked for copy number, represented with k=-1] (determining a CNV status for the first bin wherein the CNV status is indicative of whether the first bin includes at least one duplicated or deleted CNV; and identifying the at least one CNV in the genetic sequence based on the determined CNV statuses).
However, Guan does not further teach converting the read depth to a percentile and a Poisson distribution for reads as input for a CNV algorithm (HMM) or merging bins.
The prior art to Yoon teaches adjusting read depth of bin data with percentages for GC corrected read counts before inputting into his CNV detection algorithm, Event-wise testing method (EWT) [p.1587 para 3] (converting the read depth of the first bin to a percentile; and converting the percentile to a CNV status by providing the percentile as input…).
The prior art to Yoon teaches Poisson distribution of read coverage (read depth) [Yoon at p.1589], to detect CNVs by estimating the coverage or RD in nonoverlapping intervals (discretized bins) across an individual genome, then using a CNV-calling algorithm to detect events (which is an analogous operation as input[ting] to a Hidden Markov Model). The model compares data from multiple individuals to distinguish events that are polymorphic (i.e., CNVs) from those that show similarly increased or decreased copy number [indicative of whether the first bin includes at least one duplicated or deleted CNV] in all individuals in the study (i.e., monomorphic events) [p1586]. This EWT algorithm approach of RD analysis begins with a mapping estimation of the read counts to the reference and assumes it follows a Poisson distribution. While a Poisson distribution was assumed in the said first method step of RD coverage estimation, the Poisson distribution was then confirmed as a pattern in Yoon’s study by directly examining several broad genomic regions of all tested individuals (see Supplemental materials). The Poisson distribution was similarly found in another study of read depth coverage by Bentley et al. (2008) with Illumina Genome Analyzer platform. The characterization of Poisson distributed read count data (with a Poisson distribution of read depth) shows how Poisson distributed genetic data is used as input in CNV algorithms (e.g. the HMM). Deviations from Poisson distributions contributes to detection of CNV duplications or deletions, and further, to assessments of sequencing data quality.
Further, the prior art to Yoon teaches adjusting read depth of bin data with percentages for GC corrected read counts [p.1587 para 3] and merging bins of CNV data according to thresholds [p.1588 para 4].
Regarding instant claims 2, 16, and 29, the instant application recites detecting at least one copy number variation (CNV) in a genetic sequence:
a method with a CRM and a system (CRM with processor)
wherein the genetic sequence is a partial genome sequence or a whole genome sequence (WGS).
The prior art to Guan teaches examination of targeted chromosomal abnormality, such as fetal chromosomes 13 or 21 [00116], and chromosomal abnormalities across a whole genome [0007; 0060] (a partial genome sequence or a whole genome sequence (WGS)).
Regarding instant claims 4 and 18, the instant application recites detecting at least one copy number variation (CNV) in a genetic sequence:
a method with a CRM
aligning the genetic sequence with a reference genome to generate genetic sequence alignment data, wherein: dividing the genetic sequence into the plurality of bins comprises dividing the genetic sequence alignment data into the plurality of bins.
The prior art to Guan teaches in claim 1 that sample sequence reads are obtained and aligned with reference sequences [Guan at [0014; 00121: alignment unit 23]. The reads are divided and binned by predetermined window conditions for the quantity of base sequence reads (Guan in claim 1 and [0014; 0049]).
Regarding instant claims 5, 19, and 32, the instant application recites detecting at least one copy number variation (CNV) in a genetic sequence:
a method with a CRM and a system (CRM with processor)
identifying an at least one unique genetic region within an at least one autosomal chromosome associated with the genetic sequence; and calculating a read depth for the at least one unique genetic region, wherein identifying the at least one unique genetic region within the at least one autosomal chromosome comprises:
determining that each 25 k-mer of the at least one unique genetic region appears only once within the genetic sequence; and determining that the at least one unique genetic region comprises greater than 20,000 base pairs.
The prior art to Guan teaches in claim 1: sample sequence reads are obtained and aligned with reference sequences to determine in “the sample chromosomes with the human genome the base sequence reads of reference sequences [that have a] unique match” (at least one unique genetic region within an at least one autosomal chromosome). Further, Guan teaches the number of base sequence reads in each observation region bin is counted (read depth) [Guan in claim 1 and 0014].
However, Guan does not teach examination for unique regions based on presence of repeats or bp sizes.
The prior art to Yoon teaches examination for nonoverlapping calls (unique regions) with read depth methodology including screening for simple repeats (determining that each 25 k-mer of the at least one unique genetic region appears only once within the genetic sequence), and differentiated by base pair sizes, especially for >1000 bp sizes [p.1591 para. 4-6; Tables 1-2].
Regarding instant claims 7, 21, and 34, the instant application recites detecting at least one copy number variation (CNV) in a genetic sequence:
a method with a CRM and a system (CRM with processor)
calculating a read depth of the at least one autosomal chromosome based on a read depth of the at least one unique genetic region; comparing the read depth of the at least one autosomal chromosome to the read depth of the genetic sequence; and
determining whether the genetic sequence comprises an aneuploidy based on the compared read depths.
The prior art to Guan teaches in claim 1: sample sequence reads are obtained and aligned with reference sequences to determine in “the sample chromosomes with the human genome the base sequence reads of reference sequences [that have a] unique match” (at least one unique genetic region within an at least one autosomal chromosome). Further, Guan teaches the number of base sequence reads in each observation region bin is counted (read depth) [Guan at claim 1 and 0014]. Guan teaches in claims 1 and 2 a low value of base sequence reads in a bin correlates with CNV (calculating a CNV status for each bin…identifying the at least one CNV in the genetic sequence based on the calculated CNV statuses).
However, Guan does not explicitly teach a read depth-based methodology for CNV detection.
The prior art to Yoon teaches a method for CNV detection using read depth of coverage. Event-wise testing (EWT) is a method based on significance testing and works on intervals of data points, rapidly searching for specific classes of events. Deletions and duplications detected in an individual genome by EWT are examined across multiple genomes to identify polymorphism (CNVs) between individuals (determining whether the genetic sequence comprises an aneuploidy based on the compared read depths) [Yoon at Abstract and p.1586 paras. 5-6].
Regarding instant claims 8, 22, and 35, the instant application recites detecting at least one copy number variation (CNV) in a genetic sequence:
a method with a CRM and a system (CRM with processor)
wherein the two or more bins comprise adjacent pluralities of base pairs associated with the two or more bins.
The prior art to Guan teaches the number of base sequence reads in each observation region bin is counted (read depth) [Guan in claim 1 and 0014]. Guan teaches in claims 1 and 2 a low value of base sequence reads in a bin correlates with CNV (calculating a CNV status for each bin…identifying the at least one CNV in the genetic sequence based on the calculated CNV statuses).
However, Guan does not teach merging bins.
The prior art to Yoon teaches adjusting read depth of bin data with percentages for GC corrected read counts [p.1587 para 3] and merging bins of CNV data according to thresholds [p.1588 para 4].
Regarding instant claims 9 and 23, the instant application recites detecting at least one copy number variation (CNV) in a genetic sequence:
a method with a CRM
wherein converting the read depth to a percentile comprises: dividing the read depth of each bin of the plurality of bins by the number of base pairs in the plurality of base pairs and multiplying by the read depth of the genetic sequence.
The prior art to Guan teaches a data processing method and a storage medium [0030], for determining chromosomal abnormality diagnosis with CNVs correction for interferences (Guan in Abstract]. Guan teaches the number of base sequence reads in each observation region bin is counted (read depth) [Guan in claim 1 and 0014], and correcting read percentages of autosomal chromosome data against a reference genome.
However, Guan does not teach normalizing read depth of bin data with percentages.
The prior art to Yoon teaches adjusting read depth of bin data with percentages for correcting and normalizing read counts [p.1587 para 3 and p.1588 para 6].
Regarding instant claims 13, 26, and 40, the instant application recites detecting at least one copy number variation (CNV) in a genetic sequence:
a method with a CRM and a system (CRM with processor)
dividing the merged bins into a plurality of regions, each region comprising an equal number of base pairs; assigning a uniqueness value to each region; filtering out regions having a uniqueness value below a threshold value; and identifying the at least one CNV status based on one or more regions that remain following the filtering.
The prior art to Guan teaches a data processing method, a device [0029; 0121], a storage medium [0030], and processors [0031] for determining chromosomal abnormality diagnosis with CNVs corrected for interferences (Guan in Abstract). Further, Guan teaches in claims 1 and 2 a low value of base sequence reads in a bin correlates with CNV (calculating a CNV status for each bin…identifying the at least one CNV in the genetic sequence based on the calculated CNV statuses).
However, Guan does not teach filtering bin data size, merging bins, and thresholds.
The prior art to Yoon teaches using CNV call data and merging bin data. Yoon filters the bin data for thresholds, further filtering out data and merging bins of CNV data according to thresholds for small events within 500bp [p.1588 para. 4]. Yoon further tests read depth methodology with simulated data sets of known CNVs, which are filtered and have windows of specific k-mer sizes, here 100 bps [p. 1588 para. 8 -1589 para. 1].
Regarding instant claim 14, the instant application recites detecting at least one copy number variation (CNV) in a genetic sequence:
a method
wherein the uniqueness value is calculated by determining a number of unique k-mers in the regions.
The prior art to Guan teaches a data processing method for determining chromosomal abnormality diagnosis with CNVs corrected for interferences (Guan in Abstract).
However, Guan does not teach uniqueness criteria for CNVs.
The prior art to Yoon teaches examination for nonoverlapping calls (unique regions) with read depth methodology including screening for simple repeats (determining that each 25 k-mer of the at least one unique genetic region appears only once within the genetic sequence), and differentiated by base pair sizes, especially for >1000 bp sizes [p.1591 para. 4-6; Tables 1-2].
Therefore, it would have been obvious to someone of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Guan’s HMM-based CNV analysis to incorporate Yoon read depth CNV methodology. Combining these art elements would have been obvious because applying read count data with Poisson distribution, normalized percentages, or screened for repeats, can more accurately capture structural variants which are refractory to other methods (Yoon in Abstract). One of ordinary skill in the art would predict combining Guan and Yoon teachings with a reasonable expectation of success because said prior art are analogously applicable to optimizing CNV detection from large, genomic datasets. Therefore, the invention is prima facie obvious.
Response to Remarks: 102/103
Applicant's arguments (p.11-12), filed 07/14/2025, have been fully considered in view of claim amendments. Applicant amended the independent claims 1, 15, and 28 to incorporate claim 8. The Applicant asserts:
In rejecting claim 8, the Office Action (pp.19-20) asserts that Yoon teaches the merging of various bins. However, Yoon teaches merging bins only when they exhibit CNV events and are within a threshold number of base pairs (e.g., within 500 bp) in the genome, rather than merging bins based on the CNV statuses of the bins and the relative size of the bins (Yoon, p. 1588, left column, last paragraph). Yoon only merges exhibited CNV events and fails to address bins to be merged that may not exhibit any CNV events and as such, does not need to create a merged CNV status or evaluate whether the merged CNV status is "a same CNV status as the CNV statuses of each of the two or more bins over a threshold number of base pairs in the two or more bins." As such, Yoon fails to teach the above-emphasized language of independent claim 1. Guan fails to cure this deficiency of Yoon, as acknowledged by the Office Action (p. 19).
However, it is respectfully submitted that Applicant’s assertion is not persuasive as both Guan and Yoon both demonstrate feature-based binning as a routine approach to analyzing and labeling high-throughput genomic data. The concept of combining bins based on similar features (e.g. CNV status in amended independent claims by incorporating claim 8) is a basic step in classifying and collating data for simplified reporting. The similar feature (e.g. same CNV status, (non)exhibited CNV events) and merger thresholds for bin sizes, are matters of data-handling design and choice in the course of routine optimization by one of ordinary skill in the art, not matters of invention. The fact that Applicant has recognized another advantage which would flow naturally from following the suggestion of the prior art cannot be the basis for patentability when the differences would otherwise be obvious. See Ex parte Obiaya, 227 USPQ 58, 60 (Bd. Pat. App. & Inter. 1985).
Conclusion
No claims are allowed.
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/VR/
Examiner
Art Unit 1685
/MARY K ZEMAN/Primary Examiner, Art Unit 1686