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 Claims
This office action for the 18/027826 application is in response to the communications filed July 09, 2026.
Claims 1, 9-18, 20, 31-34 and 41 are currently pending and considered below.
Claim Rejections - 35 USC § 102
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 9-18, 20, 31-34 and 41 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Anderson et al. (US 2018/0218789; herein referred to as Anderson).
As per claim 1,
Anderson discloses a method of detecting a genetic variant or determining a variant allele frequency in a test sample from a subject:
(Paragraph [0003] of Anderson. The teaching describes a method is provided for detecting the presence or absence of a genetic variant, comprising: a) receiving a data input comprising sequencing data generated from a nucleic acid sample from a subject.)
Anderson further discloses receiving, at one or more processors, one or more sequencing reads that corresponds with a reference sequence and a variant sequence of the test sample, wherein the reference sequence and the variant sequence are stored in a memory:
(Paragraphs [0003], [0005], [0024], [0025], and [0072] of Anderson. The teaching describes receiving a data input comprising sequencing data [one or more sequencing reads] generated from a nucleic acid sample from a subject. The methods provide for receiving a data input comprising targeted sequencing data, untargeted sequencing data, or a combination of both. The sequencing data can be analyzed by one or more analysis methods. In some cases, the sequencing data can be mapped to a reference sequence [reference sequence]. A reference sequence can be a canonical reference sequence. Canonical reference sequences can be found in, for example, a database (e.g., GENCODE, UCSC or EMBL). The computer processor is programmed to determine a presence or absence of a genetic variant [variant sequence of the test sample] selected from the variant test panel. The systems can comprise one or more memory locations (e.g., random-access memory, read-only memory, flash memory), electronic storage unit (e.g., hard disk), communication interface (e.g., network adapter) for communicating with one or more other systems, and peripheral devices, such as cache, other memory, data storage and/or electronic display adapters.)
Anderson further discloses receiving, at the one or more processors, the reference sequence from the memory and generating, at the one or more processors, a reference match score for each of the one or more sequencing reads by aligning each sequencing read to the corresponding reference sequence:
(Paragraph [0025] of Anderson. The teaching describes that a reference sequence can be a canonical reference sequence. Canonical reference sequences can be found in, for example, a database (e.g., GENCODE, UCSC or EMBL). The sequencing data can be analyzed by one or more analysis methods. In some cases, the sequencing data can be mapped to a reference sequence. A multiple sequence alignment is performed from unaligned reads by profile Hidden Markov Model (HMM) training, using a combination of Baum-Welch, Viterbi or related approaches that use simulated annealing or consensus motif finding. In some cases, the computational complexity can be significantly reduced by subsetting the reads into gene or motif groups using a simple “best match” alignment algorithm. A multiple sequence alignment can then be performed within each subset to produce a gene-specific, or motif-specific, empirically-derived tumor reference sequence. This best match alignment algorithm output is considered to be a reference match score.)
Anderson further discloses receiving, at the one or more processors, the variant sequence from the memory; generating, at the one or more processors, a variant match score for each of the one or more sequencing reads by aligning each sequencing read to the corresponding variant sequence:
(Paragraphs [0044]-[0046] of Anderson. The teaching describes identifying one or more clinically actionable variants. In some cases, the methods and systems can be used to classify one or more clinically actionable variants. The clinically actionable variant can be in a coding region of a gene or can be in a non-coding region of the genome. For example, the presence of a variant in the UGT1A1 gene (e.g., UGT1A1*28 and/or UGT1A7*3) may suggest that the subject is at higher risk of severe hematologic toxicity when treated with irinotecan (CAMPTOSAR). This establishes that not only is the variant sequence being referenced from memory, it is being compared to the sequence read to determine a risk severity, i.e. a variant match score)
Anderson further discloses labeling, at the one or more processors, each of the one or more sequencing reads as (1) having the genetic variant, (2) not having the genetic variant, or (3) being a null read, based on the reference match score and the variant match score, wherein: a sequencing read is labeled as having the genetic variant if the reference match score and the variant match score indicate that the sequencing read more closely matches the corresponding variant sequence than the corresponding reference sequence; a sequencing read is labeled as not having the genetic variant if the reference match score and the variant match score indicate that the sequencing read more closely matches the corresponding reference sequence than the corresponding variant sequence; and a sequencing read is labeled as a null read if the reference match score and the variant match score are equal:
(Paragraph [0029] of Anderson. The teaching describes classifying one or more genetic variants may comprise comparing the quality score of each of the one or more genetic variants to the threshold value. It should be understood that any value, number, letter, word, or score can be utilized to classify a genetic variant, as long as the classification represents the class to which the genetic variant has been assigned. For example, an arbitrary number (e.g., 10) and a word (“present”) can represent the same concept (i.e., that a variant is “present”). In one example, the classification system described herein may determine whether the quality score for a given genetic variant (or genomic region) is “sufficient” or “insufficient” to proceed with analysis of the data. In some cases, genetic variants may be classified as “present”, “absent”, or “indeterminate”. A genetic variant may be classified as present, for example, if the genetic variant is present (i.e., variant is “called”) and the quality score of the called base (or a genomic region comprising the called base) is greater than the threshold value. A classification of “present” can indicate that a genetic variant is positively identified as being present with an accuracy of at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 99.9%, 99.99%, 99.999%, or 100%. This means that the label of having the genetic variant is given when the sequence read more resembles the variant sequence than the reference sequence.)
As per claim 9,
Claim 9 is substantially similar to claim 1. Accordingly, claim 9 is rejected for the same reasons as claim 1.
As per claim 10,
Anderson discloses the limitations of claim 9.
Anderson further discloses comprising storing a label associated with each sequencing read labeled as having the genetic variant and/or each sequencing read labeled as not having the variant in the memory:
(Paragraph [0003] of Anderson. The teaching describes detecting the presence or absence of a genetic variant, comprising: a) receiving a data input comprising sequencing data generated from a nucleic acid sample from a subject; b) determining a presence or absence of the genetic variant from the sequencing data, wherein the determining comprises assigning a quality score to a genomic region comprising the genetic variant, wherein the assigning is performed by a computer processor; c) classifying the genetic variant based on the quality score to generate a classified genetic variant, and d) outputting a result based on the classifying, thereby identifying the classified genetic variant. In some cases, the classifying further comprises classifying the genetic variant as present if the genetic variant is determined to be present and the quality score for the genomic region comprising the genetic variant is greater than a predetermined threshold. In some cases, the classifying further comprises classifying the genetic variant as absent if the genetic variant is determined to be absent and the quality score for the genomic region comprising the genetic variant is greater than a predetermined threshold. In some cases, the classifying further comprises classifying the genetic variant as indeterminate if the quality score for the genomic region comprising the genetic variant is less than a predetermined threshold. In some cases, the outputting a result comprises generating a report, wherein the report identifies the classified genetic variant. In some cases, the method further comprises mapping the sequencing data to a reference sequence. In some cases, the reference sequence is a consensus reference sequence. In some cases, the reference sequence is derived empirically from tumor sequencing data. In some cases, the predetermined threshold comprises a depth of coverage of the genomic region comprising the genetic variant. In some cases, the depth of coverage is at least 10×. In some cases, the depth of coverage is at least 20×. In some cases, the depth of coverage is at least 30×. In some cases, the depth of coverage is at least 50×. In some cases, the depth of coverage is at least 100×. In some cases, the predetermined threshold comprises a confidence score.)
As per claim 11,
Anderson discloses the limitations of claim 9.
Anderson further discloses further comprising calling, using the one or more processors, a presence or absence of the genetic variant in the test sample based on the labeled one or more sequencing reads; and storing a call for the genetic variant in the memory:
(Paragraph [0003] of Anderson. The teaching describes detecting the presence or absence of a genetic variant, comprising: a) receiving a data input comprising sequencing data generated from a nucleic acid sample from a subject; b) determining a presence or absence of the genetic variant from the sequencing data, wherein the determining comprises assigning a quality score to a genomic region comprising the genetic variant, wherein the assigning is performed by a computer processor; c) classifying the genetic variant based on the quality score to generate a classified genetic variant, and d) outputting a result based on the classifying, thereby identifying the classified genetic variant. In some cases, the classifying further comprises classifying the genetic variant as present if the genetic variant is determined to be present and the quality score for the genomic region comprising the genetic variant is greater than a predetermined threshold. In some cases, the classifying further comprises classifying the genetic variant as absent if the genetic variant is determined to be absent and the quality score for the genomic region comprising the genetic variant is greater than a predetermined threshold. In some cases, the classifying further comprises classifying the genetic variant as indeterminate if the quality score for the genomic region comprising the genetic variant is less than a predetermined threshold. In some cases, the outputting a result comprises generating a report, wherein the report identifies the classified genetic variant. In some cases, the method further comprises mapping the sequencing data to a reference sequence. In some cases, the reference sequence is a consensus reference sequence. In some cases, the reference sequence is derived empirically from tumor sequencing data. In some cases, the predetermined threshold comprises a depth of coverage of the genomic region comprising the genetic variant. In some cases, the depth of coverage is at least 10×. In some cases, the depth of coverage is at least 20×. In some cases, the depth of coverage is at least 30×. In some cases, the depth of coverage is at least 50×. In some cases, the depth of coverage is at least 100×. In some cases, the predetermined threshold comprises a confidence score. The quality score is based on at least one of a depth of coverage, a mapping quality, or a base call quality. In some cases, the quality score is empirically determined. The quality score is determined by a total read depth at a specific location of the genetic variant, a proportion of reads containing the genetic variant, the mean quality of non-variant base calls at the location of the genetic variant, and the difference in mean quality for variant base calls.)
As per claim 12,
Anderson discloses the limitations of claim 9.
Anderson further discloses further comprising determining, using the one or more processors, the variant allele frequency of the genetic variant in the test sample based on the labeled one or more sequencing reads; and storing the variant allele frequency in the memory:
(Paragraphs [0003]-[0006] of Anderson. The teaching describes a system is provided comprising: a) a client component, wherein the client component comprises a user interface; b) a server component, wherein the server component comprises at least one memory location configured to receive a data input comprising sequencing data generated from a nucleic acid sample; c) the user interface operably coupled to the server component; and d) a computer processor operably coupled to the at least one memory location, wherein the computer processor is programmed to map the sequencing data to a reference sequence and assign a quality score to each of a plurality of genomic regions of interest of the mapped sequencing data.)
As per claim 13,
Anderson discloses the limitations of claim 9.
Anderson further discloses comprising, using the one or more processors: selecting, using the one or more processors, the genetic variant from a variant panel stored on the memory; generating, using the one or more processors, the reference sequence or the variant sequence; and storing the reference sequence or the variant sequence in the memory:
(Paragraphs [0003]-[0006] of Anderson. The teaching describes a system is provided comprising: a) a client component, wherein the client component comprises a user interface; b) a server component, wherein the server component comprises at least one memory location configured to receive a data input comprising sequencing data generated from a nucleic acid sample; c) the user interface operably coupled to the server component; and d) a computer processor operably coupled to the at least one memory location, wherein the computer processor is programmed to map the sequencing data to a reference sequence and assign a quality score to each of a plurality of genomic regions of interest of the mapped sequencing data.)
As per claim 14,
Anderson discloses the limitations of claim 9.
Anderson further discloses wherein the one or more sequencing reads comprises a plurality of sequencing reads overlapping the variant locus, the method further comprising determining, using the one or more processors, a number of sequencing reads from the plurality of sequencing reads having the genetic variant or a number of sequencing reads from the plurality of sequencing reads not having the genetic variant:
(Paragraphs [0003], [0034] and [0132] and Figure 2 of Anderson. The teaching describes the outputting a result comprises generating a report, wherein the report identifies the classified genetic variant. The method further comprises transmitting the result over a network. In some cases, the network is the Internet. The report can be provided to a mobile device, smartphone, tablet or personal health monitor or other network enabled device. Examples of reports that can be generated by the methods and systems disclosed herein are depicted in FIGS. 2-5. Sections I and II of the report in Figure 2 demonstrates labeling of a plurality of genetic variants at different variant loci from a variant panel. A sequencing data input is received by the system of the disclosure. The sequencing data input can be from a sequencer (e.g., Illumina sequencer) or from a data repository. The system identifies the presence or absence of clinically actionable variants related to three different indications. Choosing indications that have a significant gene list overlap optimizes the cost of operating the system.)
As per claim 15,
Anderson discloses the limitations of claim 9.
Anderson further discloses comprising labeling, using the one or more processors, one or more sequencing reads associated with the test sample for a plurality of genetic variants at different variant loci selected from a variant panel:
(Paragraphs [0003] and [0034] and Figure 2 of Anderson. The teaching describes the outputting a result comprises generating a report, wherein the report identifies the classified genetic variant. The method further comprises transmitting the result over a network. In some cases, the network is the Internet. The report can be provided to a mobile device, smartphone, tablet or personal health monitor or other network enabled device. Examples of reports that can be generated by the methods and systems disclosed herein are depicted in FIGS. 2-5. Sections I and II of the report in Figure 2 demonstrates labeling of a plurality of genetic variants at different variant loci from a variant panel.)
As per claim 16,
Anderson discloses the limitations of claim 9.
Anderson further discloses comprising determining, using the one or more processors, a disease status for the subject:
(Paragraph [0003] of Anderson. The teaching describes detecting the presence or absence of a genetic variant, comprising: a) receiving a data input comprising sequencing data generated from a nucleic acid sample from a subject; b) determining a presence or absence of the genetic variant from the sequencing data, wherein the determining comprises assigning a quality score to a genomic region comprising the genetic variant, wherein the assigning is performed by a computer processor; c) classifying the genetic variant based on the quality score to generate a classified genetic variant, and d) outputting a result based on the classifying, thereby identifying the classified genetic variant. In some cases, the classifying further comprises classifying the genetic variant as present if the genetic variant is determined to be present and the quality score for the genomic region comprising the genetic variant is greater than a predetermined threshold. In some cases, the classifying further comprises classifying the genetic variant as absent if the genetic variant is determined to be absent and the quality score for the genomic region comprising the genetic variant is greater than a predetermined threshold. In some cases, the classifying further comprises classifying the genetic variant as indeterminate if the quality score for the genomic region comprising the genetic variant is less than a predetermined threshold. In some cases, the outputting a result comprises generating a report, wherein the report identifies the classified genetic variant. In some cases, the method further comprises mapping the sequencing data to a reference sequence. In some cases, the reference sequence is a consensus reference sequence. In some cases, the reference sequence is derived empirically from tumor sequencing data. In some cases, the predetermined threshold comprises a depth of coverage of the genomic region comprising the genetic variant. In some cases, the depth of coverage is at least 10×. In some cases, the depth of coverage is at least 20×. In some cases, the depth of coverage is at least 30×. In some cases, the depth of coverage is at least 50×. In some cases, the depth of coverage is at least 100×. In some cases, the predetermined threshold comprises a confidence score. In some cases, the confidence score is at least 99%. In some cases, the genetic variant comprises a clinically actionable variant. In some cases, the identifying the classified genetic variant further indicates a treatment for the subject based on the classified genetic variant. In some cases, the subject is suffering from a disease. In some cases, the disease is cancer. In some cases, the subject is administered a treatment based on the result.)
As per claim 17,
Anderson discloses the limitations of claim 9.
Anderson further discloses comprising generating, using the one or more processors, a report comprising (1) identifying information for the subject, and (2) a call for the presence or absence of the genetic variant, or a call for the variant allele frequency:
(Paragraphs [0003] and [0034] and Figure 2 of Anderson. The teaching describes the outputting a result comprises generating a report, wherein the report identifies the classified genetic variant. The method further comprises transmitting the result over a network. In some cases, the network is the Internet. The report can be provided to a mobile device, smartphone, tablet or personal health monitor or other network enabled device. Examples of reports that can be generated by the methods and systems disclosed herein are depicted in FIGS. 2-5.)
As per claim 18,
Anderson discloses the limitations of claim 17.
Anderson further discloses comprising transmitting the report to a second electronic device:
(Paragraphs [0003] and [0034] and Figure 2 of Anderson. The teaching describes the outputting a result comprises generating a report, wherein the report identifies the classified genetic variant. The method further comprises transmitting the result over a network. In some cases, the network is the Internet. The report can be provided to a mobile device, smartphone, tablet or personal health monitor or other network enabled device. Examples of reports that can be generated by the methods and systems disclosed herein are depicted in FIGS. 2-5.)
As per claim 20,
Claim 20 is substantially similar to claim 1. Accordingly, claim 20 is rejected for the same reasons as claim 1.
As per claim 31,
Anderson discloses the limitations of claim 16.
Anderson further discloses wherein the disease status is a value proportional to a percentage of circulating-tumor DNA (ctDNA) compared to total cell-free DNA (cfDNA) in the test sample:
(Paragraph [0003] of Anderson. The teaching describes detecting the presence or absence of a genetic variant, comprising: a) receiving a data input comprising sequencing data generated from a nucleic acid sample from a subject; b) determining a presence or absence of the genetic variant from the sequencing data, wherein the determining comprises assigning a quality score to a genomic region comprising the genetic variant, wherein the assigning is performed by a computer processor; c) classifying the genetic variant based on the quality score to generate a classified genetic variant, and d) outputting a result based on the classifying, thereby identifying the classified genetic variant. In some cases, the classifying further comprises classifying the genetic variant as present if the genetic variant is determined to be present and the quality score for the genomic region comprising the genetic variant is greater than a predetermined threshold. In some cases, the classifying further comprises classifying the genetic variant as absent if the genetic variant is determined to be absent and the quality score for the genomic region comprising the genetic variant is greater than a predetermined threshold. In some cases, the classifying further comprises classifying the genetic variant as indeterminate if the quality score for the genomic region comprising the genetic variant is less than a predetermined threshold. In some cases, the outputting a result comprises generating a report, wherein the report identifies the classified genetic variant. In some cases, the method further comprises mapping the sequencing data to a reference sequence. In some cases, the reference sequence is a consensus reference sequence. In some cases, the reference sequence is derived empirically from tumor sequencing data. In some cases, the predetermined threshold comprises a depth of coverage of the genomic region comprising the genetic variant. In some cases, the depth of coverage is at least 10×. In some cases, the depth of coverage is at least 20×. In some cases, the depth of coverage is at least 30×. In some cases, the depth of coverage is at least 50×. In some cases, the depth of coverage is at least 100×. In some cases, the predetermined threshold comprises a confidence score. In some cases, the confidence score is at least 99%. In some cases, the genetic variant comprises a clinically actionable variant. In some cases, the identifying the classified genetic variant further indicates a treatment for the subject based on the classified genetic variant. In some cases, the subject is suffering from a disease. In some cases, the disease is cancer. In some cases, the subject is administered a treatment based on the result. In some cases, the clinically actionable variant is in a gene that alters a response of the subject to a therapy. In some cases, the gene is a cancer gene. In some cases, a presence of a clinically actionable variant indicates the subject is a candidate for a specific therapy. In some cases, an absence of a clinically actionable variant indicates the subject is not a candidate for a specific therapy. In some cases, the nucleic acid sample is derived from blood or saliva. In some cases, the nucleic acid sample is derived from a solid tumor. In some cases, the nucleic acid sample is genomic DNA. In some cases, the genomic DNA is tumor DNA. In some cases, the nucleic acid sample is RNA. In some cases, the RNA is tumor RNA. In some cases, the nucleic acid sample is derived from circulating tumor cells.)
As per claim 32,
Anderson discloses the limitations of claim 16.
Anderson further discloses wherein the disease status is a maximum somatic allele fraction of cfDNA:
(Paragraphs [0003] and [0101] of Anderson. The teaching describes detecting the presence or absence of a genetic variant, comprising: a) receiving a data input comprising sequencing data generated from a nucleic acid sample from a subject; b) determining a presence or absence of the genetic variant from the sequencing data, wherein the determining comprises assigning a quality score to a genomic region comprising the genetic variant, wherein the assigning is performed by a computer processor; c) classifying the genetic variant based on the quality score to generate a classified genetic variant, and d) outputting a result based on the classifying, thereby identifying the classified genetic variant. In some cases, the classifying further comprises classifying the genetic variant as present if the genetic variant is determined to be present and the quality score for the genomic region comprising the genetic variant is greater than a predetermined threshold. In some cases, the classifying further comprises classifying the genetic variant as absent if the genetic variant is determined to be absent and the quality score for the genomic region comprising the genetic variant is greater than a predetermined threshold. In some cases, the classifying further comprises classifying the genetic variant as indeterminate if the quality score for the genomic region comprising the genetic variant is less than a predetermined threshold. In some cases, the outputting a result comprises generating a report, wherein the report identifies the classified genetic variant. In some cases, the method further comprises mapping the sequencing data to a reference sequence. In some cases, the reference sequence is a consensus reference sequence. In some cases, the reference sequence is derived empirically from tumor sequencing data. In some cases, the predetermined threshold comprises a depth of coverage of the genomic region comprising the genetic variant. In some cases, the depth of coverage is at least 10×. In some cases, the depth of coverage is at least 20×. In some cases, the depth of coverage is at least 30×. In some cases, the depth of coverage is at least 50×. In some cases, the depth of coverage is at least 100×. In some cases, the predetermined threshold comprises a confidence score. In some cases, the confidence score is at least 99%. In some cases, the genetic variant comprises a clinically actionable variant. In some cases, the identifying the classified genetic variant further indicates a treatment for the subject based on the classified genetic variant. In some cases, the subject is suffering from a disease. In some cases, the disease is cancer. In some cases, the subject is administered a treatment based on the result. In some cases, the clinically actionable variant is in a gene that alters a response of the subject to a therapy. In some cases, the gene is a cancer gene. In some cases, a presence of a clinically actionable variant indicates the subject is a candidate for a specific therapy. In some cases, an absence of a clinically actionable variant indicates the subject is not a candidate for a specific therapy. In some cases, the nucleic acid sample is derived from blood or saliva. In some cases, the nucleic acid sample is derived from a solid tumor. In some cases, the nucleic acid sample is genomic DNA. In some cases, the genomic DNA is tumor DNA. In some cases, the nucleic acid sample is RNA. In some cases, the RNA is tumor RNA. In some cases, the nucleic acid sample is derived from circulating tumor cells. In some cases, the classifying has a specificity of at least 99%. In some cases, the genetic variant, when classified as present, has a mutant allele fraction of at least 5%. In some cases, the genetic variant, when classified as present, has a mutant allele fraction of at least 10%. The methods and systems disclosed herein may identify variants with a mutation allelic fraction of at least 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98%, 99% or more. )
As per claim 33,
Anderson discloses the limitations of claim 16.
wherein the disease status comprises a qualitative factor indicating recurrence of a cancer in the subject, the presence of a cancer resistant to a treatment modality in the subject, or the presence of a cancer that can be treated with a particular treatment modality:
(Paragraphs [0003] and [0034] of Anderson. The teaching describes detecting the presence or absence of a genetic variant, comprising: a) receiving a data input comprising sequencing data generated from a nucleic acid sample from a subject; b) determining a presence or absence of the genetic variant from the sequencing data, wherein the determining comprises assigning a quality score to a genomic region comprising the genetic variant, wherein the assigning is performed by a computer processor; c) classifying the genetic variant based on the quality score to generate a classified genetic variant, and d) outputting a result based on the classifying, thereby identifying the classified genetic variant. In some cases, the classifying further comprises classifying the genetic variant as present if the genetic variant is determined to be present and the quality score for the genomic region comprising the genetic variant is greater than a predetermined threshold. In some cases, the classifying further comprises classifying the genetic variant as absent if the genetic variant is determined to be absent and the quality score for the genomic region comprising the genetic variant is greater than a predetermined threshold. In some cases, the classifying further comprises classifying the genetic variant as indeterminate if the quality score for the genomic region comprising the genetic variant is less than a predetermined threshold. In some cases, the outputting a result comprises generating a report, wherein the report identifies the classified genetic variant. In some cases, the method further comprises mapping the sequencing data to a reference sequence. In some cases, the reference sequence is a consensus reference sequence. In some cases, the reference sequence is derived empirically from tumor sequencing data. In some cases, the predetermined threshold comprises a depth of coverage of the genomic region comprising the genetic variant. In some cases, the depth of coverage is at least 10×. In some cases, the depth of coverage is at least 20×. In some cases, the depth of coverage is at least 30×. In some cases, the depth of coverage is at least 50×. In some cases, the depth of coverage is at least 100×. In some cases, the predetermined threshold comprises a confidence score. In some cases, the confidence score is at least 99%. In some cases, the genetic variant comprises a clinically actionable variant. In some cases, the identifying the classified genetic variant further indicates a treatment for the subject based on the classified genetic variant. In some cases, the subject is suffering from a disease. In some cases, the disease is cancer. In some cases, the subject is administered a treatment based on the result. In some cases, a treatment decision can be made based on the information in the report. In some cases, a treatment can be administered to a subject based on the report. In some examples, the patient may be receiving a therapy for a disease prior to ordering the genetic test. The report may indicate that a genetic variant is present and that the current treatment regimen should be ceased and a new treatment regimen be administered. In some cases, the patient is tested prior to receiving treatment and further tests are ordered during the course of the treatment. In this example, the patient is monitored for the presence or absence of de novo genetic variants that may indicate the current treatment regimen is no longer effective as a therapy for that patient. The report may further indicate or recommend a different course of treatment based on the presence or absence of de novo genetic variants. The report can provide additional information including, without limitation, genomic coordinates of the variant or genomic region of interest, images that locate the variant within the functional region of the protein, images that show the aligned read stack in the region of the variant, attachments or links (i.e., hyperlinks) to references (i.e., scientific literature) related to the variant of interest, the clinical evidence supporting the treatment recommendations, guidelines that support clinical use of the variant, or reimbursement codes related to the diagnosis or treatment, or any other useful information.)
As per claim 34,
Anderson discloses the limitations of claim 9.
Anderson further discloses wherein the reference match score and the variant match score are determined using a sequence alignment algorithm:
(Paragraph [0081] of Anderson. The teaching describes machine executable code (or machine readable code) can include one or more sequence alignment software. Sequence alignment software can include DNA-seq aligners. In some cases, sequence alignment software can include RNA-seq aligners)
As per claim 41,
Anderson discloses the limitations of claim 9.
Anderson further discloses comprising generating the one or more sequencing reads by sequencing nucleic acid molecules in the test sample:
(Paragraph [0081] of Anderson. The teaching describes machine executable code (or machine readable code) can include one or more sequence alignment software. Sequence alignment software can include DNA-seq aligners. In some cases, sequence alignment software can include RNA-seq aligners)
Response to Arguments
Applicant's arguments filed February July 09, 2026 have been fully considered.
Applicant’s arguments pertaining to rejections made under 35 U.S.C. 101 are persuasive.
The Applicant argues that the pending claims provide a technical solution to a technical problem with sequencing in with a PCR machine. A priori sequencing and de novo sequencing are computationally expensive as is seen in paragraphs [0070] and [0071] of the as-filed specification among other technical issues related to PCR sequencing. The claimed invention streamlines the variant calling process to generate a more efficient variant call with more efficient measurements of allele frequencies.
The Examiner agrees. The specification describes, and is reflected in the claims, elements that when looked at in an ordered combination improve technology. While the pending claims still recite an abstract idea, this abstract idea in combination with the technology that implements it results in more accurate information for allele detection at a lower computational cost. Accordingly, these rejections are withdrawn.
Applicant’s arguments pertaining to rejections made under 35 U.S.C. 102 are not persuasive.
The Applicant argues that Anderson does not teach two separate alignments, one for the reference sequence and one for the variant sequence.
The Examiner respectfully disagrees. Paragraphs [0025], [0044]-[0046] of Anderson teaches these elements under the BRI of the reference. The Applicant has read their own claims too narrowly and has not considered how the Examiner interpreted their claim language.
The Applicant further argues that the quality score in Anderson is not a match score.
The Examiner respectfully disagrees. Paragraph [0029] of Anderson clearly teaches a labeling of the match scores disclosed by Anderson. By labeling these match scores as present, absent or indeterminate based on the scores disclosed above, this is a labeling of the match scores.
The Applicant argues that the pending claims necessitate comparing the reference match score to the variant match score.
The Examiner respectfully disagrees. This is not what the claim recites. While there is a comparison between these scores in the “null” condition, this condition is not required by the claim. It is merely one optional outcome, listed in the alternative.
Conclusion
THIS ACTION IS MADE FINAL. 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 CHAD A NEWTON whose telephone number is (313)446-6604. The examiner can normally be reached M-F 8:00AM-4:00PM (EST).
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/CHAD A NEWTON/Primary Examiner, Art Unit 3681
