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 .
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.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 06/08/2026 has been entered.
Applicant's response, filed on 06/08/2026, is 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.
Status of claims
Canceled:
2-3, 5-6, 10, 12-13, 18-21, 24
Pending:
1, 4, 7-9, 11, 14-17, 22-23, 25-26
Amended:
1, 11, 15, 17
New:
25-26
Withdrawn:
none
Examined:
1, 4, 7-9, 11, 14-17, 22-23, 25-26
Independent:
1
Allowable:
25
Priority
As detailed on the 10/14/2021 filing receipt, this application claims priority to as early as 07/27/2020.
Interview Summary
On 06/26/2026, the Examiner informed Applicant's representative, Caitlin A. Hyland, about potential claim amendments that could direct the claims toward allowance. The suggested claim amendment includes amending claim 1 to include the limitations of newly added claim 25. The suggestion was discussed with SPE, Larry Riggs, prior to informing Applicant's representative. Applicant's representative would like to discuss the suggested claim amendments with the Applicant prior to making any claim amendments. No agreements were reached.
Regarding 35 USC 103 -- no prior art applied
No prior art is applied to claims 1, 4, 7-9, 11-12, 14-17, 22-23 and 25-26. The claims overcome the closest prior art to Bell (as cited on the 07/21/2022 Office Action and 02/14/2025 "Notice of References Cited" form 892) and Nicula (as cited on the 07/21/2022 "Notice of References Cited" form 89). Bell teaches determining a homologous recombination deficiency (HRD) status of a DNA sample using machine learning models. Nicula teaches binning of the reference genome and arranging coverage signals into one- and two-dimensional vectors. However, Bell and Nicula do not teach "wherein the reference genome is divided into a first set of at most 100kbp bins and further comprising a step of collapsing the 100kbp bins into a second set of bins of at least 500kbp prior to arranging the coverage signals of the chromosome arm into the coverage signal array" in claim 1. It is not clear that any combinable art of record would have rendered the claims obvious.
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, 4, 7-9, 11, 14-17, 22-23 and 26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claim 1 recites determining the HRD status of the patient DNA sample… aligning the sequencing reads of the patient DNA sample to a reference genome, wherein the reference genome is divided into a plurality of bins, each bin belonging to a same genomic region of a chromosome arm in the reference genome to be analyzed; counting and normalizing the number of aligned reads in each bin along each chromosome arm, to obtaining a coverage signal on each chromosome arm based on the step of counting and normalizing the number of aligned reads in each bin along each chromosome arm; arranging the coverage signals of the chromosome arms into a coverage data signal array for the patient DNA sample, comprising: arranging the coverage signals of the chromosome arms into a 1D coverage data signal vector or a 2D coverage data signal array, arranging the coverage signals of the chromosome arms into a the 2D coverage data signal array comprising aligning in rows the coverage data signal for each chromosome with respect to a centromeric bin of each chromosome arm, that is the bin adjacent to the centromere region of the chromosome arm; determining and outputting, via the trained machine learning model, a homologous recombination deficiency score (HRD score) of the patient DNA sample, determining a negative, a positive or an uncertain homologous recombination deficiency (HRD) status of the patient DNA sample according to the HRD score out of the trained machine learning model; and (e) selecting the patient for a cancer treatment based on the HRD status of the patient DNA sample.
Claim 4 recites wherein counting and normalizing the number of aligned reads in each bin along each chromosome arm to obtain a coverage signal on the chromosome arm comprises normalizing the coverage signal per sample, and/or normalizing by GC content to apply a GC-bias correction.
Claim 7 recites wherein the plurality of samples of known homologous recombination deficiency status are tumor data samples with a known homologous recombination deficiency status.
Claim 8 recites tumor data samples with a known homologous recombination deficiency status are augmented with artificial sample data generated by combining data from chromosomes of the tumor data samples with a known homologous recombination deficiency status label, forming data augmented samples.
Claim 9 recites wherein the data augmented samples are generated in order to represent a purity-ploidy ratio distribution as observed in the tumor data samples.
Claim 14 recites wherein the patient DNA sample is a tumor cell-free DNA (cfDNA), a fresh-frozen tissue (FFT) or a formalin-fixed paraffin-embedded (FFPE) sample.
Claim 15 recites wherein an HRD score or the HRD status of the patient DNA sample is a predictor of a tumor response to a cancer treatment regimen.
Claim 17 recites wherein the cancer is a high grade serous ovarian cancer.
Claim 23 recites converting the coverage data signal array into a coverage data signal image; and inputting the coverage data signal image to the trained machine learning model, wherein the model has been further trained using coverage signal images for a plurality of samples of known homologous recombination deficiency status to distinguish between the coverage data signal image from samples with a positive homologous recombination deficiency status and the coverage data signal image from samples with a negative homologous recombination deficiency status.
Claim 26 recites wherein the cancer is selected from high grade serous ovarian cancer, prostate cancer, breast cancer, or pancreatic cancer.
The processes of claim 1 includes aligning reads, determining HRD status and score, arranging data into an array and selecting the patient for a cancer treatment based on the HRD status of the patient DNA sample, which could be practically performed in the human mind with pen and paper. Claim 26 also includes selecting a type of cancer. Selecting could be accomplished by choosing from a list. Therefore, under the broadest reasonable interpretation, the claims can be practically carried out in the human mind or with pen and paper as claimed, which falls under the "Mental processes" grouping of abstract ideas. Although, claim 1 recites performing the method as part of a method executed on a computer, there are no additional imitations to indicate that anything other than a generic computer is required. However, merely requiring that the steps are carried out with a generic computer does not negate the mental nature of these steps and equates rather to merely using a computer as a tool to perform the mental process. The processes of claim 1 include generating a data array and trained machine learning model; claim 4 include the counting and normalizing and claim 9 includes determining a purity-ploidy ratio distribution that are mathematical concepts and/or formulas and requires carrying out a series of mathematical calculations, which falls under the “mathematical concepts” grouping of abstract ideas. Claims 14 and 15 recites the patient DNA sample and claim 12 and 17 recites the cancer patient, which are laws of nature. Claims 1, 8, 9, 12, 15, and 23 recite a correlation between the patient DNA sample and HRD status. This is similar to the concept of a correlation between a patient’s genotype and risk of QTc prolongation in Vanda Pharmaceuticals Inc. v. West-Ward Pharmaceuticals, 887 F.3d 1117, 1135-36, 126 USPQ2d 1266, 1281 (Fed. Cir. 2018) that the courts identified as a natural phenomenon. As such, claims 1, 4, 7-9, 11, 14-17, 22-23 and 26 recite an abstract idea (Step 2A, Prong 1: YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). This judicial exception is not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology or applies or uses the recited judicial exception in some other meaningful way.
Claim 1 recites A computer-based method of determining a homologous recombination deficiency (HRD) status of a patient DNA sample, the method comprising the steps of:(a) extracting and isolating fragments of DNA from the patient to obtain a patient DNA sample:(b) constructing a sequencing library comprising the fragments of DNA from the patient DNA sample overlapping a set of chromosomes (c) sequencing, via whole genome sequencing, the sequencing library to a read depth is of at least 0.1X and at most 5X; obtaining sequencing reads of the patient DNA sample to be analyzed; wherein the sequencing reads are obtained via low pass whole genome sequencing of the patient DNA sample; wherein the reference genome is divided into a first set of at most 100kbp bins and further comprising a step of collapsing the 100kbp bins into a second set of bins of at least 500kbp prior to arranging the coverage signals of the chromosome arm into the coverage signal array; inputting the coverage data signal array to a trained machine learning model, wherein the model has been trained using coverage signal arrays for a plurality of samples of known homologous recombination deficiency status to distinguish between the coverage data signal array from samples with a positive homologous recombination deficiency status and the coverage data signal array from samples with a negative homologous recombination deficiency status and (f) administering the cancer treatment to the patient based on the HRD status of the patient DNA sample.
Claim 11 recites wherein the bins of the first set of bins have a uniform size of at most 100kbp and the bins of the second set of bins have a size of between 2.5 to 3.5 Mbp and are obtained by pooling between 25 to 35 100kbp bins from the first set of bins.
Claim 16 recites wherein the cancer treatment regimen is-a radiation therapy.
Claim 22 recites wherein the trained machine learning model is a Convolutional Neural Network (CNN) model.
The processes of claim 1 include extracting and isolating fragments of DNA, constructing a sequencing library, sequencing, obtaining sequencing reads, obtaining a coverage signal and inputting the coverage data signal array to a trained machine learning model, which equate to mere data gathering and outputting activities. Claim 1 also recites a generic computer environment and methods of obtaining data that serves as input to the recited judicial exception in the claims and administering the cancer treatment to the patient based on the HRD status does not provide a particular treatment for a disease, which equates to merely an intended use of the claimed invention or a field of use limitation. The limitations of claim 11 is providing information on the size and depth of the data and do not require that the particular data generating processes be performed. Claim 16 is providing information of the cancer treatment regimen and claim 22 is providing information on the type of machine learning model. Therefore, these limitations do not change the character of the obtaining data step beyond mere data gathering activity. Claims 4, 7-9, 12, 14-15, 17, and 23 do not recite any elements in addition to the judicial exception. As such, as currently recited, the claims do not appear to recite an improvement to technology or apply or use the recited judicial exception in some other meaningful way. Therefore, claims 1, 4, 7-9, 11, 14-17, 22-23 and 26 are directed to an abstract idea (Step 2A, Prong 2: NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to well-understood, routine and conventional activities, insignificant extra-solution activity or mere instructions to implement the abstract idea on a generic computer. The instant claims recite the following additional elements:
Claim 1 recites A computer-based method of determining a homologous recombination deficiency (HRD) status of a patient DNA sample, the method comprising the steps of:(a) extracting and isolating fragments of DNA from a patient DNA sample:(b) constructing a sequencing library comprising the fragments of DNA from the patient DNA sample overlapping a set of chromosomes (c) sequencing, via whole genome sequencing, the sequencing library to a read depth is of at least 0.1X and at most 5X; obtaining sequencing reads of the patient DNA sample to be analyzed; wherein the sequencing reads are obtained via low pass whole genome sequencing of the patient DNA sample; wherein the reference genome is divided into a first set of at most 100kbp bins and further comprising a step of collapsing the 100kbp bins into a second set of bins of at least 500kbp prior to arranging the coverage signals of the chromosome arm into the coverage signal array; inputting the coverage data signal array to a trained machine learning model, wherein the model has been trained using coverage signal arrays for a plurality of samples of known homologous recombination deficiency status to distinguish between the coverage data signal array from samples with a positive homologous recombination deficiency status and the coverage data signal array from samples with a negative homologous recombination deficiency status and (f) administering the cancer treatment to the patient based on the HRD status of the patient DNA sample.
Claim 11 recites wherein the bins of the first set of bins have a uniform size of at most 100kbp and the bins of the second set of bins have a size of between 2.5 to 3.5 Mbp and are obtained by pooling between 25 to 35 100kbp bins from the first set of bins.
Claim 16 recites wherein the cancer treatment regimen is-a radiation therapy.
Claim 22 recites wherein the trained machine learning model is a Convolutional Neural Network (CNN) model.
Limitations that equate to mere data gathering and outputting via generic computer components, such as receiving data at a computer or outputting data, amount to insignificant extra-solution activity as set forth by the courts in Mayo, 566 U.S. at 79, 101 USPQ2d at 1968 and OIP Techs., Inc, v, Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015). Also, the courts have recognized that detecting DNA or enzymes in a sample, analyzing DNA to provide sequence information or detecting allelic variants and amplifying and sequencing nucleic acid sequences 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 (See MPEP 2106.05(d)). Additionally, methods for extracting and isolating fragments of DNA, constructing a sequencing library and sequencing are well-known conventional methods as disclosed by Anson ("DNA extraction from primary liquid blood cultures for bloodstream infection diagnosis using whole genome sequencing." Journal of Medical Microbiology 67.3 (2018): 347-357.; as cited on the attached “Noticed of References cited” 892 form). Also, low-pass whole genome sequencing is commercially available as disclosed by BGI (Low-Pass Whole Genome Sequencing. BGI Americas. 2018; as cited on the 06/30/2025 “Noticed of References cited” 892 form). Therefore, methods for extracting and isolating fragments of DNA, constructing a sequencing library, sequencing and lp-WGS are well-understood, routine and conventional methods. The use of machine learning models to analyze genomic data is also a known method. Evidence that these steps, in combination, are well-understood, routine and conventional in the field can be found in Leung, Michael KK, et al. "Machine learning in genomic medicine: a review of computational problems and data sets." Proceedings of the IEEE 104.1 (2015): 176-197; as cited on the 07/21/2022 “Noticed of References cited” 892 form. In particular, see Leung pg 179-180 under Section III; pg. 183-184 Figure 6 and 1) Sequencing, 2) Microarrays and 3) Basic computational models and, pg. 185 Section IV. Also, the limitation of claim 1 of administering the cancer treatment to the patient based on the HRD status does not provide a particular treatment for a disease, which equates to merely an intended use of the claimed invention or a field of use limitation. Overall, the additional elements 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. Claims 4, 7-9, 14-15, 17, and 23 do not recite additional limitations. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1, 4, 7-9, 11, 14-17, 22-23 and 26 are not patent eligible.
Response to 35 USC § 101 Remarks received 06/08/2026
Applicant amended claims 1, 11, 14-15 and 17.
It is noted that Applicant’s remarks are based on amended claims.
In Applicant's remarks for Claim Rejections under 35 U.S.C. §101, see pages 6-19, Applicant disagrees with the office action mailed 03/09/2026.
Under Step 2A, Prong 1 of the 101 analysis, Applicant disagrees that "the claims can be practically carried out in the human mind or with pen and paper as claimed, which falls under the "mental process" grouping of abstract ideas" and that the claims recite "mathematical concepts and/or formulas and requires carrying out a series of mathematical calculations, which falls under the "mathematical concepts" grouping of abstract ideas. Applicant states that when the claims are considered as a whole, and consistent with the reasoning of McRO, Inc. v. Bandai Namco Games America Inc. (Fed. Cir. 2016), the claim limitations defined a technological process of laboratory, clinical, and analytical steps and their ordered implementation to achieve the claimed outcome for determining a homologous recombination deficiency (HRD) status of a patient DNA sample.
Under Step 2A, Prong 2 of the 101 analysis, Applicant states that any judicial exception is integrated into a practical application in the amended claims because the claims recite specific clinical actions that apply the determined HRD status of the patient having or suspected of having cancer in a concrete, therapeutic context. Applicant states that the claims require selecting the patient for a cancer treatment based on the HRD status of the patient DNA sample, and administering the cancer treatment to the patient based on the HRD status of the patient DNA sample, the HRD status being determined using the instantly claimed method. Applicant also states that the claimed method represents a significant technical improvement in the technical field of HRD detection methods by enabling accurate HRD status determination from low-pass whole genome sequencing data, which was not previously achievable with comparable accuracy.
Under Step 2B, Applicant argues that the cited art Anson, BGI and Leung are directed to a different technical context than what is claimed and does not demonstrate routine or conventional methods.
In response, Applicants' remarks have been fully considered and are not persuasive. In McRO, the Federal Circuit concluded that the claims were directed to a technological improvement over earlier, manual, 3D animation practices. Therefore, the court ruled that the claims were not directed to an abstract idea and was patent-eligible. In the instant case, the asserted improvement is directed towards HRD detection methods, which is an improvement to the Judicial Exception (JE). An improvement directed to an improvement to the JE is not sufficient to provide for patent eligibility. For the JEs to be integrated into a practical application, the additional elements have to apply, rely on or use the JE in way that imposes a meaningful limit on the claims to provide an improvement. As stated in MPEP 2106.05(a), the judicial exception alone cannot provide the technical improvement. The improvement can be provided by one or more additional elements as seen in Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception as seen in Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303-04, 125 USPQ2d 1282, 1285-87 (Fed. Cir. 2018)).
Applicant appears to argue that the claims integrate the judicial exception into a practical application by applying or using a judicial exception to affect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2) under Step 2A, Second Prong, 2nd consideration of the 101 analysis. Applicants' remarks have been fully considered and are not persuasive because administering the cancer treatment to the patient based on the HRD status of the patient DNA sample does not provide a particular treatment for a disease. According to MPEP 2106.04(d)(2), in order to qualify as a "treatment" or "prophylaxis" limitation for purposes of this consideration, the claim limitation in question must affirmatively recite an action that effects a particular treatment or prophylaxis for a disease or medical condition. If the limitation does not actually provide a treatment or prophylaxis, e.g., it is merely an intended use of the claimed invention or a field of use limitation, then it cannot integrate a judicial exception under the "treatment or prophylaxis" consideration. Additionally, the treatment or prophylaxis limitation must be "particular," i.e., specifically identified so that it does not encompass all applications of the judicial exception(s). See MPEP 2106.04(d)(2) and See, e.g., Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1066–68, 100 USPQ2d 1492, 1500-01 (Fed. Cir. 2011).
Regarding the cited art Anson, BGI and Leung, although the cited arts are not directed to the same technical context as the instant claims, the art demonstrates that the methods of extracting and isolating fragments of DNA from a patient DNA sample, performing lpWGS sequencing and using machine learning to analyze genomic data are known and conventional methods. Also, the courts have recognized that detecting DNA or enzymes in a sample, analyzing DNA to provide sequence information or detecting allelic variants and amplifying and sequencing nucleic acid sequences 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 (See MPEP 2106.05(d)).
Furthermore, the process of extracting and isolating fragments of DNA from a patient DNA sample amounts are additional elements that amount to mere data gathering and is a field of use or insignificant extra solution activity. As indicated in MPEP 2106.05(g), data gathering is insignificant extra solution activity and a data gathering step that is limited to a particular data source or a particular type of data could be considered to be both insignificant extra-solution activity and a field of use limitation.
Conclusion
Claims 1, 4, 7-9, 11, 14-17, 22-23 and 26 are not allowed. Claim 25 provides a particular treatment and is allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KETTIP KRIANGCHAIVECH whose telephone number is (571)272-1735. The examiner can normally be reached 8:30am-5:00pm EDT.
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/K.K./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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