DETAILED ACTION
Applicant's response, filed 08/12/2026, 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.
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 .
Priority
This application filed 08/30/2022 is a Continuation in Part of PCT/US2022/073167, filed 06/24/2022, and claims priority from Provisional Application 63215281, filed 06/25/2021. The claims are therefore examined as filed on 06/25/2021, the effective filing date. In future actions, the effective filing date of one or more claims may change, due to amendments to the claims, or further review of the priority application(s).
Claim Status
Claims 2-7, 9, 11-12, 46, 52, 66-71, 75, 81, and 178-179 are pending.
Claims 2, 75, and 81 are withdrawn.
Claims 3-7, 9, 11-12, 46, 52, 66-71, and 178-179 are directed to the elected invention.
Claims 1, 8, 10, 13-45, 47-51, 53-65, 72-74, 76-80, and 82-177 are cancelled.
Claims 178-179 are newly added.
Claims 3-7, 9, 11-12, 46, 52, 66-71, and 178-179 are examined.
Claims 3-7, 9, 11-12, 46, 52, 66-71, and 178-179 are rejected.
Information Disclosure Statement
The Information Disclosure Statements are in compliance with the provisions of 37 CFR 1.97. Accordingly, all references have been considered.
Withdrawn Rejections
The rejection of claims 3, 4-7, 9, 11-13, 46, 52, and 66-71 under 35 U.S.C. §112(b), in the Office action mailed 5/12/2026 is withdrawn in view of the amendments and remarks filed 08/12/2026.
The rejection of claims 66-68 under 35 U.S.C. §101, in the Office action mailed 5/12/2026 is withdrawn in view of the amendments filed 08/12/2026, and persuasive argument that these claims recite a particular treatment for a medical condition, which is an additional element that integrates the abstract ideas of the claims into a practical application (remarks pg 23-24).
The rejection of claims 3-6, 9, 11-13, 46, 52, 66-67, and 69 under 35 U.S.C. 102(a)(1) and 102(a)(2) over VENKAT, in the Office action mailed 5/12/2026 is withdrawn in view of the amendments filed 08/12/2026, and persuasive argument that the cited reference does not teach the subset of features described in the amended claims.
The rejection of claim 7 under 35 U.S.C. 103 over VENKAT in view of TANG in the Office action mailed 5/12/2026 is withdrawn in view of the amendments filed 08/12/2026, and withdrawal of the rejection of claims 3-6, 9, 11-13, 46, 52, 66-67, and 69 above.
The rejection of claims 68 and 70-71 under 35 U.S.C. 103 over VENKAT in view of BELL in the Office action mailed 5/12/2026 is withdrawn in view of the amendments filed 08/12/2026, and withdrawal of the rejection of claims 3-6, 9, 11-13, 46, 52, 66-67, and 69 above.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 3-7, 9, 11-12, 46, 52, 69-71, and 178-179 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of mental processes and mathematical concepts, without significantly more.
The MPEP at MPEP 2106 sets forth steps for identifying eligible subject matter:
(1) Are the claims directed to a process, machine, manufacture or composition of matter?
(2A)(1) Do the claims recite a judicially recognized exception, i.e. a law of nature, a natural phenomenon, or an abstract idea?
(2A)(2) Do the claims recite additional elements that integrate the judicial exception into a practical application?
(2B) If the claims recite a judicial exception and do not integrate the judicial exception, do the claims recite additional elements that provide an inventive concept and amount to significantly more than the judicial exception?
With regard to step (1) (Are the claims directed to a process, machine, manufacture or composition of matter?): Yes. The claims are directed to one of the statutory classes. Claims 3-7, 9, 11-12, 46, 52, 69-71, and 178-179 are directed to a process (methods).
With regard to step (2A)(1) (Do the claims recite a judicially recognized exception?): Yes. The claims recite the abstract ideas of processing data using mental steps and mathematical concepts. Claims that recite nothing more than abstract ideas, natural phenomena, or laws of nature are not eligible for patent protection (see MPEP 2106.04).
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 particular, these abstract ideas include but are not limited to:
Determining feature importance metrics associated with each feature of a plurality of features (mental process; the human mind is capable of determining importance metrics associated with data; claim 3)
Identifying a subset of features using feature importance metrics (mental process/mathematical concept; the human mind is capable of identifying features based on metrics, using metrics to identify data is a mathematical process, as claim 5 defines the metrics as comprising statistical tests; claims 3, 5)
Classifying a tumor as HRD-positive or negative based on a model (mental process/mathematical concept, the human mind is capable of classifying a tumor based on data/a mathematical model; claims 3)
Obtaining feature rankings according to feature importance metrics (mental process/mathematical concept; the human mind is capable of ranking data according to metrics, and comparing numerical data to determine a ranking is a mathematical process; claim 6, 7)
Selecting a subset of features based on feature rankings (mental process; the human mind is capable of making a selection based on ranked data; claim 6)
Selecting a subset of features based on evaluation results (mental process, the human mind is capable of making a selection based on an evaluation output; claim 7)
Obtaining a new feature set by adding features to an existing feature set based on feature ranking (mental process; the human mind is capable of adding data to a set; claim 7)
Determining an HRD-positive likelihood score and HRD-negative likelihood score; mental process/mathematical concept; the human mind is capable of determining a likelihood score, and doing so is equivalent to performing a calculation; claim 11)
Selecting a therapy for a tumor in a subject based on the assessment of the tumor as HRD positive or negative (mental process; the human mind is capable of selecting a therapy based on data; claim 69-71)
Dependent claims 4, 52, and 178-179 further limit the abstract ideas recited in the independent claims, and do not change their characterization as abstract ideas.
Therefore, the claims recite elements that constitute one or more judicial exceptions.
With regard to step (2A)(2) (Do the claims recite additional elements that integrate the judicial exception into a practical application?): No. The claims recite the additional elements of receiving sample data (claim 3) and receiving training datasets (claim 46), storing data (claim 7), recording data in a digital electronic file (claims 11-12), and training/using a trained HRD model to process and classify data (claims 3, 7, 46). The claims also recite the additional element of using one or more processors to perform methods steps.
While the claims recite the additional element of receiving, outputting data and storing data, such steps that only amount to necessary data gathering and outputting, without any technical details of how the data is obtained/output/stored that integrate the judicial exception, are insignificant extrasolution activities that do not add a meaningful limitation to the claims (see MPEP 2106.05(g)). As a result, the judicial exception is not integrated into a practical application.
In addition, while the claims recite additional elements related to the use of computers, they do not provide any specific details by which the processors perform or carries out the judicial exception listed in step (2A)(1), nor do they provide any details of how specific structures of the computer are used to implement these functions. The judicial exception is therefore not integrated into a practical application because the generically recited computer elements do not add a meaningful limitation to the abstract idea, as they amount to simply implementing the abstract idea on a computer (see MPEP 2106.05(f)). This also applies to the use of a trained model to process and output data, as such a model, without recited physical structure outside of general computer components, is also analogous to implementing an abstract idea of data analysis on a computer using an algorithm or using the abstract idea of mathematics to train a model using a computer – the broadest reasonable interpretation of the model in light of the specification is a mathematical calculation/algorithm, as [213-214] describes that embodiments can include one or more machine-learning models, or non-machine-learning models, and can include algorithms as simple as linear regression or logistic regression. Because the claims do not recite any additional elements that integrate the judicial exception into a practical application, the claims as a whole are directed to an abstract idea.
With regard to step (2B) (Do the claims recite additional elements that provide an inventive concept and amount to significantly more than the judicial exception?): No.
The claims recite an abstract idea with additional elements; however, these additional elements are general computer elements added to abstract ideas, and non-particular instructions to apply the abstract idea by linking it to a field of use or extrasolution activity (see MPEP 2106.05(f-h)). General computer elements used to perform an abstract idea do not provide an inventive concept, and similarly, non-particular instructions to gather or produce data do not provide an inventive concept. Non-particular instructions to gather or output data are also considered well-understood, routine and conventional activities (see MPEP 2106.05(d), which indicates that limitations such as “Receiving or transmitting data over a network” from Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362, and “Storing and retrieving information in memory” from Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 are recognized as conventional activities). The claims therefore do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As a result, the claims as a whole do not provide an inventive concept.
Response to Arguments – Rejections Under 35 USC § 101
The amendments and arguments regarding the previous rejection of claims 66-68 under 35 USC § 101 have been accepted, and the rejection withdrawn.
The arguments regarding the remaining claims are not persuasive. The Applicant asserts that the claims do not recite any mental process, and that the steps of determining feature metrics, identifying a subset of features using the metrics, and classifying the tumor as HRD positive or negative using a model cannot be practically performed in the human mind (remarks pg 14-17). However, this is not persuasive because there is not anything in the claims to suggest a level of complexity outside of what can be performed in the human mind – the claims recite mental processes that are made easier when performed on a computer. Further, the broadest reasonable interpretation of these limitations in light of the specification also show these processes to be performable in the human mind – for example, the limitation of determining feature importance metrics comprise Chi-Square test or analysis of variance (ANOVA) [0009], which are performable using pen and paper, and the model used can include one or more machine-learning models, or non-machine-learning models, and can include algorithms as simple as linear regression or logistic regression [213-214] which are also performable in the human mind. Further, the more complex embodiments such as random forest and gradient boosting, while certainly more difficult without the use of a computer, are still considered mental processes; although a general-purpose computer can perform calculations at a rate and accuracy that can far outstrip the mental performance of a skilled artisan, the nature of the activity is essentially the same, and constitutes an abstract idea. See Bancorp Serves., L.L. C. v. Sun Life Assur. Co. of Canada (U.S.), 687 F.3d 1266,1278 (Fed. Cir. 2012) (holding that “the fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter”); see also See SiRF Tech., Inc. v. Int’l Trade Comm ’n, 601 F.3d 1319,1333 (Fed. Cir. 2010) (holding that: In order for the addition of a machine to impose a meaningful limit on the scope of a claim, it must play a significant part in permitting the claimed method to be performed, rather than function solely as an obvious mechanism for permitting a solution to be achieved more quickly, i.e., through the utilization of a computer for performing calculations)."
The Applicant also asserts that the claims do not recite a mathematical concept because they do not describe any mathematical relationships, calculations, formulas, or equations using words or mathematical symbols (remarks pg 18). However, the broadest reasonable interpretation of the claims is a mathematical concept, as [213-214] describes that embodiments of the model can include one or more machine-learning models, or non-machine-learning models, and can include algorithms as simple as linear regression or logistic regression. Further, the claims at least fall under the mathematical relationship category as they describe a relationship between variables (features) and a classification outcome, and performing an analysis using the broadest reasonable interpretation of the model is directed to “organizing information and manipulating information through mathematical correlations” which is considered part of the Mathematical Relationships grouping of abstract ideas (see MPEP 2106.04(a)(2)).
The Applicant also asserts that the claims integrate the judicial exception into a practical application (remarks pg 19-23). The Examiner agrees regarding claims 66-68 (as explained in the withdrawal of the rejection of these claims above). However, the remaining claims do not integrate the judicial exception, as they neither improve the technical field of cancer treatment nor provide an improved way of training a machine learning model, as asserted. The remaining claims do not recite the additional element of administering a particular treatment as claims 66-68 do, and therefore cannot integrate the judicial exception. Similarly, the claims do not actively recite an improved method of training the model, and only describe the features the model was trained on – this is just a description of data used to train the model.
As such, claims 3-7, 9, 11-13, 46, 52, 69-71, and 178-179 recite abstract ideas that are not integrated into a practical application, and remain rejected under 35 USC § 101.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim Rejection
Claims 3-6, 9, 11-12, 46, 52, 66-67, 69, and 178 are rejected under 35 U.S.C. 103 as being unpatentable over VENKAT (US 20200255909 A1, as previously cited) in view of MACINTYRE 2018 “Copy number signatures and mutational processes in ovarian carcinoma” (cited on the IDS filed 1/23/2023). This rejection is newly recited and necessitated by claim amendment.
Claim Interpretation and Scope and Contents of Prior Art
Claim 3 recites a method, comprising receiving, by one or more processors, sample data associated with a genome of a tumor in a subject; inputting, by the one or more processors, the sample data into a trained homologous recombination deficiency (HRD) model; and classifying, by the one or more processors, using the trained HRD model, the tumor as HRD- positive or HRD-negative. With respect to these limitations, VENKAT teaches methods and a computer system of processors [057] for determining HRD status in a patient, including the steps of obtaining sample data from a cancerous tissue from a subject, and inputting the data into a classifier trained to distinguish between cancers with HRD and cancers without HRD [0016, 61-69].
Claim 3 also recites the limitation wherein the HRD model has been trained using an HRD-positive training dataset and an HRD-negative training dataset, according to a training process comprising determining one or more feature importance metrics associated with each feature of a plurality of features, identifying a subset of features in the plurality of features using the one or more feature importance metrics, and training, by the one or more processors, the HRD model based on the identified subset of features. With respect to these limitations, VENKAT further teaches training the HRD model algorithm using training subjects that include the HRD status (positive or negative) and features associated with each status [0017, 72, claim 28], and by determining feature importance metrics and identifying a subset of features based on the metrics, including heterozygosity status, measure of loss of heterozygosity across the genome and measures of variant alleles [017, 72-76, 0147-149].
Claim 3 further recites the limitation wherein the identified subset of features comprises at least one of a segment minor allele frequency (segMAF) feature, a number of sequencing reads feature, a segment size feature, a breakpoint count per x megabases feature, a change point copy number feature, a segment copy number feature representing a component of a distribution of copy numbers of genomic segments, a breakpoint count per chromosome arm feature, or a number of segments with oscillating copy number feature.
Claim 178 similarly recites the limitation wherein the identified subset of features comprises at least two of these features.
VENKAT does not teach these features in its model. However, MACINTYRE teaches that a combination of structural variant and single nucleotide variant signatures improves the prediction of HRD in cancer (pg 1262-1263) and that specific features of copy number abnormalities that include breakpoint count per X megabases, the copy number of the segments, the difference in copy number between adjacent segments, the breakpoint per chromosome arm, the lengths of oscillating copy number segment chains, and the size of segments can be used as representative features of distinct mutational processes, including HRD (Fig 1 and 3, pg 1264 col 2, pg 1267, col 2 par 1, pg 1268, col 2). Therefore, it would be obvious to one of ordinary skill in the art that these features can be used in the model of VENKAT for improved HRD classification.
Claim 4 recites the limitation wherein the plurality of features comprises one or more copy number features, one or more short variant features, or a combination thereof. With respect to this limitation, VENKAT teaches that the features include measures of variant alleles [0016] that includes copy number and short variant features [0097], and MACINTYRE further teaches copy number and short variant features (Fig 1, pg 1264).
Claim 5 recites the limitation wherein the one or more feature importance metrics comprise one or more of a Chi-Square test, analysis of variance (ANOVA), random forest, or gradient boosting. With respect to this limitation, VENKAT teaches that the feature importance metrics of the classifier can be a random forest algorithm or gradient boosting [0140-142, 147].
Claim 6 recites the limitation wherein identifying the subset of features in the plurality of features comprises obtaining, by the one or more processors, one or more feature rankings according to the one or more feature importance metrics; and selecting, by the one or more processors, the subset of the plurality of features based on one or more feature rankings. With respect to this limitation, VENKAT teaches determining a ranking of features and selecting the top k-features [0174].
Claim 9 recites the limitation wherein the classifying comprises determining at least one of a HRD-positive likelihood score and a HRD-negative likelihood score. With respect to this limitation, VENKAT teaches the model returning a positive or negative HRD call [0052, 139] where a positive call is defined for samples with probability >0.5 [0174].
Claim 11 recites the limitation of recording, in a digital electronic file associated with the subject, at least one of the HRD-positive likelihood score and the HRD-negative likelihood score. With respect to this limitation, VENKAT teaches storing the HRD information output from the classifier in a digital report object for further processing or display [0150-153].
Claim 12 recites the limitation of recording, in a digital electronic file associated with the subject, a designation that the tumor is HRD-positive based on the HRD-positive likelihood score or a designation that the tumor is HRD-negative based on the HRD-negative likelihood score. With respect to this limitation, VENKAT teaches storing the HRD information output from the classifier in a digital report object for further processing or display [0150-153] which would include the designation that the tumor is HRD positive or negative [0174-175].
Claim 46 recites the limitations of training the HRD model by receiving, by the one or more processors, an HRD-positive training dataset, wherein the HRD-positive training dataset comprises a plurality of features associated with an HRD-positive tumor and an HRD-positive label; receiving, by the one or more processors, an HRD-negative training dataset, wherein the HRD-negative training dataset comprises a plurality of features associated with an HRD-negative tumor and an HRD-negative label; and training, by the one or more processors, the HRD model using the HRD-positive training dataset and the HRD-negative training dataset.
With respect to this limitation, VENKAT teaches training the HRD model using training subjects that include the HRD status (positive or negative) and features associated with each status [0017, 72, claim 28].
Claim 52 recites the limitation wherein the tumor in the subject is a prostate cancer, ovarian cancer, breast cancer, non-small cell lung cancer (NSCLC), colorectal cancer (CRC), fallopian tube cancer, endometrial cancer, or pancreatic cancer. With respect to this limitation, VENKAT teaches that the tumor can be breast cancer, ovarian cancer, or colorectal cancer (claims 19-21).
Claim 66 recites the limitation of identifying the tumor as HRD-positive according to the method of claim 3; and administering to the subject a therapeutically effective amount of a therapy effective in a HRD-positive tumor. Claim 69 similarly recites assessing the tumor as HRD-positive or HRD-negative according to the method of claim 3; and selecting a therapy that is effective in a HRD-positive tumor if the tumor is assessed as HRD positive. With respect to these limitations, VENKAT teaches identifying the HRD status and using this provide an effective treatment based on the determination of HRD-positive status [010, 015, 0157-158].
Claim 67 recites the limitation wherein the therapy effective in a HRD positive tumor comprises a platinum-based chemotherapeutic agent or a PARP inhibitor. With respect to this limitation, VENKAT teaches that the effective therapy can be a PARP inhibitor or platinum-based therapy [007, 10-11].
Resolving Ordinary Skill in the Art and Obviousness Rationale
A teaching, suggestion, or motivation in the prior art would have led one of ordinary skill in the art to modify or combine the prior art to arrive at the claimed invention. Specifically, a person of ordinary skill in mutational processes and HRD classification would have been motivated to combine the teachings of VENKAT with the teachings of MACINTYRE, in order to achieve the claimed invention, because the specific features of copy number abnormalities make up mutational signatures associated with an HRD-related cancer, and may be used as representative features of HRD (Fig 1 and 3, pg 1264 col 2, pg 1267, col 2 par 1, pg 1268, col 2). A person of ordinary skill would reasonably expect success from combining these teachings, as both VENKAT and MACINTYRE teach methods of using mutational features to predict HRD or HRD-related cancer, and as the features of MACINTYRE may be included in the model of VENKAT for potential improved HRD classification. Therefore, the claims at issue would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention as there is both a reason to modify or combine the prior art, and a reasonable expectation of success (see MPEP 2143.02 (I)).
Claim Rejection
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over VENKAT in view of MACINTYRE as applied to claims 3-6, 9, 11-12, 46, 52, 66-67, 69, and 178 above, and further in view of TANG 2014 “Feature Selection for Classification: A Review” (as previously cited). This rejection is newly recited and necessitated by claim amendment.
Claim Interpretation and Scope and Contents of Prior Art
VENKAT in view of MACINTYRE teaches the limitations of claims 3-6, 9, 11-12, 46, 52, 66-67, 69, and 178 above.
Claim 7 recites the limitations wherein identifying the subset of the plurality of features comprises:(a) obtaining, by one or more processors, a feature ranking of the plurality of features according to a feature importance metric;(b) obtaining, by the one or more processors, a new feature set by adding one or more features from the plurality of features to an existing feature set based on the feature ranking;(c) training, by the one or more processors, a new HRD model using the new feature set;(d) evaluating, by the one or more processors, the trained new HRD model to obtain an evaluation result; (e) storing, by the one or more processors, the evaluation result associated with the new HRD model and the new feature set;(f) repeating, by the one or more processors, steps (b)-(e) to obtain a plurality of evaluation results until a condition is met; and(g) selecting, by the one or more processors, the subset of the plurality of features based on the plurality of evaluation results.
With respect to this limitation, VENKAT teaches determining a ranking of features and selecting the top k-features, and that any new features can be incorporated into the model and used to expand the training set for retraining [0174].
VENKAT does not teach repeating the training process and evaluating the model until a condition is met; however, TANG teaches that this a common method feature selection for classification models, where new feature sets are added until the subset of features with the highest evaluation for the model is found (pg 10-11, Fig 3 and 5).
Resolving Ordinary Skill in the Art and Obviousness Rationale
A teaching, suggestion, or motivation in the prior art would have led one of ordinary skill in the art to modify or combine the prior art to arrive at the claimed invention. Specifically, a person of ordinary skill in classification models would have been motivated to combine the teachings of VENKAT in view of MACINTYRE with the teachings of TANG, in order to achieve the claimed invention, because the forward selection method/wrapper model of feature selection described in TANG is a known method of feature selection used to obtain better predictive accuracy estimates, and allows for selection of features that maximize quality in a classification model (pg 10-11). A person of ordinary skill would reasonably expect success from combining these teachings, as VENKAT and TANG teach feature selection for a classification model, and the methods of TANG can be used for the feature selection described in VENKAT. Therefore, the claims at issue would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention as there is both a reason to modify or combine the prior art, and a reasonable expectation of success (see MPEP 2143.02 (I)).
Claim Rejection
Claims 68 and 70-71 are rejected under 35 U.S.C. 103 as being unpatentable over VENKAT in view of MACINTYRE as applied to claims 3-6, 9, 11-12, 46, 52, 66-67, 69, and 178 above, and further in view of BELL 2021 “Systems and Methods for Predicting Homologous Recombination Deficiency Status Of A Specimen” (US 20210172024 A1, as previously cited). This rejection is newly recited and necessitated by claim amendment.
Claim Interpretation and Scope and Contents of Prior Art
VENKAT in view of MACINTYRE teaches the limitations of claims 3-6, 9, 11-12, 46, 52, 66-67, 69, and 178 above.
Claim 68 recites the limitation of identifying the tumor as HRD negative using the method of claim 3, and administering to the subject a therapeutically effective amount of a therapy that does not comprise a platinum-based chemotherapeutic agent or a PARP inhibitor.
Claim 70 similarly recites the limitation of selecting a therapy for a tumor in a subject, the method comprising: (a) assessing the tumor as HRD-positive or HRD-negative according to the method of claim 3; and(b) selecting a therapy that is effective in a HRD-positive tumor if the tumor is assessed as HRD positive. Claim 71 depends from claim 70 and recites the limitation wherein the therapy that is effective in a HRD positive tumor comprises a platinum-based chemotherapeutic agent or a PARP inhibitor.
With respect to these limitations, VENKAT teaches identifying the HRD status and using this provide an effective treatment based on the determination of HRD-positive status, and that PARP inhibitors or platinum-based treatments are more effective in HRD-positive subjects [010-11, 015, 0157-158], but does not specify a non-PARP inhibitor/platinum-based treatment for HRD-negative patients.
However, BELL similar teaches similar systems and methods for predicting HRD status, and that when cancer is assessed as HRD negative, the subject can be administered a non-PARP inhibitor therapy such as chemotherapy, an immunotherapy, a signaling pathway inhibitor, or radiation [0248].
Resolving Ordinary Skill in the Art and Obviousness Rationale
A teaching, suggestion, or motivation in the prior art would have led one of ordinary skill in the art to modify or combine the prior art to arrive at the claimed invention. Specifically, a person of ordinary skill in administering a treatment based on determined HRD status would have been motivated to combine the teachings of VENKAT in view of MACINTYRE with the teachings of BELL, in order to achieve the claimed invention, because different treatments can be more or less effective if the caner is HRD-positive vs negative, with PARP inhibitors being more effective for HRD-positive patients vs negative patients [0010-12, 35, 101, 248]. A person of ordinary skill would reasonably expect success from combining these teachings, as both VENKAT and BELL teach methods of determining HRD status in a patient and administering a treatment based on the HRD status. Therefore, the claims at issue would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention as there is both a reason to modify or combine the prior art, and a reasonable expectation of success (see MPEP 2143.02 (I)).
Claims Without an Art Rejection
No art rejection is applied to new claim 179. VENKAT in view of MACINTYRE, while addressing most of the identified subset of features in this claim, does not teach at least segment minor allele frequency (segMAF) as a feature used in training the model in combination with the other features, and no combinable art before the effective filing date could be found to render the claims as obvious.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim Rejection over Co-Pending Application 19106985
Claims 3-4, 6-7, 46, 52, 66-67, 69, and 178-179 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3, 10-12 and 16-18 of co-pending Application 19106985. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the co-pending application anticipate the instant claims.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented. This rejection was previously cited and any newly recited portions are necessitated by claim amendment.
Claims 2-3, 12, 16, and 18 of the co-pending application anticipates claim 3 of the present invention as they together teach the limitations of claim 3 receiving cancer genome data and inputting the data into a trained HRD model to characterize the cancer as HRD positive or negative, and wherein the HRD model is trained using an HRD-positive training dataset and an HRD-negative training dataset by determining feature importance metrics, identifying a subset of genomic features using the feature metrics comprising at least one of a segment minor allele frequency (segMAF) feature, a number of sequencing reads feature, a segment size feature, a breakpoint count per x megabases feature, a change point copy number feature, a segment copy number feature representing a component of a distribution of copy numbers of genomic segments, a breakpoint count per chromosome arm feature, or a number of segments with oscillating copy number feature, and training the HRD model based on the identified subset of features.
Claim 11 of the co-pending application anticipates claim 4 of the present invention.
Claim 17 of the co-pending application anticipates claims 6 and 7 of the present invention.
Claim 18 of the co-pending application anticipates claim 46 of the present invention.
Claim 10 of the co-pending application anticipates claim 52 of the present invention.
Claims 1-3 and 16 of the co-pending application anticipates claims 66-67 and 69 of the
Claim 12 of the co-pending application anticipates claims 178-179 of the present invention.
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/M.C.L./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
