DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim status
2. Claims 1-20 are currently pending and under exam herein.
Claims 1-20 are rejected.
Priority
3. This application claims domestic benefit of provisional Application No. 63/215877 filed on 06/28/2021. Acknowledgment is made of applicant’s claim for domestic priority and the effective filing date will be considered to be 06/28/2021. In this action, all claims are examined as though they had an effective filing date 28 June 2021. In future actions, the effective filing date of one or more claims may change, due to amendments to the claims, or further analysis of the disclosure(s) of the priority application(s).
Drawings
4. The objection to the drawings is withdrawn in view of the amendment to the drawings filed 26 May 2026.
Specification
5. The objection to the specification is withdrawn in view of the amendment to the specification filed 26 May 2026.
Claim interpretation
6. Claim 1 uses the term ‘several’. This is not defined in the specification. Therefore, the claim will be interpreted using the plain meaning of the word in the dictionary. As evidenced by the Meriam Webster dictionary (“Several.” Merriam-Webster.com Dictionary, Merriam-Webster, https://www.merriam-webster.com/dictionary/several. Accessed 11 Aug. 2026), the word ‘several’ means more than two and fewer than many.
Claim Objections
7. The objections to Claims 1, 13-15, and 17 are withdrawn in view of the claim amendments filed 26 May 2026.
The objections below are newly recited and necessitated by the claim amendments:
Claims 1 and 20 are objected to because of the following informalities:
Claim 1 recites “from a training set of subjects with with DLBCL treated with R-CHOP” (on lines 10-11), which is grammatically incorrect. A possible correction is to remove the duplicate word.
Claim 20 recites: “wherein treating the subject in comprises”, which is grammatically incorrect. A possible correction is to remove the word “in”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
8. The previous rejection of claims 1-20 under 35 U.S.C. 112(b) is withdrawn in view of the claim amendments filed 26 May 2026.
The rejections below are newly recited and necessitated by the claim amendments.
Claims 1-16 and 17-18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 17, limitation ‘d’ states “classifying the subject into one of several predetermined survival groups” however only two survival groups (a first group of high responders and a second group of low responders) were introduced previously in limitation 17b. Therefore, the total number of groups is unclear. For the purposes of examination and with broadest reasonable interpretation, there will be considered to be two or more groups. Claim 18 is similarly rejected by virtue of its dependence on claim 17 and its failure to resolve the indefiniteness issue.
Claim 1, limitation ‘a’ recites “the subject with the heterogeneous disease”, but ‘the subject’ lacks antecedent basis because a subject with a heterogeneous disease has not been introduced previously. Claims 2-16 are similarly rejected by virtue of their dependence on claim 1 and their failure to resolve the indefiniteness issue. For the purpose of examination, the claim will be interpreted to mean a subject with a heterogeneous disease.
Claim 1 recites treating the subject with “rituximab, cyclophosphamide, doxorubicin, vincristine, or prednisone (R-CHOP) chemotherapy”. Because ‘or’ is used instead of ‘and’, it is unclear if the treatment is with R-CHOP, which is a combination of all 5 drugs, or if it is treatment with any one of the drugs listed. Under the broadest reasonable interpretation and for the purpose of examination, the treatment will be interpreted to mean either any one of the drugs, or all the drugs in combination. Claims 2-16 are similarly rejected by virtue of their dependence on claim 1 and their failure to resolve the indefiniteness issue.
Claim 2 recites “a first group of high responders to the known therapy, and a second group of low responders to the known therapy” (lines 2-3); however ‘the known therapy’ lacks antecedent basis because ‘a known therapy’ has not been introduced previously. Claims 3-4 are similarly rejected by virtue of their dependence on claim 2 and their failure to resolve the indefiniteness issue. For the purpose of examination, and with broadest reasonable interpretation, the claim will be interpreted to mean a known therapy.
Response to argument:
The 112(b) arguments presented are persuasive and the previous rejection of claims 1-20 under 35 U.S.C. 112(b) is withdrawn. The arguments do not apply to the new grounds of rejection presented 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.
9. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Any newly recited portions herein are necessitated by claim amendments.
Step 2A, Prong 1
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: training the mathematical algorithm using machine learning by analyzing a plurality of RNA-based biomarkers from a training set of subjects with DLBCL treated with R-CHOP, each subject characterized by their respective known plurality of individual RNA-based biomarkers and known survival time
Claim 1 recites: further training the mathematical algorithm to divide all subjects from the training set of subjects into predetermined survival groups based on survival time, and further training the mathematical algorithm to define a subset of individual RNA-based biomarkers corresponding thereto
Claim 1 recites: forecasting clinical course for the subject using the subset of individual RNA-based biomarkers obtained from the subject
Claim 2 recites: the method as in claim 1, wherein in step (a) the mathematical algorithm is further trained to divide all training set subjects into a first group of high responders to the known therapy, and a second group of low responders to the known therapy, wherein the first group of high responders is characterized by survival time longer than average survival time for the entire training set of subjects, the second group of low responders is characterized by survival time shorter than average survival time for the entire training set of subjects
Claim 3 recites: the method as in claim 2, wherein the mathematical algorithm is further trained to define a first subset of RNA-based biomarkers corresponding to dividing all training set subjects into the first group of high responders and the second group of low responders
Claim 4 recites: the method as in claim 3, wherein a presence of a TP53 mutation is a predictor for the second group of low responders
Claim 5 recites: the method as in claim 2, wherein the mathematical algorithm is further trained to subdivide the first group of high responders into a third group of high responders and a fourth group of high responders, wherein the third group of high responders is characterized by survival time longer than average survival time for the entire first group of high responders, the fourth group of high responders is characterized by survival time shorter than average survival time for the entire first group of high responders.
Claim 6 recites: the method as in claim 5, wherein the mathematical algorithm is further trained to define a second subset of RNA-based biomarkers corresponding to dividing all subjects of the first group of high responders into the third group of high responders and the fourth group of high responders
Claim 7 recites: the method as in claim 6, wherein the second subset of RNA-based biomarkers is different from the first subset of RNA-based biomarkers
Claim 8 recites: .the method as in claim 7, wherein the mathematical algorithm is further trained to subdivide the second group of low responders into a fifth group of low responders and a sixth group of low responders, wherein the fifth group of low responders is characterized by survival time longer than average survival time for the entire second group of low responders, the sixth group of low responders is characterized by survival time shorter than average survival time for the entire second group of low responders
Claim 9 recites: the method as in claim 8, wherein the mathematical algorithm is further trained to define a third subset of RNA-based biomarkers corresponding to dividing all subjects of the second group of low responders into the fifth group of low responders and the sixth group of low responders
Claim 10 recites: the method as in claim 9, wherein the third subset of RNA-based biomarkers is different from the first subset of RNA-based biomarkers
Claim 12 recites: the method as in claim 1, wherein the mathematical algorithm is based on a naive Bayesian classifier that is a generalized naive Bayesian classifier defined by applying a geometric mean to a likelihood product
Claim 13 recites: the method as in claim 12, wherein the naive Bayesian classifier is trained to rank individual RNA-based biomarkers from an initial set of available RNA-based biomarkers that includes at least 500 individual genes.
Claim 14 recites: the method as in claim 13, wherein at least some of the individual RNA-based biomarkers are cross-validated by subdividing the training set of subjects into a plurality of subsets, constructing a naive Bayesian classifier for the individual RNA-based biomarkers for one of the subsets and verifying the same RNA-based biomarkers for at least some of the remaining subsets thereby reducing noise and overfitting
Claim 15 recites: the method as in claim 14, wherein: after cross-validation the number of ranked RNA-based biomarkers is between 50 and 70 for each of the subdividing steps of a first group and a second group, a third group and a fourth group, and a fifth group and a sixth group of the training set of subjects; the set of individual RNA-based biomarkers for dividing the entire training set of subjects into the first group and the second group is different from the respective set of individual RNA- based biomarkers for subdividing the first group of high responders into the third group and the fourth group; and the set of individual RNA-based biomarkers for dividing the entire training set of subjects into the first group and the second group is different from the respective set of individual RNA- based biomarkers for subdividing the second group of low responders into the fifth group and the sixth group
Claim 16 recites: the method as in claim 15, wherein: the set of RNA-based biomarkers for dividing the training set into the first group and the second group is selected from a group consisting of PPP2R1B, GOLGAS, LINGO2, HMGA1, SIN3A, ARID1A, BCL7A, CDK5RAP2, MAGEDI, CREB3L1, AMER1, DLL1, GSTT1, GPR34, DNM2, CCNB1IP1, MUTYH, RET, CDH1, POFUTI, XRCC6, KIT, RALGDS, SS18, CD22, BRCA2, HDAC3, LHX4, FAM19A2, PRG2, PRCC, TBL1XR1, HIF1A, EDIL3, ROS1, DKK4, CDC25A, WNT7B, MYBL1, MLLT10, SLCOlB3, TACC2, CANT1, NCAM1, FGF3, FGF19, PPP3R2, CRADD, ETV6, SPP1, SDHB, FGF2, SUZ12, MB21D2, MYC,BAX, CEP57, ITGA5, ABCC3, and HECW1
Claim 16 recites: the set of RNA-based biomarkers for dividing the first group of the training set into the third group of high responders and the fourth group of high responders is selected from a group consisting of DUSP22, CTNNA1, DUX2, SSX1, SSX2, CTNNB1, DCLK2, FH, DUSP9, FCGR2B, STAT5B, ESR1, CD274, TERF1, AKAP9, DGKI, HMGA1, ARNT, MAFB, PPP3CC, COL3A1, NUTM2A, CIT, MGMT, CDK6, SORT1, RCSD1, CDK5RAP2, S1N3A, RABEPI, MB21D2, KDR, SS18L1, SSBP2, SH2D5, ASXL1, AMER1, AFF1, PRKCD, 2- Sep, TPM4, FIGF, NODAL, GRM3, STAT6, GAB1, RPL22, BDNF, SNX29, MELK,ARRDC4, FGF10, MMP9, YYlAP1, HAS2, DLEC1, DEK, TLL2, BCL2L2, and ID3;the set of RNA-based biomarkers for dividing the second group of low responders of the training set into the fifth group and the sixth group is selected from a group consisting of AHI1, EPHA5, DUSP22, DUSP26, DUSP9, DUX2, MGMT, MIB1, MIPOL1, MIR1260B,MIR4321, MIR4683, MIR4758, MIR6515, MIR6752, MIR6765, BIVM-ERCC5, SSX1, SSX2, LTBP1, MAFB, TLR4, CTNNB1, ETV5, CHEK2, FUS, SS18L1, SSBP2, DGKI, CIT, TFE3, FGF19, TRIM33, CTCF, LAMA1, TBL1XR1, TOP1, RB1, OLR1, DOCK1, ARID1A, RABEPI, EP400, STK11, ETS1, MAPK1, CDC14A, LMO7, SS18, ICK, FLI1, POU5F1,RCSD1, HRAS, BACH2, CDK7, GAS5, CARS, SRSF2, and MAP3K6; or combinations thereof
Claim 17 recites: based on survival time, dividing all subjects from the training set into a first group of high responders and a second group of low responders
Claim 17 recites: using machine learning, identifying a first subset of one or more individual RNA-based biomarkers from a plurality of individual RNA-based biomarkers
Claim 17 recites wherein the first subset of one or more individual RNA-based biomarkers is identified as correlating to dividing the subjects into the first group and the second group
Claim 17 recites: classifying the subject into one of several predetermined survival groups based on predicted response to R-CHOP chemotherapy
Claim 18 recites dividing the first group of high responders into a third group of high responders and a fourth group of high responders
Claim 18 recites wherein the third group of high responders is characterized by survival time longer than average survival time for the entire first group of high responders, the fourth group of high responders is characterized by survival time shorter than average survival time for the entire first group of high responders.
Claim 19 recites using a Bayesian classifier to define the subject as a high responder or a low responder to chemotherapy using one or more of individual RNA-based biomarkers selected from a group consisting of PPP2R1B, GOLGA5, LINGO2, HMGA1, SIN3A, ARID1A, BCL7A, CDK5RAP2, MAGED1, CREB3L1, AMER1, DLL1, GSTT1, GPR34, DNM2, CCNB1IP1, MUTYH, RET, CDH1, POFUT1, XRCC6, KIT, RALGDS, SS18, CD22, BRCA2, HDAC3, LHX4, FAM19A2, PRG2, PRCC, TBL1XR1, HIF1A, EDIL3, ROS1, DKK4, CDC25A, WNT7B, MYBL1, MLLT10, SLCO1B3, TACC2, CANT1, NCAM1, FGF3, FGF19, PPP3R2, CRADD, ETV6, SPP1, SDHB, FGF2, SUZ12, MB21D2, MYC, BAX, CEP57, ITGA5, ABCC3, and HECW1
The limitations in claim 1 directed to ‘training the mathematical algorithm’, ‘further training the mathematical algorithm’ and ‘using machine learning to identify a first subset of biomolecules’ are verbal equivalents that describe a mathematical calculation that is performed as the limitation. Therefore, these limitations fall under the "Mathematical concepts" and grouping of abstract ideas. The limitations of claims 2-10 and 12-16 that further limit how the mathematical algorithms are trained (by limiting the groups, features, type of algorithm, feature ranking or how the validation is performed), merely further limit the training of the mathematical algorithms, but do not change the position of the algorithms as mathematical concepts.
The limitations in claims 1, 17 and 18 directed to ‘forecasting clinical course for the subject’, and ‘dividing all subjects from the training set’ into groups, ‘classifying the subject into one of several predetermined survival groups’ and ‘dividing the first group of high responders’ into groups equates to evaluating data and making a decision based on that evaluation which, under the broadest reasonable interpretation, can be practically performed in the human mind. Therefore, these limitations fall within the “mental process” grouping of abstract ideas because they cover concepts performed in the human mind including observation, evaluation, judgment, and opinion (MPEP 2106.04(a)(2), subsection III). The limitation of claim 18 that further limits the groups of subjects, does not change the position of the dividing subjects into groups as a mental process.
The limitation in claim 19 directed to ‘using a Bayesian classifier to define a subject as a high responder or a low responder’ equates to mathematical calculations. These include applying arithmetic calculations and functions (division, multiplication, addition, subtraction, log and square root calculations) and applying these functions to probability and correlation functions. Therefore, these limitations recite “mathematical calculations” and fall into the “mathematical concepts” grouping of abstract ideas. These limitations also fall into “mental process” grouping because the mathematical calculations are simple enough to be practically performed in the human mind. Even if most humans would use a physical aid, like a pen and paper or a calculator, to make such calculations, the use of a physical aid would not negate the mental nature of this limitation See MPEP 2106.04(a)(2), subsection III.B.
Additionally, the limitations discussed above also fall into the category of natural phenomenon and laws of nature. Claims 1c and 17-19 that are directed to identifying correlations between survival time in response to therapy or disease with the presence of RNA biomarkers and using the correlations to forecast the clinical course of a subject are natural phenomena because they describe consequence of natural processes in the human body, e.g., the naturally-occurring relationship between the presence of RNA biomarkers and response to therapy. Claims 17 and 18 also embrace a product of nature as they group subjects based on natural course of a disease or natural response to a therapy (See MPEP 2106.04(b)).
Therefore, the claims explicitly recite elements that, individually and in combination, constitute one or more judicial exceptions. As such, claims 1-20 recite an abstract idea (Step 2A, Prong 1: YES).
Step 2A, Prong 2
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) (MPEP 2106.04(d)). The claimed additional elements are analyzed alone, or in combination to determine if the judicial exception is integrated into a practical application. This judicial exception is not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to the diagnostic field and do not effect a particular treatment for either a heterogeneous disease or diffuse large B-cell lymphoma. Rather, the instant claims recite additional elements that amount to insignificant extra-solution activity and mere instructions to "apply" the exception in a generic way. Specifically, the claims recite the following additional elements:
Claim 1 recites: providing a mathematical algorithm for forecasting clinical course of the subject with the heterogeneous disease by classifying the subject into one of several predetermined survival groups based on predicted response to rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) chemotherapy
Claim 1 recites: obtaining the subset of individual RNA-based biomarkers defined in step (a) for the subject by extracting RNA from a tissue sample of the subject and analyzing the extracted RNA using a targeted RNA sequencing panel
Claim 1 recites: treating the subject forecasted in step (c) with rituximab, cyclophosphamide, doxorubicin, vincristine, or prednisone (R-CHOP) chemotherapy
Claim 11 recites: the method as in claim 2, wherein treating the subject in step (d) comprises: a step of treating the subject forecasted in step (c) as a high responder with R-CHOP chemotherapy; a step of treating the subject forecasted in step (c) as a low responder with a further therapy or an additional therapy; or a combination thereof
Claim 17 recites: providing a training set of subjects with DLBCL treated with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) chemotherapy, the heterogenous disease with known plurality of individual RNA-based biomarkers and known survival time
Claim 17 recites: obtaining the subset of individual RNA-based biomarkers defined in step (c) for the subject by extracting RNA from a tissue sample of the subject
Claim 17 recites: analyzing the extracted RNA using a targeted RNA sequencing panel to obtain expression levels of the subset of individual RNA- based biomarkers
Claim 19 recites: obtaining a subset of individual RNA-based biomarkers for the subject by extracting RNA from a tissue sample of the subject and analyzing the extracted RNA using a targeted RNA sequencing panel
Claim 19 recites: treating the subject with chemotherapy based on the classification
Claim 20 recites: the method as in claim 19, wherein treating the subject comprises: a step of treating the subject forecasted as a high responder with the chemotherapy; a step of treating the subject forecasted as a low responder with a further therapy or an additional therapy; or a combination thereof
The limitations in claims 1 and 17 are directed to ‘providing a mathematical algorithm’, ‘obtaining RNA biomarkers’ ‘extracting RNA from a tissue sample of the subject’ and ‘analyzing the extracted RNA’ to obtain expression levels and ‘providing a training set of subjects’, merely serve to gather data that is used an input for the judicial exception. Therefore, these limitations are mere data gathering activities. As set forth in MPEP 2106.05(g), mere data gathering activity has been identified by the courts as insignificant extra-solution activity that does not provide a practical application.
The limitations in claim 1 and claim 19 reciting “treating the subject forecasted in step (c) with rituximab, cyclophosphamide, doxorubicin, vincristine, or prednisone (R-CHOP) chemotherapy” and reciting “treating the subject with chemotherapy based on the classification” also fails to integrate the judicial exception into a practical application. Although the steps do affirmatively recite actions that affect particular treatments or prophylaxis for a disease or medical condition, the treatment does not depend on the outcome of the test. Instead, claim 1 is directed to “treating the subject forecasted in step (c)”, thus all subjects that were forecasted by the test are treated equally with one of the chemotherapies listed., and claim 19 is directed to “treating the subject with chemotherapy based on the classification”, which also does not make a distinction between how a low responder would be treated differently from high responder.
The limitations of claim 11 and claim 20 reciting “the step of treating the subject forecasted in step (c) as a high responder with R-CHOP chemotherapy; and a step of “treating the subject forecasted in step (c) as a low responder with a further therapy or an additional therapy; or a combination thereof” do not integrate the judicial exception into a practical application for all embodiments. In this claim, the results of the test are integrated into the treatment decision; however, there is no particular treatment executed for ‘a low responder’. Instead, the ‘low responder’ is treated with chemotherapy and a generic ‘further therapy’ or ‘additional therapy’, which is not specified. Thus the outcome for some subjects is not linked to a specific treatment indication. Thus, for these patients that are classified as ‘low responders’, this equates to "administering a suitable medication to a patient." Thus, the administration step for low responders is a mere instruction to "apply" the exception in a generic way and thus does not integrate the judicial exception into a practical application (see MPEP § 2106.05(f) and 2106.04(d)(2)).
The above recited additional elements do not provide a practical application of the recited judicial exception. As such, claims 1-20 are directed to an abstract idea (Step 2A, Prong 2: NO).
Step 2B
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 mere instructions to apply the recited exception in a generic way or are well-understood, routine and conventional activities. The additional elements identified above in claims 1 and 17 that amount to data gathering (including ‘providing a mathematical algorithm’, ‘obtaining RNA biomarkers’ ‘extracting RNA from a tissue sample of the subject’ and ‘analyzing the extracted RNA using a targeted RNA sequencing panel to obtain expression levels’ and ‘providing a training set of subjects’) do not rise to the level of significantly more than the judicial exception. These data gathering elements are routine, well understood and conventional as indicated below:
As evidenced by Xi et al. (Xi et al., Non-coding RNA, 3(1), p. 9.1-9.17, 2017; previously cited), acquiring patient samples from blood (which is considered a tissue), and isolating, amplifying and measuring RNA biomarkers using PCR or sequencing is routine, well understood and conventional in the art (sections 4, 7 and 8).
Additionally, the specification of the instant application disclosed that the data gathering step of RNA quantification can be done using a variety of known techniques including next-generation sequencing (para. 0023).
As evidenced by Xu-Monette et al. (Blood Advances, 2020, Vol. 4, p. 3391-3405), targeted RNA-seq was well understood routine and conventional before the effective filing date because kits and gene panels for targeted RNA-seq were available including the Illumina TruSightRNA Pan-Cancer Panel (p. 3393, col. 2, para. 2).
As set forth in MPEP section 2106.05(g), the courts have decided that limitations that merely add an insignificant extra-solution activity, do not amount to an inventive concept, particularly when the activities are well-understood and conventional. Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ 193, 196 (1978).
The additional elements directed to treating subjects including: “treating the subject forecasted in step (c) with rituximab, cyclophosphamide, doxorubicin, vincristine, or prednisone (R-CHOP) chemotherapy”, “treating the subject with chemotherapy based on the classification”, “treating the subject forecasted in step (c) as a high responder with R-CHOP chemotherapy; “treating the subject forecasted in step (c) as a low responder with a further therapy or an additional therapy; or a combination thereof” (in claims 1, 11 and 19-20) are recited broadly and encompass many embodiments. The treating step of claims 1 and 11, which recites “treating the subject forecasted in step (c) with rituximab, cyclophosphamide, doxorubicin, vincristine, or prednisone (R-CHOP) chemotherapy”, and “treating the subject with chemotherapy based on the classification”, include embodiments wherein all DLBCL patients are treated with the standard R-CHOP therapy regardless of the result of the test. Similarly, the ‘treating’ step in claims 11 and 20 that treats high responders with R-CHOP chemotherapy, treats low responders with a further therapy (or additional therapy) OR a combination thereof, includes embodiments wherein all forecasted subjects are treated with the standard R-CHOP therapy regardless of the test result. These ‘treating’ steps do not rise to the level of significantly more than the judicial exception, alone or in combination with the data gathering steps, because they use well-understood, routine, and conventional activity in the field as described below:
As evidenced by Grzegorz et al. (Grzegorz et al., 2015, 31 August 2022 IDS; previously cited), treating a patient having DLBCL with chemoimmunotherapy is considered a standard treatment. In addition, a number of early clinical trials evaluating combinations of novel targeted agents with standard chemotherapy (R-CHOP) have been completed (abstract; p. 1, col. 1, para. 1 – col. 2, para. 1; Table 1).
As evidenced by Cutmore et al. (Mod Pathol., 2023, Vol. 36, p. 1-15), transcriptional classification of DLBCL before treatment with R-CHOP or other chemotherapy is widely used and that the cell of origin (COO) classification system (based on transcriptional profiling) was incorporated with the WHO recommended classification of DLBCL in 2016 (abstract, p. 2, para. 2). Cutmore et al. further disclose that the COO classification provides some prognostic information, with ABC cases associated with an inferior response to R-CHOP (p. 2, para. 2). Cutmore et al. further disclose that MYC, BCL2 and BCL6 rearrangements are associated with a poor response to R-CHOP and are often treated with intensified chemotherapy regimens and that MYC rearrangements are detected by next generation sequencing (p. 2, para. 3 – p. 3, para. 2).
As evidenced by Coccaro et al. (cancers 2020, vol. 12, p. 1-20), at least 6 studies of molecular classification of DLBCL using targeted RNA-seq yielding clinical implications (including higher risk of treatment failure to R-CHOP or other suggesting other targeted therapies) had been conducted before 2020 (Table 1).
As such, the combination of additional elements recited in the claims is well-understood, routine and conventional. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (see Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 ) (MPEP 2106.05).
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. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: NO). As such, claims 1-20 are not patent eligible.
Response to Arguments
Regarding claims 1-20, applicants arguments filed 26 May 2026 have been fully considered but they are not persuasive.
Step 2A, Prong 1 arguments
Argument 1: the claimed invention is not directed to a judicial exception (Step 2A, Prong 1)
The applicant asserts that, viewed as a whole, the amended claims are not directed to any of the judicial exceptions. The applicant asserts that the invention is not merely mental processes and mathematical calculations, but rather, the claims are directed to a specific, concrete method of treating DLBCL patients with R-CHOP that involves physical laboratory steps and algorithms. Applicant further asserts that the examiner’s isolation of the judicial exceptions improperly strips away the claims’ character as a method that integrates the training of the algorithm with specific data acquisition and specific treatment (p. 16, para. 4 – p. 17, para. 1 of response to office action):
Response to argument: the presence of additional elements does not eliminate the presence of judicial exceptions. Step 2A, Prong 1 serves only to determine if there are any judicial exceptions recited in the claim. Analysis of the additional elements and whether they integrate the judicial exception into a practical application is considered, however in Step 2A, Prong 2. The additional elements were found not to integrate the judicial exception into a practical application for the reasons discussed in the Step 2A, Prong 2 section above. Specifically, regarding the treatment steps, they either did not depend on the result of the test (as recited in claim 1) or there was no particular treatment claimed for low responders to chemotherapy (claim 11). One way to overcome this rejection would be to claim different and particular treatments for both low and high responders that are dependent on the results of the test.
Argument 2: The applicant asserts that machine learning limitations and RNA extraction limitations cannot practically be performed in the human mind as characterized by the examiner.
Response to argument: Note that in the original claims, the training of the classifier was not considered to be part of the invention and RNA extraction was not claimed. Regarding the amended claims, the examiner agrees that training a machine learning classifier is math and not a mental process and it was categorized as such in this final OA. However, ‘using a Bayesian classifier to define a subject as a high responder or a low responder’ was considered to be both a mental process and a mathematical concept because it involves analyzing one sample using a set of predefined probability calculations to determine if the final value is above or below a threshold, which are analysis steps that can be performed in the human mind or with a calculator. Regarding RNA extraction, the examiner agrees that this is an additional element, and it was treated as such in this final OA.
Argument 3: the applicant asserts that extracting and analyzing RNA is not a law of nature.
Applicant’s assertion that extracting and analyzing RNA is not a law of nature is based on case law (Vanda pharmaceuticals v. West-Ward Pharmaceuticals). Applicant further asserts that Vanda was patent eligible at Step 2A because it required a step of treating with iloperidone.
Response to argument: Extracting and analyzing RNA was not in the original claims, so analysis of extracting and analyzing RNA for patent eligibility was necessitated by the claim amendments and these steps were considered to be additional elements. Additionally, the cited case law pertains to Step 2A Prong 2 and does not affect if a limitation is considered to be a judicial exception in Step 2A Prong 1. In fact, the natural relationship between a patient’s CYP2D6 metabolizer genotype and the risk that the patient will suffer QTc prolongation after administration of a medication called iloperidone, Vanda Pharmaceuticals Inc. v. West-Ward Pharmaceuticals, 887 F.3d 1117, 1135-36, 126 USPQ2d 1266, 1281 (Fed. Cir. 2018) is cited as an example of when the courts have identified products as examples of laws of nature or natural phenomena in the MPEP 2106.04(b). Regarding the Step 2A Prong 2 assessment, the examiner disagrees with the assertion that this case is pertinent to Vanda case law because the Vanda patent claim was a genetic test that assigned specific, differentiated medical treatments based on whether a patient fell above or below a specific metabolic threshold. The instant application is different because either 1) the test is not actually used to decide between two treatment decisions (claims 1 and 19) or 2) there is not a particular treatment claimed for low responders (claims 11 and 20).
Step 2a Prong 2 arguments
Argument 4: The claims integrate the judicial exception into a practical application
Applicant asserts that the claims integrate the judicial exception into a practical application in the amended claims because claim 1 now identifies a particular treatment for a disease (i.e. DLBCL as the disease and the therapy as R-CHOP), in addition to using a specific machine-learning algorithm.
Applicant further asserts that the data-gathering steps are not “insignificant extra-solution activities” because the amended claims now recite physical laboratory techniques, not generic data gathering and because RNA-seq is more reproducible and practical than microarrays, so it is a concrete technical implementation.
Response to argument: As discussed in the U.S.C 101 rejection above, claim 1, fails to integrate the judicial exception into a practical application. Although the steps do affirmatively recite actions that affect particular treatments or prophylaxis for a disease or medical condition, there is no recited selection of treatments that depend on the outcome of the test. Instead, claim 1 is directed to merely “treating the subject forecasted in step (c)”, thus all subjects that were forecasted by the test are treated with chemotherapy in a manner that is potentially independent of the results of the test.
Regarding the argument pertaining to data gathering, the examiner disagrees. Standard laboratory techniques can be data gathering steps and they were treated as such in this final OA because as indicated in the response to office action (p. 19, para. 4), the RNA analysis is done by a commercial method (Illumina TruSight RNA PanCancer Panel) without any indicated modifications. Therefore, standard commercially available laboratory methods are not considered to impart an inventive concept because they are widely used (See MPEP 2106.05(g)).
Step 2B arguments
Argument 5: unconventionality and technical improvement
Applicant asserts that all the limitations of amended claim 1 together (including training a mathematical algorithm using machine learning on RNA biomarkers from DLBCL patient treated with R-CHOP to define survival groups and biomarkers and administering R-CHOP chemotherapy based on the forecasting) amount to significantly more than any abstract idea or other exception and that it is both unconventional and a technical improvement in the field of DLBCL classification and treatment. Applicant further asserts that hierarchical subdivision of classes in claims 5-10 and the specific biomarkers in claim 16 further support eligibility. Applicant further asserts that examiner’s reference to Buzdin et al. inappropriately imports breast cancer-related profiling methods to DLBCL treatment (response to OA, p. 20, para. 1-3).
Response to argument: The Step 2B assessment for well understood, routine and conventional elements applies only to the additional elements and not the judicial exceptions. Therefore, the conventionality of the biomarkers, machine learning classification and subdivision of classes were not under consideration in this step. The additional elements considered were the data gathering activities and the treatment steps, and these additional elements in combination were found to be well understood routine and conventional as discussed in the Step 2B section in the above U.S.C. 101 analysis for patent eligibility.
Regarding the examiner citing a reference to breast cancer (Buzdin et al.) instead of DLBCL, the original claims were broad and did not restrict to DLBCL, thus this argument not persuasive for the original claims. Regarding the amended claims, in this final office action, new art was applied, which was necessitated by the claim amendments and the relevance of the breast cancer reference is a moot point.
Regarding the assertion that the combination of elements in claim 1 is a technical improvement in the field of DLBCL classification and treatment, this was considered in Step 2A, Prong 2. However, no technical improvement was identified as there was no explanation set forth in the specification or in the response to OA that described the nature of the technical improvement, such as improved patient survival rate, faster diagnosis or reduced cost of diagnosis or treatment over existing state of the art. It should be noted that while the additional elements are considered in the context of the invention as a whole in the assessment of an improvement to technology, an improvement in the abstract idea itself is not considered to be an improvement to technology (2106.05(a)(II)).
Claim Rejections - 35 USC § 102
The rejection of claims 1 and 20 under 35 U.S.C. 102(a)(1) and 102(a)(2) as being unpatentable over Gutin et al. (US 2020/0283855 A1; previously cited), is withdrawn in view of the claim amendments filed 26 May 2026.
The rejection of claims 17 under 35 U.S.C. 102(a)(1) and 102(a)(2) as being unpatentable over Smyth et al. (US 2020/0239968 A1; previously cited), is withdrawn in view of the claim amendments filed 26 May 2026.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C.
103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or
nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The rejection of claims 2-3 and 11 under 35 U.S.C. 103 as being unpatentable over Gutin et al. (US 2020/0283855 A1; previously cited), in view of Smyth et al. (US 2020/0239968 A1; previously cited) is withdrawn in view of the claim amendments filed 26 May 2026.
The rejection of claim 4 under 35 U.S.C. 103 as being unpatentable over Gutin et al. (US 2020/0283855 A1; previously cited), as applied to claim 1 above, further in view of Smyth et al. (US 2020/0239968 A1; previously cited), as applied to claims 2-3 and 11 above, and further in view of Chapuy et al. (Nature Medicine, vol 24, p679-690, 2018 in the IDS; previously cited) is withdrawn in view of the claim amendments filed 26 May 2026.
The rejection of claims 5-10 under 35 U.S.C. 103 as being unpatentable over Gutin et al. (US 2020/0283855 A1; previously cited), as applied to claim 1 above, further in view of Smyth et al. (US 2020/0239968 A1; previously cited), as applied to claims 2-3 and 11 above, and further in view of Roder et al. (US 2017/0039345 A1; previously cited) is withdrawn in view of the claim amendments filed 26 May 2026.
The rejection of claims 12-14 under 35 U.S.C. 103 as being unpatentable Gutin et al. (US 2020/0283855 A1; previously cited), as applied to claim 1 above, and further in view of Cohan (Proc. of the 12th Intl. Conference on Natural Language Processing, pages 118–123, 2015; previously cited) is withdrawn in view of the claim amendments filed 26 May 2026.
The rejection of claims 15 and 16 under 35 U.S.C. 103 as being unpatentable over Gutin et al. (US 2020/0283855 A1; previously cited), as applied to claim 1 above, and further in view of Cohan (Proc. of the 12th Intl. Conference on Natural Language Processing, pages 118–123, 2015; previously cited), as applied to claims 12-14 above, and further in view of Rosenwald et al. (N Engl. J Med. 2002, vol. 346, p. 1937-47; previously cited) as evidenced by Staudt et al. (biomarker database Immunol Rev. 2006 210:67-85; previously cited) is withdrawn in view of the claim amendments filed 26 May 2026.
The rejection of claim 17 under 35 U.S.C. 103 as being unpatentable over Smyth et al. (US 2020/0239968 A1; previously cited) is withdrawn in view of the claim amendments filed 26 May 2026.
The rejection of claim 18 under 35 U.S.C. 103 as being unpatentable over Smyth et al. (US 2020/0239968 A1; previously cited), as applied to claim 17 above, and further in view of Roder et al. (US 2017/0039345 A1; previously cited) is withdrawn in view of the claim amendments filed 26 May 2026.
The rejection of claims 19 and 20 under 35 U.S.C. 103 as being unpatentable over Rosenwald et al. (N Engl. J Med. 2002 346(25):1937-47; previously cited) as evidenced by (Staudt et al., biomarker database Immunol Rev. 2006 210:67-85; previously cited) in view of Gutin et al. (US 2020/0283855 A1; previously cited) Is withdrawn in view of the claim amendments filed 26 May 2026.
10. Claim 1 is rejected under 35 U.S.C. 103 as being unpatentable over Xu-Monette et al. (Blood Advances, 2020, Vol. 4, p. 3391-3405), in view of Staudt et al. (US10607717B2). The italicized text corresponds to the instant claim limitations.
With respect to claim 1, Xu-Monette et al. teaches a method using high-throughput genetic and gene expression signature analysis to improve the DLBCL classification for prognostic stratification and therapeutic implication. Xu-Monette et al. further disclosed that two models (a cell of origin model (COO) model and a survival model) and that both models stratify patients treated with R-CHOP into high survival and low survival groups (abstract; p. 3393, col. 1, para. 2-3; a method for treating a subject with diffuse large B-cell lymphoma (DLBCL)).
Regarding claim 1a, Xu-Monnet et al. discloses building prognostic models aggregating small contributions of a large number of variables directly to patient survival, using neural network modeling to develop models in the training set and test the performance in the validation set based on both gene expression and genetic variables plus 2 additional factors: age and sex of patients. Xu-Monnet et al. further discloses that the patients classified were DLBCL patients treated with R-CHOP and that 3 groups were predetermined based on overall survival (OS); (p. 3398, col. 1, para. 1-p. 3399, col. 1, para. 3; p. 3393, col. 1, para. 2-3; (a) providing a mathematical algorithm for forecasting clinical course of the subject with the heterogeneous disease by classifying the subject into one of several predetermined survival groups based on predicted response to rituximab, cyclophosphamide, doxorubicin, vincristine and prednisone (R-CHOP) chemotherapy).
Regarding claim 1a, Xu-Monnet et al. discloses that they used samples from 252 DLBCL patients treated with R-CHOP to develop risk stratification models directly correlating with overall survival (OS) (or progression free survival (PFS)) by randomly selected 60% (152) of subjects as the training set to fit the model and tested the performance in the remaining 40% (100) patients. Xu-Monnet et al. further discloses using mRNA levels of 55 genes as features in the OS classifier and that mRNA levels were measured for patients of the training set and that their survival times were known (p. 3393, col. 1, para. 2-3; p. 3394, col. 1, para. 2; p. 3398, col. 1, para. 1-p. 3399, col. 1, para. 3; Table 5; p. 3393, col. 2, para. 1; Fig. 5; training the mathematical algorithm using machine learning by analyzing a plurality of RNA biomarkers from a training set of subjects with DLBCL treated with R-CHOP, each subject characterized by their respective known plurality of individual RNA-based biomarkers and known survival time).
Regarding claim 1a, Xu-Monnet et al. discloses selecting 252 DLBCLs to develop risk stratification models directly correlating with survival by randomly selected 60% (152) of subjects as the training set to fit the model and tested the performance in the remaining 40% (100) patients. Xu-Monette et al. further discloses that the patients were divided into three equal groups based on risk scores for either overall survival or progression-free survival and that high-risk group had strikingly poorer survival than the low- and intermediate-risk groups. Xu-Monnet et al. further discloses that Kaplan-Meier and Cox proportional hazards (CPH) analysis was used to identify variables with significant prognostic impact and predictive models were built through deep learning with autoencoders for nonlinear transformations of autoencoded features into 2-dimensional latent space. Xu-Monnet et al. further discloses that Logistic regression and CPH models were used for building the clinical risk models and that 50 and 57 were used as features in the OS and PFS models, respectively, including expression levels of several genes (p. 3399, col. 1, para. 1; p. 3394, col. 1, para. 2-3; supplementary Figure 1; further training the mathematical algorithm to divide all subjects from the training set of subjects into predetermined survival groups based on survival time and further training the mathematical algorithm to define a subset of individual RNA-based biomarkers corresponding thereto).
Regarding claim 1b, Xu-Monnet et al. discloses the Agencourt FormaPure Total 96-Prep Kit was used to extract both DNA and RNA from the same FFPE tissue lysates using an automated KingFisher Flex and that samples were selectively enriched for 1408 cancer-associated genes using reagents provided in an Illumina TruSightRNA Pan-Cancer Panel followed by sequencing by RNA-seq (Supplemental Fig. 1; p. 3393, col. 2, para. 2 (b) obtaining the subset of individual RNA-based biomarkers defined in step (a) for the subject by extracting RNA from a tissue sample of the subject and analyzing the extracted RNA using a targeted RNA sequencing panel).
Regarding claim 1c, Xu-Monnet et al. discloses that the robustness of the OS and NGS predictive models were tested in validation cohorts and that the validation cohorts were split into three different groups based on predicted OS or NGS (p. 3393, col. 1, para. 2, Fig. 5; Supplementary Figure 1 (c) forecasting clinical course for the subject using the subset of individual RNA-based biomarkers obtained from the subject).
Regarding claim 1d, as the claim recites ‘treating the subjects forecasted in step (c)’, this implies that step (c) occurs before step (d); however, there is no indication that the treatment is dependent on the results of step (c), therefore, by broadest reasonable interpretation, it will be interpreted to mean that the treatment step occurs after step (c), but is not dependent on the results of step (c).
Regarding claim 1d, Xu-Monnet et al. discloses that the samples used in the method, including those in the training set and validation set, were from patients with DLBCL treated with R-CHOP chemotherapy. However, due to the nature of a retrospective study, Xu-Monnet et al. did not disclose treating with R-CHOP after the forecasting was done (p. 3393, col. 1, para. 3; (d) treating the subject forecast in step (c) with rituximab, cyclophosphamide, doxorubicin, vincristine, or prednisone (R-CHOP) chemotherapy).
Regarding claim 1d, it would have been obvious for Xu-Monnet et al. to try treating with R-CHOP after the forecasting instead of before because: (1) at the time of the invention, there had been a recognized problem or need in the art, including a design need or market pressure to solve a problem. Xu-Monnet et al. disclose that Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous entity of B-cell lymphoma and that reliable tools are needed for precision medicine and decision making by clinicians. (abstract; p. 3392, col. 1, para. 1- p. 3393, col. 1, para. 2; p. 3400, col. 2, para. 2 – 3401, col. 1, para. 1) (2) there were a finite number of chemotherapy treatments for DLBCL and R-CHOP was the standard treatment and the treatment investigated in this study (abstract; p. 3392, col. 1, para. 1- p. 3393, col. 1, para. 3) (i.e. a finding that there had been a finite number of identified, predictable potential solutions to the recognized need or problem); and (3) one of ordinary skill in the art could have pursued the known potential solutions with a reasonable expectation of success because Xu-Monnet et al. disclosed that the clinical grade assays and NGS models integrating both genetic and transcriptional factors they developed may eventually support precision medicine in DLBCL (abstract), suggesting that the results of the predictive model could drive treatment plans. Therefore, the invention is prima facie obvious.
11. Claims 2-4 are rejected under 35 U.S.C. 103 as being unpatentable over Xu-Monette et al. (Blood Advances, 2020, Vol. 4, p. 3391-3405) as applied to claim 1 above, further in view of Smyth et al. (US 2020/0239968 A1; previously cited). The italicized text corresponds to the instant claim limitations. This rejection is newly recited and necessitated by claim amendment.
The limitations of claim 1 have been taught by Xu-Monette et al. above.
With respect to claim 2, Xu-Monette et al. teaches that samples used for the study were from patients previously treated with R-CHOP chemotherapy. Xu-Monette et al. further teaches training two different neural network models to divide training set subjects into three risk groups based on progression free survival or overall survival, wherein 1/3 of patients are in each group (corresponding to high, intermediate and low risk and correlating with survival time) (p. 3393, col. 1, para. 3; p. 3398, col. 1, para. 1-p. 3399, col. 1, para. 3; Fig. 5; Supplementary Fig. 1; the method of claim 1 wherein in step (a) the mathematical algorithm is further trained to divide training set subjects into groups depending on the level of response to a known therapy measured by survival time).
With respect to claim 3, Xu-Monette et al. teaches that the neural network classifier is trained to define gene expression features for both classifiers based on risk (i.e. based either on overall survival or progression-free survival) (Supplementary Fig. 1; the algorithm is further trained to define a first subset of RNA-based biomarkers corresponding to dividing all training set subjects into groups of low and high responders).
With respect to claims 2 and 3, Xu-Monette et al. do not explicitly disclose the limitation in claim 2 wherein the groups are two groups characterized by survival time that is longer or shorter than average survival time of the training set (claim 2) and wherein there are two groups (claim 3). However, this limitation was known in the art at the time of the effective filing date of the invention, as taught by Smyth et al.
Regarding claim 2, Smyth et al. teaches training a classifier using a training set of gastroesophageal cancer patients separated into two groups by survival time following tumor resection (i.e. the treatment) that are classified as having greater or shorter survival time than is typical for gastroesophageal cancer patients in the general population. Smyth et al. further teaches that classes can be “good prognosis” versus “bad prognosis” corresponding to overall survival that is longer or shorter than average for that stage of cancer and cancer type (para. 0062-0068, 0080 and 0151-0152; wherein the groups are characterized by survival time that is longer or shorter than average survival time of the training set).
Regarding claim 3, Smyth et al. teaches separating cancer patients into two groups (para. 0062-0068, 0080 and 0151-0152; dividing into the first group and the second group).
An invention would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Smyth et al. taught modelling with a training set containing classes defined by average survival times after tumor resection is useful to predict treatment response and survival of gastric cancer patients (para. 0010). Therefore, one of ordinary skill in the art would have been motivated to apply training the classifier with two groups defined by average survival time in the training set after a known therapy taught by Smyth et al. to the modeling method taught by Xu-Monette et al. in order to enable predicting treatment response of DLBCL cancer patients. Furthermore, one of ordinary skill in the art would predict that the training classes taught by Smyth et al. could readily be added to the classification method taught by Xu-Monette et al. with reasonable expectation of success because they both pertain to developing classifiers to predict response of cancer patients to treatment. The invention is therefore prima facie obvious. (see MPEP 2143(I)(C)).
With respect to claim 4, Xu-Monette et al. teaches that TP53 mutation showed a significant adverse prognostic effect by log-rank test and was selected as a feature in both the overall survival (NGS-OS) model and the progression-free survival (NGS-PFS) models (p. 3395, col. 1, para. 2; p. 3398, col. 1, para. 1; Supplementary Fig. 1; the method as in claim 3, wherein a presence of a TP53 mutation is a predictor for the second group of low responders).
12. Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Xu-Monette et al. (Blood Advances, 2020, Vol. 4, p. 3391-3405) as applied to claim 1 above, in view of Smyth et al. (US 2020/0239968 A1; previously cited), as applied to claims 2-4 above, and further in view of Sha et al. (J Clin Oncol 2018, Vol. 37, p. 202-212). The italicized text corresponds to the instant claim limitations. This rejection is newly recited and necessitated by claim amendment.
The limitations of claim 1 have been taught by Xu-Monette et al. above.
The limitations of claims 2-4 have been taught by Xu-Monette et al. and Smyth et al. above.
With respect to claims 11, Xu-Monette et al. and Smyth et al. are silent to the method of claim 2, wherein treating the subject in step (d) comprises: a step of treating the subject forecasted in step (c) as a high responder or with R-CHOP chemotherapy; a step of treating the subject forecasted in step (c) as a low responder with a further therapy, or other therapy; or a combination thereof. However, this limitation was known in the art at the time of the effective filing date of the invention as taught by Sha et al.
With respect to claims 11, Sha et al. teaches developing a gene expression-based classifier for patients with DLBCL treated with R-CHOP therapy to identify a molecular high-grade (MHG) sub-group of patients. Sha et al. further discloses that MHG defines a biologically coherent high-grade B-cell lymphoma group with distinct molecular features and clinical outcomes and that patients with MHG may benefit from intensified chemotherapy or novel targeted therapies. (abstract; the method of claim 2, wherein treating the subject in step (d) comprises: a step of treating the subject forecasted in step (c) as a high responder or with R-CHOP chemotherapy; a step of treating the subject forecasted in step (c) as a low responder with a further therapy, or other therapy; or a combination thereof).
An invention would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Sha et al. taught that biologic heterogeneity is a feature of diffuse large B-cell lymphoma (DLBCL), and the existence of
a subgroup with poor prognosis and phenotypic proximity to Burkitt lymphoma is well known and that a more biologically coherent and clinically useful definition of this group is required for targeting more intensive chemotherapies than standard R-CHOP (abstract). Therefore, one of ordinary skill in the art would have been motivated to apply different treatment plans based on DLBCL subclassification taught by Sha et al. to the modeling method taught by Xu-Monette et al. and Smyth et al. in order to enable suggesting personalized treatments to DLBCL patients that best suit their severity. Furthermore, one of ordinary skill in the art would predict that the method of recommending different treatment plans taught by Sha et al. could readily be added to the classification method taught by Xu-Monette et al. and Smyth et al. with reasonable expectation of success because they both pertain to developing classifiers to predict response of DLBCL patients to treatment. The invention is therefore prima facie obvious.
14. Claims 5-10 are rejected under 35 U.S.C. 103 as being unpatentable over Xu-Monette et al. (Blood Advances, 2020, Vol. 4, p. 3391-3405), as applied to claim 1 above, further in view of Smyth et al. (US 2020/0239968 A1; previously cited), as applied to claims 2-4 above, and further in view of Roder et al. (US 2017/0039345 A1; previously cited). The italicized text corresponds to the instant claim limitations. This rejection is newly recited and necessitated by claim amendment.
The limitations of claim 1 were taught by Xu-Monette et al. above.
The limitations of claims 2-4 have been taught by Xu-Monette et al. and Smyth et al. above.
Regarding claim 6-7 and 9-10, Xu-Monette et al. teaches that the biomarkers/features of the classifiers are RNA-based biomarkers (Supplementary Fig. 1; RNA-based biomarkers).
Regarding claims 5-10, Xu-Monette et al. and Smyth et al. are silent to the binary decision tree model of first dividing samples into high and low responders and then subdividing each group based on survival time. (i.e. the method as in claim 2, wherein the mathematical algorithm is further trained to subdivide the first group of high responders into a third group of high responders and a fourth group of high responders, wherein the third group of high responders is characterized by survival time longer than average survival time for the entire first group of high responders, the fourth group of high responders is characterized by survival time shorter than average survival time for the entire first group of high responders (claim 5); the method as in claim 5, wherein the mathematical algorithm is further trained to define a second subset of biomarkers corresponding to dividing all subjects of the first group of high responders into the third group of high responders and the fourth group of high responders (claim 6); the method of claim 6, wherein the second subset of biomarkers is different from the first subset of biomarkers (claim 7); the method as in claim 7, wherein the mathematical algorithm is further trained to subdivide the second group of low responders into a fifth group of low responders and a sixth group of low responders; wherein the fifth group of low responders is characterized by survival time longer than average survival time for the entire second group of low responders, the sixth group of low responders is characterized by survival time shorter than average survival time for the entire second group of low responders (claim 8); the method as in claim 8, wherein the mathematical algorithm is further trained to define a third subset of biomarkers corresponding to dividing all subjects of the second group of low responders not the fifth group of low responders and the sixth group of low responders (claim 9) and the method as in claim 9, wherein the third subset of biomarkers is different from the first subset of biomarkers (claim 10). However, these limitations were known in the art at the time of the effective filing date of the invention, as taught by Roder et al.
Regarding claim 5, Roder et al. teaches training a multi-stage classifier for predicting response to therapy including a first stage classifier for stratifying patients into either an early or late group; a second stage classifier for further stratifying the early group of the first stage classifier into early and late groups; and a third stage classifier for further stratifying the late group of the first stage classifier into early and late groups. Roder et al. further teaches that each early and late group can correspond to shorter and longer overall survival, respectively (para. 0002, 0044 0014; the method as in claim 2, wherein the mathematical algorithm is further trained to subdivide the first group of high responders into a third group of high responders and a fourth group of high responders, wherein the third group of high responders is characterized by survival time longer than average survival time for the entire first group of high responders, the fourth group of high responders is characterized by survival time shorter than average survival time for the entire first group of high responders).
Regarding claim 6, Roder et al. describes generation of a multitude of different classifiers, each using different feature subsets related to different protein subsets to stratify patients with melanoma/nivolumab in response to treatment. Roder et al. further discloses that each classifier selects features/biomarkers by filtering out those that do not contribute to classifier performance. For example, they train a first level classifier to split patients into early (poor performing) and late (good performing) using features associated with acute response function. A sample which tests Late (or good performing) on the first level classifier is then classified by the second level classifier, which is trained using a different set of features (associated with wound healing protein function (but not associated with acute response) to stratify patients into better (late) or worse (early) time to progression (Fig. 1 of this office action). They then defined a final classifier as a hierarchical combination of classifiers 1 and 2. (para. 0026, 0456, 0499-0503, appendix D, Fig 46 (shown in Fig. 1 of this office action); the method as in claim 5, wherein the mathematical algorithm is further trained to define a second subset of RNA-based biomarkers corresponding to dividing all subjects of the first group of high responders into the third group of high responders and the fourth group of high responders).
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[AltContent: textbox (Figure 1. Two stage classifier of Roder at al.)]Regarding claim 7, Roder et al. describes creation of a multitude of different classifiers, each using different feature subsets related to different protein subsets to stratify patients with melanoma/nivolumab in response to treatment. For example, they train a first level classifier to split patients into early (poor performing) and late (good performing) using features associated with acute response function. A sample which tests Late (or good performing) on the first level classifier is then classified by the second level classifier, which is trained using a different set of features (associated with wound healing protein function (but not associated with acute response) to stratify patients into better (late) or worse (early) time to progression (Fig. 1 of this office action). They then defined a final classifier as a hierarchical combination of classifiers 1 and 2. (para. 0456, 0499-0503, appendix D, Fig 46 (shown in Fig. 1 of this office action); the method as in claim 6, wherein the second subset of RNA-based biomarkers is different from the first subset of RNA-based biomarkers).
Regarding claim 8, Roder et al. teaches training a multi-stage classifier for predicting response to therapy including a first stage classifier for stratifying patients into either an early or late group; a second stage classifier for further stratifying the early group of the first stage classifier into early and late groups; and a third stage classifier for further stratifying the late group of the first stage classifier into early and late groups. Roder et al. further teaches that each early and late group can correspond to shorter and longer overall survival, respectively (para. 0002, 0044, 0014; the method as in claim 7, wherein the mathematical algorithm is further trained to subdivide the second group of low responders into a fifth group of low responders and a sixth group of low responders, wherein the fifth group of low responders is characterized by survival time longer than average survival time for the entire second group of low responders, the sixth group of low responders is characterized by survival time shorter than average survival time for the entire second group of low responders).
Regarding claim 9, Roder et al. describes creation of a multitude of different classifiers, each using different feature subsets related to different protein subsets to stratify patients treated with melanoma/nivolumab based on their response to treatment. Roder et al. further discloses that each classifier selects features/biomarkers by filtering out those that do not contribute to classifier performance. (para. 0026; 0456, 0499-0503; the method as in claim 8, wherein the mathematical algorithm is further trained to define a third subset of biomarkers corresponding to dividing all subjects of the second group of low responders into the fifth group of low responders and the sixth group of low responders).
Regarding claim 10, Roder et al. describes creation of a multitude of different classifiers, each using different feature subsets related to different protein subsets to stratify patients treated with melanoma/nivolumab based on their response to treatment. Roder et al. further discloses that each classifier selects features/biomarkers by filtering out those that do not contribute to classifier performance (para. 0026; 0456, 0499-0503; the method as in claim 9, wherein the third subset of RNA-based biomarkers is different from the first subset of RNA-based biomarkers).
An invention would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Roder et al. teaches that creation of multiple different classifiers using different feature subsets related to different functional groups and the combination of them, by a rule-based system, to produce and overall classification that combines the information content across various biological functions, can enable taking out main effects and thus enabling identification of newly relevant peaks, thus creating a hierarchy of biological functions. Thus, this approach can disentangle compound effects. For example, regarding Figure 1 of this OA), the method yields a classifier with classifier level 1 based on features associated with acute response and classifier 2 based on wound healing but not acute response, thus having two biological pathways disentangled (para. 0502-0503). Therefore, one of ordinary skill in the art would have been motivated to utilize the sample partitioning method taught by Roder et al. to the methods to predict response to cancer treatment taught by Xu-Monette et al. and Smyth et al. in order to measure a new signal revealed by removing a main effect (thus improving signal to noise in biomarker detection) and to disentangle compound effects to create a hierarch of classification with a different biological process represented at each level (to improve interpretability). Furthermore, one of ordinary skill in the art would predict that the multi-stage classification approach taught by Roder et al. could be readily combined with the method of Xu-Monette et al. and Smyth et al. with a reasonable expectation of success because they both pertain to machine learning analysis of molecular data to predict response of cancer patients to treatment. The invention is therefore prima facie obvious.
15. Claims 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Xu-Monette et al. (Blood Advances, 2020, Vol. 4, p. 3391-3405), as applied to claim 1 above, and further in view of Cohan (Proc. of the 12th Intl. Conference on Natural Language Processing, pages 118–123, 2015; previously cited). The italicized text corresponds to the claim limitations. This rejection is newly recited and necessitated by claim amendment.
The limitations of claim 1 have been taught by Xu-Monette et al. above.
With respect to claim 13, Xu Monette et al. discloses that RNA for the method was extracted from FFPE tissue lysates using an automated KingFisher Flex and that samples were selectively enriched for 1408 cancer-associated genes using reagents provided in an Illumina TruSight RNA Pan-Cancer Panel before sequencing by RNA-seq. Xu-Monette et al. further discloses that the RNA feature were ranked by the classifier during training (p. 3393, col. 2, para. 2; Supplementary Fig. 1; wherein the naïve Bayesian classifier is trained to rank individual RNA-based biomarkers from an initial set of available RNA-based biomarkers that includes at least 500 individual genes).
With respect to claims 12 and 13, Xu-Monette et al. is silent to: the method as in claim 1, wherein the mathematical algorithm is based on a naïve Bayesian classifier that is a generalized naïve Bayesian classifier defined by applying a geometric mean to a likelihood product (claim 12) and wherein the classifier is a Naïve Bayesian classifier (Claim 13). However, these limitations were known in the art at the time of the effective filing date of the invention, as taught by Cohan.
With respect to claims 12 and 13, Cohan discloses a Perplexed Bayes Classifier that applies a geometric mean to a likelihood product. The classifier makes decisions that are identical to those of a Naive Bayes classifier without assuming that the features used are class-conditionally independent, by combining the class-conditional feature probabilities into posterior probabilities using their geometric mean unlike the Naive Bayes classifier that takes their product. Equation 7 describing the algorithm (shown below in Figure 2 of this office action) is effectively the same as the equation shown in the instant application in paragraph 0061 (p. 5, section 6, para. 1, Equation 7, wherein the mathematical algorithm is based on a naïve Bayesian classifier that is a generalized naïve Bayesian classifier defined by applying a geometric mean to a likelihood product (claim 12) and wherein the classifier is a naïve Bayesian classifier (claim 13)).
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Figure 2. Equation 7 from Cohan
An invention would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Cohan taught that Perplexed bayes classifier (i.e. combining class conditional feature probabilities using the geometric mean) improves the classifier’s posterior probability estimates without affecting its performance and can produce better calibrated posterior probabilities than a Naive Bayes classifier for datasets with higher feature counts. (Abstract, para. 2, Conclusion p. 122). Therefore, one of ordinary skill in the art would have been motivated to use the algorithm taught by Cohan in the process to predict response to treatment for cancer patients taught by Xu-Monette et al. in order to improve probability estimates because RNA-based datasets used by Xu-Monette et al. typically have high feature counts. Furthermore, one of ordinary skill in the art would predict that the algorithm taught by Cohan et al. could readily be added to the process of Xu-Monette et al. with reasonable expectation of success because Naive Bayes type classification is commonly used as a baseline algorithm for many classification tasks and is reported to perform surprisingly well, and the Perplexed Bayes classifier works particularly well when there are a lot of features (p. 1, col. 2, para. 3; p. 5, col. 2, para. 3). The invention is therefore prima facie obvious.
16. Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Xu-Monette et al. (Blood Advances, 2020, Vol. 4, p. 3391-3405), as applied to claim 1 above, in view of Cohan (Proc. of the 12th Intl. Conference on Natural Language Processing, pages 118–123, 2015; previously cited) as applied to claims 12-13 above, and further in view of Sha et al. (Genome Medicine, 2015, Vol. 7, p. 1-13). The italicized text corresponds to the claim limitations. This rejection is newly recited and necessitated by claim amendment.
The limitations of claim 1 have been taught by Xu-Monette et al. above.
The limitations of claims 12-13 have been taught by Xu-Monette et al. and Cohan et al. above.
Regarding claim 14, Xu-Monette et al. and Cohan et al. are silent to wherein at least some of the individual RNA biomarkers are cross-validate by subdividing the training set of subjects into a plurality of subsets, constructing a naïve Bayesian classifier for the individual RNA-based biomarkers for one of the subsets and verifying the same RNA-based biomarkers for at least some of the remaining subsets thereby reducing noise and overfitting. However, this limitation was known in the art at the time of the effective filing date of the invention as taught by Sha et al.
Regarding claim 14, Sha et al. teaches developing a naïve Bayesian classifier based on gene expression profiles to distinguish Burkitt Lymphoma from DLBCL (with and without myc rearrangement. Sha et al. further discloses that performance in classifier training was estimated by using 10-fold cross validation) (abstract, p. 3, col. 2, para. 2-3; wherein at least some of the individual RNA biomarkers are cross-validated by subdividing the training set of subjects into a plurality of subsets, constructing a naïve Bayesian classifier for the individual RNA-based biomarkers for one of the subsets and verifying the same RNA-based biomarkers for at least some of the remaining subsets thereby reducing noise and overfitting).
An invention would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Sha et al. taught that cross-validation within the training data enabled parameter optimization, which enabled avoiding potential over-fitting that could otherwise result from using a large data set. (p. 4, col. 1, para. 1). Therefore, one of ordinary skill in the art would have been motivated to use the cross validation taught by Sha et al. in the process to predict response to treatment for cancer patients taught by Xu-Monette et al. and Cohan in order to avoid classification errors due to over fitting when using large data sets. Furthermore, one of ordinary skill in the art would predict that the cross-validation could readily be added to the classification taught by Xu-Monette et al. and Cohan with reasonable expectation of success because both methods relate to generating gene expression classifiers for characterizing DLBCL patients. The invention is therefore prima facie obvious.
17. Claims 15 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Xu-Monette et al. (Blood Advances, 2020, Vol. 4, p. 3391-3405), as applied to claim 1 above, and further in view of Cohan (Proc. of the 12th Intl. Conference on Natural Language Processing, pages 118–123, 2015; previously cited), as applied to claims 12-13 above, further in view of Sha et al. (Genome Medicine, 2015, Vol. 7, p. 1-13), and further in view of Rosenwald et al. (N Engl. J Med. 2002, vol. 346, p. 1937-47; previously cited) as evidenced by Staudt et al. (biomarker database Immunol Rev. 2006 210:67-85; previously cited). The italicized text corresponds to the instant claim limitations. This rejection is newly recited and necessitated by claim amendment.
The limitations of claim 1 are taught by Xu-Monette et al. above.
The limitations of claims 12-14 are taught by Xu-Monette et al., Cohan and Sha et al. above.
With respect to claims 15-16, Xu-Monette et al., Cohan and Sha et al. are silent to: the method as in claim 14, wherein: after cross-validation the number of ranked RNA-based biomarkers is between 50 and 70 for each of the subdividing steps of a first group and a second group, a third group and a fourth group, and a fifth group and a sixth group of the training set of subjects; the set of individual RNA-based biomarkers for dividing the entire training set of subjects into the first group and the second group is different from the respective set of individual RNA- based biomarkers for subdividing the first group of high responders into the third group and the fourth group; and the set of individual RNA-based biomarkers for dividing the entire training set of subjects into the first group and the second group is different from the respective set of individual RNA- based biomarkers for subdividing the second group of low responders into the fifth group and the sixth group (claim 15); and the method as in claim 15, wherein: the set of RNA-based biomarkers for dividing the training set into the first group and the second group is selected from a group consisting of PPP2R1B, GOLGAS, LINGO2, HMGA1, SIN3A, ARID1A, BCL7A, CDK5RAP2, MAGEDI, CREB3L1, AMER1, DLL1, GSTT1, GPR34, DNM2, CCNB
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IP1, MUTYH, RET, CDH1, POFUTI, XRCC6, KIT, RALGDS, SS18, CD22, BRCA2, HDAC3, LHX4, FAM19A2, PRG2, PRCC, TBL1XR1, HIF1A, EDIL3, ROS1, DKK4, CDC25A, WNT7B, MYBL1, MLLT10, SLCOlB3, TACC2, CANT1, NCAM1, FGF3, FGF19, PPP3R2, CRADD, ETV6, SPP1, SDHB, FGF2, SUZ12, MB21D2, MYC,BAX, CEP57, ITGA5, ABCC3, and HECW1; the set of RNA-based biomarkers for dividing the first group of the training set into the third group of high responders and the fourth group of high responders is selected from a group consisting of DUSP22, CTNNA1, DUX2, SSX1, SSX2, CTNNB1, DCLK2, FH, DUSP9, FCGR2B, STAT5B, ESR1, CD274, TERF1, AKAP9, DGKI, HMGA1, ARNT, MAFB, PPP3CC, COL3A1, NUTM2A, CIT, MGMT, CDK6, SORT1, RCSD1, CDK5RAP2, S1N3A, RABEPI, MB21D2, KDR, SS18L1, SSBP2, SH2D5, ASXL1, AMER1, AFF1, PRKCD, 2- Sep, TPM4, FIGF, NODAL, GRM3, STAT6, GAB1, RPL22, BDNF, SNX29, MELK,ARRDC4, FGF10, MMP9, YYlAP1, HAS2, DLEC1, DEK, TLL2, BCL2L2, and ID3;the set of RNA-based biomarkers for dividing the second group of low responders of the training set into the fifth group and the sixth group is selected from a group consisting of AHI1, EPHA5, DUSP22, DUSP26, DUSP9, DUX2, MGMT, MIB1, MIPOL1, MIR1260B,MIR4321, MIR4683, MIR4758, MIR6515, MIR6752, MIR6765, BIVM-ERCC5, SSX1, SSX2, LTBP1, MAFB, TLR4, CTNNB1, ETV5, CHEK2, FUS, SS18L1, SSBP2, DGKI, CIT, TFE3, FGF19, TRIM33, CTCF, LAMA1, TBL1XR1, TOP1, RB1, OLR1, DOCK1, ARID1A, RABEPI, EP400, STK11, ETS1, MAPK1, CDC14A, LMO7, SS18, ICK, FLI1, POU5F1,RCSD1, HRAS, BACH2, CDK7, GAS5, CARS, SRSF2, and MAP3K6; or combinations thereof) (claim 16). However, these limitations were known in the art at the time of the effective filing date of the invention, as taught by Rosenwald et al.
Regarding claim 15, Rosenwald et al. teaches a method of training and validating a classifier for response of cancer patients to treatment whereby ranking/selecting RNA features for classification using the preliminary set (training set) is done using a cox proportional hazard model combined with hierarchical clustering. Rosenwald et al. further teaches that using the cox model, 670 genes with a good or bad outcome are identified from the training set (P<0.01). Rosenwald et al. further teaches that there is redundancy in the gene expression readout and that genes similar signatures (and similar functions) as identified by hierarchical clustering can be either pruned or combined into a representative feature by averaging their expression. This method was used to reduce feature number down to 16 genes, which were used for generating a final model. Because the starting number of genes was 670, which was pruned to 16 based on redundancy, the method is compatible with pruning to between 50 and 70 genes (page 1942 para. 2-5, Table 2; the method as in claim 14, wherein: after cross-validation the number of ranked RNA-based biomarkers is between 50 and 70 for each of the subdividing steps of a first group and a second group, a third group and a fourth group, and a fifth group and a sixth group of the training set of subjects; the set of individual RNA-based biomarkers for dividing the entire training set of subjects into the first group and the second group is different from the respective set of individual RNA- based biomarkers for subdividing the first group of high responders into the third group and the fourth group; and the set of individual RNA-based biomarkers for dividing the entire training set of subjects into the first group and the second group is different from the respective set of individual RNA- based biomarkers for subdividing the second group of low responders into the fifth group and the sixth group).
Regarding claim 16, Rosenwald et al. teaches using a training set (preliminary group) to identify microarray gene expression features (RNA features) to identify those that are predictive of survival time. Rosenwald et al. identified 892 genes with significant correlation with either a good or bad outcome (overall survival after chemotherapy) using the Cox proportional-hazards model. Therefore, the 892 identified genes are capable of separating patients into two groups based on response to treatment. These 892 genes have been curated into a database curated by Staudt et al. Of the genes identified by Rosenwald et al., 5 are in the first gene set of claim 16 (first group versus second group) including HDAC3, MYBL1, SDHB, MYC, ITGA5; 9 are in the second gene set of claim 16 (third group versus fourth group) including CTNNB1, FH, HMGA1, COL3A1, SIN3A, RPL22, MELK, MMP9, YY1AP1; and 4 are in the third gene set of claim 16 (fifth group versus sixth group) including CTNNB1, TOP1, HRAS, and CDK7 (Rosenwald et al. page 1942 para. 2-5, as evidenced by Staudt et al. spreadsheet signatureDB annotation (version updated Jan 21, 2026), The method as in claim 15, wherein: the set of RNA-based biomarkers for dividing the training set into the first group and the second group is selected from a group consisting of PPP2R1B, GOLGAS, LINGO2, HMGA1, SIN3A, ARID1A, BCL7A, CDK5RAP2, MAGEDI, CREB3L1, AMER1, DLL1, GSTT1, GPR34, DNM2, CCNB
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IP1, MUTYH, RET, CDH1, POFUTI, XRCC6, KIT, RALGDS, SS18, CD22, BRCA2, HDAC3, LHX4, FAM19A2, PRG2, PRCC, TBL1XR1, HIF1A, EDIL3, ROS1, DKK4, CDC25A, WNT7B, MYBL1, MLLT10, SLCOlB3, TACC2, CANT1, NCAM1, FGF3, FGF19, PPP3R2, CRADD, ETV6, SPP1, SDHB, FGF2, SUZ12, MB21D2, MYC,BAX, CEP57, ITGA5, ABCC3, and HECW1; the set of RNA-based biomarkers for dividing the first group of the training set into the third group of high responders and the fourth group of high responders is selected from a group consisting of DUSP22, CTNNA1, DUX2, SSX1, SSX2, CTNNB1, DCLK2, FH, DUSP9, FCGR2B, STAT5B, ESR1, CD274, TERF1, AKAP9, DGKI, HMGA1, ARNT, MAFB, PPP3CC, COL3A1, NUTM2A, CIT, MGMT, CDK6, SORT1, RCSD1, CDK5RAP2, S1N3A, RABEPI, MB21D2, KDR, SS18L1, SSBP2, SH2D5, ASXL1, AMER1, AFF1, PRKCD, 2- Sep, TPM4, FIGF, NODAL, GRM3, STAT6, GAB1, RPL22, BDNF, SNX29, MELK,ARRDC4, FGF10, MMP9, YYlAP1, HAS2, DLEC1, DEK, TLL2, BCL2L2, and ID3;the set of RNA-based biomarkers for dividing the second group of low responders of the training set into the fifth group and the sixth group is selected from a group consisting of AHI1, EPHA5, DUSP22, DUSP26, DUSP9, DUX2, MGMT, MIB1, MIPOL1, MIR1260B,MIR4321, MIR4683, MIR4758, MIR6515, MIR6752, MIR6765, BIVM-ERCC5, SSX1, SSX2, LTBP1, MAFB, TLR4, CTNNB1, ETV5, CHEK2, FUS, SS18L1, SSBP2, DGKI, CIT, TFE3, FGF19, TRIM33, CTCF, LAMA1, TBL1XR1, TOP1, RB1, OLR1, DOCK1, ARID1A, RABEPI, EP400, STK11, ETS1, MAPK1, CDC14A, LMO7, SS18, ICK, FLI1, POU5F1,RCSD1, HRAS, BACH2, CDK7, GAS5, CARS, SRSF2, and MAP3K6; or combinations thereof).
An invention would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention if some motivation in the prior art would have led that person to combine the prior art teaching to arrive at the claimed invention. Rosenwald et al. taught that using RNA features for predicting response to treatment using a training set applied to patients with diffuse large-B-cell lymphoma could improve treatment plans, since these patients have molecularly distinct diseases that may require individualized and molecularly targeted therapies. Rosenwald et al. taught that using their specific set of RNA biomarkers, they successfully generated a molecular predictor of survival after chemotherapy for DLBCL (p. 1946, para. 6; abstract). Therefore, one of ordinary skill in the art would have been motivated to combine the RNA biomarkers for large-B-cell lymphoma taught by Rosenwald et al. to the methods for predicting chemotherapy outcome taught by Xu-Monette et al., Cohan and Sha et al. in order to use validated RNA biomarkers to improve prediction of response to therapy and develop individualized treatment plans for patients with heterogeneous cancers (breast and large-B-cell lymphoma). Furthermore, one of ordinary skill in the art would predict that the RNA biomarkers for predicting response to treatment of large-B-cell lymphoma taught by Rosenwald et al. could be combined with the classifier development methods of Xu-Monette et al., Cohan and Sha et al. for DLBCL could be applied with a reasonable expectation of success because they both develop multivariate RNA-based classifiers for DCLBL with heterogeneous responses to treatment. The invention is therefore prima facie obvious.
18. Claims 17 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Xu-Monette et al. (Blood Advances, 2020, Vol. 4, p. 3391-3405), in view of Roder et al. (US 2017/0039345 A1; previously cited) and Smyth et al. (US 2020/0239968 A1; previously cited). The italicized text corresponds to the instant claim limitations. This rejection is newly recited and necessitated by claim amendment.
Concerning claim 17, Xu-Monette et al. teaches a method of generating two classifiers to separate DLBCL patients based on response to R-CHOP, the classifiers each trained to stratify patients based on survival risk scores (either overall or progression free survival) into 3 groups. Xu-Monette et al. teaches that high-risk group had strikingly poorer survival than the low- and intermediate-risk groups. Xu-Monette et al. teaches that for each classifier, at least some of the features are a plurality of gene expression features that were identified by the model during training. Xu-Monette et al. further teaches that the patients in the study were treated with R-CHOP chemotherapy and that the survival times and levels of expression of the genes were known/quantified (abstract; p. 3393, col. 1, para. 3; p. 3399, col. 1, para. 1; p. 3393, col. 2, para. 2; Table 4; Fig. 5; Supplementary Fig. 1; a method for identifying one or more individual RNA-based biomarkers for forecasting clinical course of a subject with diffuse large B-cell lymphoma (DLBCL); the method comprising the following steps: (a) providing a training set of subjects with DLBCL treated with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) chemotherapy with known plurality of individual RNA-based biomarkers and known survival times; (b) based on survival time, dividing subjects from the training set into a first group of high responders and a second group of low responders).
Regarding claim 17, Xu-Monette et al. teaches training a neural network model to identify variables (features), including a plurality of gene expression features, that correlate with dividing the patients into the 3 risk groups (Supplemental Fig. 1; (c) using machine learning, identifying a first subset of one or more individual RNA- based biomarkers from a plurality of individual RNA-based biomarkers, wherein the first subset of one or more individual RNA-based biomarkers is identified as correlating to dividing the subjects into the first group and the second group).
Regarding claim 17, Xu-Monette et al. disclose that the Agencourt FormaPure Total 96-Prep Kit was used to extract RNA from FFPE tissue lysates using an automated KingFisher Flex and that samples were selectively enriched for 1408 cancer associated genes using reagents provided in an Illumina TruSight RNA Pan-Cancer Panel, followed by RNA-Seq to obtain gene expression levels. Xu-Monette et al. further teaches using the expression values to classify DLBCL patients treated with R-CHOP into three equal subgroups with high, intermediate or low risk scores (of progression free survival or overall survival). Xu-Monette et al. further discloses that high-risk group had strikingly poorer survival than the low- and intermediate-risk groups (abstract; p. 3393, col. 2, para. 2; p. 3394, col. 1, para. 2; p. 3393, col. 1, para. 3; Supplementary Fig. 1; p. 3399, col. 1, para. 1; (d) obtaining the subset of individual RNA-based biomarkers defined in step (c) for the subject by extracting RNA from a tissue sample of the subject and analyzing the extracted RNA using a targeted RNA sequencing panel to obtain expression levels of the subset of individual RNA-based biomarkers, and classifying the subject into one of several predetermined survival groups based on the predicted response to R-CHOP chemotherapy).
Regarding claims 17 and 18, Xu-Monette et al. is silent to: dividing all subjects from the training set into a first group of high responders and a second group of low responders (i.e., wherein there are only two groups) (claim 17), and the method as in claim 17 further comprising a step (d) of dividing the first group of high responders into a third group of high responders and a fourth group of high responders, wherein the third group of high responders is characterized by survival time that is longer than average survival time for the entire first group of high responders, the fourth group of high responders is characterized by survival time shorter than average survival time for the entire first group of high responders (claim 18). However, these limitations were known in the art at the time of the effective filing date of the invention, as taught by Roder et al.
Regarding claim 17, Roder et al. teaches training a multi-stage classifier for predicting response to therapy including a first stage classifier for stratifying patients into either an early or late group (para. 0002, 0044 0014; dividing all subjects from the training set into a first group of high responders and a second group of low responders (i.e., wherein there are only two initial groups).
Regarding claim 18, Roder et al. teaches training a multi-stage classifier for predicting response to therapy including a first stage classifier for stratifying patients into either an early or late group; a second stage classifier for further stratifying the early group of the first stage classifier into early and late groups; and a third stage classifier for further stratifying the late group of the first stage classifier into early and late groups. Roder et al. further teaches that each early and late group can correspond to shorter and longer overall survival, respectively (para. 0002, 0044 0014, the method as in claim 17 further comprising a step (d) of dividing the first group of high responders into a third group of high responders and a fourth group of high responders).
An invention would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Roder et al. teaches that creation of multiple different classifiers using different feature subsets related to different functional groups and the combination of them, by a rule-based system, to produce and overall classification that combines the information content across various biological functions, can enable taking out main effects and thus enabling identification of newly relevant peaks, thus creating a hierarchy of biological functions. Thus, this approach can disentangle compound effects. For example, regarding Figure 1 of this OA), the method yields a classifier with classifier level 1 based on features associated with acute response and classifier 2 based on wound healing but not acute response, thus having two biological pathways disentangled (Roder et al., para. 0502-0503). Therefore, one of ordinary skill in the art would have been motivated to utilize the sample partitioning method taught by Roder et al. to the methods to predict response to cancer treatment taught by Xu-Monette et al. in order to measure a new signal revealed by removing a main effect (thus improving signal to noise in biomarker detection) and to disentangle compound effects to create a hierarch of classification with a different biological process represented at each level (to improve interpretability). Furthermore, one of ordinary skill in the art would predict that the multi-stage classification approach taught by Roder et al. could be readily added to the system of Xu-Monette et al. with a reasonable expectation of success because they both pertain to analysis of molecular data using machine learning to predict response of cancer patients to treatment. The invention is therefore prima facie obvious.
Regarding claim 18, Xu-Monette et al. and Roder et al. are silent to: wherein the third group of high responders is characterized by survival time that is longer than average survival time for the entire first group of high responders, the fourth group of high responders is characterized by survival time shorter than average survival time for the entire first group of high responders (i.e. wherein average survival time is used as the threshold for dividing the samples). However, this limitation was known in the art at the time of the effective filing date of the invention as taught by Smyth.
Regarding claim 18, Smyth et al. teaches training a classifier using a training set of gastroesophageal cancer patients separated into two groups by survival time following tumor resection (i.e. the treatment) that are classified as having greater or shorter survival time than is typical for gastroesophageal cancer patients in the general population. Smyth et al. further teaches that classes can be “good prognosis” versus “bad prognosis” corresponding to overall survival that is longer or shorter than average for that stage of cancer and cancer type (para. 0062-0068, 0080 and 0151-0152; wherein the threshold of separating the groups is average survival time).
An invention would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Smyth et al. taught modelling with a training set containing classes defined by average survival times after tumor resection is useful to predict treatment response and survival of gastric cancer patients (para. 0010). Therefore, one of ordinary skill in the art would have been motivated to apply training the classifier with two groups defined by average survival time in the training set after a known therapy taught by Smyth et al. to the modeling method taught by Xu-Monette et al. and Roder et al. in order to enable predicting treatment response of DLBCL cancer patients. Furthermore, one of ordinary skill in the art would predict that the training classes taught by Smyth et al. could readily be added to the classification method taught by Xu-Monette et al. and Roder et al. with reasonable expectation of success because they both pertain to developing classifiers to predict response of DLBCL patients to treatment. The invention is therefore prima facie obvious. (see MPEP 2143(I)(C)).
20. Claims 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Rosenwald et al. (N Engl. J Med. 2002 346(25):1937-47; previously cited) as evidenced by Staudt et al. (biomarker database Immunol Rev. 2006 210:67-85; previously cited) in view of Sha et al. (Genome Medicine, 2015, Vol. 7, p. 1-13), and further in view of Schweighofer et al. (US 2016/0032404 A1). The italicized text corresponds to the instant claim limitations. This rejection is newly recited and necessitated by claim amendment.
Regarding Claim 19, Rosenwald et al. teaches predicting survival after chemotherapy for patients with diffuse large-B-cell lymphoma using gene expression features from patient biopsy samples. Rosenwald teaches generating microarray gene expression data and analyzing the data using a Cox proportional-hazards model to identify individual genes whose expression correlated with the outcome. Data from 670 of 7399 microarray features were significantly associated with a good or a bad outcome in the training group (P < 0.01). These 670 features are curated in a database of predictive biomarkers by Staudt et al. The 670 features include 10 RNA biomarkers selected from the group claimed in claim 19 of the instant application (HMGA1, SIN3A, BCL7A, ITGA5, XRCC6, RALGDS, HDAC3, MYBL1, SDHB, and MYC) (Staudt et al., spreadsheet signatureDB annotation, version updated January 21, 2026). Rosenwald teaches that molecular diagnosis by analyzing gene expression data (RNA biomarkers) can predict individualized and molecularly targeted therapies for diffuse large b-cell lymphoma (Rosenwald, results para. 6, discussion para. 7, methods; A method for treating a subject with diffuse large B-cell lymphoma; define the subject as a high responder or a low responder to chemotherapy using one or more of individual RNA-based biomarkers selected from a group consisting of PPP2R1B, GOLGA5, LINGO2, HMGA1, SIN3A, ARIDIA, BCL7A, CDK5RAP2, MAGEDI, CREB3L1, AMERI, DLL1, GSTT1, GPR34, DNM2, CCNB1IP1, MUTYH, RET, CDH1, POFUTI, XRCC6, KIT, RALGDS, SS18, CD22, BRCA2, HDAC3, LHX4, FAM19A2, PRG2, PRCC, TBL1XR1, HIF1A, EDIL3, ROS1,DKK4, CDC25A, WNT7B, MYBL1, MLLT10, SLCO1B3, TACC2, CANT1, NCAM1, FGF3, FGF19, PPP3R2, CRADD, ETV6, SPP1, SDHB, FGF2, SUZ12, MB21D2, MYC, BAX, CEP57, ITGA5, ABCC3, and HECW1).
Pertaining to claims 19 and 20, Rosenwald et al. is silent to using a Bayesian classifier. However, this limitation was known in the art at the time of the effective filing date of the invention as taught by Sha et al.
As to claim 19, Sha et al. teaches developing a naïve Bayesian classifier based on gene expression profiles to distinguish Burkitt Lymphoma (BL) from DLBCL in patients, many of whom were pretreated with R-CHOP. Sha et al. further discloses that performance in classifier training was estimated by using 10-fold cross validation (abstract, p. 3, col. 2, para. 2-3; Table 5; using a Bayesian classifier).
An invention would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Sha et al. taught that in a comparison to 9 other machine learning algorithms, a naïve bayes algorithm had similar error rates to most other algorithms as measured in 10-fold cross validation in a binary classification problem to separate DLBCL patients from BL patients. Furthermore, the naïve bayes algorithm was not one of the two algorithms wit noticeably higher error rates (Fig. 1). Therefore, one of ordinary skill in the art would have been motivated to combine the method of predicting survival after chemotherapy for patients with DLBCL taught by Rosenwald et al. with the Bayesian classifier taught by Sha et al., because the Bayesian classifier showed a low error rate compared to other machine learning algorithms. Furthermore, one of ordinary skill in the art would predict that modeling algorithm taught by Sha et al., could readily be added to the method of predicting survival of DLBCL patients taught by Rosenwald et al. with a reasonable expectation of success because they both pertain to prediction of response to therapy for patients with DLBCL. The invention is therefore prima facie obvious.
Regarding claims 19 and 20, Rosenwald et al. and Sha et al. are silent to: comprising a step of obtaining a subset of individual RNA-based biomarkers for the subject by extracting RNA from a tissue sample of the subject and analyzing the extracted RNA using a targeted RNA sequencing panel and treating the subject with chemotherapy based on the classification (claim 19) and the method as in claim 19, wherein treating the subject comprises: a step of treating the subject forecasted as a high responder with the chemotherapy; a step of treating the subject forecasted as a low responder with a further therapy or an additional therapy; or a combination thereof) (claim 20). However, these limitations were known in the art at the time of the effective filing date of the invention as taught by Schweighofer et al.
Regarding claim 19, Schweighofer et al. teaches a method of selecting an individual having diffuse large B cell lymphoma (DLBCL) for treatment with ibrutinib by determining the presence or absence of a modified biomarker gene and administering ibrutinib. Schweighofer et al. further teaches that RNA was extracted from FFPE tissue from DLBCL tumor biopsies and analyzed by targeted RNA-seq using a FoundationOne Heme panel, to interrogate DNA sequences of 405 genes and RNA sequence of 256 gene by next generation sequencing. Schweighofer et al. further teaches that DLBCL patients are classified as having absence or presence of modification of one or more biomarker genes (indicating resistance to therapy) and administering a therapeutically effective amount of ibrutinib if there is absence of modifications in the one or more biomarker genes (i.e. chemotherapy) (para. 0004; 0006; 0533; comprising a step of obtaining a subset of individual RNA-based biomarkers for the subject by extracting RNA from a tissue sample of the subject and analyzing the extracted RNA using a targeted RNA sequencing panel and treating the subject with chemotherapy based on the classification).
With respect to claim 20, Schweighofer et al. teaches that DLBCL patients are classified as having absence or presence of modification of one or more biomarker genes (indicating resistance to therapy) and administering a therapeutically effective amount of ibrutinib if there is absence of modification s in the one or more biomarker genes (para. 0004; the method as in claim 19, wherein treating the subject comprises: a step of treating the subject forecasted as a high responder with the chemotherapy; a step of treating the subject forecasted as a low responder with a further therapy or an additional therapy; or a combination thereof).
An invention would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Schweighofer et al. taught that their assay is based on the expression of biomarker genes involved in negatively regulating B-cell proliferation through stimulation of the B-cell receptor and that B-cell receptor (BCR) signaling is an important growth and survival pathway in various B cell malignancies, including DLBCL (para. 0003 and 0192). Therefore, one of ordinary skill in the art would have been motivated to apply the targeted RNA-seq analysis taught by Schweighofer et al. to the development of biomarkers for predicting response to treatment in patients with DLBCL as taught by Rosenwald et al. and Sha et al. in order to monitor mechanistically relevant biomarkers. Furthermore, one of ordinary skill in the art would predict that the targeted RNA-seq methods taught by Schweighofer et al. could be readily added to the method of predicting chemotherapy benefit for DLBCL patients as taught by Rosenwald et al. and Sha et al. with a reasonable expectation of success because they both pertain to developing and testing assays for predicting chemotherapy benefit for patients with DLBCL. The invention is therefore prima facie obvious.
Response to Arguments
Applicant’s arguments filed 26 May 2026 have been fully considered but they are not persuasive.
Response to argument that the prior art cited does not teach the method wherein the disease is DLBCL and the treatment is R-CHOP
Applicant’s arguments that prior art does not teach DLBCL treated with R-CHOP (throughout the response to office action on p. 21-29 (for example, see response to OA p. 24, para. 4 – p. 25, para. 2)), filed 26 May 2026, with respect to the rejections of claims 1-20 under 35 U.S.C. 102 and 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, new grounds of rejection is made in view of the claim amendments.
Response to argument that the prior art cited does not teach the method wherein the RNA is isolated and analyzed by targeted RNA-seq
Applicant’s arguments that prior art does not teach RNA isolation or sequencing (made throughout the response to Office Action on p. 21-29 pertaining to claims 2-19 (for example, see response to OA p. 23, para. 3-4 – p. 24, para. 1-3 and p. 28, para. 2)), filed 26 May 2026, with respect to the rejections of claims 1, 17 and 19 under 35 U.S.C. 102 and 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, new grounds of rejection is made in view of the claim amendments.
Response to 35 U.S.C. 102 arguments (other than those pertaining to DLBCL, R-CHOP or RNA isolation and analysis above): The applicant asserts that the cited art does not teach the invention of the amended claims
Applicant’s arguments that prior art does not disclose training to define a biomarker subset based on survival (see response to OA, p. 21, para. 5 – p. 22, para. 2), filed 26 May 2026, with respect to the rejections of claim 1 under 35 U.S.C. 102 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, new grounds of rejection is made in view of the claim amendments.
Applicant’s arguments that prior art does not teach a particular definition of “high responders” and “low responders” (i.e. patients whose survival time is longer or shorter than average survival time for the entire training set based on response to a known therapy) (see response to OA, p. 23, para. 2), filed 26 May 2026, with respect to the rejections of claim 17 under 35 U.S.C. 103 have been fully considered and are not persuasive. This is because this definition of high and low responders is not commensurate with the scope of the claim as this specific restriction was not introduced until claim 18 (wherein this limitation was taught by Roder et al. as described in the 35 U.S.C. 103 section). There is mention of the specific definition of high and low responders in the specification (para. 0027 of specification), but this was not considered to be a limiting definition as it is the context of an example (wherein confidence intervals and p-values are given of a specific experiment). Additionally, a different definition is given in para. 0049 of the specification: “it is important to accurately predict who will respond well to a known therapy (high responders) and who will not (low responders)”.
Response to 35 U.S.C. 103 argument (other than those pertaining to DLBCL, R-CHOP or RNA isolation and analysis): The applicant asserts that the cited art does not teach the limitations of the amended claims.
Applicant’s arguments that Gutin et al. does not teach the claimed machine learning algorithm and training sets used to train the algorithm (with the same biomarkers) (response to OA p. 23, para. 3-4 – p. 24, para. 1-3), filed 26 May 2026, with respect to the rejections of claim 2-3 and 11 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, new grounds of rejection is made in view of the claim amendments.
Applicant’s arguments that there was no adequate rationale for combining the teachings of Gutin et al. and Smyth et al. to arrive at a method for treating DLBCL with R-CHOP with the specific processing of RNA) and furthermore that there would be no expectation of success due to the cancer types being different and the lack of success in DLBCL models previously (response to OA p. 23, para. 3-4 – p. 24, para. 1-3), filed 26 May 2026, with respect to the rejections of claim 2-3 and 11 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, new grounds of rejection is made in view of the claim amendments.
Applicant’s arguments that the prior art does not teach training a mathematical algorithm using machine learning on prior DLBCL subject treated with R-CHOP in order to divide subjects into predetermined survival groups and define corresponding RNA biomarkers (response to OA p. 24, para. 4 – p. 25, para. 1-2), filed 26 May 2026, with respect to the rejection of claim 4 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, new grounds of rejection is made in view of the claim amendments.
Applicant’s arguments that the prior art does not teach training a mathematical algorithm using machine learning on prior DLBCL subject treated with R-CHOP in order to divide subjects into predetermined survival groups and define corresponding RNA biomarkers (response to OA p. 25, para. 3-4), filed 26 May 2026, with respect to the rejection of claims 5-10 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, new grounds of rejection is made in view of the claim amendments.
Applicant’s arguments that the prior art does not teach training a mathematical algorithm using machine learning on prior DLBCL subject treated with R-CHOP in order to divide subjects into predetermined survival groups and define corresponding RNA biomarkers (response to OA p. 25, para. 5-6), filed 26 May 2026, with respect to the rejection of claims 12-14 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, new grounds of rejection is made in view of the claim amendments.
Applicant’s arguments that the prior art does not teach training a mathematical algorithm using machine learning on prior DLBCL subject treated with R-CHOP in order to divide subjects into predetermined survival groups and define corresponding RNA biomarkers (response to OA p. 25, para. 7 – p. 26, para. 2), filed 26 May 2026, with respect to the rejection of claims 15-16 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, new grounds of rejection is made in view of the claim amendments.
Applicant’s arguments that claim 18 is not rendered obvious by the combination of Smyth et al. and Roder et al. because the rationale does not explain why a person of ordinary skill would use serum mass spectrometry classification methods (Roder) to further subdivide RNA biomarker-defined groups in context of DLBCL with targeted RNA sequencing (see response to OA p. 22, para. 4 – p. 23, para. 1, filed 26 May 2026), with respect to the rejection of claim 18 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, new grounds of rejection is made in view of the claim amendments.
With respect to the 35 U.S.C. 103 rejection of claim 19, the applicant asserts that only 10 out of 670 analyzed genes in Rosenwald overlap with the list of genes in claim 19. Therefore, 50-60 of the biomarkers claimed are not found in Rosenwald. The examiner has not explained how a person having ordinary skill in the art would have selected the specific 60 biomarkers from the claimed list when the vast majority of them are not in Rosenwald’s dataset (see p. 27, para. 3-p. 28, para. 1 of response to OA, filed 26 May 2026). The examiner agrees that all 60 claimed biomarkers are not taught by Rosenwald, however this argument is not commensurate with the scope of the claim. The claim recites “using one or more of individual RNA-based biomarkers selected from a group consisting of [the list]”. Therefore, Rosenwald needs to teach using only one or more of the biomarkers (not all of the biomarkers). In addition, there is no limitation in the claim indicating that the one or more individual RNA-based biomarkers cannot be used in combination with other biomarkers (for example, other biomarkers identified by Rosenwald), therefore a motivation for selecting only those individual biomarkers is not required.
With respect to the 35 U.S.C. rejection of claim 19, the applicant asserts that Rosenwald’s Cox regression model is different from a Bayesian classifier and that Gutin et al. teaches using Bayes machine learning models generally for breast cancer predictions, not for DLBCL. Therefore, neither Rosenwald nor Gutin et al., alone or in combination, teaches using a Bayesian classifier with the specific set of 60 RNA biomarkers to classify DLBCL patients as high or low responders to chemotherapy (see response to OA, p. 28, para. 1, filed 26 May 2026). The examiner agrees that Gutin et al. in combination with Rosenwald et al. does not teach using a Bayesian classifier with all 60 RNA biomarkers to classify DLBCL patients. However, these limitations are not commensurate with the scope of the claim because the claim recites “using one or more of individual RNA-based biomarkers selected from a group consisting of [the list]”. Therefore, Rosenwald needs to teach using only one or more of the biomarkers (not all of the biomarkers). Regarding the other arguments, the examiner disagrees that the combination of prior art does not teach all limitations. However, the rejection has been withdrawn and new grounds of rejection is made in view of the claim amendments.
Regarding the 35 U.S.C 103 rejection of claim 19, the applicant asserts that “the specific combination of 60 biomarkers was discovered by the inventors through their novel application of their machine learning algorithm with cross validation to the problem of DLBCL prognosis and cannot be reconstructed from the prior art without impermissible hindsight. Accordingly, claim 19 is not rendered obvious by the combination of Rosenwald and Gutin et al.” (see response to OA, p. 28, para. 2). The examiner disagrees with this assertion of improper hindsight reasoning for three reasons: 1) According to the MPEP 2145 XA regarding impermissible hindsight: "any judgment on obviousness is in a sense necessarily a reconstruction based on hindsight reasoning, but so long as it takes into account only knowledge which was within the level of ordinary skill in the art at the time the claimed invention was made and does not include knowledge gleaned only from applicant’s disclosure, such a reconstruction is proper." In re McLaughlin, 443 F.2d 1392, 1395, 170 USPQ 209, 212 (CCPA 1971). The applied art teaches all limitations of the claim without including other knowledge from the application. 2) It would have been obvious to a person having ordinary skill in the art to use the 670 features (or some subset thereof) taught by Rosenwald et al. for predicting survival after chemotherapy for patients with diffuse large-B-cell lymphoma using gene expression features from patient biopsy samples because Rosenwald et al. teaches this exact application (see Rosenwald et al. abstract). The missing element provided by Gutin et al. was the use of a Bayesian classifier rather than a Cox regression model, therefore the impermissible hindsight argument regarding applying the specific biomarkers to DLBCL is not supported. 3) Furthermore, as stated above, any argument related to the specific 60-gene panel is a moot point because only “one or more of the individual RNA-based biomarkers selected from [the] group” are claimed. Therefore, the limitation is met by using the entire 670 RNA panel taught by Rosenwald without modification and thus no information is imported from the instant application. Note that limitations of the amended claims related to isolating and analyzing RNA were not discussed here as they were not relevant to this particular argument. However, the rejection has been withdrawn, and new grounds of rejection is made in view of the claim amendments.
Conclusion
21. In 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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/J.J.S./ Examiner, Art Unit 1685
/OLIVIA M. WISE/ Supervisory Patent Examiner, Art Unit 1685
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