Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Status of Claims
The present Office Action is pursuant to Applicant’s communication on 09-15-2025; current application filed on 09-15-2025. This application has PRO 63/694,456 09/13/2024.
Examiner’s Note
The rejections below group claims that may not be identical, but whose language and scope are so substantively similar as to lend themselves to grouping, in the interests of clarity and conciseness.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1
Claim(s) 1-20 is/are within the four statutory categories. Claim(s) 1-20 is/are drawn to a system1, method2 and non-transitory computer-readable medium3 which means that said claims(s) is/are within the four statutory categories (i.e. process). However, as will be shown below, arguendo, Aforementioned claim(s) is/are nonetheless unpatentable under 35 U.S.C. 101.
Prong 1 of Step 2A
What is claimed:
1. A system for predicting a treatment-related outcome for a patient after a cancer therapy (e.g., chemotherapy), including an overall survival outcome, the system comprising:
a processor; and
a memory having instructions stored thereon, wherein execution of the instructions causes the processor to:
receive, via the processor, a methylation signature comprising methylated nucleic acid sequences (e.g., DNA, cell-free DNA (cfDNA), or RNA) or RNA sequencing signature acquired from a sample of a patient for at least one gene selected from the group consisting of BNIP3, CES2, CHFR, CXCL5, GSTM2, ITGB4, MUC4, MUC5AC, ONECUT2, PRMT1, RUNX1, SFN, SLC22A3, SOX8, TACC3, KCNH2, PROKR2, IGF1R, and TMEM139;
determine, via a trained AI model, using the received sequences, an indicator corresponding to an overall survival outcome of the patient from pancreatic cancer and/or associated treatments; and
output the determined indicator via a report or graphical user interface, wherein the output is subsequently employed to direct or adjust treatment of the pancreatic cancer for the patient.
The underlined limitations as shown above, given the broadest reasonable interpretation, cover the abstract ideas of a mental process and/or a certain method of organizing human activity because they recite a process that is a manner of organizing human activity, comprising employing pattern recognition of signatures associated with cancer phenotypes directed to pancreatic cancer, but for the recitation of generic computer components (i.e. the computer), e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract ideas are deemed “additional elements,” and will be discussed in further detail below.
Dependent claim(s) 2-16 and 18-19, include other limitations, for example:
2. The system of claim 1, wherein the trained AI model was trained using sequences for a plurality of genes, including at least 5 of BNIP3, CES2, CHFR, CXCL5, GSTM2, ITGB4, MUC4, MUC5AC, ONECUT2, PRMT1, RUNX1, SFN, SLC22A3, SOX8, TACC3, KCNH2, PROKR2, IGF1R, TMEM139, wherein the methylation signature or RNA sequencing signature is stratified for a patient population having a high risk group label and a lower risk group label for overall survival.
3. The system of claim 1, wherein the overall survival is determined at 6 months, 1 year, or 2 years from date of diagnosis of the pancreatic cancer.
4. The system of claim 1, wherein execution of the instructions further causes the processor to additionally predict at least one of a predicted duration of response, a predicted progression-free survival time, and predicted time to progression.
5. The system of claim 4, wherein the instructions to determine the additional prediction for the at least one of the predicted duration of response, the predicted progression-free survival time, and the predicted time to progression includes:
instructions to determine, via a second trained AI model, the received methylated sequences or RNA sequences for at least one gene selected from the group consisting of BNIP3, CES2, CHFR, CXCL5, GSTM2, ITGB4, MUC4, ONECUT2, PRMT1, RUNX1, SFN, SLC22A3, SOX8, TACC3, IGF1R, KCNH2, MUC5AC, SLC22A2, SST, TMEM139, ISG15, PROKR2, SLC38A5, and SMARCA2.
6. The system of claim 4, wherein the instructions to determine the additional prediction for the predicted duration of response, includes:
instructions to determine, via a second trained AI model, the received methylated sequences or RNA sequences for at least one gene in a gene selected from the group consisting of
BNIP3, CES2, CHFR, CXCL5, GSTM2, ITGB4, MUC4, ONECUT2, PRMT1, RUNX1, SFN, SLC22A3, SOX8, TACC3, IGF1R, KCNH2, MUC5AC, SST, and TMEM139.
7. The system of claim 4, wherein the instructions to determine the additional prediction for the predicted progression-free survival time includes:
instructions to determine, via a second trained AI model, the received methylated sequences or RNA sequences for at least one gene in a gene selected from the group consisting of BNIP3, CES2, IGF1R, ISG15, ITGB4, KCNH2, ONECUT2, PROKR2, RUNX1, SFN, SLC22A3, SLC38A5, SMARCA2, SOX8, SST, and TACC3.
8. The system of claim 4, wherein the instructions to determine the additional prediction for the predicted time to progression includes:
instructions to determine, via a second trained AI model, the received methylated sequences or RNA sequences for at least one gene in a gene selected from the group consisting of BNIP3, CES2, CHFR, CXCL5, GSTM2, IGF1R, ISG15, ITGB4, KCNH2, MUC4, ONECUT2, PROKR2, RUNX1, SFN, SLC22A3, SLC38A5, SMARCA2, SOX8, SST, and TACC3.
9. The system of claim 5, wherein the predicted duration of response, the predicted progression-free survival time, and the predicted time to progression is determined at 6 months, 1 year, or 2 years from a date of diagnosis or a date of initial treatment.
10. The system of claim 1, wherein the trained AI model is a convolutional neural network.
11. The system of claim 1, wherein the methylated sequences or RNA sequences were acquired via a sequencing operation.
12. The system of claim 11, wherein the sequencing operation comprises an Enzymatic Methylation Sequencing operation.
13. The system of claim 1, wherein the sample comprises blood plasma and/or tissues.
14. The system of claim 1, wherein pancreatic cancer comprises pancreatic ductal adenocarcinoma (PDA).
15. The system of claim 4, wherein the instructions to determine the additional prediction for the at least one of the predicted duration of response, the predicted progression-free survival time, and the predicted time to progression includes:
instructions to determine, via a second trained AI model, the received methylated sequences or RNA sequences for at least one gene selected from the group consisting of ABCB1, ABCB4, ABCC1, ABCC10, ABCC3, ABCC5, ABCC6, ABCC8, ABCC9, ABCG2, ANGPTL4, ARID1A, ASXL2, ATM, BCL2L1, BICC1, BNIP3, BRCA1, CADM1, CD44, CES2, CHFR, CTNNB1, CTPS2, CXCL5, DCK, DKK3, DPYD, EGFR, EIF5A, ENO1, GLO1, GSDME, GSTM1, GSTM2, HMGA1, HNF1A, HSPA5, HSPB1, IGF1R, IGFBP3, ISG15, ITGA3, ITGB4, JAG1, KCNH2, LDHA, MAP2, MAP3K7, MCL1, METTL3, MLH1, MUC4, MUC5AC, NOTCH2, NRP1, NT5C1A, ONECUT2, PRMT1, PROKR2, PTGES2, PYCARD, RELL2, RRM1, RRM2, RRP9, RUNX1, SFN, SLC22A2, SLC22A3, SLC29A1, SLC2A1, SLC38A5, SMARCA2, SNRPF, SOX8, SST, TACC3, TET1, TFAM, TGM2, TMEM139, TPX2, TRIM31, TYMS, UBE2T, USP8, VASH2, YEATS4, and ZEB1.
16. The system of claim 1, wherein the trained AI model was trained using cfDNA gene methylation signature comprising the methylated sequences from isolated cfDNA from plasma of a patient.
18. The method of claim 17, wherein the trained AI model was trained using methylated sequences or RNA sequences for a plurality of genes, including at least 5 of BNIP3, CES2, CHFR, CXCL5, GSTM2, ITGB4, MUC4, MUC5AC, ONECUT2, PRMT1, RUNX1, SFN, SLC22A3, SOX8, TACC3, KCNH2, PROKR2, IGF1R, TMEM139, wherein the methylation signature or RNA sequencing signature is stratified for a patient population having a high risk group label and a lower risk group label for overall survival.
19. The method of claim 17, wherein the overall survival is determined at 6 months, 1 year, or 2 years from date of diagnosis of the pancreatic cancer.
However these dependent claims only serve to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g. see MPEP 2106.04. Additionally, any limitations in dependent claim(s) 2-16 and 18-19 are deemed additional elements to the abstract idea, and will be further addressed below. Hence dependent claim(s) 2-16 and 18-19 are nonetheless directed towards fundamentally the same abstract idea as independent Claim(s) 1, 17, 20.
Prong 2 of Step 2A
Claim(s) 1, 17, 20 is/are not integrated into a practical application because the additional elements (i.e. comprising non-underlined limitations above – in this case a processor, a memory, a trained AI model, A non-transitory computer-readable medium) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of a computer, which amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see ¶¶1-118 of the present Specification, see MPEP 2106.05(f);
generally link the abstract idea to a particular technological environment or field of use, which amounts to limiting the abstract idea to the field of healthcare, see MPEP 2106.05(h); and/or
add insignificant extra-solution activity to the abstract idea, see MPEP 2106.05(g).
Additionally, dependent claim(s) 2-16 and 18-19 include other limitations, but these limitations also amount to no more than generally linking the abstract idea to a particular technological environment or field of use, and/or do not include any additional elements beyond those already recited in independent Claim(s) 1, 17, 20, hence also do not integrate the aforementioned abstract idea into a practical application.
Step 2B
Claim(s) 1, 17, 20 do/does not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. comprising non-underlined limitations above – in this case a processor, a memory, a trained AI model, A non-transitory computer-readable medium), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, generally link the abstract idea to a particular technological environment or field of use, and/or add insignificant extra-solution activity to the abstract idea, wherein the insignificant extra-solution activity comprises limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The Specification expressly disclosing that the additional elements are well-understood, routine, and conventional in nature:
¶¶1-118 of the Specification discloses that the additional elements (i.e. the computer) comprise a plurality of different types of generic computing systems that are configured to perform generic computer functions (i.e. receive and process data) that are well-understood, routine, and conventional activities previously known to the pertinent industry (i.e. healthcare);
Relevant court decisions: The following are examples of court decisions demonstrating well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II):
Storing and retrieving information in memory, e.g. see Versata Dev. Group, Inc. v. SAP Am., Inc. – similarly, the current invention recites storing or uploading media;
Dependent claim(s) 2-13 and 15-20 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because, as stated above, the limitations of the aforementioned dependent claims amount to no more than generally linking the abstract idea to a particular technological environment or field of use, and/or do not recite any additional elements not already recited in independent Claim(s) 1, 17, 20 hence does not amount to “significantly more” than the abstract idea.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claim(s) 1-20 is/are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 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 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.
Claim(s) 1-5, 7-14 and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Graeber4 in view of Yassi5.
Regarding claim(s) 1, 17, 20, Graeber A system for predicting a treatment-related outcome for a patient after a cancer therapy (e.g., chemotherapy), including an overall survival outcome, the system comprising a processor6; and
a memory having instructions stored thereon, wherein execution of the instructions, A method for predicting a treatment-related outcome for a patient after a cancer therapy (e.g., chemotherapy), including an overall survival outcome, the method comprising, A non-transitory computer-readable medium having instructions stored thereon, wherein execution of the instructions causes a processor to:
receive, via the processor, a methylation signature comprising methylated nucleic acid sequences (e.g., DNA, cell-free DNA (cfDNA), or RNA) or RNA sequencing signature acquired from a sample of a patient for at least one gene selected from the group consisting of BNIP3, CES2, CHFR, CXCL5, GSTM2, ITGB4, MUC4, MUC5AC, ONECUT2, PRMT1, RUNX1, SFN, SLC22A3, SOX8, TACC3, KCNH2, PROKR2, IGF1R, and TMEM139; [BNIP37, CES28, CHFR9, CXCL510, GSTM211 associated with “methylation signatures [associated with] “methylation changes in SCNs across multiple tissues”12]
Graeber does not explicitly disclose as disclosed by Yassi:
determine, via a trained AI model13, using the received sequences, an indicator corresponding to an overall survival outcome of the patient from pancreatic cancer and/or associated treatments; [Wherein a computer14 system employs a convolutional neural network in receiving cfdna15 samples for use in training associated with prediction of multi-omics signatures correlation to a stratification of cancer types]and
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Graeber, including mechanism(s) [b] as taught by Yassi. One of ordinary skill would have been so motivated to employ said mechanism(s), employing Deep Learning techniques to analyze the multi-omics data and DNA methylation data in epigenetic cancer16 directed to treatment.
Graeber discloses:
output the determined indicator via a report or graphical user interface, wherein the output is subsequently employed to direct or adjust treatment of the pancreatic cancer for the patient. [Providing “5-year survival rates for SCN”17 for a plurality of cancer strata, wherein survival is calculated based on “Kaplan-Meier” and/or “Cox regression… [wherein] [s]urvival [is plotted] on a continuous scale… based on SCN score”18 wherein survival times including treatment times include months19 20 of treatment]
Regarding claim(s) 2, 18, Graeber-Yassi as a combination discloses: The system of claim 1, The method of claim 17, Graeber disclosing: wherein the trained AI model was trained using sequences for a plurality of genes, including at least 5 of BNIP3, CES2, CHFR, CXCL5, GSTM2, ITGB4, MUC4, MUC5AC, ONECUT2, PRMT1, RUNX1, SFN, SLC22A3, SOX8, TACC3, KCNH2, PROKR2, IGF1R, TMEM139, wherein the methylation signature or RNA sequencing signature is stratified for a patient population having a high risk group label and a lower risk group label for overall survival. [BNIP321, CES222, CHFR23, CXCL524, GSTM225]
Regarding claim(s) 3, 19, Graeber-Yassi as a combination discloses: The system of claim 1, Graeber disclosing: The method of claim 17, wherein the overall survival is determined at 6 months, 1 year, or 2 years from date of diagnosis of the pancreatic cancer. [Providing “5-year survival rates for SCN”26 for a plurality of cancer strata, wherein survival is calculated based on “Kaplan-Meier” or “Cox regression… [wherein] [s]urvival [is plotted] on a continuous scale… based on SCN score”27 wherein survival times including treatment times including months28 29 of treatment]
Regarding claim(s) 4, Graeber-Yassi as a combination discloses: The system of claim 1, Yassi disclosing [a]: wherein execution of the instructions further causes the processor to additionally predict at least one of a predicted duration of response, a predicted progression-free survival time, and predicted time to progression. [Wherein a “time-to-diagnosis” corresponds to a predicted time to progression to facilitate correlation of “DNA methylation patterns in blood associated with the duration between the onset of a disease and its detection or diagnosis [to optimize] survival outcomes”30]
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Graeber, including mechanism(s) [a] as taught by Yassi. One of ordinary skill would have been so motivated to employ said mechanism(s), employing Deep Learning techniques to analyze the multi-omics data and DNA methylation data in epigenetic cancer31 directed to treatment.
Regarding claim(s) 5, Graeber-Yassi as a combination discloses: The system of claim 4, Graeber disclosing: wherein the instructions to determine the additional prediction for the at least one of the predicted duration of response, the predicted progression-free survival time, and the predicted time to progression includes: instructions to determine, via a second trained AI model, the received methylated sequences or RNA sequences for at least one gene selected from the group consisting of BNIP3, CES2, CHFR, CXCL5, GSTM2, ITGB4, MUC4, ONECUT2, PRMT1, RUNX1, SFN, SLC22A3, SOX8, TACC3, IGF1R, KCNH2, MUC5AC, SLC22A2, SST, TMEM139, ISG15, PROKR2, SLC38A5, and SMARCA2. [BNIP332, CES233, CHFR34, CXCL535, GSTM236: employing “Kaplan-Meier of overall survival [survival time] for predicted SCN”37, wherein said predictions were made with a machine learning model employing a trained model38]
Regarding claim(s) 7, Graeber-Yassi as a combination discloses: The system of claim 4, Graeber disclosing: wherein the instructions to determine the additional prediction for the predicted progression-free survival time includes: instructions to determine, via a second trained AI model, the received methylated sequences or RNA sequences for at least one gene in a gene selected from the group consisting of BNIP3, CES2, IGF1R, ISG15, ITGB4, KCNH2, ONECUT2, PROKR2, RUNX1, SFN, SLC22A3, SLC38A5, SMARCA2, SOX8, SST, and TACC3. [BNIP339, CES240, CHFR41, CXCL542, GSTM243]
Graeber does not explicitly disclose as disclosed by Yassi [a]: progression-free survival time; [Predicting patient “clinical outcomes, including Progression-Free Interval [corresponding to progression-free survival time]”44]
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Graeber, including mechanism(s) [a] as taught by Yassi. One of ordinary skill would have been so motivated to employ said mechanism(s), employing Deep Learning techniques to analyze the multi-omics data and DNA methylation data in epigenetic cancer45 directed to treatment.
Regarding claim(s) 8, Graeber-Yassi as a combination discloses: The system of claim 4, Graeber disclosing: wherein the instructions to determine the additional prediction for the predicted time to progression includes: instructions to determine, via a second trained AI model, the received methylated sequences or RNA sequences for at least one gene in a gene selected from the group consisting of BNIP3, CES2, CHFR, CXCL5, GSTM2, IGF1R, ISG15, ITGB4, KCNH2, MUC4, ONECUT2, PROKR2, RUNX1, SFN, SLC22A3, SLC38A5, SMARCA2, SOX8, SST, and TACC3. [BNIP346, CES247, CHFR48, CXCL549, GSTM250]
Graeber does not explicitly disclose as disclosed by Yassi [a]: time to progression; [Wherein a “time-to-diagnosis” corresponds to a predicted time to progression to facilitate correlation of “DNA methylation patterns in blood associated with the duration between the onset of a disease and its detection or diagnosis [to optimize] survival outcomes”51]
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Graeber, including mechanism(s) [a] as taught by Yassi. One of ordinary skill would have been so motivated to employ said mechanism(s), employing Deep Learning techniques to analyze the multi-omics data and DNA methylation data in epigenetic cancer52 directed to treatment.
Regarding claim(s) 9, Graeber-Yassi as a combination discloses: The system of claim 5, Yassi disclosing [a]: wherein the predicted duration of response, the predicted progression-free survival time, and the predicted time to progression is determined at 6 months, 1 year, or 2 years from a date of diagnosis or a date of initial treatment. [Predicting patient “clinical outcomes, including Progression-Free Interval [corresponding to progression-free survival time]”53]
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Graeber, including mechanism(s) [b] as taught by Yassi. One of ordinary skill would have been so motivated to employ said mechanism(s), employing Deep Learning techniques to analyze the multi-omics data and DNA methylation data in epigenetic cancer54 directed to treatment.
Regarding claim(s) 10, Graeber-Yassi as a combination discloses: The system of claim 1, Yassi disclosing [a]: wherein the trained AI model is a convolutional neural network. [Employing a “convolutional neural network”55 associated with predicting cancer strata correlated with methylation data analyses]
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Graeber, including mechanism(s) [a] as taught by Yassi. One of ordinary skill would have been so motivated to employ said mechanism(s), employing Deep Learning techniques to analyze the multi-omics data and DNA methylation data in epigenetic cancer56.
Regarding claim(s) 11, Graeber-Yassi as a combination discloses: The system of claim 1, Yassi disclosing [a]: wherein the methylated sequences or RNA sequences were acquired via a sequencing operation. [Wherein “Enzyme-seq [EM-seq57]”58 is employed]
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Graeber, including mechanism(s) [a] as taught by Yassi. One of ordinary skill would have been so motivated to employ said mechanism(s), employing Deep Learning techniques to analyze the multi-omics data and DNA methylation data in epigenetic cancer59.
Regarding claim(s) 12, Graeber-Yassi as a combination discloses: The system of claim 11, Yassi disclosing [a]: wherein the sequencing operation comprises an Enzymatic Methylation Sequencing operation. [Wherein “Enzyme-seq [EM-seq60]”61 is employed]
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Graeber, including mechanism(s) [a] as taught by Yassi. One of ordinary skill would have been so motivated to employ said mechanism(s), employing Deep Learning techniques to analyze the multi-omics data and DNA methylation data in epigenetic cancer62.
Regarding claim(s) 13, Graeber-Yassi as a combination discloses: The system of claim 1, Yassi disclosing [a]: wherein the sample comprises blood plasma and/or tissues. [Multi-Omics “biomarker discovery in tissue and cFDNA samples [which comprise blood plasma and/or tissues]” 63]
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Graeber, including mechanism(s) [a] as taught by Yassi. One of ordinary skill would have been so motivated to employ said mechanism(s), employing Deep Learning techniques to analyze the multi-omics data and DNA methylation data in epigenetic cancer64.
Regarding claim(s) 14, Graeber-Yassi as a combination discloses: The system of claim 1, Graeber disclosing wherein pancreatic cancer comprises pancreatic ductal adenocarcinoma (PDA). [Wherein a cancer characterization presents itself as “pancreatic adenocarcinoma”65]
Regarding claim(s) 16, Graeber-Yassi as a combination discloses: The system of claim 1, Yassi disclosing [a]: wherein the trained AI model was trained using cfDNA gene methylation signature comprising the methylated sequences from isolated cfDNA from plasma of a patient. [Multi-Omics “biomarker discovery in tissue and cFDNA samples” 66]
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Graeber, including mechanism(s) [a] as taught by Yassi. One of ordinary skill would have been so motivated to employ said mechanism(s), employing Deep Learning techniques to analyze the multi-omics data and DNA methylation data in epigenetic cancer67.
Claim(s) 6, 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Graeber in view of Yassi and further in view of Kim68.
Regarding claim(s) 6, 15, Graeber-Yassi as a combination discloses: The system of claim 4, Graeber disclosing: wherein the instructions to determine the additional prediction for the at least one of the predicted duration of response, the predicted progression-free survival time, and the predicted time to progression includes:
instructions to determine, via a second trained AI model, the received methylated sequences or RNA sequences for at least one gene selected from the group consisting of ABCB1, ABCB4, ABCC1, ABCC10, ABCC3, ABCC5, ABCC6, ABCC8, ABCC9, ABCG2, ANGPTL4, ARID1A, ASXL2, ATM, BCL2L1, BICC1, BNIP3, BRCA1, CADM1, CD44, CES2, CHFR, CTNNB1, CTPS2, CXCL5, DCK, DKK3, DPYD, EGFR, EIF5A, ENO1, GLO1, GSDME, GSTM1, GSTM2, HMGA1, HNF1A, HSPA5, HSPB1, IGF1R, IGFBP3, ISG15, ITGA3, ITGB4, JAG1, KCNH2, LDHA, MAP2, MAP3K7, MCL1, METTL3, MLH1, MUC4, MUC5AC, NOTCH2, NRP1, NT5C1A, ONECUT2, PRMT1, PROKR2, PTGES2, PYCARD, RELL2, RRM1, RRM2, RRP9, RUNX1, SFN, SLC22A2, SLC22A3, SLC29A1, SLC2A1, SLC38A5, SMARCA2, SNRPF, SOX8, SST, TACC3, TET1, TFAM, TGM2, TMEM139, TPX2, TRIM31, TYMS, UBE2T, USP8, VASH2, YEATS4, and ZEB1. [BNIP369, CES270, CHFR71, CXCL572, GSTM273: employing “Kaplan-Meier of overall survival for predicted SCN”74, wherein said predictions were made with a machine learning model employing a trained model75]
Aforementioned combination does not explicitly disclose as disclosed by Kim [a]: predicted duration of response; [Evaluating “duration of treatment”76 in facilitating treatment strategies directed to optimal patient outcomes]
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Graeber, including mechanism(s) [a] as taught by Kim. One of ordinary skill would have been so motivated to employ said mechanism(s), employing Deep Learning techniques to analyze the multi-omics data and DNA methylation data in epigenetic cancer77.
Conclusion
The prior art made of record78 and NOT relied upon is considered pertinent to applicant's disclosure:
Rai79:
Survival analysis is a collection of statistical procedures employed on time-to-event data. The outcome variable of interest is time until an event occurs. Conventionally, it dealt with death as the event, but it can handle any event occurring in an individual like disease, relapse from remission, and recovery. Survival data describe the length of time from a time of origin to an endpoint of interest. By time, we mean years, months, weeks, or days from the beginning of being enrolled in the study. The major limitation of time-to-event data is the possibility of an event not occurring in all the subjects during a specific study period. In addition, some of the study subjects may leave the study prematurely. Such situations lead to what is called censored observations as complete information is not available for these subjects. Life table and Kaplan–Meier techniques are employed to obtain the descriptive measures of survival times. The main objectives of survival analysis include analysis of patterns of time-to-event data, evaluating reasons why data may be censored, comparing the survival curves, and assessing the relationship of explanatory variables to survival time. Survival analysis also offers different regression models that accommodate any number of covariates (categorical or continuous) and produces adjusted hazard ratios for individual factor.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL EZEWOKO whose telephone number is 571 272 7850. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fonya Long can be reached on 571 270 5096. The fax phone number for the organization where this application or proceeding is assigned is 571-273-7850.
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/MICHAEL I EZEWOKO/Primary Examiner, Art Unit 3682
1 Claim(s) 1-16
2 Claim(s) 17-19
3 Claim(s) 20
4 US 2022/0244263
5 See Form 892: Non-Patent Literature
6 Processing Pipeline (FIG 5A), ¶221
7 Page 39: Table 1
8 Page 126, Table 1
9 Page 62, Table 1
10 Page 150, Table 1
11 Page 115, Table 1
12 ¶¶179-180
13 Comprising a deep neural network such as a convoluted neural network (CNN): consistent with Applicant Specification, ¶57
14 Pages 2-11
15 Pages 2-11
16 Pages 2-11
17 ¶7
18 ¶250
19 ¶120
20 It is noted that a time-to-event is a derivative event and a continuous variable may be subdivided from years to months so long as the survival data is uncensored
21 Page 39: Table 1
22 Page 126, Table 1
23 Page 62, Table 1
24 Page 150, Table 1
25 Page 115, Table 1
26 ¶7
27 ¶250
28 ¶120
29 It is noted that a time-to-event is a derivative event and a continuous variable may be subdivided from years to months so long as the survival data is uncensored
30 Page 14
31 Pages 2-11
32 Page 39: Table 1
33 Page 126, Table 1
34 Page 62, Table 1
35 Page 150, Table 1
36 Page 115, Table 1
37 ¶39
38 ¶54
39 Page 39: Table 1
40 Page 126, Table 1
41 Page 62, Table 1
42 Page 150, Table 1
43 Page 115, Table 1
44 Page 13
45 Pages 2-11
46 Page 39: Table 1
47 Page 126, Table 1
48 Page 62, Table 1
49 Page 150, Table 1
50 Page 115, Table 1
51 Page 14
52 Pages 2-11
53 Page 13
54 Pages 2-11
55 Page 2
56 Pages 2-11
57 Consistent with Applicant Specification, ¶74
58 Page 1
59 Pages 2-11
60 Consistent with Applicant Specification, ¶74
61 Page 1
62 Pages 2-11
63 Pages 2-11
64 Pages 2-11
65 ¶185
66 Pages 2-11
67 Pages 2-11
68 See Form 892: Non-Patent Literature
69 Page 39: Table 1
70 Page 126, Table 1
71 Page 62, Table 1
72 Page 150, Table 1
73 Page 115, Table 1
74 ¶39
75 ¶54
76 Page 937-940
77 Page 937-940
78Please see Form 892 for complete listing
79 See Form 892: Non-Patent Literature
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