DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Claim 1, 39, 40 has been amended.
Claim 41 has been newly added.
Claim 21-41 are still pending for consideration.
Response to Arguments
Applicant’s arguments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 21- 29, and 32-41 are rejected under 35 U.S.C. 103 as being unpatentable over Levy‑Jurgenson1 et al. NPL “Spatial transcriptomics inferred from pathology whole-slide images links tumor heterogeneity to survival in breast and lung cancer” herein after Levy in view of Drake et al. (US 20210210205 A1).
Regarding claim 21, Levy teaches a method for stratifying a patient regarding a disease of interest (see Fig. 4, Abstract; “Applying our methods to breast and lung cancer slides, we show a significant statistical link between heterogeneity and survival”), implemented using a computer system comprising one or more processors, a memory, and one or more programs stored in the memory (see para [0050]; “we train deep learning models” Note: deep-learning system implemented on computers); the method comprising: receiving, by the compute system, a medical image from the patient (see page 7 “Discussion”; “This work offers a method for analyzing tumor heterogeneity from the rich spatial data available in H&E WSIs….. We applied our method to both breast and lung cancer pathology whole-slide images (H&E)”, and para 9, last para; “Where patients were associated with more than one whole-slide image”); inputting, by the compute system, the medical image from the patient into a machine learning model (see Abstract; “First, we train deep learning models to spatially resolve bulk mRNA and miRNA expression levels on pathology whole-slide images (WSIs)”); predicting, by the computer system, one or more activities of one or more molecular analytes of the patient, wherein the machine learning model is trained to predict activities of a plurality of molecular analytes using the medical image from the patient without using data of multiple molecular analytes of the patient during model inference (see page 2, 2nd para; “Briefly, we train deep neural networks to provide molecular cartographies of mRNA and miRNA expression from WSIs”, see also page 7, “Discussion” “Using deep learning we created high resolution maps of multiple mRNA and miRNA expression levels within a whole-slide image and combined these maps into a tensor molecular cartograph” Note: multiple mRNA and miRNA expression levels implies plurality of molecular analytes with image-only inference). Levy additionally disclose (see Abstract; “Applying our methods to breast and lung cancer slides, we show a significant statistical link between heterogeneity and survival”), but does not specifically disclose stratifying the patient regarding the disease of interest based on the predicted one or more activities of the one or more molecular analytes of the patient.
In the same field of endeavor Drake et al. teach and stratifying the patient regarding the disease of interest based on the predicted one or more activities of the one or more molecular analytes of the patient (see para [0016]; “methods of incorporating machine learning approaches with analyte data sets to develop and refine classifiers for use in stratifying individual populations and detecting disease such as cancer”). Accordingly, it would have been obvious to one of ordinary skill in the before the effective filing date of the claimed invention to modify a method Spatial transcriptomics inferred from pathology whole-slide images links tumor heterogeneity to survival in breast and lung cancer of Levy in view of the use a method for a clinical prediction based on molecular and biopsy data using a machine learning model of Drake et al. in order accurately predict patient's response to a cancer treatment (see para [0016]).
Regarding claim 22, the rejection of claim 21 is incorporated herein.
Levy in the combination further teach wherein the predicted one or more activities of the one or more molecular analytes comprise: gene expression data; copy number amplification (CNA) data; chromatin accessibility data; DNA methylation data; histone modification; RNA data; protein data; spatial biology data; whole-genome sequencing (WGS) data; somatic mutation data; germline mutation data; genetic sequence data; or any combinations thereof (see page 1, 2nd para; “identify molecular traits that are not known to be associated with cell/tissue morphology, such as mutations17,18, copy-number alterations18,19, gene expression18–20 and hormone receptor status”, see also page 2, 1st para “combined single cell RNA sequencing (scRNA-seq) with spatial transcriptomics to map and characterize the different cell populations in heterogeneous pancreatic tumors3”).
Regarding claim 23, the rejection of claim 21 is incorporated herein.
Drake et al. in the combination further teach wherein the predicted one or more activities of the one or more molecular analytes comprise: a gene expression value (see Drake et al. para [0110]; “FIGS. 36A-36G show cfDNA derived V-plots around TSS regions used to predict gene expression”) comprising an abundance of a transcript; a copy number amplification value; an amplification signature value; abundance of one or more histone modifications comprising a ChIP-seq value abundance of one or more mRNA sequences (see Drake et al. para [0249]; “the measured values relate to the abundance for these cfRNAs. Their transcripts are of a certain size, and each transcript is stored, and the number of cfRNAs found for each can be counted”); abundance of one or more proteins; the presence of one or more somatic mutations; the presence of one or more germline mutations; the presence or absence of one or more specific DNA methylation marks in one or more specific genomic regions; or any combinations thereof (see Drake et al. para [0144]; “Four highly informative classes of molecular biomarkers include: 1) genomic biomarkers based on the analysis of DNA profiles, sequences or modifications; 2) transcriptomic biomarkers based on the analysis of RNA expression profiles, sequences or modifications; 3) proteomic or protein biomarkers based on the analysis of protein profiles, sequences or modifications and 4) metabolomic biomarkers based on the analysis of metabolites abundance”).
Regarding claim 24, the rejection of claim 21 is incorporated herein.
Levy in the combination further teach wherein stratifying the patient comprises predicting a patient outcome of the patient based on the plurality of molecular analytes (see page 7, “Discussion” “Using deep learning we created high resolution maps of multiple mRNA and miRNA expression levels within a whole-slide image and combined these maps into a tensor molecular cartograph” Note: multiple mRNA and miRNA expression levels implies plurality of molecular analytes).
Regarding claim 25, the rejection of claim 24 is incorporated herein.
Levy in the combination further teach wherein the patient outcome comprises: a response to treatment, a mortality, a survival, a disease diagnosis, a disease progression, a disease prognosis, a disease risk, or any combinations thereof (see Abstract; “other spatial molecular properties and their link to clinical characteristics, including treatment susceptibility and survival”).
Regarding claim 26, the rejection of claim 21 is incorporated herein.
Levy in the combination further teach wherein the disease of interest comprises a cancer, an immune disease, or a fibrosis-associated disease (see Abstract; “Our models reach up to 0.95 AUC on held-out test sets from two cancer cohorts”).
Regarding claim 27, the rejection of claim 21 is incorporated herein.
Drake et al. in the combination further teach the one or more programs further comprising instructions for: identifying a therapy for the patient (see para [0330]; “After such a classification, treatment may be provided to the subject. Example treatment regimens can include surgical intervention, chemotherapy with a given drug or drug combination, and/or radiation therapy”).
Regarding claim 28, the rejection of claim 27 is incorporated herein.
Drake et al. in the combination further teach wherein the one or more molecular analytes are indicative of an activity level of a target of the identified therapy or a responsiveness to the identified therapy (see para [0378]; “a method for determining the efficacy of a drug designed to treat a disease class, comprising obtaining a sample from an individual having the disease class; subjecting the sample to the drug; assessing the drug-exposed sample for the level of gene expression for at least one gene”).
Regarding claim 29, the rejection of claim 28 is incorporated herein.
Drake et al. in the combination further teach wherein the target of the identified therapy comprises an mRNA or a protein (see para [0423]; “For RNA, assays detecting mRNA or short RNAs can be applied. For proteins, enzyme-linked immunosorbent assay (ELISA) can be used”).
Regarding claim 32, the rejection of claim 27 is incorporated herein.
Drake et al. in the combination further teach wherein the therapy comprises an antibody or bi-specific antibody therapy (see [0153]; “In cancer-patients serum-antibody profiles change, as well as autoantibodies against the cancerous tissue are generated”).
Regarding claim 33, the rejection of claim 29 is incorporated herein.
Drake et al. in the combination further teach wherein the therapy comprises an antibody or bi-specific antibody therapy (see [0153]; “In cancer-patients serum-antibody profiles change, as well as autoantibodies against the cancerous tissue are generated”).
Regarding claim 34, the rejection of claim 21 is incorporated herein.
Drake et al. in the combination further teach the one or more programs further comprising instructions for: identifying a therapeutic that inhibits a gene for the patient (see para [0383]; “the systems and methods described herein that relate to classifying a population based on treatment responsiveness refer to cancers that are treated with chemotherapeutic agents of the classes DNA damaging agents, DNA repair target therapies, inhibitors of DNA damage signaling, inhibitors of DNA damage induced cell cycle arrest and inhibition of processes indirectly leading to DNA damage, but not limited to these classes”).
Regarding claim 35, the rejection of claim 21 is incorporated herein.
Drake et al. in the combination further teach wherein the medical image comprises: one or more histopathology images; one or more magnetic resonance imaging (MRI) images; one or more computerized tomography (CT) scans; or any combination thereof (see para [0352]; “This clinical test may comprise an imaging test, a blood test, a computed tomography (CT) scan, a magnetic resonance imaging (MRI) scan, an ultrasound scan, a chest X-ray, a positron emission tomography (PET) scan, a PET-CT scan, or any combination thereof”).
Regarding claim 36, the rejection of claim 21 is incorporated herein.
Drake et al. in the combination further teach wherein the medical image is unlabeled (see para [0299]; “the computer processing method is an unsupervised machine learning method including, for example, clustering, network, principal component analysis, and matrix factorization” Note: unsupervised machine learning method implies unlabeled image).
Regarding claim 37, the rejection of claim 21 is incorporated herein.
Drake et al. in the combination further teach wherein the machine learning model comprises a first module that is trained to receive as input the medical image and provide as output an embedding (see para [0310]; “the machine learning model that is trained using training vectors obtained from training biological samples. The training samples can have the same measurements performed, and thus the same feature vector can be generated” Note; feature vector implies embedding).
Regarding claim 38, the rejection of claim 37 is incorporated herein.
Drake et al. in the combination further teach wherein the machine learning model comprises a second module that is trained to receive as input the embedding and provide as output the activities of the plurality of molecular analytes (see para [0025]; “inputting the feature vector into the machine learning model to obtain an output classification of whether the biological sample has the specified property, thereby distinguishing a population of individuals having the specified property”, see also para [0437]; “Multiple models can be used, e.g., where models can be used sequentially (e.g., output of one model that into input of another model) or used in parallel (e.g., using voting to determine final classification)”) and wherein the second module comprises a plurality of heads (see para [0298]; “a machine learning technique can include.. multilayer perceptron (MLP)”).
Regarding claim 39, the scope of claim 39 is fully encompassed by the scope of claim 21, accordingly, the rejection of claim 21 is fully applicable here (see also Drake et al. [0050]: “a system comprising one or more computer processors and computer memory coupled thereto. The computer memory comprises machine-executable code that, upon execution by the one or more computer processors, implements any of the methods above or elsewhere herein”).
Regarding claim 40, the scope of claim 40 is fully encompassed by the scope of claim 21, accordingly, the rejection of claim 21 is fully applicable here (see also Drake et al. [0617]; “The software code may be stored as a series of instructions or commands on a computer readable medium for storage and/or transmission. A suitable non-transitory computer readable medium can include random access memory (RAM)”).
Regarding claim 41, the rejection of claim 21 is incorporated herein.
Levy in the combination further teach wherein the machine learning model is trained to predict activities of the plurality of molecular analytes without using any molecular analyte data of the patient during model inference (see page 2, 2nd para; “Briefly, we train deep neural networks to provide molecular cartographies of mRNA and miRNA expression from WSIs”, see also page 7, “Discussion” “Using deep learning we created high resolution maps of multiple mRNA and miRNA expression levels within a whole-slide image and combined these maps into a tensor molecular cartograph” Note: multiple mRNA and miRNA expression levels implies plurality of molecular analytes with image-only inference) .
Claims 30-31 are rejected under 35 U.S.C. 103 as being unpatentable over Levy in view of Drake et al. as applied in claim 21 above, and further in view of Hegde et al. (US 20190369098 A1).
Regarding claim 30, the rejection of claim 27 is incorporated herein.
The combination of Levy and Drake et al. as a whole does not specifically teach wherein the therapy comprises an antibody-drug conjugate (ADC) therapy.
In the same field of endeavor, Hegde et al. teach wherein the therapy comprises an antibody-drug conjugate (ADC) therapy (see para [0512]; “an immunoconjugate is an antibody-drug conjugate (ADC) in which an antibody is conjugated to one or more drugs”). Accordingly, it would have been obvious to one of ordinary skill in the before the effective filing date of the claimed invention to modify a method Spatial transcriptomics inferred from pathology whole-slide images links tumor heterogeneity to survival in breast and lung cancer of Levy in view of the use a method for a clinical prediction based on molecular and biopsy data using a machine learning model of Drake et al. and diagnostic methods, therapeutic methods, and compositions for the treatment of cancer of Hegde et al. in order to increase therapeutic efficacy of the conjugate in killing the cancer cell to which it binds (see para [0512]).
Regarding claim 31, the rejection of claim 29 is incorporated herein.
Hegde et al. in the combination further teach wherein the therapy comprises an ADC therapy (see Hegde et al. para [0512]; “an immunoconjugate is an antibody-drug conjugate (ADC) in which an antibody is conjugated to one or more drugs”). Accordingly, it would have been obvious to one of ordinary skill in the before the effective filing date of the claimed invention to modify a method Spatial transcriptomics inferred from pathology whole-slide images links tumor heterogeneity to survival in breast and lung cancer of Levy in view of the use a method for a clinical prediction based on molecular and biopsy data using a machine learning model of Drake et al. and diagnostic methods, therapeutic methods, and compositions for the treatment of cancer of Hegde et al. in order to increase therapeutic efficacy of the conjugate in killing the cancer cell to which it binds (see para [0512]).
Conclusion
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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/WINTA GEBRESLASSIE/Examiner, Art Unit 2677
/ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677