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 .
Status of the Claims
Claims 21-40 are currently pending and under exam herein.
Claims 21-40 are rejected.
Claims 1-20 are cancelled.
Priority
The instant application is a continuation of application 17/707,623 filed on 3/09/2022 which is a continuation of 17/200,492 filed on 3/12/2021 which claims priority from provisional applications 63/108,262 filed on 10/30/2020 and 62/988,700 filed on 3/12/2020. Thus, the earliest effective filing date is 3/12/2020.
Drawings
The Drawings filed on 12/15/2022 and 02/21/2023 were considered.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 12/15/2022, 05/15/2023, 09/25/2023, 04/23/2024, 07/29/2024, and 03/30/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements have been considered by the examiner.
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 21-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion).
Subject matter eligibility evaluation in accordance with MPEP 2106:
Eligibility Step 1: Claims 21-40 are directed to a method, system, and non-transitory computer readable medium for deconvolution of expression data.
[Step 1: YES]
Eligibility Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if
so, then it is determined in Prong Two whether the recited judicial exception is integrated into a
practical application of that exception.
Eligibility Step 2A Prong One: In determining whether a claim is directed to a judicial exception,
examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a
law of nature, natural phenomenon, or abstract idea is set forth or described in the claim.
Independent claim 1 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
and determining a first cell composition percentage for the first cell type using the first RNA expression data, the first cell composition percentage indicating an estimated percentage of cells of the first cell type in the biological sample, wherein determining the first cell composition percentage for the first cell type comprises: (mathematical concept, in the broadest reasonable interpretation one can simply add up total expression in order to get a crude approximation)
providing only the first RNA expression data as input to a first non-linear regression model to obtain a corresponding output representing the first cell composition percentage for the first cell type, wherein the first non-linear regression model was trained using gradient boosting, (mathematical concept, gradient boosting is a mathematical algorithm)
Dependent claim 22 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
and the first non-linear regression model comprises: a first sub-model configured to generate, using the first RNA expression data as input, a first value for the estimated percentage of RNA from the first cell type; (mathematical concept)
and a second sub-model configured to generate, using the second RNA expression data and the first value for the estimated percentage of RNA from the first cell type as input, a second value for the estimated percentage of RNA from the first cell type; (mathematical concept)
Dependent claim 23 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
and wherein determining a second cell composition percentage for the second cell type comprises: processing the second RNA expression data with a second non-linear regression model to determine the second cell composition percentage for the second cell type (mathematical concept)
Dependent claim 25 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the RNA expression data includes RNA expression data associated with a plurality of gene sets associated with a respective plurality of cell types, the plurality of gene sets including the first set of genes and the plurality of cell types including the first cell type (mathematical concept, this just limits what the math is done on)
wherein the method further comprises determining a plurality of cell composition percentages for the plurality of cell types using the RNA expression data associated with the plurality of gene sets, the plurality of cell composition percentages including the first cell composition percentage (mathematical concept)
wherein determining the plurality of cell composition percentages comprises: for each cell type of the plurality of cell types, determining a respective cell composition percentage for the cell type at least in part by processing RNA expression data associated with a set of genes associated with the cell type using a respective non-linear regression model to determine the cell composition percentage for the cell type (mathematical concept)
Dependent claim 26 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the first non-linear regression model was trained at least in part by generating training data comprising simulated RNA expression data, (mathematical concept)
generating simulated microenvironment cell RNA expression data using the microenvironment cell RNA expression data; (mathematical concept)
generating simulated malignant cell RNA expression data using the malignant cell RNA expression data; (mathematical concept)
and combining the simulated microenvironment cell RNA expression data and the simulated malignant cell RNA expression data to produce at least a part of the simulated RNA expression data. (mathematical concept)
Dependent claim 27 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
determining a malignancy expression profile using an RNA expression profile for the first cell type and the first cell composition percentage for the first cell type. (mathematical concept, mental process)
Dependent claim 28 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the first RNA expression data consists of expression data for at least 10 genes selected from the group of genes listed in Table 2. (mathematical concept)
Dependent claim 29 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the first RNA expression data consists of expression data for at least 25 genes selected from the group of genes listed in Table 2. (mathematical concept)
Dependent claim 30 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the first RNA expression data consists of expression data for at least 50 genes selected from the group of genes listed in Table 2. (mathematical concept)
Dependent claim 31 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the first RNA expression data consists of expression data for at least 100 genes selected from the group of genes listed in Table 2. (mathematical concept)
Dependent claim 32 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the first non-linear regression model has been trained by: obtaining training data comprising simulated RNA expression data, the simulated RNA expression data including second RNA expression data for the first set of genes associated with the first cell type; (mathematical concept)
training the first non-linear regression model to estimate a percentage of RNA from the first cell type, the training comprising: (mathematical concept)
generating, using the first non-linear regression model and the second RNA expression data, an estimated percentage of RNA from the first cell type; (mathematical concept)
and updating parameters of the first non-linear regression model using the estimated percentage of RNA from the first cell type. (mathematical concept)
Dependent claim 33 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the first non-linear regression model comprises a first ensemble of prediction models trained using gradient boosting. (mathematical concept)
Independent claim 35 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
and determining a first cell composition percentage for the first cell type using the first RNA expression data, the first cell composition percentage indicating an estimated percentage of cells of the first cell type in the biological sample, wherein determining the first cell composition percentage for the first cell type comprises: (mathematical concept)
providing only the first RNA expression data as input to a first non-linear regression model to obtain a corresponding output representing the first cell composition percentage for the first cell type, wherein the first non-linear regression model was trained using gradient boosting, (mathematical concept)
Dependent claim 36 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
and the first non-linear regression model comprises: a first sub-model configured to generate, using the first RNA expression data as input, a first value for the estimated percentage of RNA from the first cell type; (mathematical concept)
and a second sub-model configured to generate, using the second RNA expression data and the first value for the estimated percentage of RNA from the first cell type as input, a second value for the estimated percentage of RNA from the first cell type (mathematical concept)
Dependent claim 37 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the method further comprises determining a plurality of cell composition percentages for the plurality of cell types using the RNA expression data associated with the plurality of gene sets, the plurality of cell composition percentages including the first cell composition percentage (mathematical concept)
wherein determining the plurality of cell composition percentages comprises: for each cell type of the plurality of cell types, determining a respective cell composition percentage for the cell type at least in part by processing RNA expression data associated with a set of genes associated with the cell type using a respective non-linear regression model to determine the cell composition percentage for the cell type (mathematical concept)
Independent claim 38 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
and determining a first cell composition percentage for the first cell type using the first RNA expression data, the first cell composition percentage indicating an estimated percentage of cells of the first cell type in the biological sample, wherein determining the first cell composition percentage for the first cell type comprises: (mathematical concept)
providing only the first RNA expression data as input to a first non-linear regression model to obtain a corresponding output representing the first cell composition percentage for the first cell type, wherein the first non-linear regression model was trained using gradient boosting (mathematical concept)
Dependent claim 39 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
and the first non-linear regression model comprises: a first sub-model configured to generate, using the first RNA expression data as input, a first value for the estimated percentage of RNA from the first cell type; (mathematical concept)
and a second sub-model configured to generate, using the second RNA expression data and the first value for the estimated percentage of RNA from the first cell type as input, a second value for the estimated percentage of RNA from the first cell type (mathematical concept)
Independent claim 40 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the method further comprises determining a plurality of cell composition percentages for the plurality of cell types using the RNA expression data associated with the plurality of gene sets, the plurality of cell composition percentages including the first cell composition percentage (mathematical concept)
wherein determining the plurality of cell composition percentages comprises: for each cell type of the plurality of cell types, determining a respective cell composition percentage for the cell type at least in part by processing RNA expression data associated with a set of genes associated with the cell type using a respective non-linear regression model to determine the cell composition percentage for the cell type (mathematical concept)
It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a ‘‘process of organizing information through mathematical correlations’’ are directed to an abstract idea); and Bancorp Servs., LLC v. Sun Life Assurance Co. of Can. (U.S.), 687 F.3d 1266, 1280, 103 USPQ2d 1425, 1434 (Fed. Cir. 2012) (identifying the concept of ‘‘managing a stable value protected life insurance policy by performing calculations and manipulating the results’’ as an abstract idea) for more information see MPEP 2106.04(a)(2)
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pencil and paper, and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Therefore, claims 21-40 recite an abstract idea as the dependent claims will inherit the abstract ideas from the independent claims.
[Step 2A Prong One: YES]
Eligibility Step 2A Prong Two: In determining whether a claim is directed to a judicial exception, further
examination is performed that analyzes if the claim recites additional elements that when examined as a
whole integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that
integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception
in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements
are analyzed to determine if the abstract idea is integrated into a practical application (MPEP
2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract
idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A Prong One are not integrated into a practical application because of the reasons noted below.
The additional element in independent claim 21 includes:
A method, comprising: using at least one computer hardware processor to perform:
obtaining RNA expression data for a biological sample, wherein the RNA expression data includes first RNA expression data associated with a first set of genes associated with a first cell type, wherein the first RNA expression data consists of expression data for at least some genes selected from the group of genes for the first cell type listed in Table 2;
The additional element in dependent claim 22 includes:
the RNA expression data includes second RNA expression data associated with the first set of genes associated with the first cell type
The additional element in dependent claim 23 includes:
wherein the RNA expression data includes second RNA expression data associated with a second set of genes associated with a second cell type, wherein the second RNA expression data includes expression data for at least some genes selected from the group of genes for the second cell type listed in Table 2;
The additional element in dependent claim 24 includes:
wherein the second cell type is selected from the group consisting of B cells, CD4+ T cells, CD8+ T cells, endothelial cells, fibroblasts, lymphocytes, macrophages, monocytes, NK cells, neutrophils, and T cells.
The additional element in dependent claim 26 includes:
wherein generating the training data comprises: obtaining a set of RNA expression data from one or more biological samples, the set of RNA expression data comprising microenvironment cell RNA expression data and malignant cell RNA expression data;
The additional element in dependent claim 34 includes:
wherein the first cell type is selected from the group consisting of B cells, CD4+ T cells, CD8+ T cells, endothelial cells, fibroblasts, lymphocytes, macrophages, monocytes, NK cells, neutrophils, and T cells.
The additional element in independent claim 35 includes:
A system, comprising: at least one hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform:
obtaining RNA expression data for a biological sample, wherein the RNA expression data includes first RNA expression data associated with a first set of genes associated with a first cell type, wherein the first RNA expression data consists of expression data for at least some genes selected from the group of genes for the first cell type listed in Table 2
The additional element in dependent claim 36 includes:
wherein: the RNA expression data includes second RNA expression data associated with the first set of genes associated with the first cell type;
The additional element in dependent claim 37 includes:
wherein the RNA expression data includes RNA expression data associated with a plurality of gene sets associated with a respective plurality of cell types, the plurality of gene sets including the first set of genes and the plurality of cell types including the first cell type;
The additional element in independent claim 38 includes:
At least one non-transitory computer-readable storage medium storing processor- executable instructions that, when executed by at least one hardware processor, cause the at least one hardware processor to perform:
obtaining RNA expression data for a biological sample, wherein the RNA expression data includes first RNA expression data associated with a first set of genes associated with a first cell type, wherein the first RNA expression data consists of expression data for at least some genes selected from the group of genes for the first cell type listed in Table 2
The additional element in dependent claim 39 includes:
wherein: the RNA expression data includes second RNA expression data associated with the first set of genes associated with the first cell type
The additional element in dependent claim 40 includes:
wherein the RNA expression data includes RNA expression data associated with a plurality of gene sets associated with a respective plurality of cell types, the plurality of gene sets including the first set of genes and the plurality of cell types including the first cell type
The additional elements of obtaining RNA expression data for a biological sample, wherein the RNA expression data includes first RNA expression data associated with a first set of genes associated with a first cell type, wherein the first RNA expression data consists of expression data for at least some genes selected from the group of genes for the first cell type listed in Table 2; (Claim 21), the RNA expression data includes second RNA expression data associated with the first set of genes associated with the first cell type (Claim 22), wherein the RNA expression data includes second RNA expression data associated with a second set of genes associated with a second cell type, wherein the second RNA expression data includes expression data for at least some genes selected from the group of genes for the second cell type listed in Table 2; (Claim 23), wherein the second cell type is selected from the group consisting of B cells, CD4+ T cells, CD8+ T cells, endothelial cells, fibroblasts, lymphocytes, macrophages, monocytes, NK cells, neutrophils, and T cells. (Claim 24), wherein generating the training data comprises: obtaining a set of RNA expression data from one or more biological samples, the set of RNA expression data comprising microenvironment cell RNA expression data and malignant cell RNA expression data; (Claim 26), wherein the first cell type is selected from the group consisting of B cells, CD4+ T cells, CD8+ T cells, endothelial cells, fibroblasts, lymphocytes, macrophages, monocytes, NK cells, neutrophils, and T cells. (Claim 34), obtaining RNA expression data for a biological sample, wherein the RNA expression data includes first RNA expression data associated with a first set of genes associated with a first cell type, wherein the first RNA expression data consists of expression data for at least some genes selected from the group of genes for the first cell type listed in Table 2 (Claim 35), wherein: the RNA expression data includes second RNA expression data associated with the first set of genes associated with the first cell type; (Claim 36), wherein the RNA expression data includes RNA expression data associated with a plurality of gene sets associated with a respective plurality of cell types, the plurality of gene sets including the first set of genes and the plurality of cell types including the first cell type; (Claim 37), obtaining RNA expression data for a biological sample, wherein the RNA expression data includes first RNA expression data associated with a first set of genes associated with a first cell type, wherein the first RNA expression data consists of expression data for at least some genes selected from the group of genes for the first cell type listed in Table 2 (Claim 38), wherein: the RNA expression data includes second RNA expression data associated with the first set of genes associated with the first cell type (Claim 39), wherein the RNA expression data includes RNA expression data associated with a plurality of gene sets associated with a respective plurality of cell types, the plurality of gene sets including the first set of genes and the plurality of cell types including the first cell type (Claim 40) are insignificant extra-solution activity that are part of the data gathering process used in the recited judicial exceptions (see MPEP 2106.05(g)).
The additional elements of a method, comprising: using at least one computer hardware processor to perform: (Claim 21), a system, comprising: at least one hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform: (Claim 35), at least one non-transitory computer-readable storage medium storing processor- executable instructions that, when executed by at least one hardware processor, cause the at least one hardware processor to perform: (Claim 38) fail to integrate a judicial exception into a practical application merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
Thus, the additionally recited elements merely invoke a computer as a tool, and/or amount to insignificant extra-solution data gathering activity, and as such, when all limitations in claims 21-40 have been considered as a whole, the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application, and therefore claims 21-40 are directed to an abstract idea (MPEP 2106.04(d)).
[Step 2A Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
The additional elements recited in claims 21-40 are identified above, and carried over from Step 2A: Prong Two along with their conclusions for analysis at Step 2B. Any additional element or combination of elements that was considered to be insignificant extra-solution activity at Step 2A: Prong Two was re-evaluated at Step 2B, because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and all additional elements and combination of elements were evaluated to determine whether any additional elements or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP 2106.05(d).
The additional elements of obtaining RNA expression data for a biological sample, wherein the RNA expression data includes first RNA expression data associated with a first set of genes associated with a first cell type, wherein the first RNA expression data consists of expression data for at least some genes selected from the group of genes for the first cell type listed in Table 2; (Claim 21), the RNA expression data includes second RNA expression data associated with the first set of genes associated with the first cell type (Claim 22), wherein the RNA expression data includes second RNA expression data associated with a second set of genes associated with a second cell type, wherein the second RNA expression data includes expression data for at least some genes selected from the group of genes for the second cell type listed in Table 2; (Claim 23), wherein the second cell type is selected from the group consisting of B cells, CD4+ T cells, CD8+ T cells, endothelial cells, fibroblasts, lymphocytes, macrophages, monocytes, NK cells, neutrophils, and T cells. (Claim 24), wherein generating the training data comprises: obtaining a set of RNA expression data from one or more biological samples, the set of RNA expression data comprising microenvironment cell RNA expression data and malignant cell RNA expression data; (Claim 26), wherein the first cell type is selected from the group consisting of B cells, CD4+ T cells, CD8+ T cells, endothelial cells, fibroblasts, lymphocytes, macrophages, monocytes, NK cells, neutrophils, and T cells. (Claim 34), obtaining RNA expression data for a biological sample, wherein the RNA expression data includes first RNA expression data associated with a first set of genes associated with a first cell type, wherein the first RNA expression data consists of expression data for at least some genes selected from the group of genes for the first cell type listed in Table 2 (Claim 35), wherein: the RNA expression data includes second RNA expression data associated with the first set of genes associated with the first cell type; (Claim 36), wherein the RNA expression data includes RNA expression data associated with a plurality of gene sets associated with a respective plurality of cell types, the plurality of gene sets including the first set of genes and the plurality of cell types including the first cell type; (Claim 37), obtaining RNA expression data for a biological sample, wherein the RNA expression data includes first RNA expression data associated with a first set of genes associated with a first cell type, wherein the first RNA expression data consists of expression data for at least some genes selected from the group of genes for the first cell type listed in Table 2 (Claim 38), wherein: the RNA expression data includes second RNA expression data associated with the first set of genes associated with the first cell type (Claim 39), wherein the RNA expression data includes RNA expression data associated with a plurality of gene sets associated with a respective plurality of cell types, the plurality of gene sets including the first set of genes and the plurality of cell types including the first cell type (Claim 40) are insignificant extra-solution activity that are conventional and part of the data gathering process used in the recited judicial exceptions (see MPEP 2106.05(g)). Evidence for conventionality is shown by Ziegenhain et al. (Ziegenhain et al. Comparative Analysis of Single-Cell RNA Sequencing Methods. Molecular Cell 2017, 65 (4), 631-643.e4.) which is a comparison of different single cell RNA sequencing data analysis methods. It shows how individual cell types can be identified and sequenced and discusses how different groups handle data gathered from this routine and conventional practice (pg. 631, col. 1, paragraph 1).
The additional elements of a method, comprising: using at least one computer hardware processor to perform: (Claim 21), a system, comprising: at least one hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform: (Claim 35), at least one non-transitory computer-readable storage medium storing processor- executable instructions that, when executed by at least one hardware processor, cause the at least one hardware processor to perform: (Claim 38) are conventional fail to integrate a judicial exception into a practical application merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
When taken alone, all additional elements in claims 21-40 do not amount to significantly more than the above-identified judicial exception(s). Even when evaluated as a combination, the additional elements fail to transform the exception(s) into a patent-eligible application of that exception. Thus, claims 21-40 are deemed to not contribute an inventive concept, i.e., amount to significantly more than the judicial exception(s) (MPEP 2106.05(II)).
[Step 2B: NO]
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.
Claims 21, 23-25, 28-31, 33-35, 37, 38, 40 are rejected under 35 U.S.C. 103 as being unpatentable over Monaco et al. (Monaco et al, RNA-Seq Signatures Normalized by MRNA Abundance Allow Absolute Deconvolution of Human Immune Cell Types. Cell Reports 2019, 26 (6), 1627-1640.e7.) in view of Chen et al. (Chen, T.; Guestrin, C. XGBoost: A Scalable Tree Boosting System; 2016, arxiv). The italicized text corresponds to the instant claim limitations.
With respect to the limitations of Claims 21, 23-25, 28-31, 34, 35, 37, 38, 40, Monaco et al. teaches a method and a system that uses the aid of a computer program for the characterization of 29 immune cells using RNA-sequencing. A person of ordinary skill in the art would use this method to gather a second set of data which is an obvious variation that requires no inventive step. (abstract, method/ system/ non-transitory computer readable media for obtaining RNA expression data for a biological sample, wherein the RNA expression data includes first RNA expression data associated with a first set of genes associated with a first cell type (Claim 21, Claim 35, Claim 38), wherein the RNA expression data includes second RNA expression data associated with a second set of genes associated with a second cell type (Claim 23), Monaco et al. also teaches identifying the following genes are associated with specific immune cells such as PAX5 with B cells, other genes identified with immune cells are ADAP2,ADGRE3,ADGRG3,AIF1,BANK1,BLK,C1QA,C1QC,CAMK4,CCR1,CD160,CD19,CD1D,CD22,CD300C,CD300E,CD300LB,CD302,CD33,CD5,CD79A,CLEC5A,CLEC7A,CMKLR1,CPNE5,CSF1R,CSF2RA,CXCR1,CXCR2,DERL3,FCGR1A,FCGR1B,FCGR2A,FCGR3B,FCRL1,FCRL2,FCRL5,FCRLA,FPR1,FPR2,FPR3,GLT1D1,HLADOB,IGHG1,IGHG3,IGLL5,ITGAX,KCNJ15,KIR2DL1,KIR2DL3,KIR2DL4,KIR3DL1,KIR3DL2,KLRC2,KLRC3,KLRF1,LAG3,LRRC25,MEFV,MRC1,MS4A1,MS4A4A,MSR1,MZB1,NCAM1,NCR1,NMUR1,P2RY13,PADI2,PADI4,PAX5,PGLYRP1,PHOSPHO1,PILRA,PLA2G7,POU2AF1,PTGDR,RASGRP4,S1PR5,SIGLEC1,SIGLEC5,SIGLEC7,SIGLEC9,SIRPB2,SIRPG,SPI1,STAP1,TBX21,TCF7,TLR2,TNFRSF10C,TNFRSF13B,TNFRSF17,TRAT1,TXNDC5,UBASH3A,VNN3,VPREB3,VSIG4,XCL2 (Supplemental Data, Table 3, wherein the first RNA expression data consists of expression data for at least some genes selected from the group of genes for the first cell type listed in Table 2; (Claim 21, Claim 35, Claim 38), wherein the second RNA expression data includes expression data for at least some genes selected from the group of genes for the second cell type listed in Table 2; and (Claim 23), wherein the second cell type is selected from the group consisting of B cells, CD4+ T cells, CD8+ T cells, endothelial cells, fibroblasts, lymphocytes, macrophages, monocytes, NK cells, neutrophils, and T cells (Claim 24 – it is an obvious variant to rerun with another set of data gathered), wherein the RNA expression data includes RNA expression data associated with a plurality of gene sets associated with a respective plurality of cell types, the plurality of gene sets including the first set of genes and the plurality of cell types including the first cell type (Claim 25, Claim 37, Claim 40) wherein the first RNA expression data consists of expression data for at least 10 genes selected from the group of genes listed in Table 2 (Claim 28), wherein the first RNA expression data consists of expression data for at least 25 genes selected from the group of genes listed in Table 2 (Claim 29), wherein the first RNA expression data consists of expression data for at least 50 genes selected from the group of genes listed in Table 2 (Claim 30), wherein the first RNA expression data consists of expression data for at least 100 genes selected from the group of genes listed in Table 2 (Claim 31), wherein the first cell type is selected from the group consisting of B cells, CD4+ T cells, CD8+ T cells, endothelial cells, fibroblasts, lymphocytes, macrophages, monocytes, NK cells, neutrophils, and T cells (claim 34) Monaco et al. also teaches absolute deconvolution on a suitable set of immune cell types using transcriptomics signatures normalized by mRNA abundance (abstract, determining a first cell composition percentage for the first cell type using the first RNA expression data, the first cell composition percentage indicating an estimated percentage of cells of the first cell type in the biological sample, wherein determining the first cell composition percentage for the first cell type comprises (Claim 21, Claim 35, Claim 38) Monaco et al. also teaches the use of a Non-linear least square regression as well as support vector regression models for determining the percentage of cell types from RNA expression data. It is an obvious variant to reuse the model on new data tow get information of a second prediction. (providing only the first RNA expression data as input to a first non-linear regression model to obtain a corresponding output representing the first cell composition percentage for the first cell type (Claim 21, Claim 35, Claim 38), wherein determining a second cell composition percentage for the second cell type comprises: processing the second RNA expression data with a second non-linear regression model to determine the second cell composition percentage for the second cell type (Claim 23) wherein the method further comprises determining a plurality of cell composition percentages for the plurality of cell types using the RNA expression data associated with the plurality of gene sets, the plurality of cell composition percentages including the first cell composition percentage, wherein determining the plurality of cell composition percentages comprises: for each cell type of the plurality of cell types, determining a respective cell composition percentage for the cell type at least in part by processing RNA expression data associated with a set of genes associated with the cell type using a respective non-linear regression model to determine the cell composition percentage for the cell type (Claim 25, Claim 37, Claim 40)
Monaco et al. does not explicitly teach
wherein the first non-linear regression model was trained using gradient boosting (Claim 21, Claim 35, Claim 38)
wherein the first non-linear regression model comprises a first ensemble of prediction models trained using gradient boosting (Claim 33)
With respect to the limitations of Claims 21, 33, 35, 38, Chen et al. teaches the use of gradient boosting algorithm using XGboost which uses regression and gradient boosting. (pg. 2, col. 1, paragraphs 8-10, wherein the first non-linear regression model was trained using gradient boosting (Claim 21, Claim 35, Claim 38), wherein the first non-linear regression model comprises a first ensemble of prediction models trained using gradient boosting (Claim 33)
A person of ordinary skill in the art would be motivated to combine with method of deconvolution of RNA sequencing data taught by Monaco et al. by substituting the non-linear least square regression with XGboost. Chen et al. even suggests applying XGBoost to other machine learning problems in order to improve performance using minimal computational resources (pg. 10, col. 1, paragraph 1) and that it will scale beyond billions of examples (abstract). There is a reasonable expectation of success because a person of ordinary skill in the art would understand how to switch the model without changing how the overall pipeline works so XGboost will continue to work combined as it does seprately. Therefore, XG boost would work as a substitution for another regression model. XG boost is just another regression model a person of ordinary skill in the art would be motivated to use as a substitute.
Claim 22, 36, 39 are rejected under 35 U.S.C. 103 as being unpatentable over Monaco et al. in view of Chen et al. as applied to claims 21, 23-25, 28-31, 33-35, 37, 38, 40 above in further view of Gama et al. (Gama, J., Brazdil, P. Cascade Generalization. Machine Learning 41, 315–343 (2000)) The italicized text corresponds to the instant claim limitations.
The limitations of claims 21, 23-25, 28-31, 33-35, 37, 38, 40 have been taught Monaco et al. in view of Chen et al. above.
Monaco et al. in view of Chen et al. does not explicitly teach
wherein: the RNA expression data includes second RNA expression data associated with the first set of genes associated with the first cell type; and the first non-linear regression model comprises: a first sub-model configured to generate, using the first RNA expression data as input, a first value for the estimated percentage of RNA from the first cell type; and a second sub-model configured to generate, using the second RNA expression data and the first value for the estimated percentage of RNA from the first cell type as input, a second value for the estimated percentage of RNA from the first cell type (Claim 22, Claim 36, Claim 39)
However, these limitations were known in the art at the time of the effective filing date of the invention, as taught by Gama et al.
With respect to the limitations of Claims 22, 36, 39, Gama et al. teaches cascade generalization which is a method that would processes the first RNA expression data to generate a baseline prediction (the first value for the estimated percentage of RNA from the first cell type) and uses the second RNA expression data in combination with the intermediate prediction from the first sub-model as a new, expanded feature input to generate a refined final estimate. Gama et al. teaches the method of cascade generalization which can easily be applied to RNA-seq data. This teaches the use of sequential model processing in order to improve prediction results. (abstract, wherein: the RNA expression data includes second RNA expression data associated with the first set of genes associated with the first cell type; and the first non-linear regression model comprises: a first sub-model configured to generate, using the first RNA expression data as input, a first value for the estimated percentage of RNA from the first cell type; and a second sub-model configured to generate, using the second RNA expression data and the first value for the estimated percentage of RNA from the first cell type as input, a second value for the estimated percentage of RNA from the first cell type (Claim 22, Claim 36, Claim 39)
A person of ordinary skill in the art would be motivated to combine with method of deconvolution of RNA sequencing data taught by Monaco et al. in view of Chen et al. with the method of cascade generalization taught by Gama et al. As Cascade generalization is a known method to improve regression models accessible to any person having ordinary skill in the art. Gama et al. even specifically researched how cascade generalization improved the performance of tree-based algorithms (pg. 1, paragraph 1) and XGboost is a well-known tree-based algorithm. This gives both a motivation to combine as well as a reasonable expectation of success. There is also reasonable expectation of success because cascade generalization is expected in designed to improve regression algorithms so there is a high probability of success.
Claim 26, 27, 32 are rejected under 35 U.S.C. 103 as being unpatentable over Monaco et al. in view of Chen et al. in further view of Gama et al. as applied to claims 21-25, 28-31, 33-40 above in further view of Finotello et al. (Finotello et al. Molecular and Pharmacological Modulators of the Tumor Immune Contexture Revealed by Deconvolution of RNA-Seq Data. Genome Medicine 2019, 11 (1). The italicized text corresponds to the instant claim limitations.
The limitations of claims 21-25, 28-31, 33-40 have been taught Monaco et al. in view of Chen et al. in further view of Gama et al. above.
Monaco et al. in view of Chen et al. in further view of Gama et al. does not explicitly teach
wherein the first non-linear regression model was trained at least in part by generating training data comprising simulated RNA expression data, wherein generating the training data comprises: obtaining a set of RNA expression data from one or more biological samples, the set of RNA expression data comprising microenvironment cell RNA expression data and malignant cell RNA expression data; generating simulated microenvironment cell RNA expression data using the microenvironment cell RNA expression data; generating simulated malignant cell RNA expression data using the malignant cell RNA expression data; and combining the simulated microenvironment cell RNA expression data and the simulated malignant cell RNA expression data to produce at least a part of the simulated RNA expression data. (Claim 26)
determining a malignancy expression profile using an RNA expression profile for the first cell type and the first cell composition percentage for the first cell type (Claim 27)
wherein the first non-linear regression model has been trained by: obtaining training data comprising simulated RNA expression data, the simulated RNA expression data including second RNA expression data for the first set of genes associated with the first cell type; training the first non-linear regression model to estimate a percentage of RNA from the first cell type, the training comprising: generating, using the first non-linear regression model and the second RNA expression data, an estimated percentage of RNA from the first cell type; and updating parameters of the first non-linear regression model using the estimated percentage of RNA from the first cell type (Clam 32)
With respect to the limitations of Claims 26, 27, 32, Finotello et al. teaches that they simulated RNA-seq data from breast tumors with different purity values and immune infiltrates by mixing pre-processed reads from immune cell types and from a tumor cell line (G41726.MCF7.5) of the RNA-seq compendium. We simulated 100 different immune cell mixtures by sampling the cell fractions from a uniform distribution in the [0–1] interval. This data was used in a regression model for deconvolution of RNA-seq data. The data is used to determine malignancy expression profile and how the immune cell types change with cancer progression, (pg. 3, col. 2, paragraph, 2,wherein the first non-linear regression model was trained at least in part by generating training data comprising simulated RNA expression data, wherein generating the training data comprises: obtaining a set of RNA expression data from one or more biological samples, the set of RNA expression data comprising microenvironment cell RNA expression data and malignant cell RNA expression data; generating simulated microenvironment cell RNA expression data using the microenvironment cell RNA expression data; generating simulated malignant cell RNA expression data using the malignant cell RNA expression data; and combining the simulated microenvironment cell RNA expression data and the simulated malignant cell RNA expression data to produce at least a part of the simulated RNA expression data. (Claim 26), determining a malignancy expression profile using an RNA expression profile for the first cell type and the first cell composition percentage for the first cell type (Claim 27), wherein the first non-linear regression model has been trained by: obtaining training data comprising simulated RNA expression data, the simulated RNA expression data including second RNA expression data for the first set of genes associated with the first cell type; training the first non-linear regression model to estimate a percentage of RNA from the first cell type, the training comprising: generating, using the first non-linear regression model and the second RNA expression data, an estimated percentage of RNA from the first cell type; and updating parameters of the first non-linear regression model using the estimated percentage of RNA from the first cell type (Clam 32, this is obvious when viewed with cascade generalization for improved training results)
A person of ordinary skill in the art would be motivated to combine with method of deconvolution of RNA sequencing data taught by Monaco et al. in view of Chen et al. in view of Gama et al. with the method of RNA-seq data simulation taught by Finotello et al. As Finotello et al. was also solving the same problem of deconvolution of expression data using simulated RNA-seq data. Furthermore, Finotello et al. demonstrated the value of using simulated RNA-seq data for the deconvolution of expression data (pg. 4, col. 2, paragraph 2) which would lend a person of ordinary skill in the art to try similar methods as well as giving a reasonable expectation of success. There is a reasonable expectation of success because each method works independently and the methods are not changing all Finotello et al. adds is the use of simulated RNA-seq data for RNA-seq deconvolution. Therefore, it is expected to function when joined as they function together.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 21, 22-27, 29-32, 35-40 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 22-33, 35, 37, 38, 40 of U.S. Patent No. US11587642B2. Although the claims at issue are not identical, they are not patentably distinct from each other because the instant application just has broader claims than U.S. Patent No. US11587642B2 which just require the subject was at previous risk of cancer and that the cell types consist of B cells, CD4+ T cells, CD8+ T cells, endothelial cells, fibroblasts, lymphocytes, macrophages, monocytes, NK cells, neutrophils, and T cells.
Instant application. The claim mapping is in the table below.
18/082,157 (Instant Application)
U.S. Patent No. US11587642B2
Claim 21
Claim 22
Claim 22
Claim 23
Claim 23
Claim 24
Claim 25
Claim 25
Claim 26
Claim 26
Claim 27
Claim 27
Claim 29
Claim 28
Claim 30
Claim 29
Claim 31
Claim 30
Claim 32
Claim 31
Claim 35
Claim 32
Claim 36
Claim 33
Claim 37
Claim 35
Claim 38
Claim 37
Claim 39
Claim 38
Claim 40
Claim 40
Conclusion
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/C.H.B./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
/YVONNE L EYLER/Director, TC 1600
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