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 .
Claim Status
Claims 33-56 are cancelled.
Claims 1-32 are currently pending and under exam herein.
Claims 1-32 are rejected.
Priority
Applicant’s claim for the benefit of prior-filed application, U.S. Provisional Application No. 63/216,091 filed on June 29, 2021, under 35 U.S.C. 119(e) is acknowledged. At this point in examination, the effective filing date of claims 1-32 is June 29, 2021.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on August 2, 2022, December 14, 2022, and January 31, 2023 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Drawings
The drawings are objected to under 37 CFR 1.83(a) because they fail to show the “biological component score module 122” and the “biological component scores 124” as described in paragraph 0026 of the specification. Any structural detail that is essential for a proper understanding of the disclosed invention should be shown in the drawing. MPEP § 608.02(d). Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance. Applicant is required to check all drawings compliance.
Specification
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code (e.g., paragraph 0042-0043, 0047-0048, and 0053). Applicant is required to check the specification and delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
The use of the term SMARTer®, Covaris®, mosquito® HV, and NovaSeq® (paragraph 0040) which are trade names or marks used in commerce, has been noted in this application. The term should be accompanied by the generic terminology; furthermore the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks. Applicant is required to place the proper symbol by all trade names or marks.
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 1-32 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1:
The first part of the eligibility analysis evaluates whether a claim falls withing any statutory
category (See MPEP 2106.03). Claims 1-16 recite a tumor classifying method executed by a computer and claims 17-32 recite a computer system that executes a tumor classifying method. The claims are directed to a process and a computer system, which is a machine, and fall within one of the statutory categories of invention (Step 1: YES).
Step 2A, prong 1:
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES)
are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of
nature, or natural phenomenon (Step 2A, prong 1). In the instant application, the claims recite the
following limitations that equate to an abstract idea:
Claims 1 (and its’ dependent claims 2-16) and 17 (and its’ dependent claims 18-32) recite (A) using the map to extract, from the plurality of training tumors, a plurality of biological components, thereby generating, for each of the plurality of training tumors and each of the plurality of biological components, a corresponding biological component score representing the training tumor as a compilation of non-gene terms describing a plurality of biological programs.
Claims 2 (and its’ dependent claims 3-6) and 18 (and its’ dependent claims 19-22) recite (C) before A, generating the map.
Claims 3 (and its’ dependent claims 4-6) and 19 (and its’ dependent claims 20-22) recite wherein generating the map comprises mapping each of the plurality of training tumors to at least one of the plurality of mammalian developmental trajectories in the single-cell atlas.
Claims 4 (and its’ dependent claims 5-6) and 20 (and its’ dependent claims 21-22) recite (C) (3) generating the map based on the single-cell atlas and the tumor atlas.
Claims 12 and 28 recite wherein the plurality of known biological programs comprises a plurality of known developmental programs.
Claims 15 and 31 recite wherein using the map to extract, from the plurality of training tumors, the plurality of biological components comprises using the map to deconvolute the plurality of training tumors into the plurality of biological components.
The limitation of using the map to extract a plurality of biological components from the training tumors in, claims 1 and 17, doesn’t put any limitations on how the map is used to obtain the biological components. The limitation encompasses actions that can be practically performed in the human mind using observation, evaluation, judgment, and opinion as well as performing mathematical calculation that may be performed by a person with aid of a pen and paper; such as those in paragraph 0066 of the specification. The limitations of claims 15 and 31 also encompass the same mental processes and mathematical calculations. The limitations of claims 12 and 28 only describe the end result after performing the abstract ideas to obtain biological components.
The limitation of generating the map, in claims 2 and 18, doesn’t impose any constraints on how to create the map and, likewise, encompasses mental processes and mathematical calculations (See paragraphs 0051-0056 of the specification) that may be performed by a person with a pen and paper. The limitations of claims 3 and 19 impose the requirement of mapping each training tumor to at least one mammalian development trajectory, but don’t limit how the mapping is done and only describe what the abstract ideas need to accomplish when generating the map. The limitations of claim 4 and 20 only describe the data used by the abstract ideas to generate the map.
Therefore, these limitations fall under the “Mathematical concepts” and “Mental processes” groupings of abstract ideas (Step 2A, prong 1: YES).
Step 2A, prong 2:
Claims found to recite a judicial exception under Step 2A, prong 1 are then further analyzed to
determine if the claims as a whole integrate the recited judicial exception into a practical application
(Step 2A, prong 2). The claims recite the following additional elements:
Claims 1 (and its’ dependent claims 2-16) and 17 (and its’ dependent claims 18-32) recite at least one computer processor executing computer program instructions stored on at least one non-transitory computer-readable medium, for using a map which maps from gene expression data for a plurality of training tumors in a tumor atlas to gene expression data representing single cells derived from mammal samples in developmental stages in a single-cell atlas, a system comprising at least one non-transitory computer-readable medium having computer program instructions stored thereon, the computer program instructions being executable by at least one computer processor to perform a method for using a map which maps from gene expression data for a plurality of training tumors in a tumor atlas to gene expression data representing single cells derived from mammal samples in developmental stages in a single-cell atlas, and (B) constructing, based on the map, a machine learning perceptron classifier that outputs a tumor type of an input tumor based on gene expression data for the input tumor, wherein the input tumor is not among the plurality of training tumors.
Claims 4 (and its’ dependent claims 5-6) and 20 (and its’ dependent claims 21-22) recite (C) (1) receiving the single-cell atlas and (C) (2) receiving the tumor atlas.
Claims 5 and 21 recite wherein the gene expression data for the plurality of training tumors comprises, for each of the plurality of training tumors: (1) gene sequencing data for the training tumor; and (2) a label indicating a type of cancer of the training tumor.
Claims 6 and 22 recite wherein the gene expression data representing single cells derived from mammal samples in developmental stages in the single-cell atlas comprises gene expression data representing organogenesis of a plurality of mammalian developmental trajectories.
Claims 7 and 23 recite wherein the tumor atlas comprises a version of The Cancer Genome Atlas (TCGA).
Claims 8 and 24 recite wherein the single-cell atlas comprises a developmental atlas.
Claims 9 and 25 recite wherein the single-cell atlas comprises a single-cell organogenesis atlas.
Claims 10 and 26 recite wherein the single-cell atlas comprises data representing normal development for each of a plurality of mammalian developmental trajectories.
Claims 11 and 27 recite wherein the single-cell atlas comprises a version of the Mouse Organogenesis Cell Atlas (MOCA).
Claims 13 and 29 recite (C) applying the machine learning perceptron classifier to the gene expression data for the input tumor to generate the tumor type of the input tumor.
Claims 14 and 30 recite wherein the input tumor comprises a sample of a cancer of unknown primary.
Claims 16 and 32 recite wherein the developmental stages in the single-cell atlas comprise prenatal developmental stages in the single-cell atlas.
The additional elements of a processor executing program instructions stored on a non-transitory computer-readable medium, in claims 1 and 17, require performing all the recited abstract ideas in a computer. The system is recited at a high level of generality and is used as a tool to perform generic computer functions of running a program and receiving and outputting data such that it amounts to no more than mere instructions to apply the exception in a generic computer (See MPEP 21069.05f). The additional element of constructing, based on the map, a machine learning perceptron classifier also amounts to instructions to apply the judicial exception because the limitation doesn’t impose any limitations on how the perceptron classifier is constructed and merely recited the idea of a solution or outcome without details of how it is accomplished.
The additional elements in claims 4 and 20 of receiving atlases and the additional element in claims 13 and 29 of generating a prediction of tumor type are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity because they don’t impose meaningful limits on the claims and all uses of the recited judicial exception require such data gathering and output (See MPEP 2106.05g). The limitations in claims 5 and 21 and 6 and 22 only describe the data being gathered in the data gathering step of claims 4 and 20; respectively. The limitations of claims 14 and 30 describe the type of data the perceptron classifier is applied to in claims 13 and 29; respectively. Likewise, the limitations of claims 7-11, 16 and 23-27, 32 describe the data the computer program can use in claims 1 and 17; respectively.
Therefore, the judicial exception is not integrated into a practical application because the claims do not recite any additional elements that reflects an improvement to technology or applies/uses the recited judicial exception in some other meaningful way and the claims are directed to the judicial exception (Step 2A, prong 2: NO).
Step 2B:
Claims found to be directed to a judicial exception are then further evaluated to determine if
the claims recite an inventive concept that provides significantly more than the judicial exception itself
(Step 2B). The additional elements of a processor executing program instructions stored on a non-transitory computer-readable medium equate to mere instructions to apply the recited
judicial exception in a generic computing environment. Claims that amount to nothing more than
instructions to apply the judicial exception using a generic computer do not render an abstract idea
eligible. Alice Corp., 576 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. The additional elements of receiving atlases and applying the perceptron classifier to generate a tumor type do not put limit on how the atlas data is received or how the tumor type is generated and the plain meaning encompasses receiving or transmitting data over a network and performing repetitive calculations, which courts have found to be computer functions that are well-understood, routine, and conventional (WURC). Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network; Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.").
Lastly, the additional element of constructing a machine learning perceptron classifier that outputs a tumor type is WURC because several publications teach applying machine learning techniques like artificial neural networks to diagnose cancer. Grewal et al. teaches using an ensemble of neural networks trained on data from The Cancer Genome Atlas (p. 3 Methods; 8/2/2022 IDS document), Hwang et al. teaches using neural trees on gene expression data to diagnose cancer (Abstract; 8/2/2022 IDS document), Fakoor et al. teaches autoencoder neural networks trained on gene expression data (4.1 Feature Learning; 8/2/2022 IDS document), and Kourou et al. teaches artificial neural networks with multiple hidden layers have been used (p. 12, 3.1 ML and cancer prediction/prognosis, paragraph 3; Fig. 3).
As such, the combination of additional elements recited in the claims is well-understood,
routine and conventional. The additional elements do not comprise an inventive concept when
considered individually or as an ordered combination that transform the claimed judicial exception into
a patent-eligible application of the judicial exception. Therefore, the claims do not amount to
significantly more than the judicial exception itself (Step 2B: NO) and claims 1-32 are not patent eligible.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The 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.
Claims 1-32 are rejected under 35 U.S.C. 103 as being unpatentable over Yeatman et al (US20110106740A1; IDS document 8/2/2022) in view of Regev et al. (US20210071255A1), Cao et al. (Nature, vol. 566, no. 7745, p. 496-502), and Venteicher et al. (Science, vol. 355, no. 6332, p. eaai8478). The italicized text corresponds to the instant claim limitations.
Concerning claims 1-2, 4-5, 13-14, 17-18, 20-21, and 29-30:
Yeatman et al. teach a computer implemented method and system to carryout a method to classify unknown cancers by training a neural network classifier on gene expression data of known tumor types and then inputting gene expression data from an unknown tumor sample (p. 4, paragraph 0037-0038; p.5, paragraph 0044) which partially reads on the limitations of claims 1 and 17 of: A method, performed by at least one computer processor executing computer program instructions stored on at least one non-transitory computer-readable medium…, A system comprising at least one non-transitory computer-readable medium having computer program instructions stored thereon, the computer program instructions being executable by at least one computer processor to perform a method…, and (B) constructing, …, a machine learning perceptron classifier that outputs a tumor type of an input tumor based on gene expression data for the input tumor, wherein the input tumor is not among the plurality of training tumors. In addition, Yeatman et al. also teach performing pre-classification statistical analysis on the known gene expression data of tumors to identify the set of genes that best distinguishes each tumor prior to training the neural network (p. 4, paragraph 0035-0036) which reads on the limitations of claims 2 and 18 of: step (C) before A, generating the map and the limitations of claims 1 and 17 of: (B) … based on the map … The stated teachings of Yeatman et al. also read on the limitations of claims 13-14 and 29-30 of: (C) applying the machine learning perceptron classifier to the gene expression data for the input tumor to generate the tumor type of the input tumor and wherein the input tumor comprises a sample of cancer of unknown primary.
Yeatman et al. teach before creating a map they receive genetic expression data corresponding to the cellular phenotype of a plurality of known tumor type classes (p.4, paragraph 0038) reading on the limitations of claims 4-5 and 20-21 of: wherein (C) comprises: … (C) (2) receiving the tumor atlas;… and wherein the gene expression data for the plurality of training tumors comprises, for each of the plurality of training tumors: (1) gene sequencing data for the training tumor; and (2) a label indicating a type of cancer of the training tumor.
Yeatman et al. are silent on the claims 1 and 17 limitations of : … a map which maps from gene expression data for a plurality of training tumors in a tumor atlas to gene expression data representing single cells derived from mammal samples in developmental states in a single-cell atlas, and (A) using the map to extract, from the plurality of training tumors, a plurality of biological components, thereby generating, for each of the plurality of training tumors and each of the plurality of biological components, a corresponding biological component score representing the training tumor as a compilation of non-gene terms describing a plurality of biological programs, the claims 4 and 20 limitations of: (C) (1) receiving the single-cell atlas; … (C) (3) generating the map based on the single-cell atlas and the tumor atlas. Yeatman et al. is also silent on the limitations of claims 3, 6-12, 15-16, 19, 22-28 and 31-32. However, these limitations were known in the art at the effective filing date of the invention, as taught by Regev et al., Cao et al., and Venteicher et al.
Concerning claims 1, 3-4, 6-12, 15-17, 19-20, 22-28, and 31-32:
Regev et al. teach using single cell atlases to generate gene modules which are groups of genes associated with specific cell type expression, gene program, biological program expression (e.g., an activation program, cell differentiation program, proliferation program), or expression in a cell type having a specific cell state and a gene module can include signature genes whose expression profile is associated with a specific cell type, subtype, or cell state of a specific cell type or subtype within a population of cells (p. 18, paragraphs 0119-0121). Regev et al. also teach the gene signatures can be used to indicate the presence of a cell type or a subtype of the cell type and such presence may be determined by applying the signature genes to bulk sequencing data in a sample (e.g., tumor samples) which creates a map between the gene expression of tumor samples and the single cell gene expression and represents the tumor sample by the biological programs associated with signature genes (p. 18-19, paragraph 0123); which reads on the claims 1 and 17 limitations of: … a map which maps from gene expression data for a plurality of training tumors in a tumor atlas to gene expression data representing single cells derived from mammal samples … in a single-cell atlas, and (A) using the map to extract, from the plurality of training tumors, a plurality of biological components, thereby generating, for each of the plurality of training tumors and each of the plurality of biological components, a corresponding biological component score representing the training tumor as a compilation of non-gene terms describing a plurality of biological programs. Regev et al. teach the signature genes can be used to deconvolute the network of cells present in a tumor based on comparing them to data from bulk analysis of a tumor sample (p. 20, paragraph 132) which reads on the claims 15 and 31 limitations of: wherein using the map to extract, from the plurality of training tumors, the plurality of biological components comprises using the map to deconvolute the plurality of training tumors into the plurality of biological components. The teachings of Regev et al. also read on the limitations of claims 4 and 20 of: (C) (1) receiving the single-cell atlas; … (C) (3) generating the map based on the single-cell atlas and the tumor atlas and the limitations of claims 3 and 19 of: wherein generating the map comprises mapping each of the plurality of training tumor to at least one of the plurality of mammalian … in the single-cell atlas.
Cao et al. teach creating the Mouse Organogenesis Cell Atlas (MOCA), which provides a global view of mammalian organogenesis and developmental trajectories during prenatal development in mice (p. 6, Reconstructing developmental trajectories; p. 8, Discussion). The teachings of Cao et al. read on the claims 1 and 17 limitations of: …in developmental stages…, the limitations of claims 3 and 19 of: …developmental trajectories…, the limitations of claims 6 and 22, 8 and 24, 9 and 25, 10 and 26, 11 and 27, 12 and 28, and the limitations of claims 16 and 32.
Venteicher et al. teach comparing bulk expression from tumor samples from The Cancer Genome Atlas to single-cell samples to deconvolute the gene expression from different cells in the tumors (p. 1, Deciphering differences between bulk IDH-mutant glioma samples with single-cell RNA-seq) which reads on the claims 7 and 23 limitations of: wherein the tumor atlas comprises a version of The Cancer Genome Atlas (TCGA).
The method and system of Yeatman et al. differ from those in claims 1-32 because they map gene expression directly to the known tumor type by performing statistical analysis prior to training their neural network. A map which uses gene expression from single cells atlases to identify cell types and subtypes in tumor samples was known in the art at the effective filing data of the invention as taught by Regev et al. Additionally, a single-cell atlas with gene expression data derived from mammal samples in developmental stages was known in the art as taught by Cao et al. and using samples from The Cancer Genome Atlas and comparing them with single-cell RNA-seq data was known in the art as taught by Venteicher et al. One of ordinary skill in the art could have substituted the map taught by Yeatman et al. with the map taught by Regev et al. with the result having the predictable outcome of a neural network predicting a tumor type of an unknown sample based on input gene expression because both maps serve the same purpose of classifying tumors and a skilled artisan could have substituted the single-cell atlas for the Mouse Organogenesis Cell Atlas and the known tumor gene expression data (i.e., tumor atlas) for samples from The Cancer Genome Atlas with predictable results of creating a map with those datasets. The invention is therefore prima facie obvious.
Conclusion
No claims are allowed.
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/T.Y.O./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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