amoDETAILED 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 .
Acknowledgements
This communication is in response to Application No. 18/879,529 filed on 12/27/2024.
Claims 1-29 are currently pending.
Claims 1-29 have been rejected as follows.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/27/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being 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 1-29 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1, 15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites a system and method for predict an efficacy of a biological agent for gastrointestinal cancer.
The limitations of […] compute expression values for a set of metagenes from target differentially expressed genes (DEGs) for a gastrointestinal cancer, where the set of metagenes exhibit co- expression patterns with respect to response to a biological agent for patients with the gastrointestinal cancer; and
[…] to predict an efficacy of the biological agent for the gastrointestinal cancer, such that an output of the […] is usable by a clinician for determining a treatment for a new patient with the gastrointestinal cancer. as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for recitation of generic computer components. That is, other than reciting a processor and memory (computer), the claimed invention amounts to managing personal behavior or interaction between people. For example, but for the processor and memory, this claim encompasses a person computing expression values for metagenes and predicting efficacy of a biological agent for gastrointestinal cancer in the manner described in the identified abstract idea, supra. The Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A2
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of (claim 1) a processor and memory, that implements the identified abstract idea. The processor and memory is not described by the applicant and is recited at a high-level of generality (i.e., a generic computer performing a generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim further recites the additional element of using the trained machine learning model to predict an efficacy of the biological agent for the gastrointestinal cancer. This represents mere instructions to implement the abstract idea on a generic computer. Implementing an abstract idea using a generic computer or components thereof does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of a processor and memory to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). Accordingly, even in combination, this additional element does not provide significantly more. As such the claim is not patent eligible.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the trained machine learning model was found to represent mere instructions to implement the abstract idea on a generic computer. This has been re-evaluated under the “significantly more” analysis and determined to be insufficient to provide significantly more. MPEP 2106.05(I) indicates that mere instructions to implement the abstract idea on a generic computer and/or confining the use of the abstract idea to a particular technological environment or field of use cannot provide significantly more. Accordingly, even in combination, this additional element does not provide significantly more. As such the claim is not patent eligible.
Dependent Claims
Claims 2-14, 16-29 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Claim 2, 3, 16, 17 merely describes identify the target DEGs. Claims 4, 18 merely describes extract the set of metagenes. Claims 5, 19 merely describes the reduced set of metagenes. Claims 6, 20 merely describes compute the expression values. Claims 9, 23 merely describes gastrointestinal cancer comprises a colorectal cancer. Claims 10, 24 merely describes the biological agent comprises FOLFOX. Claims 11, 25 merely describes the biological agent comprises oxaliplatin. Claims 12, 26 merely describes the biological agent comprises bevacizumab. Claims 14, 28 merely describes each patient in the cohort is labelled as responder or non-responder.
Claim 7, 8, 13, 21, 22, 27, 29 includes the additional element of “model” which is analyzed the same as the as in the independent claim and does not provide a practical application or significantly more for the same reasons. Claim 7, 21 merely describes model comprises a classifier. Claims 8, 22 merely describes classifier comprises a logistic regression model. Claims 13, 27 merely describes GSVA scores for the set of metagenes for a cohort of patients are feature vectors for training the model. Claim 29 merely describes classifying, with the model, the efficacy of the biological agent for the new patient.
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.
Claims 1-2, 4, 7-12, 14-16, 18, 21-26, 29-29 are rejected under 35 U.S.C. 103 as being unpatentable over Abraham (US 20210295979) in view of Sadanandam (US 20150354009)
CLAIM 1, 15
Abraham teaches A computer system comprising: one or more processor cores; and computer memory in communication with the one or more processor cores, wherein the computer memory stores instructions that when executed by the one or more processor cores cause the one or more processor cores to: (Abraham para 14 teaches processing apparatus including one or more processors and one or more storage devices storing instructions that when executed by the one or more processors cause the one or more processors to perform operations)
compute expression values for a set of […] from target differentially expressed genes (DEGs) for a gastrointestinal cancer, (Abraham para 113 teaches the biomarker data records 220, 222, 224 may include RNA data such as gene expression or gene fusion, including without limitation whole transcriptome sequencing. Para 100 teaches the disease or disorder may include a type of cancer. Para 234 teaches colorectal cancer.)
where the set of […] with respect to response to a biological agent for patients with the gastrointestinal cancer; and (Abraham para 212 teaches a treatment known to have an effect on cells that differentially express genes as identified by molecular profiling techniques, an experimental drug, a government or regulatory approved drug or any combination of such drugs, which may have been studied and approved for a particular indication that is the same as or different from the indication of the subject from whom a biological sample is obtain and molecularly profiled. Para 100 teaches the disease or disorder may include a type of cancer. Para 234 teaches colorectal cancer. Para 100 teaches treatment for the subject may include one or more therapeutic agents, e.g., small molecule drugs, biologics, and various combinations thereof)
train a model, through machine learning, to predict an efficacy of the biological agent for the gastrointestinal cancer, such that an output of the model is usable by a clinician for determining a treatment for a new patient with the gastrointestinal cancer. (Abraham para 97 teaches identifying therapeutic agents for use in treatments and using a trained machine learning model to predict the effectiveness of a treatment for a disease or disorder of a subject. Para 100 teaches the disease or disorder may include a type of cancer. Para 234 teaches colorectal cancer. para 237 teaches molecular profiling results for new patients in order to predict benefit from the applicable treatment and thus guide treatment decisions. Examiner notes “such that an output of the model is usable by a clinician for determining a treatment for a new patient with the gastrointestinal cancer” is intended use holds no patentable weight. )
Abraham does not teach, however Sadanandam does teach compute expression values for a set of metagenes from target differentially expressed genes (DEGs) for a gastrointestinal cancer, where the set of metagenes exhibit co-expression patterns with respect to response to a biological agent for patients with the gastrointestinal cancer; and (Sadanandam para 62 teaches metagenes to capture the salient functional properties of a high-dimensional gene expression profile. Para 55 teaches clustering of gene expression profiles)
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the genes as taught by Abraham with metagenes as taught by Sadanandam because it would be beneficial to predict response to treatment based on expression level of one or a combination of genes as taught by Sadanandam para 22-23.
CLAIM 2, 16
Abraham teaches The computer system of claim 1, the computer memory stores instructions that when executed by the one or more processor cores cause the one or more processor cores to identify the target DEGs for the gastrointestinal cancer. (Abraham para 113 teaches the biomarker data records 220, 222, 224 may include RNA data such as gene expression or gene fusion, including without limitation whole transcriptome sequencing. Para 100 teaches the disease or disorder may include a type of cancer. Para 234 teaches colorectal cancer.)
CLAIM 4, 18
Abraham teaches extract the set of metagenes by, using a clustering analysis, identifying a reduced set of metagenes that exhibit clean co-expression patterns and strong drug response signals. (Abraham para 113 teaches the biomarker data records 220, 222, 224 may include RNA data such as gene expression or gene fusion, including without limitation whole transcriptome sequencing. Para 100 teaches the disease or disorder may include a type of cancer. Para 234 teaches colorectal cancer. para 212 teaches a treatment known to have an effect on cells that differentially express genes as identified by molecular profiling techniques, an experimental drug, a government or regulatory approved drug or any combination of such drugs, which may have been studied and approved for a particular indication that is the same as or different from the indication of the subject from whom a biological sample is obtain and molecularly profiled. Para 100 teaches treatment for the subject may include one or more therapeutic agents, e.g., small molecule drugs, biologics, and various combinations thereof)
Abraham does not teach, however Sadanandam teaches extract the set of metagenes by, using a clustering analysis, identifying a reduced set of metagenes that exhibit clean co-expression patterns and strong drug response signals. (Sadanandam para 62 teaches capture the salient functional properties of a high-dimensional gene expression profile using a relatively small number of “metagenes. Para 55 teaches clustering of gene expression profiles)
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the genes as taught by Abraham with metagenes using clustering analysis as taught by Sadanandam because it would be beneficial to predict response to treatment based on expression level of one or a combination of genes as taught by Sadanandam para 22-23.
CLAIM 7, 21
Abraham teaches The computer system of claim 1, wherein the model comprises a classifier. (Abraham para 143 teaches classifiers model)
CLAIM 8, 22
Abraham teaches The computer system of claim 7, wherein the classifier comprises a logistic regression model. (Abraham para 104 teaches a logistic regression model)
CLAIM 9, 23
Abraham teaches The computer system of claim 1, wherein the gastrointestinal cancer comprises a colorectal cancer. (Para 100 teaches the disease or disorder may include a type of cancer. Para 234 teaches colorectal cancer.)
CLAIM 10, 24
Abraham teaches The computer system of claim 9, wherein the biological agent comprises FOLFOX. (Abraham para 57 teaches treatment comprises FOLFOX)
CLAIM 11, 25
Abraham teaches The computer system of claim 9, wherein the biological agent comprises oxaliplatin. (Abraham para 57 teaches treatment comprises oxaliplatin)
CLAIM 12, 26
Abraham teaches The computer system of claim 9, wherein the biological agent comprises bevacizumab. (Abraham claim 4 teaches biological agent comprises bevacizumab)
CLAIM 14, 28
Abraham teaches The computer system of claim 13, wherein each patient in the cohort is labelled as responder or non-responder with respect to the biological agent. (Abraham para 208 teaches classify patients as a treatment provides a benefit to a patient (a “responder” or “benefiter”) or has a lack of benefit to the patient (a “non-responder” or “non-benefiter”))
CLAIM 29
Abraham teaches The method of claim 15, further comprising, after training the model:
collecting tumor tissue samples of the new patient diagnosed with the gastrointestinal cancer; (Abraham para 237 teaches tissue samples of tumor)
profiling transcriptome of the samples; (Abraham para 113 teaches whole transcriptome sequencing)
mapping the transcriptome to the set of […]; and (Abraham para 113 teaches whole transcriptome sequencing)
classifying, with the model, the efficacy of the biological agent for the new patient based on the mapping of the transcriptome for the new patient to the set of […]. (Abraham para 113 teaches whole transcriptome sequencing. para 97 teaches identifying therapeutic agents for use in treatments and using a trained machine learning model to predict the effectiveness of a treatment for a disease or disorder of a subject. para 237 teaches molecular profiling results for new patients in order to predict benefit from the applicable treatment and thus guide treatment decisions.)
Abraham does not teach, however Sadanandam does teach
mapping the transcriptome to the set of metagenes; and
classifying, with the model, the efficacy of the biological agent for the new patient based on the mapping of the transcriptome for the new patient to the set of metagenes. ( (Sadanandam para 62 teaches metagenes to capture the salient functional properties of a high-dimensional gene expression profile. Para 55 teaches clustering of gene expression profiles)
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the genes as taught by Abraham with metagenes as taught by Sadanandam because it would be beneficial to predict response to treatment based on expression level of one or a combination of genes as taught by Sadanandam para 22-23.
Claims 3, 17 are rejected under 35 U.S.C. 103 as being unpatentable over Abraham (US 20210295979) in view of Sadanandam (US 20150354009) in view of Cai (Cai, Systematic discovery of the functional impact of somatic genome alterations in individual tumors through tumor-specific causal inference, 2019 Jul 5)
CLAIM 3, 17
Abraham teaches identify the target DEGs for the gastrointestinal cancer using a […] algorithm applied to genomic data and transcriptomic data. (Abraham para 113 teaches the biomarker data records 220, 222, 224 may include RNA data such as gene expression or gene fusion, including without limitation whole transcriptome sequencing. Para 100 teaches the disease or disorder may include a type of cancer. Para 234 teaches colorectal cancer. Para 117 teaches an extraction unit may perform one or more information extraction algorithms such as keyed data extraction, pattern matching, natural language processing to identify and obtain data and para 120 teaches processing the extracted data to correlate the biomarker data with the outcome data )
Abraham does not teach, however Cai does teach identify the target DEGs for the gastrointestinal cancer using a tumor-specific causal inference algorithm applied to genomic data and transcriptomic data. (Cai page 3, 3rd paragraph teaches TCI algorithm to discover the causal relationships between SGAs and DEGs observed in an individual tumor)
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the identify target DEGs as taught by Abraham with tumor-specific causal inference as taught by Cai because it would be beneficial to guide precision oncology as taught by Cai pg. 1, para 1.
Claims 5,19 are rejected under 35 U.S.C. 103 as being unpatentable over Abraham (US 20210295979) in view of Sadanandam (US 20150354009) in view of Lu (Lu, Testing of a machine learning (ML) model for ability to predict oxaliplatin and bevacizumab (bev) benefit in NRG Oncology/NSABP C-07 and C-08, June 02, 2022)
CLAIM 5, 19
Abraham teaches The computer system of claim 4, (see claim 4)
Abraham does not teach, however Sadanandam does teach wherein the reduced set of metagenes comprises […] metagenes. (Sadanandam para 62 teaches capture the salient functional properties of a high-dimensional gene expression profile using a relatively small number of “metagenes. Para 55 teaches clustering of gene expression profiles. Para 56 teaches 5 subtypes)
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the genes as taught by Abraham with metagenes as taught by Sadanandam because it would be beneficial to predict response to treatment based on expression level of one or a combination of genes as taught by Sadanandam para 22-23.
Abraham in view of Sadanandam does not teach, however Lu does teach wherein the reduced set of metagenes comprises ten to twenty, inclusive, metagenes. (Lu Background teaches 15 metagenes)
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the metagenes as taught by Abraham in view of Sadanandam with 15 metagenes as taught by Lu because it would be beneficial to reflect the transcriptomic impact of major driver genes of CRC as taught by Lu Background line 1.
Claims 6, 13, 20, 27 are rejected under 35 U.S.C. 103 as being unpatentable over Abraham (US 20210295979) in view of Sadanandam (US 20150354009) in view of Owen (US 20240282453)
CLAIM 6, 20
Abraham teaches to compute the expression values […] (Abraham para 113 teaches the biomarker data records 220, 222, 224 may include RNA data such as gene expression or gene fusion, including without limitation whole transcriptome sequencing. Para 100 teaches the disease or disorder may include a type of cancer. Para 234 teaches colorectal cancer.)
Abraham does not teach, however Owen does teach to compute the expression values using a gene set variation analysis (GSVA). (Owen para 340 teaches Gene Set Variation Analysis (GSVA) may be performed)
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the metagenes as taught by Abraham in view of Sadanandam with gene set variation analysis (GSVA) as taught by Owen because it would be beneficial to identify modules of genes to represent a specific process as taught by Owen para 340.
CLAIM 13, 27
Abraham teaches The computer system of claim 6, wherein […] for the set of […] for a cohort of patients are feature vectors for training the model through machine learning. (Abraham para 102 teach eature vector includes a set of features derived from, and representative of, a training sample. The training sample may include, for example, one or more biomarkers of a subject, a disease or disorder of the subject, and a proposed treatment for the disease or disorder.)
Abraham does not teach, however Sadanandam does teach The computer system of claim 6, wherein […] for the set of metagenes for a cohort of patients are feature vectors for training the model through machine learning (Sadanandam para 62 teaches metagenes to capture the salient functional properties of a high-dimensional gene expression profile. Para 55 teaches clustering of gene expression profiles)
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the genes as taught by Abraham with metagenes as taught by Sadanandam because it would be beneficial to predict response to treatment based on expression level of one or a combination of genes as taught by Sadanandam para 22-23.
Abraham in view of Sadanandam does not teach, however Owen does teach The computer system of claim 6, wherein GSVA scores for the set of metagenes for a cohort of patients are feature vectors for training the model through machine learning. (Owen para 340 teaches Gene Set Variation Analysis (GSVA) may be performed. Para 123-124 teaches training on scores)
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the metagenes as taught by Abraham in view of Sadanandam with gene set variation analysis (GSVA) as taught by Owen because it would be beneficial to identify modules of genes to represent a specific process as taught by Owen para 340.
Prior Art Made of Record and Not Relied Upon
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20150105272 Anastassiou
[0058] This algorithmic behavior with convergence properties occurs due to the fact that if a metagene contains some co-expressed genes with high weights, then the next iteration will naturally "attract" even more genes with the same properties, and so forth, until the process will eventually converge to a metagene representing a potential underlying biological event reflected by this co-expression. Therefore, in certain embodiments, this methodology provides an unsupervised algorithm of identifying biomolecular events from rich biological data.
Xue, Tumour-specific Causal Inference Discovers Distinct Disease Mechanisms Underlying Cancer Subtypes, 13 September 2019
[Abstract] tumour-specific causal inference algorithm (TCI) to identify causal relationships between SGAs and differentially expressed genes (DEGs) within tumours
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW KYLE TAPIA whose telephone number is (703)756-1662. The examiner can normally be reached 830 - 530.
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/A.K.T./Examiner, Art Unit 3687
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687
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