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 Claim(s)
Claims 1-20 have been examined.
Claim Objections
Claims 19-26 are objected to because of the following informalities:
Claim 19 recites wherein the plurality of features are selected from Tables 4A-4C, Tables 5A-5B, Tables 6A- 6B, Tables 7A-7B, Table 8, Table 9, Tables 13A-13B, Table 14, Table 15, Tables 18A-18B or a combination thereof. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 19-26 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 19 recites wherein the plurality of features are selected from Tables 4A-4C, Tables 5A-5B, Tables 6A- 6B, Tables 7A-7B, Table 8, Table 9, Tables 13A-13B, Table 14, Table 15, Tables 18A-18B or a combination thereof. However, there nowhere in Specification and Drawings to disclose the structures, functions of the tables. The disclosure does not provide adequate structure to perform the claimed function of the pluralities of features selected from these tables. The specification does not demonstrate that the applicant has made an invention that achieves the claimed function because the invention is not described with sufficient detail such that one of ordinary skill in the art can reasonably conclude that the invention had possession of the claimed function.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claim(s) 1 recite(s) a computer-implemented method, which is a statutory category (i.e. process). Claim 5 recites a computer-implemented method, which is a statutory category (i.e. process). Claim 10 recites a system, which is a statutory category (i.e. machine). Claim 14 recites a system, which is a statutory category (i.e. process). Claim 19 recites a method, which is a statutory category (i.e. process). Accordingly, claims 1, 5, 10, 14 are all within at least one of the four statutory categories.
Step 2A - Prong One:
Regarding Prong One of Step 2A (MPEP2106.04-.7), the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
The limitation of Independent claims 1, 5, 10, 14 recites at least one abstract idea. Specifically, Claim 1 recites the steps of
A computer-implemented method comprising:
determining available medical tests at a medical institution, the available medical tests being at least a subset of known medical tests performed at various medical institutions;
selecting, from the available medical tests, selected medical tests based on a trained parsimonious model for pancreatic cancer;
obtaining one or more biological samples from a subject for the selected medical tests;
assaying the one or more biological samples via the selected medical tests to obtain one or more factors; and
prognosticating the subject as having a higher likelihood of survival, the subject as having a higher likelihood of recurrence, or a combination thereof based on the trained parsimonious model and the one or more factors.
The limitations “determining available medical tests at a medical institution, the available medical tests being at least a subset of known medical tests performed at various medical institutions; selecting, from the available medical tests, selected medical tests based on a trained parsimonious model for pancreatic cancer; obtaining one or more biological samples from a subject for the selected medical tests; assaying the one or more biological samples via the selected medical tests to obtain one or more factors” constitutes b) certain methods of organizing human activity because receiving data can be practically performed in the human mind. Accordingly, the claim is directed toward at least one abstract idea.
Further the limitations “prognosticating the subject as having a higher likelihood of survival, the subject as having a higher likelihood of recurrence, or a combination thereof based on the trained parsimonious model and the one or more factors” constitutes a) mathematical concepts because these limitations could be performed by the algorithm process, analyze data sources on a piece of paper by healthcare providers. Accordingly, the claim is directed toward at least one abstract idea
Furthermore, the abstract idea for claims 10 is identical as the abstract idea for claim 1, because the only difference between claim 1 and claim 10 is that claims 1 recites method, whereas claim 10 recites a system.
Furthermore, the following depending claims further define the at least one abstract idea, and thus fail to make the abstract idea any less abstract.
For dependent claims 2-4, 9-13 the recitation of weighting factors, thus merely define steps that were indicated as being part of the abstract idea, and thus part of mathematical concept add the element in addition to the judicial exception(s) of repeating the process of the independent claim. This element fails to rise to the level of significantly more than the judicial exception(s) as the steps are further abstract limitations comprising mathematical or algorithmic steps, therefore these elements are a part of the judicial exception
Claim 5 recites the steps of
A computer-implemented method comprising:
processing a plurality of analytes from a plurality of individuals with cancer to obtain a plurality of features;
training one or more machine learning models with single-omic and mult-omic combinations of the plurality of features to predict binary survival and disease recurrence outcomes of the plurality of individuals;
evaluating the one or more machine learning models for positive predictive value and accuracy in predicting the survival and disease recurrence outcomes and feature proportions; and
recursively eliminating features from the plurality of features based on the evaluating of the one or more machine learning models to develop a parsimonious machine learning model for predicting survival and disease recurrence outcome.
The limitations “training one or more machine learning models with single-omic and mult-omic combinations of the plurality of features to predict binary survival and disease recurrence outcomes of the plurality of individuals” constitutes a) mathematical concepts because these limitations could be performed by the algorithm process, analyze data sources on a piece of paper by healthcare providers because predicting data can be practically performed in the human mind by providers. Accordingly, the claim is directed toward at least one abstract idea.
Further, the limitation “recursively eliminating features from the plurality of features based on the evaluating of the one or more machine learning models to develop a parsimonious machine learning model for predicting survival and disease recurrence outcome” constitutes a) mathematical concepts because these limitations could be performed by the algorithm process, analyze data sources on a piece of paper by healthcare providers because predicting data can be practically performed in the human mind by providers. Accordingly, the claim is directed toward at least one abstract idea.
Furthermore, the abstract idea for claims 14 is identical as the abstract idea for claim 5, because the only difference between claim 5 and claim 14 is that claims 5 recites method, whereas claim 14 recites a system.
For dependent claims 2-4, 9-13 the recitation of weighting factors, thus merely define steps that were indicated as being part of the abstract idea, and thus part of mathematical concept add the element in addition to the judicial exception(s) of repeating the process of the independent claim. This element fails to rise to the level of significantly more than the judicial exception(s) as the steps are further abstract limitations comprising mathematical or algorithmic steps, therefore these elements are a part of the judicial exception
Step 2A - Prong Two:
Regarding Prong Two of Step 2A (MPEP2106.04-.07), it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted in MPEP2106.04-07, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted at least one abstract idea are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
A computer-implemented method comprising:
determining available medical tests at a medical institution, the available medical tests being at least a subset of known medical tests performed at various medical institutions;
selecting, from the available medical tests, selected medical tests based on a trained parsimonious model for pancreatic cancer (merely invokes use of computer and computer components as a tool as noted below, see MPEP 2106.05(f));
obtaining one or more biological samples from a subject for the selected medical tests;
assaying the one or more biological samples via the selected medical tests to obtain one or more factors (merely invokes use of computer and computer components as a tool as noted below, see MPEP 2106.05(f)); and
prognosticating the subject as having a higher likelihood of survival, the subject as having a higher likelihood of recurrence, or a combination thereof based on the trained parsimonious model and the one or more factors (merely data gathering steps as noted below, see MPEP 2106.05(g) and Symantec).
For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted at least one abstract idea into a practical application.
Regarding the additional limitation of prognosticating the subject as having a higher likelihood of survival, the subject as having a higher likelihood of recurrence, or a combination thereof based on the trained parsimonious model and the one or more factors , this is merely post-solution activity. The examiner submits that this additional limitation merely adds insignificant extra-solution activity of impractical application to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea of a mental process (see MPEP § 2106.05(g)).
Particularly, the use of a trained parsimonious pancreatic model, a parsimonious machine learning model, as described in claims 1, 10 and 5, 14 is not positively claimed in the claims as it defines the service but is claimed at such a high level of generality that it represents mere instructions to implement an abstract idea MPEP 2106.05(f). The Specification describes these models as generic component (‘Spec.; Para 0022).
The remaining dependent claim limitations are not addressed above fail to integrate the abstract idea into a practical application
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to implement and revise a treatment plan, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (see 2019 PEG and MPEP § 2106.05).
For these reasons, representative independent claim 1 and analogous independent claims 12 do not recite additional elements that integrate the judicial exceptions into a practical application.
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set below:
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Step 2B:
Regarding Step 2B, independent claims 1, 5, 10, 14 do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
For claims 1, 5, 10, 14 and dependent claims 2-4, 6-9, 11-13, 15-18 limit the use of the parsimonious mdels, machine learning model as well-understood, routine, conventional activity (Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018).), and MPEP 2106.05(d)(I)(2)
For the reasons stated, the claims fail the Subject Matter Eligibility Test and are consequently rejected under 35 USC 101. Therefore, claims 1-18 are rejected under 35 USC 101 as being patent ineligibility.
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.
Claim(s) 1-4, 10-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stern et al. (US20220033915A1 hereinafter Stern) in view of Amri et al. (US20070259363A1 hereinafter Amri).
With respect to claim 1, Stern teaches a computer-implemented method comprising:
determining available medical tests at a medical institution, the available medical tests being at least a subset of known medical tests performed at various medical institutions (‘915; Para 0023: FIG. 1 is a flowchart showing a summary of the methods used for differential expression analysis and predictive model development how different operations in processing test samples may be grouped to be handled by different elements of a system);
Amri teaches
selecting, from the available medical tests, selected medical tests based on a trained parsimonious model for pancreatic cancer (‘363; Para 0056; Para 0058: Parsimony analysis produced one most parsimonious cladogram (requiring the least amount of steps in constructing a classification of specimens) for each of the pancreatic and prostate specimens (FIGS. 2A & B), 5 equally parsimonious cladograms for ovarian specimens (FIG. 2C shows only one), and several equally parsimonious cladograms for the inclusive analysis (FIG. 3, summarizes only one). Multiple equally parsimonious cladograms were fundamentally similar in topography and differed only in the internal arrangement of some minor branche)s;
obtaining one or more biological samples from a subject for the selected medical tests (‘915; Para 0094: biological samples in addition to or instead of prostate tissue may be used to determine the expression levels of the signature genes. In some embodiments, the suitable biological samples include, but are not limited to, circulating tumor cells (CTCs) isolated from the blood, urine of the patients or other body fluids, exosomes, and circulating tumor nucleic acids).;
It would have been obvious to one of ordinary skill in the art before the effective filing date of claimed invention to modify the method of Stern with the technique of utilizing the principles of parsimony to reveal susceptibility to cancer development as taught by Amri in order to provide the prognosis of pancreatic cancer to the subject.
Stern in view of Amri teaches
assaying the one or more biological samples via the selected medical tests to obtain one or more factors (‘915; Para 0086: If required, a nucleic acid sample having the signature gene sequence(s) are prepared using known techniques. For example, the sample can be treated to lyse the cells, using known lysis buffers, sonication, electroporation, etc., with purification and amplification as outlined below occurring as needed, as will be appreciated by those in the art. In addition, the reactions can be accomplished in a variety of ways, as will be appreciated by those in the art. Components of the reaction may be added simultaneously, or sequentially, in any order, with preferred embodiments outlined below. In addition, the reaction can include a variety of other reagents which can be useful in the assays. These include reagents like salts, buffers, neutral proteins, e.g. albumin, detergents, etc., which may be used to facilitate optimal hybridization and detection, and/or reduce non-specific or background interactions. Also reagents that otherwise improve the efficiency of the assay, such as protease inhibitors, nuclease inhibitors, anti-microbial agents, etc., can be used, depending on the sample preparation methods and purity.); and
prognosticating the subject as having a higher likelihood of survival, the subject as having a higher likelihood of recurrence, or a combination thereof based on the trained parsimonious model and the one or more factors (‘915; Abstract; Para 0057: a method for predicting progression of prostate cancer in an individual, the method comprising: (a) receiving expression levels of a collection of signature genes from a biological sample taken from said individual…. In some embodiments, the output of the predictive model predicts a likelihood of clinical recurrence of prostate cancer in the individual after said individual has undergone treatment for prostate cancer., Para 0092: Although the use of the 28 genes, and subsets thereof, has been exemplified with respect to prognosis and diagnosis methods utilizing expression levels of mRNA species produced by these genes, it will be understood that similar diagnostic and prognostic methods can utilize other measures such as methylation levels for the genes which can be correlated with expression levels or a measure of the level or activities of the protein products of the genes.)
Claim 10 is rejected as the same reason with claim 1.
With respect to claim 2, the combined art teaches the method of claim 1, further comprising weighting each factor of the one or more factors based on the selected medical tests (‘363; Para 0039A weighted score may be provided (i.e., other than 1) for each derived state if more or less weight should be factored for a particular MS state (e.g., to reduce scatter); Para 0137: TABLE1B).
Claim 11 is rejected as the same reason with claim 2.
With respect to claim 3, the combined art teaches the method of claim 1, further comprising selecting a pancreatic cancer treatment method from among a plurality of pancreatic cancer treatment methods based on the trained parsimonious model and the one or more factors (‘363; Para 0056, 0058).
Claim 12 is rejected as the same reason with claim 3.
With respect to claim 4, the combined art teaches the method of claim 1, further comprising administering the pancreatic cancer treatment method (‘363; Para 0088).
Claim 11 is rejected as the same reason with claim 2.
Claim(s) 5-9, 14-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Harley et al. (US20210319907A1 hereinafter Harley) in view of Stern et al. (US20220033915A1 hereinafter Stern).
With respect to claim 5, Harley teaches a computer-implemented method comprising:
processing a plurality of analytes from a plurality of individuals with cancer to obtain a plurality of features (‘907; Abstract: utilizing multi-omic data indices for tumor profiling. The method can comprise storing a plurality of multi-omic data indices, wherein each of the plurality of multi-omic data indices comprises cancer-specific tokenized data; ingesting additional multi-omic data and any annotations associated with the additional multi-omic data, the additional multi-omic data related to one or more indices; indexing the ingested additional multi-omic data and annotations while preserving gene names, gene variant names and multi-omic mapping between different data streams for the same patient in the specific index, to produce tokenized ingested additional multi-omic data; receiving a user query; Para 0091: deriving cancer analytics for the selected one or more multi-omic data indices. The cancer analytics can comprise tumor characteristics selected from the group consisting of quality control, tumor mutation burden, genomic mutation signatures, microsatellite instability status, neo-antigens and their binding affinities, HLA-allele typing, RNA confirmed variants, copy number variants, structural variants, non-coding regulatory variants, gene fusions, pathway enrichment, cancer driver identification, mutation summary, differential gene expression, immune signatures, and combinations thereof. In accordance with various embodiments, the cancer analytics can be derived for an individual sample or a cohort of samples. Moreover, cancer analytics can include matching information about treatment outcomes for similar patients);
training one or more machine learning models with single-omic and mult-omic combinations of the plurality of features to predict binary survival and disease recurrence outcomes of the plurality of individuals (‘907; Para 0072: a general search engine architecture is provided that can be configured to adapt to the specific needs for cancer multi-omic data. The general architecture, discussed below in more detail with reference to FIG. 1, can include various components. For example, the general architecture can include a web-based user interface, a query engine, an indexing pipeline that can index cancer multi-omic data with all annotations, a cancer analytics software module, and a ranking engine. The query engine can be configured to respond to requests to search any combination of multi-omic data streams available for individual samples or cohorts.);
recursively eliminating features from the plurality of features based on the evaluating of the one or more machine learning models to develop a parsimonious machine learning model for predicting survival and disease recurrence outcome .(‘907; Para 0091: the cancer analytics can comprise machine learning predictions and machine learning model features ranked in the order of their relevance to a particular prediction).
Stern discloses
evaluating the one or more machine learning models for positive predictive value and accuracy in predicting the survival and disease recurrence outcomes and feature proportions (‘915; Abstract; Para 0057: a method for predicting progression of prostate cancer in an individual, the method comprising: (a) receiving expression levels of a collection of signature genes from a biological sample taken from said individual…. In some embodiments, the output of the predictive model predicts a likelihood of clinical recurrence of prostate cancer in the individual after said individual has undergone treatment for prostate cancer; and
It would have been obvious to one of ordinary skill in the art before the effective filing date of claimed invention to modify the method of Harley with the technique of prognosis of prostate cancer as taught by Stern in order to use machine learning model for predicting survival and disease recurrence outcomes for pancreatic cancer.
With respect to claim 6, the combined art method of claim 5, wherein the plurality of analytes are derived from serum, plasma, blood and/or tissue samples subjected to targeted NGS DNA sequencing, whole transcriptome RNA sequencing, paired tissue proteomics, unpaired serum proteomics, lipidomics, surgical pathology, and/or computational pathology (‘907; Paras 0074, 0104; (‘907; Paras 0035-0036, 0074, 0104, 0219).
Claim 15 is rejected as the same reason with claim 6.
With resepct to claim 7, the combined art teaches the method of claim 5, wherein the plurality of analytes include plasma or serum lipids, RNA gene expressions, CNVs, INDELS,SNVs, and or tumor nuclei characteristics (‘907; Paras 0074, 0104; (‘907; Paras 0035-0036, 0074, 0104, 0219).
Claim 16 is rejected as the same reason with claim 7.
With respect to claim 8, the combined art teaches the method of claim 5, wherein the feature proportions evaluated using a leave-one-patient-out cross- validation strategy.
Claim 17 is rejected as the same reason with claim 8.
With respect to claim 9, the combined art teaches the method of claim 5, wherein the one or more machine learning models Support Vector Machine (SVM), Principal Component Analysis (PCA) + Logistic Regression, L1-Normalized SVM, L1- Normalized Random Forest, 5-hidden-layer Deep Neural Network, Recursive Feature Elimination (RFE) Logistic Regression and/or RFE Random Forest (‘907; Abstract, 0093).
Claim 18 is rejected as the same reason with claim 9.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HIEP VAN NGUYEN whose telephone number is (571)270-5211. The examiner can normally be reached Monday through Friday between 8:00AM and 5:00PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jason B Dunham can be reached at 5712728109. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HIEP V NGUYEN/Primary Examiner, Art Unit 3686
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