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 .
Information Disclosure Statement
The IDS received on April 9, 2024 and February 21, 2025 are proper and are being considered by the Examiner.
Drawings
New corrected drawings in compliance with 37 CFR 1.121(d) are required in this application because Figure 6 contains texts which are too small with low quality, making them not clearly legible. Applicant is advised to employ the services of a competent patent draftsperson outside the Office, as the U.S. Patent and Trademark Office no longer prepares new drawings. The corrected drawings are required in reply to the Office action to avoid abandonment of the application. The requirement for corrected drawings will not be held in abeyance.
Claim Objections
Claims 1-13, 15-23, 25-27, 60, and 72 are objected to.
Where possible, claims are to be complete in themselves. Incorporation by reference to a specific figure or table “is permitted only in exceptional circumstances where there is no practical way to define the invention in words and where it is more concise to incorporate by reference than duplicating a drawing or table into the claim. Incorporation by reference is a necessity doctrine, not for applicant’s convenience.” Ex parte Fressola, 27 USPQ2d 1608, 1609 (Bd. Pat. App. & Inter. 1993) (citations omitted). The claims should recite the list of gene names and the miRNAs without referencing to the Tables.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 6-8, 13, 15, 17, 20, 23, 26, 27, and 72 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 6 is indefinite. Claim 6 depends from claim 1. Claim 1 recites that the method is based on the measurement of one or more gene expression level or of one or more miRNAs. Claim 6 now requires that expression levels of both the one or more mRNA and the one or more miRNA are measured. The embodiment covered by claim 6 is not within the embodiment covered by the parent claim and therefore, appears to be disjointed.
Claim 8 is indefinite for the same reason.
Claim 7 is indefinite. Claim 7 depends from claim 1. Claim 1 recites that the method comprises the step of “measuring expression levels of one or more genes” (in Table 1) or one or more miRNAs (in Table 2). Therefore, the method of claim 1 requires the measurement of the subject genes or miRNAs.
Claim 7, however, recites that these very genes (or miRNAs) “are excluded from being measured.” The very genes or miRNAs which must be measured for the method in the independent claim is now recited as being excluded in the dependent claim 7. No meaningful interpretation could be made for this claim and for the purpose of applying prior art.
Claim 13 is indefinite because an embodiment of the claim has no difference in scope with that of the claim 12. While claim 13 recites that an expression “signature” of the one or more genes or one or more miRNAs, the claim embraces a single gene expression. A single marker expression does not have a “signature” in an actual application and because an observed expression of a single gene (or miRNA) that is indicative of canonical, immune, or stromal metastatic phenotype is no different from that of claim 12 that makes the same conclusion based on the same single gene or miRNA expression level.
Claims 15 and 23 recite the term, “the patient.” There is an insufficient antecedent basis for this limitation in the claim.
Claim 17 is indefinite because the claim explicitly recites what is already an implicit process. Claim 17 depends from claim 16. Claim 16 recites that the expression levels of one or more genes or miRNAs are analyzed using a multi-layer neural network classification process that includes an input layer, one or more hidden layer, and an output layer. Because a neural network is based on the input of a training set, that is an input, reciting that that the training data (i.e., expression levels of the one of more genes or miRNA) is an input layer is already implicit the parent claim 16. For this reason, claim 17 does not add additional limitation over its parent claim.
Claim 20 is indefinite because the claim is missing an antecedent basis for the term, “metastatic phenotype.” It would appear that the term should be “stromal metastatic phenotype”. This interpretation has been assumed for prosecution.
Claims 26 and 27 recite the phrase, “the expression levels of the mRNAs”. There is an insufficient antecedent basis for this limitation in the claims. While a gene can be expressed via mRNA molecules, their actual usage of the term in claims 26 and 27 do not find an explicit antecedent basis. For the purpose of prosecution, the Office adopts the term, “genes” as recited in their parent claim 1.
Claim 72 is indefinite because the claim does not recite an active final step which agrees back with the preamble of “diagnosing a patient” as having a liver metastasis from a primary colorectal tumor. The claim recites a single step of inputting of data into a “classifier”.
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 1-13, 15-23, 25-27, 60, and 72 are rejected under 35 U.S.C. 101 because the claimed invention is directed to the natural phenomenon and abstract idea without significantly more. The claims recite: 1) a natural correlation of markers to primary, metastatic colon cancer’s phenotype; and 2) gathering of data and their manipulation involving neural network. This judicial exception is not integrated into a practical application because the judicial exception of the natural correlation discussed above and/or the gathering of the data do not involve additional elements that are sufficient to amount to significantly more than the judicial exception based on the analysis under the current Patent Eligibility Guidelines (herein, “PEG”) as discussed below.
Preliminarily, the Office notes that claim 1 has been construed to encompass the judicial exception of the markers correlation to metastatic cancer phenotypes based on its dependent claim 23. Claim 23 depends from claim 1 and further comprises step of, “administering a cancer therapy to the patient”. Not withstanding the term, “the patient” lacking an antecedent basis in the parent claim 1, the fact that claim 23 goes from measuring an expression of the recited markers of claim 1 into a treatment of cancer of a subject (i.e., patient) from which said expression was measured implicates claim 1 as an implicit nature of judicial exception in the step of measuring the expression level of said markers.
Step 1 Inquiry under PEG
Step 1 inquiry under Patent Eligibility Guidelines (herein, “PEG”) determines whether or not the claimed invention is drawn to one of the recognized statutory classes of invention. All of the presently rejected claims satisfy the present inquiry as being drawn to a method.
Step 2A Inquiry under PEG
A recently revised PEG now performs step 2A inquiry under a 2-prong analysis, and the subject claims analyzed accordingly as follows:
Prong 1:
Prong-1 inquiry under step 2A determines whether the claim(s) recites an abstract idea, a law of nature, or a natural phenomenon. As stated above, the method involves the natural correlation of the markers expression levels/signature with a metastatic cancer phenotypes; and a method of classifying a patient based on analysis of data collected, which is considered an abstract idea.
Therefore, claim recites a judicial exception.
Prong 2:
Prong-2 inquiry under step 2A determines whether or not the claims recite additional elements that integrate the judicial exception into a practical application in a manner that imposes a meaningful limit on the judicial exception.
Re: Claims 1-6 & 8-13:
Claims 1-6 and 8-13 do not recite any additional elements than the judicial exception (i.e., natural correlation) in that the claims recite utilizing the expression levels/signatures of any sub-combinations of genes and/or miRNA recited in Table 1 and 2 (respectively), but their levels/signatures are naturally tied to the phenotypes of a metastatic cancer which exist in nature, and while Applicants may have discovered them, the fact remains that they are natural correlations and thus a judicial exception.
Re: Claims 15-22:
Claims 15-22 recite additional elements of calculating a clinical risk score based on the expression levels/signatures of the genes/miRNAs, utilizing a neural network data analysis which involves input layer, hidden layers, and an output layer for the classification, which calculates a probability score of three phenotypes of said metastatic cancer (i.e., canonical, immune, or stromal metastatic), with a number of nodes.
The application of a computerized algorithm to calculate a probably score of a diagnosis, be it via use of a neural network involving Hidden Markov Model with various numbers of nodes and layers, are not deemed to significantly add a meaningful limit to the judicial exception recited in the claims because they are recited in a highly generalized without any specific means of achieving the probably score or actual algorithms involved in the neural network. Application of neural network for deriving a probably score based on gene expression levels/signatures have been conventionalized in the art.
For example, Mandal et al. (International Journal of Emerging Research and Technology, 2015, vol. 3, no. 7, pages 172-178) evidence that application of neural network for cancer classification has been widely known:
“Artificial Neural Network (ANN) is impended to classify the data of Breast Cancer and Lung Adenocarcinoma … to simplify and solve problems in pattern classification, function approximation, pattern matching …” (page 173)
“Among many different ANN models, the multilayer feed forward neural networks (MLFF) have been mainly used due to their well-known universal approximation capability” (page 173)
“The input units (neurons) are fully connected to the hidden layer with the hidden units. The hidden units (neurons) are also fully connected to the output layer. The output layer supplies the response of neural network to the activation pattern applied to the input layer” (page 173)
As well, the determination of an optimal number of layers and nodes involved in such a determination is deemed a conventional analysis:
“we see that with the increase of no. of nodes and hidden layers of NN, the accuracy increases up to a certain level … it is observed that accuracy can be increased if we insert hidden layer between input and output layer as the data set is noisy and non-linear … After an optimum value, if we further increase the number of hidden layers and the number of node for experiment purpose, it is seen that the accuracy does not increase and the complexity of the network becomes high and it takes more time to be trained.
Therefore, such generic recitation do not amount to more than capturing the judicial exception by implying “apply” this exception.
Re: Claims 26 and 27:
Claims 26 and 27 recite a means of determining the expression levels, involving PCR and microarray.
As well, the use of RT-PCR, PCR and microarray for determining expression levels of genes (i.e., mRNA) and miRNAs have been well established long before the effective filing date of the instant application. This fact is so well-established the Office relies on the Official Notice.
Re: Claims 23, 25, and 60:
Claims 23, 25, 60 are directed to further providing a cancer therapy (claim 23), such as immunotherapy (claim 25), or “appropriate therapy” (claim 60) based on the above discussed natural correlation.
However, these are not specifically recited as embracing “do nothing” approach as well as for treatment of metastatic cancer of any primary origin to any site. Further, the specification is clear in the form of cancer drug which is tailored for the different phenotypes of metastatic cancer (chemotherapy with or without cetuximab, see section [0074], for example) for a specific type of metastatic cancer of colorectal cancer (canonical, immune, stromal metastatic) to liver cancer (CRCLM).
Therefore, the treatment embraces a generalized treatment that is not tied to a specific types of cancer, thus failing to significantly apply the judicial exception.
Re: Claim 72:
Claim 72 do not recite any additional elements other that “inputting the expression levels from the genes/miRNAs into a “classifier” which has been trained.
As explained by the Supreme Court, in order to transform a judicial exception into a patent-eligible application, the additional element or combination of elements must do ‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’”. Alice Corp. v. CLS Bank, 573 U.S. __, 134 S. Ct. 2347, 2357, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 134 S. Ct. at 2358, 110 USPQ2d at 1983. See also 134 S. Ct. at 2389, 110 USPQ2d at 1984 (warning against a § 101 analysis that turns on “the draftsman’s art”) (MPEP 2106.05(f))
Step 2B Inquiry under PEG
Step 2B inquiry of the PEG determines whether or not additional elements are provided and whether such elements amount to significantly more than the judicial exception in the claims.
Presently, the additional elements which are provided in the claims that recite that the judicial exceptions applied by detection means of microarray/PCR that are routine and conventionally employed in the art of molecular diagnostics or utilizing a neural network which also are conventionally employed for generating a prediction score, or treated via use of a generic treatment means that are widely applicable and not specific to the judicial exception, and therefore fail to further add more than the judicial exception.
Therefore, these elements are not deemed significantly more than inclusion of that are commonly used, routine and conventional.
Therefore, the present claims lack patent eligibility.
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 1-13, 15-23, 25-27, 60, and 72 are 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. This is a Written Description Rejection.
The written description requirement ensures that, “an applicant invented the subject matter which is claimed. Further, the written description requirement for a claimed genus may be satisfied through a sufficient description of a representative number of species by 1) reduction to practice; 2) reduction to drawing; or 3) disclosure of relevant identifying characteristics (i.e., structure of other physical and/or chemical properties, functional characteristics coupled with a known or disclosed correlation between function and structure) (MPEP 2163 at II(A)(3)(a)(ii)).
Reduction to Practice
The present invention is directed to the metastases of a particular type of metastases and the ability to determine a sub-phenotype based on the expression levels and/or signature of one or more markers.
In particular, the metastases is “oligometastases” where cancer metastasis is limited in number and organ involvement, which has a better prognosis:
“Metastases are the leading cause of cancer-related deaths and are frequently widely disseminated, which has led to the prevailing view that metastases are always widespread. The oligometastasis hypothesis, in contrast, suggests that metastatic spread is a spectrum of virulence where some metastases are limited both in number and organ involvement and potentially curable with surgical resection or other loco-regional therapies. This paradigm is in stark contrast to the outcomes of patients with solid tumors where widespread metastases are largely fatal despite recent advances in systemic therapy. To date, the oligometastasis concept has been challenged, in large part, due to the lack of supporting molecular data to identify metastases associated with restricted spread. (section [0003])
The instant specification seeks to improve the inventors’ previous findings of a group of markers that are indicative of sub-phenotypes of a primary colorectal cancer metastases to liver, so as to allow fewer markers that are indicators of sub-phenotypes:
“Expression signatures based on mRNA or miRNA expression levels in metastatic tissue have been developed. Ptiroda … Nature Communications 9:1793 (2018) … describes identification of three subtypes of liver metastases from colorectal cancer primary tumors using expression levels of mRNA and miRNA in metastatic tissue (see also PCT Publication WO2019/204576 to Pitroda et al. …). Classification of the subtypes depend on mRNA or miRNA signatures requiring analysis of approximately 50 to 200 miRNAs or mRNAs, and it was only possible to classify patients into one of two groups (one SNF2 group, and one SNF1 + SNF3 group).” (section [0005])
“The inventors’ prior studies found that mRNA data alone or miRNA data alone were insufficient to classify patients into three molecular subtypes of CRCLM. By contrast, integration of both mRNA and miRNA data accurately classified the molecular subtypes of CRCLM. In the present study, the inventors aimed to minimize the number of input mRNA and miRNA features while maintaining a high accuracy for classification into the three molecular subtypes. The inventors first overlapped the mRNA and miRNA features that were present in the Pitroda 2018 Publication with the data from the UK randomized trial Xcel platform. This provided the full set of potential input mRNA and miRNA features. The inventors utilized a neural network classifier (a machine learning algorithm) to derive a classifier in the cohort from the Pitroda 2018 Publication that could then be validated in the UK validation cohort. In this context, 2018 study cohort was split into a training a testing set (60% and 40% of samples respectively) from which a signature was discovered and iteratively optimized. The model was first derived by training the neural network containing a hidden layer of 25 neurons and using as the input standardized z-scores of 400 mRNAs were selected from approximately 20,000 mRNAs on the basis of having the highest principal component (PC1 and PC2) using a principal component analysis. The 41 miRNAs were selected as being present in both platforms used in the 2018 study and the UK study. At this initial stage the average model accuracy using 400 mRNAs and 41 miRNAs as input features was 83% in the 2018 cohort testing set. In order to improve the model prediction, a recursive feature elimination was performed where input features that did not contribute significantly to the model accuracy were successively eliminated. The final model contained only 24 mRNAs (listed in Table 1 below) and 7 miRNA (listed in Table 2 below), for a total of only 31 features.” (section [0071])
In addition, in this finding, the instant specification discloses that data which pertains to the metastasis of a particular type of primary cancer, colorectal cancer to a particular type of organ, that is liver.
All of the markers which have been trained and determined to be indicative of three different types of metastatic phenotypes (canonical, immune, and stromal) pertains to colorectal cancer metastasis observed in liver.
Therefore, the specification does not contain any discussion nor show evidence of reduction to practice for a method comprising measurement of expression levels of one or more genes or miRNAs that pertain to metastasis of any origin of primary cancer to any organ.
While the specification does disclose that this method can be applied in, “metastatic cancers beyond only colorectal liver cancer” and that, “methods disclosed herein can be used to identify molecular subtypes of other metastatic cancers and to guide prognosis and treatment decisions for patients having such cancers, section [0007]), the application as filed does not contain any such markers which are correlated to such metastatic cancers to which the claims embrace.
Neither is the claim reproducing the assertion made in the specification justify written description as the Federal Circuit reiterated that mere use of the same words in the specification and the claim (an in ipsis verbis test) is not sufficient to establish written description.
Reduction to Drawing
The specification makes reference to various Figures which pertain to the markers and their correlation to colorectal cancer metastasis to liver. There are no figures which show demonstration of markers being correlated with any other primary cancer and its metastasis to any other organs.
Disclosure of Relevant Identifying Characteristics
While one could argue that a skilled artisan would be able to identify the “representative number of species” of such markers that correlate with other primary cancers and their metastasis to other organs, such argument would not satisfy the written description for the genus claims when, “the claims require an essential or critical feature which is not adequately described in the specification and which is not conventional in the art or known to one of ordinary skill in the art” (MPEP 2163(I)(A)). For the claims at issue, such essential or critical feature is markers and their demonstrated correlation. Applicants have not disclosed enough number of species within the claimed genus so as to justify the present claims directed to the subject-genus.
As stated in University of California v. Eli Lilly and Co. at page 1404:
An adequate written description of a DNA ... "requires a precise definition, such as by structure, formula, chemical name, or physical properties," not a mere wish or plan for obtaining the claimed chemical invention. Fiers v. Revel, 984 F.2d 1164, 1171, 25 USPQ2d 1601, 1606 (Fed. Cir. 1993). Accordingly, "an adequate written description of a DNA requires more than a mere statement that it is part of the invention and reference to a potential method for isolating it; what is required is a description of the DNA itself." Id. at 1170, 25 USPQ2d at 1606.
Therefore, for the foregoing reasons, the genus embraced by the claims is not sufficiently described by the number of species disclosed in the specification, and therefore, the specification lacks written description of the claims.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1 and 26 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Liu et al. (World J. Gastroenterol., October 2010, vol. 16, no. 39, pages 4986-4991).
With regard to claim 1, Liu et al. teach a method of measuring the expression level of PIK3CA (found in instant Table 1, see page 35 of specification) in a sample (distant lymph node) comprising tissue from a metastasis from a gastric cancer (primary cancer tumor):
“Transcript abundance of PIK3CA and b-actin (internal control was quantified by RT-qPCR on total RNA isolated from … gastric cancer tissues of primary foci, lymph node, and distant metastasis” (page 4987, 2nd column)
“PIK3CA mRNA expression was detected by RT-qPCR in … gastric cancer tissues of primary foci, lymph node and distant metastasis. The primary gastric cancer specimens showed higher expression of PIK3CA mRNA in comparison with the normal gastric mucosa. The lymph node metastasis tissues displayed the strongest expression of PIK3CA mRNA, which was approximately 5 and 2 folds higher, respectively, than that in normal gastric mucosa and primary gastric cancer tissue” (page 4988).
With regard to claim 26, the means for determining the expression level is via RT-PCR using RNA obtained from the sample as a template (“total RNA isolated from … gastric cancer tissues of primary foci, lymph node and distant metastasis … total RNA was reversely transcribed in a reaction volume … PCR amplification and fluorescence detection were carried out”, page 4987).
Therefore, the invention as claimed is anticipated by Liu et al.
Claims 1-7, 9-13, 15, 23, 25-27, and 60 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Pitroda et al. (WO 2019/204576 A1, published October 2019; IDS ref).
With regard to claims 1-3, Pitroda et al. teach a method comprising measuring expression levels of one or more genes listed in Table 1 or one or more of miRNAs listed in Table 2 in a sample comprising tissue from a metastasis from a primary cancer tumor (“a method comprising measuring expression levels of one or more genes listed in Table 10A or one or more miRNAs listed in Table 11A in a sample comprising tissue from a metastasis from a primary cancer tumor”, section [0007], also “any of the methods disclosed herein may involve measuring the expression of one or more genes listed in Tables 3A-C, which lists genes that are differentially expressed in SNF1, SNF2, and SNF3 liver metastases from colorectal cancer primary tumors”, section [0007]).
Table 3A of Pitroda et al. lists genes DDR2, FAP, ITPR1, LRRC8C, PREX2; and Table 3B of Pitroda et al. lists genes TCIRG1.
Tables 11A and 4A of Pitroda et al. lists hsa-miR-21-5p (or MIR21), hsa-miR-762 (or MIR762), respectively.
With regard to claims 4-7, the above Tables disclose at least two markers from the genes of Table 1 and two markers from Table 2.
With regard to claim 9, the artisans explicitly disclose an embodiment where “one” of the genes listed in Tables listed for the genes and “one of the miRNA listed in the Table of the specification is utilized and therefore, would necessarily anticipate the embodiment of no expression level of gene other than those listed in Table 1 and 2 are utilized.
With regard to claims 10-13, Pitroda et al. teach that the expression levels of the one or more genes or the one or more miRNAs are within a predetermined amount of a mean expression levels in metastases of a cohort of patients having one of the canonical, immune, or stromal metastatic phenotypes, as well as looking at the signature of the genes (“expression levels of one or more genes or one or more miRNAs are within a predetermined amount of mean expression levels of the … patients having an oligometastatic phenotype … measuring the expression levels of genes in metastases of patients in the cohort and calculating a mean expression level for each gene”, section [0010]; also “characteristic of an SNF1, SNF2, or SNF31”, section [0010]).
With regard to claim 11, the number of cohort of patients comprise at least 50, 100, 150, 200, to 1000 (see section [0010]).
With regard to claim 15, Pitroda et al. teach calculating a risk score (“some embodiments, the method further comprises calculating a Clinical Risk Score (‘CRS’)”, section [0012]).
With regard to claim 23, Pitroda et al. teach providing cancer therapy based on the analysis (“method further comprise administering a cancer therapy to the patient”, section [0013]).
With regard to claim 25, the therapy comprises immunotherapy (“the cancer therapy comprises an immunotherapy”, section [0013]).
With regard to claims 26 and 27, the means of detecting the expression levels is via PCR using RNA obtained from a sample of metastatic tissue as a template (“the measurement comprises performing PCR using RNA obtained from a sample of metastatic tissue as a template”, section [0014]), or via microarray (“[m]ensuring expression may also comprise hybridizing nucleic acids to a microarray”, section [0014]).
With regard to claim 60, the artisans teach treatment of a patient with the above discussed gene expression or miRNA levels, as well as the correlated phenotypes (see above discussion).
Therefore, the invention as claimed is anticipated by Pitroda et al.
Claim Rejections - 35 USC § 103
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.
Claims 16-22 and 72 are rejected under 35 U.S.C. 103 as being unpatentable over Pitroda et al. (WO 2019/204576 A1, published October 2019; IDS ref) in view of Mandal et al. (International J. of Emerging Engineering Research and Technology, July 2015, vo. 3, no. 7, pages 172-178).
The teachings of Pitroda et al. have already been discussed above.
Pitroda et al. explicitly teach the use of a classifier programmed to perform a statistical analysis to determine whether expression levels of a sufficient number of genes and/or miRNAs in a sample metastasis are sufficiently close to the reference expression levels of a particular molecular subtype to classify the sample as belonging to that subtype (see section [0010], bottom).
However, Pitroda et al. do not teach the use of a multi-layer neural network classifier process that includes an input layer, one or more hidden layers, and an output layer (claim 16), wherein expression levels of the one of more genes or miRNA comprise the input layer (claim 17), with output providing the classification (claim 18) and its probability of the phenotypes (claims 19 and 20), involving a first hidden and second hidden layer (claim 21), with varying number of nodes (claim 22).
Pitroda et al. do not utilize using a trained classifier to classify the above-discussed phenotypes (claim 72).
Mandal et al. teach a well-known adoption of neural network for classifying cancers (“Artificial Neural Networks (ANN) is impended to classify the data of Breast Cancer and Lung Adenocarcinoma …”, page 173).
Mandal et al. teach that neural network involves, “three different layers with feed forward architecture … input layer of this network is a set of input units, which accepts the elements of input feature vectors … connected to the hidden layer with the hidden units … fully connected to the output layer … suppl[ying] the response of neural network to the activation pattern applied to the input layer”, page 173).
Mandal et al. teach training ANN with a set of data in order to provide a desired input and output mapping and adjusting the algorithm accordingly (see page 174) and state that healthcare industry who collects a vast amount of healthcare data can be exploited to train ANN:
“For Classification task, ANN needs to be trained for the networks to be able to produce the desired input output mapping. For training purpose a set of example data are feed to the network and connection weights, which is also called synaptic weight, are adjusted by using a learning algorithm. The objective of a neural network system is to give an output due some input signals. Before the training of the neural network, the system is initialized to its defaults values, and all the outputs (possible answers of the system) have the same probability. While the network is trained, the weights that define the connection between notes modified the value, and depending on the input and hidden values, the structure can be also changed. That implies that it is possible to optimize the neural networks …” (page 174)
“The healthcare industry collects huge amounts of healthcare data and that need to be mined to discover hidden information for effective decision making. Discover of hidden patterns and relationships often go unexploited. There are several online databases available for the researchers. These databases are supported and contributed by different cancer institute and government
Organization” (page 175)
Mandal et al. also teach that such optimization of ANN involving modification of number of layers and nodes is also routine:
“one can increase the number of hidden layers and corresponding nodes to deal with more non linearity and noise of dataset … Fig. 4(a) and 4(b) shows accuracy vs. no. of hidden layers … and accuracy vs. no. of nodes in each hidden layer …” (page 176)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Pitroda et al. with the teachings of Mandal et al., thereby arriving at the invention as claimed for the following reasons.
In KSR, the Supreme Court particularly emphasized “the need for caution in granting a patent based on the combination of elements found in the prior art,” Id. at 415, 82 USPQ2d at 1395, and discussed circumstances in which a patent might be determined to be obvious. Importantly, the Supreme Court reaffirmed principles based on its precedent that “[t]he combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.” Id. at 415-16, 82 USPQ2d at 1395. The Supreme Court stated that there are “[t]hree cases decided after Graham [that] illustrate this doctrine.” Id. at 416, 82 USPQ2d at 1395. (1) “In United States v. Adams, . . . [t]he Court recognized that when a patent claims a structure already known in the prior art that is altered by the mere substitution of one element for another known in the field, the combination must do more than yield a predictable result.”
As discussed above, Pitroda et al. already teach that a computer-based classifier can be utilized to, “perform a statistical analysis for determining whether expression levels of sufficient number of genes and/or miRNAs in a sample metastasis are sufficiently close to the reference expression levels of a particular molecular subtype to classify the sample as belonging to that subtype” (section [0010])
Since neural network had been known to be utilized “to simplify and solve problems in pattern classification, function approximation, pattern matching and associative memories”, Mandal et al. at page 173) with an explicit application of ANN for disease classification, one of ordinary skill in the art would have been motivated to combine the teachings of Pitroda et al. with Mandal et al. to apply ANN as an alternate computerized means to train and employ in classifying cancer phenotypes of Pitroda et al. with a reasonable expectation of success.
Conclusion
No claims are allowed.
Claim 8 is free of prior art as the prior art does not teach, suggest, or provide motivation to assay for all of the genes in Table 1 and all of the miRNAs listed in Table 2 from a tissue from a metastasis from a primary cancer tumor.
Inquiries
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Young J. Kim whose telephone number is (571) 272-0785. The Examiner can best be reached from 7:30 a.m. to 4:00 p.m (M-F). The Examiner can also be reached via e-mail to Young.Kim@uspto.gov. However, the office cannot guarantee security through the e-mail system nor should official papers be transmitted through this route.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner's supervisor, Gary Benzion, can be reached at (571) 272-0782.
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/YOUNG J KIM/Primary Examiner
Art Unit 1637 January 14, 2026
/YJK/
1 “SNF1” is canonical, see sections [0010], [0086], and [0087]; “SNF2” is described as having expression associated with high innate and adaptive immune genes (see Pitroda et al. section [0063]), thus synonymous with immune phenotype; and “SNF3” is described as having expression patterns associated with high stromal infiltration, which is synonymous with stromal phenotype, id [0063], Pitroda et al.