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 .
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 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.
Claim Status
Claims 30-34 and 106-115 are pending.
Claims 1-29 and 35-105 are canceled.
Claims 30-34 and 106-115 are under examination.
Claims 30-34 and 106-115 are rejected.
Priority
Applicant's claim for the benefit of a prior-filed application, PCT/US2021/018263, filed 02/16/2021, claims domestic benefit to US provisional application 63/145305, filed 02/03/2021, claims domestic benefit to US provisional application 63/052363, filed 07/15/2020, claims domestic benefit to US provisional application 63014515, filed 04/23/2020, and claims domestic benefit to US provisional application 62/977015, filed 02/14/2020 is acknowledged.
Accordingly, each of claims 30-34 and 106-115 are afforded the effective filing date of 02/14/2020.
Information Disclosure Statement
The listing of references in the specification is not considered to be an IDS as it is not a proper IDS. 37 CFR 1.98(b) requires a list of all patents, publications, or other information submitted for consideration by the Office and MPEP § 609.04(a) states, "the list may not be incorporated into the specification but must be submitted in a separate paper." Therefore, unless the references have been cited by the examiner on form PTO-892, they have not been considered. See pages 239-240 of the published specification.
Drawings
The Drawings submitted on 07/08/2021 and 11/26/2025 are accepted.
Specification
The disclosure is objected to for the following informalities. It is noted that for purposes of the instant Office Action, any reference to the specification pertains to the clean copy of the substitute specification as originally filed on 08/12/2022.
Hyperlinks
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01. Non-limiting examples include paragraphs [0234, 0238, 0424, 0337, 0391, 0418, 0450, and 0547]. Applicant will note that this is exemplary and other instances may exist. It is requested that all instances be corrected.
Appropriate correction for all objections to the specification is required.
Claim Rejections - 35 USC § 112
35 U.S.C. 112(b)
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.
Claims 33, 109, and 114 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 33, 109, and 114, limitation, recites “wherein training the dynamic voting engine comprises”. It is unclear whether the wherein clause is intended to require the training of the dynamic voting engine is within the metes and bounds of the claimed invention, or if it is only further limiting the trained dynamic voting engine that is used such that the training is not required within the metes and bounds of the invention. As set forth in MPEP 2111.04.I, “wherein” clauses raise the question as to the limiting effect of the language in a claim. The metes and bounds of the claims are unclear. The rejection may be overcome by clarifying what steps are required to be performed. For compact examination, it is assumed that the performance of the training processes is required. Including the features of claims 33, 109, and 114 into claims 30, 106, and 111 would overcome this rejection.
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 30-34 and 106-115 are rejected under 35 U.S.C. 101 because the claimed invention is directed to one or more judicial exceptions without significantly more.
MPEP 2106 organizes judicial exception analysis into Steps 1, 2A (Prongs One and Two) and 2B as follows below. MPEP 2106 and the following USPTO website provide further explanation and case law citations: uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials.
Framework with which to Evaluate Subject Matter Eligibility:
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter;
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e. a law of nature, a natural phenomenon, or an abstract idea;
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application (Prong Two); and
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept.
Framework Analysis as Pertains to the Instant Claims:
Step 1
With respect to Step 1: yes, the claims are directed to method, system, and process, i.e., a process, machine, or manufacture within the above 101 categories [Step 1: YES; See MPEP § 2106.03].
Step 2A, Prong One
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. The MPEP at 2106.04(a)(2) further explains that abstract ideas are defined as:
mathematical concepts (mathematical formulas or equations, mathematical relationships and mathematical calculations);
certain methods of organizing human activity (fundamental economic practices or principles, managing personal behavior or relationships or interactions between people); and/or
mental processes (procedures for observing, evaluating, analyzing/ judging and organizing information).
With respect to the instant claims, under the Step 2A, Prong One evaluation, the claims are found to recite abstract ideas that fall into the grouping of mental processes (in particular procedures for observing, analyzing and organizing information) and mathematical concepts (in particular mathematical relationships and formulas) are as follows:
Independent claims 30, 106, and 111:
providing, by one or more computers, at least a portion of the first data and the second data as an input to a dynamic voting engine that has been trained to predict a target biological sample classification based on processing of multiple initial biological sample classifications;
processing, by one or more computers, the provided input data through the dynamic voting engine
determining, by one or more computers, a target biological sample classification for the biological sample based on the obtained output data
Dependent claim 31, 107, and 112:
providing the obtained data representing the cancer type classification, the obtained data representing the organ from which the biological sample originated, the obtained data representing the histology, and the second data as an input to the dynamic voting engine.
Dependent claims 33, 109, and 114:
generating training input data for input to the dynamic voting engine based on the obtained training data item;
processing the generated training input data through the dynamic voting engine
adjusting one or more parameters of the dynamic voting engine based on the level of similarity between the output data and the label of the obtained training data item.
Dependent claims 34, 110, and 115:
performing, by one or more computers and using a pairwise-analysis model, pairwise analysis of the biological signature using the first cancerous biological signature and the second cancerous biological signature;
generating, by one or more computers and based on the performed pairwise analysis, a likelihood that the cancerous neoplasm in the first portion of the body was caused by cancer in a second portion of the body
Dependent claims 32, 108, and 113 recite further steps that limit the judicial exceptions in independent claims 30, 106, and 111 and, as such, also are directed to those abstract ideas. For example, claims 32, 108, and 113 further limit the dynamic voting engine of claims 30, 106, and 111.;
The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation (BRI) and determined to each cover performance either in the mind and/or by mathematical operation because the method only requires a user to manually generate, determine, and adjust. Without further detail as to the methodology involved in “providing, by at least a portion of the first data and the second data as an input to a dynamic voting engine”, “determining, a target biological sample classification for the biological sample based on the obtained output data”, and “generating training input data for input” under the BRI, one may simply, for example, use pen and paper to provide input and determine a classification. Some of these steps and those recited in the dependent claims require mathematical techniques such as “processing, the provided input data through the dynamic voting engine”, processing the generated training input data through the dynamic voting engine”, “adjusting one or more parameters of the dynamic voting engine”, “using a pairwise-analysis model, pairwise analysis of the biological signature”, and “generating, by one or more computers and based on the performed pairwise analysis, a likelihood”.
Therefore, claims 30, 106, and 111 and those claims dependent therefrom recite an abstract idea [Step 2A, Prong 1: YES; See MPEP § 2106.04].
Step 2A, Prong Two
Because the claims do recite judicial exceptions, direction under Step 2A, Prong Two, provides that the claims must be examined further to determine whether they integrate the judicial exceptions into a practical application (MPEP 2106.04(d)). A claim can be said to integrate a judicial exception into a practical application when it applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception. This is performed by analyzing the additional elements of the claim to determine if the judicial exceptions are integrated into a practical application (MPEP 2106.04(d).I.; MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the judicial exceptions, the claim is said to fail to integrate the judicial exceptions into a practical application (MPEP 2106.04(d).III).
Additional elements, Step 2A, Prong Two
With respect to the instant recitations, the claims recite the following additional elements:
Independent claims 30, 106, and 111:
obtaining, by one or more computers, first data representing one or more initial classifications for the biological sample that were previously determined based on RNA sequences of the biological sample;
obtaining, by one or more computers, second data representing another initial classification for the biological sample that were previously determined based on DNA sequences of the biological sample;
obtaining, by one or more computers, output data generated by the dynamic voting engine based on the dynamic voting engine's processing of the provided input data;
Dependent claims 31, 107, and 112:
obtaining data representing a cancer type classification for the biological sample based the RNA sequences of the biological sample
obtaining data representing an organ from which the biological sample originated based on the RNA sequences of the biological sample
obtaining data representing a histology for the biological sample based on the RNA sequences of the biological sample
Dependent claims 33, 109, and 114:
obtaining a labeled training data item that includes (I) one or more initial classifications that include data indicating a cancer classification type, data indicating an initial organ of origin, data indicating a histology, or data indicating output of a DNA analysis engine and (II) a target biological sample classification
obtaining output data generated by the dynamic voting engine based on the dynamic voting engine's processing of the generated training input data
Dependent claims 34, 109, and 115:
receiving, by one or more computers, a biological signature representing the biological sample that was obtained from a cancerous neoplasm in a first portion of a body, wherein the model includes a cancerous biological signature for each of multiple different types of cancerous biological samples, wherein each of the cancerous biological signatures include at least a first cancerous biological signature representing a molecular profile of a cancerous biological sample from the first portion of one or more other bodies and a second cancerous biological signature representing a molecular profile of a cancerous biological sample from a second portion of one or more other bodies
storing, by one or more computers, the generated likelihood in a memory device
The claims also include non-abstract computing elements. For example, independent claim 30 includes a computer, claim 106 includes a system, and claim 110 includes computer-readable medium.
Considerations under Step 2A, Prong Two
With respect to Step 2A, Prong Two, the additional elements of the claims do not integrate the judicial exceptions into a practical application for the following reasons. Those steps directed to data gathering, such as “obtaining” and “receiving”, and to data outputting, such as “storing”, perform functions of collecting the data needed to carry out the judicial exceptions. Data gathering and outputting do not impose any meaningful limitation on the judicial exceptions, or on how the judicial exceptions are performed. Data gathering and outputting steps are not sufficient to integrate judicial exceptions into a practical application (MPEP 2106.05(g)).
Further steps directed to additional non-abstract elements of “a computer, system, and computer-readable medium” do not describe any specific computational steps by which the “computer parts” perform or carry out the judicial exceptions, nor do they provide any details of how specific structures of the computer, such as the computer-readable recording media, are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, and therefore the claim does not integrate that judicial exceptions into a practical application. The courts have weighed in and consistently maintained that when, for example, a memory, display, processor, machine, etc.… are recited so generically (i.e., no details are provided) that they represent no more than mere instructions to apply the judicial exception on a computer, and these limitations may be viewed as nothing more than generally linking the use of the judicial exception to the technological environment of a computer (MPEP 2106.05(f)).
Thus, none of the claims recite additional elements which would integrate a judicial exception into a practical application, and the claims are directed to one or more judicial exceptions [Step 2A, Prong 2: NO; See MPEP § 2106.04(d)].
Step 2B (MPEP 2106.05.A i-vi)
According to analysis so far, the additional elements described above do not provide significantly more than the judicial exception. A determination of whether additional elements provide significantly more also rests on whether the additional elements or a combination of elements represents other than what is well-understood, routine, and conventional. Conventionality is a question of fact and may be evidenced as: a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
With respect to the instant claims, the courts have found that receiving and outputting data are well-understood, routine, and conventional functions of a computer when claimed in a merely generic manner or as insignificant extra-solution activity (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network), Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, as discussed in MPEP 2106.05(d)(II)(i)).
As such, the claims simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (MPEP2106.05(d)). The data gathering steps as recited in the instant claims constitute a general link to a technological environment which is insufficient to constitute an inventive concept which would render the claims significantly more than the judicial exception (MPEP2106.05(g)&(h)).
With respect to claims 30, 106, and 111 and those claims dependent therefrom, the computer-related elements or the general purpose computer do not rise to the level of significantly more than the judicial exception. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, which the courts have found to not provide significantly more when recited in a claim with a judicial exception (see MPEP 2106.06(A)). The specification also notes that computer processors and systems, as example, are commercially available or widely used at [0011]. The additional elements are set forth at such a high level of generality that they can be met by a general purpose computer. Therefore, the computer components constitute no more than a general link to a technological environment, which is insufficient to constitute an inventive concept that would render the claims significantly more than the judicial exceptions (see MPEP 2106.05(b)I-III).
Taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claims as a whole do not amount to significantly more than the exception itself [Step 2B: NO; See MPEP § 2106.05].
Therefore, the instant claims are not drawn to eligible subject matter as they are directed to one or more judicial exceptions without significantly more. For additional guidance, applicant is directed generally to the MPEP § 2106.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 30, 106, and 111 is/are rejected under 35 U.S.C. 102(a)1 as being anticipated by Cappelli et al. (Cappelli, Eleonora, Giovanni Felici, and Emanuel Weitschek. "Combining DNA methylation and RNA sequencing data of cancer for supervised knowledge extraction." BioData mining 11.1 (2018), newly cited).
Claim 106 is directed to a system.
Cappelli discloses a system herein, the software and data require a system to be performed [p. 20 , additional files].
Claim 111 is directed to one or more computer-readable storage media
Cappelli discloses a “Software and data are available online” [p. 20, additional files] refers to a process that can be embodied in software that to analyze the combined data through tree- and rule-based classification algorithms.
Claims 30, 106, and 111 are directed to classifying a biological sample, the method comprising:
Cappelli discloses extracting more than 15,000 classification models (composed of gene sets), which allow to distinguish the tumoral samples from the normal ones") [abstract]. Cappelli further discloses to focus on DNA methylation and RNA sequencing, as these two NGS experiments have been proven to play an important role in knowledge discovery in cancer [p. 2, par. 2].
obtaining, by one or more computers, first data representing one or more initial classifications for the biological sample that were previously determined based on RNA sequences of the biological sample;
Cappelli discloses RNA sequencing is a next generation sequencing technique for the analysis of the transcriptome and its quantification [p. 2, par. 4]. Cappelli further discloses four main methods for measuring gene expression are used in practice [p. 2, par. 4]. Cappelli also discloses RNA sequencing matrix, "Class" discloses data representing one or more initial classifications based on RNA sequences [p. 5, tbl 1].
obtaining, by one or more computers, second data representing another initial classification for the biological sample that were previously determined based on DNA sequences of the biological sample;
Cappelli discloses "DNA methylation", "NGS methods based on bisulfite conversion to determine the percentage of methylated cytosines in a CpG island" [p.2, par. 3] in combination "DNA methylation matrix", "Class")discloses data representing another initial classification based on ONA sequences [p.6, tbl. 2].
providing, by one or more computers, at least a portion of the first data and the second data as an input to a dynamic voting engine that has been trained to predict a target biological sample classification based on processing of multiple initial biological sample classifications; processing, by one or more computers, the provided input data through the dynamic voting engine; obtaining, by one or more computers, output data generated by the dynamic voting engine based on the dynamic voting engine's processing of the provided input data; and determining, by one or more computers, a target biological sample classification for the biological sample based on the obtained output data.
Cappelli discloses "combine RNA sequencing and DNA methylation data … and test our method on genomic data related to three types of cancer", "analyze the combined data by means of supervised classification algorithms, extracting classification models, which are able to distinguish the samples in two classes (tumoral and normal) and which are composed of features that represent the genes related to the disease and the different NGS experiment.", wherein "supervised classification algorithms" are trained machine learning models for "distinguish[ing] the samples in two classes", i.e. determining a biological sample classification for the biological sample [p. 3, par. 3-4]. Cappelli further discloses the classification results of a Random Forest execution are computed by counting the votes for the most popular class predicted by the different trees and by assigning that class to the considered instance [p. 7, par. 4- p. 8, par. 1].
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
A. Claim(s) 31-34, 107-110, and 112-115 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cappelli et al. (Cappelli, Eleonora, Giovanni Felici, and Emanuel Weitschek. "Combining DNA methylation and RNA sequencing data of cancer for supervised knowledge extraction." BioData mining 11.1 (2018), newly cited), in view of Aharonov et al. (US 2014/0315739 Al, published on 10/23/2014, newly cited).
Claims 31, 107, and 112 are directed to wherein obtaining, by one or more computers, first data representing one or more initial classifications for the biological sample that were previously determined based on RNA sequences of the biological sample comprises: obtaining data representing a cancer type classification for the biological sample based the RNA sequences of the biological sample;
Cappelli discloses every BED file is related to an experiment on a given sample identified by its TCGA barcode, which contains several information about the sample including the type [p. 8, par. 4]. Cappelli further discloses the sample type permits to distinguish between normal and tumoral samples, which are the two classes used for classifying the experiments [p. 8,par. 4].
obtaining data representing an organ from which the biological sample originated based on the RNA sequences of the biological sample; and
Cappelli discloses the performed experiments to test our method and the results of the classification algorithms applied to the RNA sequencing and DNA methylation data of three cancer types from the Breast Invasive Carcinoma (BRCA), the Kidney Renal Papillary Cell Carcinoma (KIRP), and the Thyroid Carcinoma (THCA). [p. 8, par. 4] which reads on organ identification.
obtaining data representing a histology for the biological sample based on the RNA sequences of the biological sample, and
Cappelli discloses performing binary classifications (two classes, normal and tumoral), and we considered three cancers with both normal and tumoral samples [p. 9, par. 1] but is silent on histology for the biological sample.
However, Aharonov discloses gene expression signature for classification of tissue of origin of tumor samples [title]. Aharonov further discloses a validation set of 255 new FFPE tumor samples was used to assess the performance of the assay, representing 26 different tumor origins or “classes' where about half of the samples in the set were metastatic tumors to different sites (e.g., lung, bone, brain and liver) [0194].
wherein providing at least a portion of the first data and the second data as an input to the dynamic voting engine comprises: providing the obtained data representing the cancer type classification, the obtained data representing the organ from which the biological sample originated, the obtained data representing the histology, and the second data as an input to the dynamic voting engine.
Cappelli discloses "combine RNA sequencing and DNA methylation data … and test our method on genomic data related to three types of cancer", "analyze the combined data by means of supervised classification algorithms, extracting classification models, which are able to distinguish the samples in two classes (tumoral and normal) and which are composed of features that represent the genes related to the disease and the different NGS experiment.", wherein "supervised classification algorithms" are trained machine learning models for "distinguish[ing] the samples in two classes", i.e. determining a biological sample classification for the biological sample [p. 3, par. 3-4]. Cappelli further discloses the classification results of a Random Forest execution are computed by counting the votes for the most popular class predicted by the different trees and by assigning that class to the considered instance [p. 7, par. 4- p. 8, par. 1]. Cappelli doesn’t explicitly disclose a dynamic voting engine.
However, Aharonov discloses the KNN algorithm calculated the distance (Pearson correlation) of any sample to all samples in the training set, and classifies the sample by the majority Vote of the k samples which are most similar [0189], which reads on a dynamic voting engine.
Claims 32, 108, and 113 are directed to wherein the dynamic voting engine comprises one or more machine learning models.
Cappelli discloses the application of machine learning algorithms, we show the advantage of combining DNA methylation and RNA sequencing data, i.e., the increase of extracted knowledge resulting in combinations of genes from both experimental strategies [p. 4, par. 2].
However, Aharonov discloses the KNN algorithm calculated the distance (Pearson correlation) of any sample to all samples in the training set, and classifies the sample by the majority Vote of the k samples which are most similar [0189], which reads on a dynamic voting engine.
Claims 33, 109, and 114 are directed to wherein training the dynamic voting engine comprises: obtaining a labeled training data item that includes (I) one or more initial classifications that include data indicating a cancer classification type, data indicating an initial organ of origin, data indicating a histology, or data indicating output of a DNA analysis engine and
Cappelli discloses "combine RNA sequencing and DNA methylation data … and test our method on genomic data related to three types of cancer", "analyze the combined data by means of supervised classification algorithms, extracting classification models, which are able to distinguish the samples in two classes (tumoral and normal) and which are composed of features that represent the genes related to the disease and the different NGS experiment.", wherein "supervised classification algorithms" are trained machine learning models for "distinguish[ing] the samples in two classes", i.e. determining a biological sample classification for the biological sample [p. 3, par. 3-4]. Cappelli further discloses the classification results of a Random Forest execution are computed by counting the votes for the most popular class predicted by the different trees and by assigning that class to the considered instance [p. 7, par. 4- p. 8, par. 1]. Cappelli doesn’t explicitly disclose a dynamic voting engine.
However, Aharonov discloses the KNN algorithm calculated the distance (Pearson correlation) of any sample to all samples in the training set, and classifies the sample by the majority Vote of the k samples which are most similar [0189], which reads on a dynamic voting engine.
(II) a target biological sample classification; generating training input data for input to the dynamic voting engine based on the obtained training data item; processing the generated training input data through the dynamic voting engine; obtaining output data generated by the dynamic voting engine based on the dynamic voting engine's processing of the generated training input data;
Cappelli discloses "combine RNA sequencing and DNA methylation data … and test our method on genomic data related to three types of cancer", "analyze the combined data by means of supervised classification algorithms, extracting classification models, which are able to distinguish the samples in two classes (tumoral and normal) and which are composed of features that represent the genes related to the disease and the different NGS experiment.", wherein "supervised classification algorithms" are trained machine learning models for "distinguish[ing] the samples in two classes", i.e. determining a biological sample classification for the biological sample [p. 3, par. 3-4]. Cappelli further discloses the classification results of a Random Forest execution are computed by counting the votes for the most popular class predicted by the different trees and by assigning that class to the considered instance [p. 7, par. 4- p. 8, par. 1]. Cappelli discloses C4.5 is an algorithm for the generation of decision trees used for classification. The algorithm takes as input a set of classified data (training set) and the output is composed by leaf nodes of the tree, which define the belonging to a class attribute.
Cappelli doesn’t explicitly disclose a dynamic voting engine.
However, Aharonov discloses the KNN algorithm calculated the distance (Pearson correlation) of any sample to all samples in the training set, and classifies the sample by the majority Vote of the k samples which are most similar [0189], which reads on a dynamic voting engine.
adjusting one or more parameters of the dynamic voting engine based on the level of similarity between the output data and the label of the obtained training data item.
Cappelli discloses optimizing an arbitrary number of parameters according to input data and number of cross validation folds [p. 9, par. 2] but is silent on it being based on the level of similarity between the output data and the label of the obtained training data item.
However, Aharonov discloses the training samples are then ranked according to this metric, and the samples with the highest values of the metric (or lowest values, according to the type of metric) are identified, indicating those samples that are most similar to the test sample [0090].
Claims 34, 110, and 115 are directed to wherein previously determining an initial classification for the biological sample based on DNA sequences of the biological sample comprises: receiving, by one or more computers, a biological signature representing the biological sample that was obtained from a cancerous neoplasm in a first portion of a body,
wherein the model includes a cancerous biological signature for each of multiple different types of cancerous biological samples, wherein each of the cancerous biological signatures include at least a first cancerous biological signature representing a molecular profile of a cancerous biological sample from the first portion of one or more other bodies and a second cancerous biological signature representing a molecular profile of a cancerous biological sample from a second portion of one or more other bodies; performing, by one or more computers and using a pairwise-analysis model, pairwise analysis of the biological signature using the first cancerous biological signature and the second cancerous biological signature; generating, by one or more computers and based on the performed pairwise analysis, a likelihood that the cancerous neoplasm in the first portion of the body was caused by cancer in a second portion of the body; and storing, by one or more computers, the generated likelihood in a memory device.
Cappelli is silent on at least a first cancerous biological signature representing a molecular profile of a cancerous biological sample from the first portion of one or more other bodies and a second cancerous biological signature representing a molecular profile of a cancerous biological sample from a second portion of one or more other bodies.
However, Aharonov discloses tumor samples used in the study included primary tumors and metastases of defined origins, according to clinical records [0177]. Aharonov also discloses a metric which is then compared to previously measured samples or to a threshold [0078 and 0089] which reads on an evaluation. Aharonov further discloses expression level of the nucleic acids is used to classify a test sample by comparison to a training set of samples [0090]. Aharonov also discloses the test sample is compared in turn to each one of the training set samples, where each such pairwise comparison is performed by comparing the expression levels of one or multiple nucleic acids between the test sample and the specific training sample [0090]. Aharonov further discloses comparing said expression profile to a reference expression profile by using a classifier algorithm whereby the expression of any of said nucleic acid sequences or combinations thereof allows the identification of the tissue of origin of said sample [0014] which reads on a different subject. Aharonov also discloses a validation set of 255 new FFPE tumor samples was used to assess the performance of the assay, representing 26 different tumor origins or “classes' where about half of the samples in the set were metastatic tumors to different sites (e.g., lung, bone, brain and liver) [0194].
In regards to claim(s) 31-34, 107-110, and 107-115, 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 Cappelli with Aharonov as they both discloses gene expression signature for classification of tissue of origin of tumor samples. The motivation would have been to include the histology data of Aharonov with the sample data of Cappelli to allow optimization of treatment, and determination of specific therapy as disclosed by Aharonov [abstract], said practitioner would have readily predicted that the combination would successfully result in tissue of origin identification because it is a known sampling method as disclosed by Aharonov [0177].
Conclusion
No claims are allowed.
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/D.M.B./Examiner, Art Unit 1685
/Soren Harward/Primary Examiner, TC 1600