DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-10 are pending and examined herein.
Claims 1-10 are rejected.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. Claims 1-10 are granted the claim to the benefit of priority to Foreign applications KR10-2022-0059550 filed 16 May 2022 and KR10-2021-0126398 filed 24 September 2021. Thus, the effective filling date of claims 1-10 is 24 September 2021.
Information Disclosure Statement
The information disclosure statements (IDS) were received on 25 August 2023 and 05 November 2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements have been considered by the examiner.
Drawings
The drawings received 25 August 2023 are objected to for the reasons provided below.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: “S230” (in Fig. 2), “S240” (in Fig. 2). Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: “S540” (in [0060]) and “125” (in [0116]). Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 112
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.
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 1-10 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 1 recites “generating gene expression pattern information of a sample collected from tissue in which metastatic cancer has occurred” and claim 10 recites “a processor, by executing one or more of the stored instructions, performing: an operation of generating gene expression information of a sample collected from tissue in which metastatic cancer has occurred” which renders the metes and bounds of the claim indefinite. The indefiniteness arises because it is unclear what steps are encompassed by “generating gene expression pattern information of a sample collected from tissue in which metastatic cancer has occurred” (e.g., is this meant to be a step of performing a physical assaying step on the sample or is this meant to be a step of analyzing produced assay results to generate information as a gene expression pattern). Dependent claims 2-9 are rejected by virtue of their dependency on a rejected claim without alleviating the indefiniteness. For the sake of furthering examination this limitation will be interpreted as a step of analyzing produced assay results to generate information as a gene expression pattern.
Claims 1 and 10 recites “for each cancer type”, claim 4 recites “for each cancer type”, and claim 7 “for each primary site” which renders the metes and bounds of the claims indefinite. The indefiniteness arises because it is unclear what cancer types and primary sites are encompassed in “for each cancer type” and “for each primary site” (e.g., is this meant to encompass all possible cancer types/primary sites or is this meant to encompass cancer types in a set of cancer types and primary sites in a set of primary sites). Dependent claims 2, 3, and 5-9 are rejected by virtue of their dependency on a rejected claim without alleviating the indefiniteness. For the sake of furthering examination, this limitation will be interpreted as “comparing the gene expression pattern information… with pre-learned gene expression pattern information for each cancer type in a set of cancer types”.
Claims 1 and 10 recite “the gene expression pattern information derived from the tissue has been removed”, claim 3 recites “wherein the gene expression pattern information derived from the tissue is…” and claim 5 recites “converting the gene expression pattern information derived from the tissue into…” which renders the metes and bounds of the claim indefinite. The indefiniteness arises because it is unclear if “the gene expression pattern information derived from the tissue” is referring to “gene expression pattern information derived from pre-learned tissue” or if “the gene expression pattern information derived from the tissue” is referring to “gene expression pattern information of the sample collected from the tissue in which metastatic cancer has occurred” (i.e., which tissue is “the tissue” referring to in the claim). Dependent claims 2-9 are rejected by virtue of their dependency on a rejected claim without alleviating the indefiniteness. For the sake of furthering examination, this limitation will be interpreted as referring to gene expression pattern information derived from the pre-learned tissue.
Examiners Comment
Regarding instant claim 10, if the limitation of “an operation of generating gene expression information of a sample collected from tissue in which metastatic cancer has occurred” is meant to be a physical assaying step on the sample, then the claim will fail to comply with the enablement requirement under 35 U.S.C. 112(a) because the claim will contain subject matter which was not described in such a way as to enable one skilled in the art to make and/or use the invention. The claim will fail to comply with the enablement requirement because the subject matter of a system with a memory and processor to perform an operation of performing a physical assaying step to generate gene expression data from a sample is not described in such a way as to enable one skilled in the art to make and/or use the invention. One skilled in the art would recognize to perform a physical assaying step to generate gene expression data from a sample requires a specific machine, such as a next generation sequencing machine, as shown by Costa et al. (BioMed Research International, 2010, 853916, 19 pages, 2010). If this limitation in claim 10 is meant to be a physical assaying step then the apparatus of claim 10 should include positively recited structure of a sequencer which is capable of performing the process of performing the physical assay.
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
(Step 1)
Claims 1-9 fall under the statutory category of a process and claim 10 falls under the statutory category of a machine.
(Step 2A Prong 1)
Under the BRI, the instant claims recite judicial exceptions that are an abstract idea of the type that is in the grouping of a “mental process”, such as procedures for evaluating, analyzing or organizing information, and forming judgement or an opinion. The instant claims further recite judicial exceptions that are an abstract idea of the type that is in the grouping of a “mathematical concept”, such as mathematical relationships and mathematical equations.
Independent claims 1 and 10 recite mental processes of generating gene expression pattern information of a sample collected from tissue in which metastatic cancer has occurred, removing gene expression pattern information derived from pre-learned tissue from the gene expression pattern information of the sample collected from the tissue in which the metastatic cancer has occurred, comparing the gene expression pattern information from which the gene expression pattern information derived from the tissue has been removed with pre-learned gene expression pattern information for each cancer type, and specifying a primary site of the sample collected from the tissue in which the metastatic cancer has occurred.
Independent claims 1 and 10 recite mathematical concepts of removing gene expression pattern information derived from pre-learned tissue from the gene expression pattern information of the sample collected from the tissue in which the metastatic cancer has occurred and specifying a primary site of the sample collected from the tissue in which the metastatic cancer has occurred.
Dependent claim 5 recites mental processes of converting the gene expression pattern information of the sample collected from the tissue in which the metastatic cancer has occurred into a first vector, converting the gene expression pattern information derived from the tissue into second vector, and performing a difference calculation of the second vector with respect to the first vector. Dependent claim 5 recites mathematical concepts of converting the gene expression pattern information of the sample collected from the tissue in which the metastatic cancer has occurred into a first vector, converting the gene expression pattern information derived from the tissue into second vector, and performing a difference calculation of the second vector with respect to the first vector. Dependent claim 6 recites a mental process and mathematical concept of specifying at least one of a plurality of pre-learned primary sites. Dependent claim 7 recites a mental process and mathematical concept of outputting a probability value for each primary site.
The claims recite mental processes of analyzing/evaluating gene expression pattern information, making observations/judgments about gene expression pattern information, and organizing gene expression pattern information as generating gene expression pattern information of a sample collected from tissue in which metastatic cancer has occurred (which encompasses analyzing information produced by an assay on a sample to generate gene expression pattern information such as identifying the number of transcripts for genes measured in the assay or analyzing intensity data produced from a microarray platform), removing gene expression pattern information derived from pre-learned tissue from the gene expression pattern information of the sample collected from the tissue in which the metastatic cancer has occurred (which encompasses converting the gene expression pattern information from the pre-learned tissue sample and the tissue sample in which the metastatic cancer has occurred into vectors through organizing the number/abundance of transcripts for each gene in the samples into numerical vectors and then calculating the difference between these numerical vectors utilizing mathematical operations), comparing the gene expression pattern information from which the gene expression pattern information derived from the tissue has been removed with pre-learned gene expression pattern information for each cancer type (which encompasses making an observation and judgment through comparing the resulting vector from the difference calculation representing gene expression pattern information to gene expression pattern information for each cancer type), and specifying a primary site of the sample collected from the tissue in which the metastatic cancer has occurred (which encompasses generating a numerical value such as a percentage to represent the determined primary site of the sample based on the comparison). The human mind is capable of analyzing/evaluating gene expression information, organizing information into numerical feature vectors and calculating a difference between numerical feature vectors, making comparisons between gene expression information through observations of gene expression data and judgments based on observations, and specifying a primary site of a sample through analyzing/evaluating the gene expression data to generate a value representing a classification specifying a primary site (or multiple values representing a classification specifying multiple primary sites). Thus, the claims recite mental processes.
The claims recite mathematical concepts of removing gene expression pattern information derived from pre-learned tissue from the gene expression pattern information of the sample collected from the tissue in which the metastatic cancer has occurred (which encompasses mathematical calculations of converting gene expression pattern information into a first vector and second vector through adding up transcripts for each gene in the gene expression pattern information to produce vectors with numerical values representing the amount of gene expression for each gene in the gene expression pattern information and calculating a difference between these vectors holding numerical values to produce a vector holding numerical values as a result see instant disclosure [0080]-[0087], [0091], and [0093]-[0097] and dependent claim 5) and specifying a primary site of the sample collected from the tissue in which the metastatic cancer has occurred (which encompasses calculating a numerical probability value/result value such as a percent for a primary site of the sample see instant disclosure [0049]-[0050] and dependent claim 7). The MPEP states that “There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation” (see MPEP 2106.04(a)(2)(I)(C)). Thus, the claims recite mathematical concepts.
Dependent claims 2-4, 8, and 9 further limit the mental process/mathematical concept recited in the independent claim but do not change their nature as a mental process/mathematical concept. Thus, claims 1-10 recite abstract ideas.
(Step 2A Prong 2)
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). Integration into a practical application is evaluated by identifying whether there are any additional elements recited in the claim and evaluating those additional elements to determine whether they integrate the exception into a practical application.
The additional element in claim 10 of a generic computer system (i.e., a memory storing one or more instructions and a processor which executes one or more of the stored instructions) to perform the judicial exceptions does not integrate the judicial exceptions into a practical application because this is using a generic computer to perform judicial exceptions without an improvement to computer functionality (see MPEP 2106.04(d)(1)). This additional element only interacts with the judicial exceptions in a manner by invoking a computer as a tool to perform the abstract data analysis.
Thus, the additional elements do not integrate the judicial exceptions into a practical application and claims 1-10 are directed to the abstract idea.
(Step 2B)
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because:
The additional element in claim 10 of a generic computer system (i.e., a memory storing one or more instructions and a processor which executes one or more of the stored instructions) to perform judicial exceptions is conventional as shown by MPEP 2106.05(b) and MPEP 2106.05(d)(II).
Thus, the additional elements are not sufficient to amount to significantly more than the judicial exception because they are conventional.
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.
Claims 1-8 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Michuda et al. (US20210142904A1) in view of Igartua et al. (US20200210852A1).
Independent claim 1 is directed to method for diagnosing cancer of unknown primary site using artificial intelligence, wherein the method comprises: generating gene expression pattern information of a sample collected from tissue in which metastatic cancer has occurred
Michuda et al. shows determining a cancer type of a somatic tissue a subject having unknown origins which includes determining sequence features from RNA sequence reads (Michuda et al. [0020], [0026], [0077]). Michuda et al. shows that somatic biopsies are often a heterogenous mixture including stomal cells and tumor cells (Michuda et al. [0145]). Michuda et al. shows performing the classification using liver metastatic samples and liver is a representative type of cancer because many tumors of unknown origins may be found in this organ (Michuda et al. [0137]). Michuda et al. further shows that the classification model works well in metastatic, low-purity settings (Michuda et al. [0137]).
removing gene expression pattern information derived from pre-learned tissue from the gene expression pattern information of the sample collected from the tissue in which the metastatic cancer has occurred
Michuda et al. shows the sequencing information is deconvoluted prior to feature identification where deconvolution comprises identifying sequence reads in the sequencing information that originate from healthy tissue and removing said sequence reads from the sequencing information to decrease background noise (Michuda et al. [0216]).
comparing the gene expression pattern information from which the gene expression pattern information derived from the tissue has been removed with pre-learned gene expression pattern information for each cancer type in the set of cancer types and specifying a primary site of the sample collected from the tissue in which the metastatic cancer has occurred.
Michuda et al. shows applying a set of sequence features to a classification model trained to distinguish between each cancer type in a set of cancer types, thus determining the cancer type of the somatic tissue and that the classification model provides, for each respective cancer type in the set of cancer types an indication, such as a probability or a binary prediction, that the somatic tissue is the respective cancer type which is interpreted as specifying a primary site of the sample (e.g., if the cancer type is predicted as being lung cancer then the primary site of the sample is the lung) (Michuda et al. [0367]-[0371]). It is interpreted that the classification model is a process of comparing gene expression pattern information with pre-learned gene expression information for each cancer type because the classification model was trained with RNA expression data from training subjects where the training dataset is prepared by matching diagnostic labels to each respective tissue sample and/or each plurality of sequence reads derived therefrom, for each training subject in the plurality of training subjects where the diagnostic labels include a patient’s disease condition (Michuda et al. [0256] and [0395]-[0396]).
Michuda et al. does not show that the step of removing gene expression pattern information is removing gene expression pattern information derived from pre-learned tissue.
Like Michuda et al., Igartua et al. shows analyzing RNA expression data of cancer tissue samples of subjected. Igartua et al. shows for a particular sample, a machine learning algorithm (which was trained to cluster samples with similar gene expressions) may identify the percentage of membership in each cluster (Igartua et al. [0087]). Igartua et al. shows if a cluster was derived from 1000 samples, each sample may be plotted as a data point with the grade of membership percentage in that cluster one the x-axis and the expression level of a given gene in the sample on the y-axis and the equation of a regression line may be calculated to approximate the plotted data points and using the equation of the regression line to calculate the expression level of the gene that is explained by that percentage of membership in that cluster by inputting the membership percentage of the newest sample (Igartua et al. [0087]). Igartua et al. further shows to remove the effect of that cluster the calculated expression level may be subtracted from the total gene expression level measured in the mixture sample for that gene (Igartua et al. [0087]). Igartua et al. shows by calculating each cluster's effect on the expression levels of all genes associated with the cluster, these factors may be regressed out resulting in deconvoluted RNA expression data (Igartua et al. [0088]). This is interpreted as a process of removing this gene expression information from pre-learned tissue (which is modeled and learned by the machine learning model/algorithm).
Claim 10 is directed to an apparatus for diagnosing cancer of unknown primary site using artificial intelligence, the apparatus comprising: a memory storing one or more instructions; and a processor, by executing one or more of the stored instructions, performing: the method of claim 1.
Michuda et al. shows an apparatus including memory with instructions and a processor for executing stored instructions for performing cancer classifications (Michuda et al. [0116]). It is noted that the steps performed are addressed above and are obvious over Michuda et al. in view of Igartua et al. as set out above.
Claim 2 is directed to wherein the sample collected from the tissue in which the metastatic cancer has occurred comprises normal tissue and cancer tissue of an organ in which the metastatic cancer has occurred.
Michuda et al. shows that somatic biopsies are often a heterogenous mixture of stomal cells (which is interpreted as being normal tissue) and tumor cells (which is interpreted as cancer tissue) (Michuda et al. [0145]).
Claim 3 is directed to wherein the gene expression pattern information derived from the pre-learned tissue is specific gene expression pattern information expressed in normal tissue of an organ.
Michuda et al. shows the sequencing information is deconvoluted prior to feature identification where deconvolution comprises identifying sequence reads in the sequencing information that originate from healthy tissue and removing said sequence reads from the sequencing information (Michuda et al. [0216]).
Claim 4 is directed to wherein the gene expression pattern information for each cancer type in the set of cancer types is specific gene expression pattern information expressed in cancer tissue of which the primary site is specified.
Michuda et al. shows that the training dataset is prepared by matching diagnostic labels to each respective tissue sample and/or each plurality of sequence reads derived therefrom, for each training subject in the plurality of training subjects where the diagnostic labels include a patient’s disease condition which is interpreted as the gene expression information for each cancer type in the set of cancer types that are used in the training of the cancer classification model is specific to the cancer type (i.e., the diagnostic label given to the training data which identifies the cancer type) (Michuda et al. [0256] and [0395]-[0396]).
Claim 5 is directed to wherein the removing of the gene expression pattern information derived from the pre-learned tissue from the gene expression pattern information of the sample collected from the tissue in which the metastatic cancer has occurred comprises: converting the gene expression pattern information of the sample collected from the tissue in which the metastatic cancer has occurred into a first vector;
Igartua et al. shows the RNA expression data may be configured in a N×G matrix, where N is the number of samples and G is the number of genes and it is interpreted that for a single particular sample this is represented as a 1xG data structure which is interpreted as being a vector (Igartua et al. [0072]).
converting the gene expression pattern information derived from the pre-learned tissue into a second vector;
Igartua et al. shows for a particular sample, a machine learning algorithm (which was trained to cluster samples with similar gene expressions) may identify the percentage of membership in each cluster (Igartua et al. [0087]). Igartua et al. shows if a cluster was derived from 1000 samples, each sample may be plotted as a data point with the grade of membership percentage in that cluster one the x-axis and the expression level of a given gene in the sample on the y-axis and the equation of a regression line may be calculated to approximate the plotted data points and using the equation of the regression line to calculate the expression level of the gene that is explained by that percentage of membership in that cluster by inputting the membership percentage of the newest sample (Igartua et al. [0087]). This is interpreted as converting gene expression information from the pre-learned tissue (which is interpreted as being the clusters which are learned by the machine learning algorithm) to a vector holding a specific value of a calculated expression level of a particular gene based on percent membership to that cluster.
and performing a difference calculation of the second vector with respect to the first vector.
Igartua et al. further shows to remove the effect of that cluster (which is interpreted as pre-learned tissue expression information) the calculated expression level may be subtracted from the total gene expression level measured in the mixture sample for that gene (Igartua et al. [0087]). Igartua et al. shows by calculating each cluster's effect on the expression levels of all genes associated with the cluster, these factors may be regressed out resulting in deconvoluted RNA expression data (Igartua et al. [0088]).
Claim 6 is directed to wherein the specifying of the primary site of the sample collected from the tissue in which the metastatic cancer has occurred comprises specifying at least one of a plurality of pre-learned primary sites. Claim 7 is directed to wherein the specifying of the primary site of the sample collected from the tissue in which the metastatic cancer has occurred comprises outputting a probability value for each primary site.
Michuda et al. shows that the classification model provides, for each respective cancer type in the set of cancer types an indication, such as a probability, that the somatic tissue is the respective cancer type/primary site (Michuda et al. [0367]).
Claim 8 is directed to wherein the gene expression pattern information of the sample collected from the tissue in which the metastatic cancer has occurred, the gene expression pattern information derived from the tissue, and the gene expression pattern information from which the gene expression pattern information derived from the tissue has been removed are RNA sequence information.
Michuda et al. shows that the gene expression pattern information is RNA sequence information produced by RNA-seq methodologies or microarray platforms (Michuda et al. [0152] and [0153]).
An invention would have been obvious to one or ordinary skill in the art if some motivation in the prior art would have led that person to modify reference teachings to arrive at the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to have modified the process of deconvolution process which identifies sequence reads in the sequencing information that originate from healthy tissue and removes said sequence reads from the sequencing information to decrease background noise of Michuda et al. with the specific deconvolution process which removes gene expression pattern information from pre-learned tissue data using a machine learning model to assign membership identify based on gene expression data of a sample which contains a mixture of cell types and correcting this data by subtracting gene expression values based on percent membership to clusters (which are pre-learned by a machine learning model) of Igartua et al. because this would allow for a process that removes background tissue gene expression values for multiple genes from the sample containing a mixture of background tissue and cancer cells based on a percentage of membership to multiple clusters (which are clustered pre-learned tissue expression pattern information by a machine learning model). One would be motivated to make this modification because Igartua et al. shows the deconvoluted gene expression data may be used in downstream gene expression data analyses and may yield more accurate results than analyzing mixed sample gene expression data (Igartua et al. [0064]). One would have a reasonable expectation of success because Michuda et al. shows performing a deconvolution process to remove background tissue gene expression information before cancer type prediction while Igartua et al. provides a specific deconvolutional process using gene expression data from pre-learned tissue to remove the contribution of gene expression information based on percent membership to clusters of pre-learned tissue which includes background tissue.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Michuda et al. in view of Igartua et al. as applied to claim 1 above, and further in view of Dumur et al. (The Journal of Molecular Diagnostics 10.1 (2008): 67-77).
Claim 9 is directed to wherein the RNA sequence information is mRNA sequence information.
Michuda et al. in view of Igartua et al. as applied to claim 1 does not explicitly show wherein the RNA sequence information is mRNA sequence information.
Like Michuda et al. in view of Igartua et al., Dumur et al. shows using microarrays to derive gene expression profiles to analyze the tissue of origin in cancer. Dumur et al. shows utilizing mRNA marker data, which is interpreted at mRNA sequence information, to analyzing tissue of origin of a tumor (Dumur et al. page 70 left col. and page 74 right col.).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to have substituted the RNA sequence information which provides the gene expression pattern information utilized in the method of specifying a primary site of the sample collected from the tissue in which the metastatic cancer has occurred of Michuda et al. in view of Igartua et al. to be mRNA marker data of Dumur et al. because Michuda et al. in view of Igartua et al. shows utilizing microarray data to derived gene expression pattern information from a sample to analyze to specify a primary site of the sample (Michuda et al. [0153]) while Dumur et al. shows generating gene expression pattern information for mRNA markers using a microarray platform to determine the tissue of origin of the sample in which the mRNA was extracted and would yield predictable results of deriving gene expression pattern information to be used in specifying the primary site of a cancer sample using gene expression information produced by mRNA markers measured using a microarray platform.
Conclusion
No claims are allowed.
This Office action is a Non-Final action. A shortened statutory period for reply to this action is set to expire THREE MONTHS from the mailing date of this action.
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