DETAILED ACTION
Status of Claims
This is the first office action on the merits in response to the application filed on 31 May 2023.
Claim(s) 1-15 are currently pending and have been examined.
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 .
Priority
This application claims priority of KR Application No. 10-202-0183149 filed on 24 December 2020 and PCT/KR2021/019494 filed on 21 December 2021. Applicant’s claim for the benefit of this prior filed application is acknowledged.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 31 May 2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “learning data generation unit” initially recited in claim 1, “model training unit” initially recited in claim 1, “classification unit” initially recited in claim 1, and ”model verification unit” initially recited in claim 7.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claim(s) 1-15 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 9, which is representative of claim(s) 1, recites in part: a diagnostic classification method, comprising: a learning data generation step extracting each expressed gene specifically expressed in a diagnosis name using gene expression amount information obtained from each patient group corresponding to a diagnosis name for each case and generating the expressed gene and an expression amount of the expressed gene according to the diagnosis name as learning data; and a classification step performing classification with the diagnosis name by applying new gene expression amount information to the classification model. This limitation describes evaluation and judgement, and as such sets forth a mathematical concept. Alternatively, this limitation describes instructions for classifying data to generate a diagnosis, which falls within the managing personal behavior sub-grouping, and thus sets forth a certain method of organizing human activity.
The claim also recites a model training step training a classification model for classifying the diagnosis name using the learning data. When given its broadest reasonable interpretation in light of the background, these limitations describe a series of mathematical calculations. As such, this limitation sets forth a mathematical concept.
While the above limitations set forth concepts that fall within different groupings of abstract ideas, they all set forth abstract ideas. As such, per MPEP 2106.04(II)(B), these concepts are considered together as a single abstract idea for further analysis. Therefore the claims are determined to recite an abstract idea.
MPEP 2106, reflecting the 2019 PEG, directs examiners at Step 2A Prong Two to consider whether the additional elements of the claims integrate a recited abstract idea into a practical application.
Claim 1 recites a device comprising units. This device may be interpreted as a generic computing device used to implement the abstract idea. Per MPEP 2106.05(f), implementing an abstract idea on a generic computing device does not integrate an abstract idea into a practical application in Step 2A Prong Two, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea on a generic computer. As such, this additional element does not integrate the abstract idea into a practical application.
There are no further additional elements. As the additional element does not integrate the abstract idea into a practical application, the claims are determined to be directed to an abstract idea.
At Step 2B of the Mayo/Alice analysis, examiners are to consider whether the additional elements amount to significantly more than the abstract idea.
As previously noted, the claims recite an additional element which may be interpreted as generic computing devices used to implement the abstract idea. However, per MPEP 2106.05(f), implementing an abstract idea on a generic computing does not add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea on a generic computer. As such, this additional element does not amount to significantly more.
There are no further additional elements. Therefore, when considered individually and as an ordered combination, the additional elements of the independent claims do not amount to significantly more than the judicial exception. Thus the independent claims are not patent eligible.
Dependent claims 2-8 and 10-15 further describe the abstract idea, but the claims continue to recite an abstract idea. Dependent claims 2-8 and 10-15 do not recite any further additional elements. The previously identified additional elements, individually and as a combination, fail to integrate the narrowed abstract idea into a practical application. Therefore the claims remain directed to an abstract idea. The previously identified additional elements, individually and as a combination, do not amount to significantly more than the narrowed abstract idea. Thus as the dependent claims remain directed to a judicial exception, and as the additional elements of the claims do not amount to significantly more, the dependent claims are not patent eligible.
Claim Rejections - 35 USC § 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.
Claim(s) 1-15 is/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. Claims not listed below are rejected for dependency.
Claim limitations “learning data generation unit” (claim 1), “model training unit” (claim 1), “classification unit” (claim 1), ”model verification unit” (claim 7), “learning data generation step” (claim 9), “model training step” (claim 9), “classification step” (claim 9), and “model verification step” (claim 15) invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The disclosure does not clearly link structure and algorithms sufficient for performing the functions of the “unit” limitations, and does not clearly link acts sufficient for the “step” limitations. One of ordinary skill in the art could not identify what structure and algorithm, or what act, of the written disclosure performs the claimed functions. Therefore claims 1, 7, 9, and 15 are indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim 1 recites “extracting each expressed gene specifically expressed in a diagnosis name”. Claim 1 further recites “classifying the diagnosis name using the learning data”. However, one of ordinary skill in the art would not understand the “diagnosis name” to express genes, nor would one of ordinary skill in the art understand the claim to involve determining a type of class of the “diagnosis name”. These disclosures, along with the disclosure of the dependent claims, suggests that the term “diagnosis name” is being used in a non-standard way contrary to its ordinary meaning. However, the disclosure does not appear to provide a definition for the non-standard way the term is being used. As such, one of ordinary skill in the art would not be able to determine the boundaries of the claim, rendering the claim indefinite.
For the purpose of examination, the “diagnosis name” is understood to refer to a condition. Thus the first identified limitation is interpreted as “extracting each expressed gene specifically expressed [in conjunction with a condition]”, and the second identified limitation is interpreted as “classifying the [condition] using the learning data]”.
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.
Claim(s) 1, 6-9, 14, and 15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Golub et al. (US 2003/0225526 A1).
Regarding Claim 1 and 9: Golub discloses a diagnostic classification device, comprising: a learning data generation unit extracting each expressed gene specifically expressed in a diagnosis name using gene expression amount information obtained from each patient group corresponding to a diagnosis name for each case and generating the expressed gene and an expression amount of the expressed gene according to the diagnosis name as learning data; a model training unit training a classification model for classifying the diagnosis name using the learning data; and a classification unit performing classification with the diagnosis name by applying new gene expression amount information to the classification model (Expression data of 144 primary tumors was obtained using oligonucleotide microarrays containing 16,063 oligonucleotide probe sets. Centralized histological review was used to confirm each clinical diagnosis. See at least [0059]. Also: Expression data can be obtained by assaying for the level of a gene expression product (e.g., RNA, peptide or protein). For example, a large expression database containing the expression profiles of more than 16,000 markers from 218 tumor samples representing 14 common human cancer classes was created as a suitable database for use in methods described herein. See at least [0031]. Also: The gene expression datasets were obtained following an experimental protocol shown schematically in FIG. 1. See at least [0071]. Also: The dataset is preferably manipulated using a supervised learning algorithm (see FIG. 2) because this class of algorithms was found to more accurately predict tumor class (FIG. 3 and Examples). Supervised learning involves "training" a classifier to recognize distinctions among, for example, the 14 clinically-defined tumor classes in the dataset described in the Exemplification, based on gene expression patterns. See at least [0035]. Also: "training" a classifier to recognize the distinctions among the 14 clinically-defined tumor classes based on gene expression patterns. See at least [0061]. Also: This result was confirmed by training the multi-class SVM classifier on the entire set of 144 samples and applying this classifier to an independent test set of 54 tumor samples. See at least [0064]. Also: The steps of the methods described herein can be performed in a computer system. See at least [0009]).
Regarding Claim 6 and 14: Golub discloses the above limitations. Golub further discloses wherein the model training unit calculates a difference between diagnosis names using a support vector machine (SVM) and generates a classification model for performing classification with the diagnosis name from the gene expression amount information based on the difference, and wherein the classification model plots the learning data as a dot in a specific dimensional space and classifies the dot based on a hyperplane (Each classifier uses the support vector machine (SVM) algorithm to define a hyperplane that best separates training samples in these two classes. Test samples are sequentially presented to each of 14 OVA classifiers and the sample's class is determined by the classifier with the highest confidence, as determined by the distance from the hyperplane. In the example shown, the sample is predicted to be breast cancer. See at least [0020] and Fig. 5. Also: The SVM algorithm considers all profiled markers and defines a hyperplane that best separates tumor samples from two classes (FIG. 5). An unknown sample's position relative to this hyperplane determines its membership in one or other class (e.g., `breast cancer` versus `not breast cancer`). 14 separate OVA classifiers classify each sample. The confidence of each OVA SVM prediction is based on the distance of the test sample to each hyperplane, with a value of 0 indicating that a sample falls on a hyperplane. The classifier then assigns a sample to the class with the highest confidence among the 14 pairwise OVA analyses. See at least [0062]).
Regarding Claim 7 and 15: Golub discloses the above limitations. Golub further discloses a model verification unit dividing the learning data into K groups, re-dividing each group into K groups, and designating a learning set and a verification set to perform a verification process, wherein each group designates the learning set and the verification as different and repeatedly performs the verification process (To confirm the stability and reproducibility of the prediction results for this collection of samples, the train and test procedure for 100 random splits of a combined dataset were repeated. The results were similar to the reported case. See at least [0106]. Also: Of note, classification of 100 random splits of a combined training and test dataset gave similar results, confirming the stability of the predictor for this collection of samples (FIGS. 8A and 8B). See at least [0064]).
Regarding Claim 8: Golub discloses the above limitations. Golub further discloses wherein the model verification unit generates a confusion matrix by comparing a verification result of the verification set with an actual diagnosis result and calculates a prediction value based on a probability value of the confusion matrix to determine a reliability of the classification model (FIGS. 8A and 8B are graphical representations of confusion matrices for the OVA/SVM classifier based on the samples described in FIG. 7. The confusion matrices for the "Train" and "Test" sets are shown. See at least [0023] and Fig. 8B. Also: Proportional chance criterion. In order to compute p-values for multi-class prediction, a "proportional chance criterion" was used to evaluate the probability that a random predictor will produce a confusion matrix with the same row and column counts as the gene expression predictor. For example, for a binary class (A vs. B) problem, if .alpha. is the prior probability of a sample being in class A and p is the -true proportion of samples in class A, then C.sub.p=p.alpha.+(1-p) (1-.alpha.) is the proportion of the overall sample that is expected to receive correct classification by chance alone. Then if C.sub.model is the proportion of correct classifications achieved by the gene expression predictor one can estimate its significance by using a Z statistic of the form: (C.sub.model-C.sub.p)/Sqrt(C.sub.p(1-C.sub.p)/n), where n is the total sample count. For more details see chapter VII of Huberty's Applied Discriminant Analysis. See at least [0092]).
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.
Claim(s) 2 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Golub et al. (US 2003/0225526 A1) in view of Golub et al. (US 2003/0134300 A1) [hereafter referenced as “Armstrong”].
Regarding Claim 2 and 10: Golub discloses the above limitations. Golub further discloses wherein the learning data generation unit obtains the gene expression amount information measured from each patient group corresponding to acute myeloid leukemia (AML), acute lymphoblastic leukemia (ALL) (generate models used in making pairwise distinctions with gene expression data (e.g., the distinction between acute lymphoblastic leukemia (ALL) and acute mycloid leukemia. See at least [0061]. Also: Three classes contained known cancer subtypes: … leukemia (acute myelogenous, acute lymphocytic (B-cell and T-cell))).
Golub does not expressly disclose obtaining information corresponding to mixed phenotype acute leukemia (MPAL).
However, Armstrong teaches obtaining the gene expression amount information measured from each patient group corresponding to acute myeloid leukemia (AML), acute lymphoblastic leukemia (ALL), and mixed phenotype acute leukemia (MPAL) (In one embodiment, the invention relates to a method of diagnosing mixed lineage leukemia, acute lymphoblastic leukemia or acute myelogenous leukemia, comprising determining a gene expression profile of a gene expression product from at least one informative gene from one or more cells, wherein the cells are selected from the group consisting of mononuclear blood cells and bone marrow cells, and wherein the gene expression profile is correlated with mixed lineage leukemia, acute lymphoblastic leukemia or acute myelogenous leukemia. See at least [0006]. Also: Gene expression profiles of lymphoblastic leukemias which possess an MLL translocation are remarkably consistent and differ significantly from those of other leukemias. Consequently, it is appropriate that they be considered a distinct disease entitled MLL for "Mixed Lineage Leukemia." This is supported by their comparison to conventional B cell precursor ALL that lacks MLL rearrangement, where .about.1000 genes proved underexpressed and .about.200 overexpressed in the MLL rearranged group. Moreover, evaluation of the expression profiles using principal component analysis indicated that MLL was clearly separable from conventional ALL and also AML. The expression differences are so robust that .about.95% of leukemic samples were correctly classified as MLL, ALL or AML. See at least [0181]).
Golub provides a system for diagnosing cancers including acute myeloid leukemia and acute lymphoblastic leukemia based on received gene expressions information associated with these cancers, upon which the claimed invention’s further inclusion of receiving data relating to mixed phenotype acute leukemia can be seen as an improvement. However, Armstrong demonstrates that the prior art already knew of receiving gene expression data associated with mixed phenotype acute leukemia and using such data to diagnose such cancer. One of ordinary skill in the art could have trivially incorporated the mixed phenotype acute leukemia data of Armstrong into the gene expression classifiers of Golub to produce a system that could classify mixed phenotype acute leukemia. Further, one of ordinary skill in the art would have recognized that such an application of Armstrong would have resulted in an improved system which could more accurately diagnose mixed phenotype acute leukemia so that patients could be provided with better treatment (Armstrong, [0004]). As such, the application of Armstrong and the claimed invention would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Golub and the teaching of Armstrong.
Claim(s) 3-5 and 11-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Golub et al. (US 2003/0225526 A1) in view of Szabo et al. (US 2004/0265830 A1) and Perou et al. (US 2011/0145176 A1).
Regarding Claim 3 and 11: Golub discloses the above limitations. Golub does not appear to disclose wherein the learning data generation unit performs first normalization on the gene expression amount information corresponding to the diagnosis name using a housekeeping gene and extracts the expressed gene by comparing the first normalized expression amount.
Szabo teaches performs normalization on the gene expression amount information corresponding to the diagnosis name and extracts the expressed gene by comparing the first normalized expression amount (Statistical pattern recognition can be formulated within the framework of discriminant analysis. Each pattern is considered as an entity that belongs to one of a number of predefined classes or groups of patterns (tissues or states, for example) and can be represented by a vector of feature variables. In the context of microarray data analysis, a set of microarray data (e.g., signals of expression levels) on a distinct set of genes can be represented by a random vector. Certain rules for initial feature vector selection in pattern recognition have been proposed. For example, van der Laan and Bryan (Public Health series 86, Univ. of Cal., Berkeley 2000) suggested the following procedure: (1) select those genes which are at least m-fold differentially expressed (m is predetermined by the user). See at least [0007]. Also: Effective microarray data analysis often requires preprocessing of raw data from array or chip images. Various background reduction, normalization, and other adjustment procedures may be used. Such data adjustment is transforms the measurements of gene expression such that they are placed on the same scale. See at least [0049]).
Golub provides a system for diagnosing cancers based on a gene expression analysis, upon which the claimed invention’s use of normalized gene expression feature selection can be seen as an improvement. However, Szabo demonstrates that the prior art already knew of using normalized gene expression data to select key features. One of ordinary skill in the art could have easily applied the techniques of Szabo to the system of Golub to select variables for Golub’s classifiers. Further, one of ordinary skill in the art would have recognized that such an application of Szabo would have resulted in an improved system which would user computationally smaller classifier models. As such, the application of Szabo would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Golub and the teaching of Szabo.
Additionally, Perou teaches normalization using a housekeeping gene ("Normalization" may be used to remove sample-to-sample variation. For microarray data, the process of normalization aims to remove systematic errors by balancing the fluorescence intensities of the two labeling dyes. The dye bias can come from various sources including differences in dye labeling efficiencies, heat and light sensitivities, as well as scanner settings for scanning two channels. Some commonly used methods for calculating normalization factor include: (i) global normalization that uses all genes on the array; (ii) housekeeping genes normalization that uses constantly expressed housekeeping/invariant genes. See at least [0050]).
Golub and Szabo suggest a system which generates gene expression classifiers using normalized gene expression data, which differs from the claimed invention by the substitution of Szabo’s generic normalization for a housekeeping gene based normalization. However, Perou demonstrates that the prior art already knew of housekeeping gene based normalization. One of ordinary skill in the art could have trivially substituted Perou’s normalization in to the system of Golub and Szabo. Further, one of ordinary skill in the art would have recognized that such a substitution would have predictably resulted in a system which would select genes to use according to data normalized by the particular normalization technique. As such, the identified substitution and the claimed invention would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Golub and the teaching of Szabo and Perou.
Regarding Claim 4 and 12: Golub in view of Szabo and Perou makes obvious the above limitations. As previously noted in combination with Golub, Szabo teaches extracting a gene in which a difference in a median value of the first normalized expression amount is more than or equal to N fold change (FC) as the expressed gene, and wherein a gene in which the first normalized expression amount is less than or equal to a specific value is excluded from the expressed gene (Statistical pattern recognition can be formulated within the framework of discriminant analysis. Each pattern is considered as an entity that belongs to one of a number of predefined classes or groups of patterns (tissues or states, for example) and can be represented by a vector of feature variables. In the context of microarray data analysis, a set of microarray data (e.g., signals of expression levels) on a distinct set of genes can be represented by a random vector. Certain rules for initial feature vector selection in pattern recognition have been proposed. For example, van der Laan and Bryan (Public Health series 86, Univ. of Cal., Berkeley 2000) suggested the following procedure: (1) select those genes which are at least m-fold differentially expressed (m is predetermined by the user). See at least [0007]).
Regarding Claim 5 and 13: Golub discloses the above limitations. Golub does not appear to disclose wherein the learning data generation unit performs second normalization on the expression amount of the expressed gene using an expression average of all genes included in the gene expression amount information and generates the second normalized expression amount as the learning data.
Szabo teaches performs normalization on the expression amount of the expressed gene and generates the normalization expression amount as the learning data (Statistical pattern recognition can be formulated within the framework of discriminant analysis. Each pattern is considered as an entity that belongs to one of a number of predefined classes or groups of patterns (tissues or states, for example) and can be represented by a vector of feature variables. In the context of microarray data analysis, a set of microarray data (e.g., signals of expression levels) on a distinct set of genes can be represented by a random vector. Certain rules for initial feature vector selection in pattern recognition have been proposed. For example, van der Laan and Bryan (Public Health series 86, Univ. of Cal., Berkeley 2000) suggested the following procedure: (1) select those genes which are at least m-fold differentially expressed (m is predetermined by the user). See at least [0007]. Also: Effective microarray data analysis often requires preprocessing of raw data from array or chip images. Various background reduction, normalization, and other adjustment procedures may be used. Such data adjustment is transforms the measurements of gene expression such that they are placed on the same scale. See at least [0049]).
Golub provides a system for diagnosing cancers based on a gene expression analysis, upon which the claimed invention’s use of normalized gene expression feature selection can be seen as an improvement. However, Szabo demonstrates that the prior art already knew of using normalized gene expression data to select key features. One of ordinary skill in the art could have easily applied the techniques of Szabo to the system of Golub to select variables for Golub’s classifiers. Further, one of ordinary skill in the art would have recognized that such an application of Szabo would have resulted in an improved system which would user computationally smaller classifier models. As such, the application of Szabo would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Golub and the teaching of Szabo.
Additionally, Perou teaches normalization using an expression average of all genes included in the gene expression amount information ("Normalization" may be used to remove sample-to-sample variation. For microarray data, the process of normalization aims to remove systematic errors by balancing the fluorescence intensities of the two labeling dyes. The dye bias can come from various sources including differences in dye labeling efficiencies, heat and light sensitivities, as well as scanner settings for scanning two channels. Some commonly used methods for calculating normalization factor include: (i) global normalization that uses all genes on the array; (ii) housekeeping genes normalization that uses constantly expressed housekeeping/invariant genes. See at least [0050]).
Golub and Szabo suggest a system which generates gene expression classifiers using normalized gene expression data, which differs from the claimed invention by the substitution of Szabo’s generic normalization for a global average based normalization. However, Perou demonstrates that the prior art already knew of global average based normalization. One of ordinary skill in the art could have trivially substituted Perou’s normalization in to the system of Golub and Szabo. Further, one of ordinary skill in the art would have recognized that such a substitution would have predictably resulted in a system which would select genes to use according to data normalized by the particular normalization technique. As such, the identified substitution and the claimed invention would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Golub and the teaching of Szabo and Perou.
Additional Considerations
The prior art made of record and not relied upon that is considered pertinent to applicant’s disclosure can be found in the PTO-892 Notice of References Cited.
Weinberg and Arber (Mixed-phenotype acute leukemia: historical overview and a new definition) provides additional background information regarding mixed phenotype acute leukemia.
Ross et al. (Classification of pediatric acute lymphoblastic leukemia by gene expression profiling) discusses classifying gene expression data to diagnose leukemia.
Showe et al. (US 2006/0271309 A1) provides additional background on using gene expression data to diagnose conditions.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Bion A Shelden whose telephone number is (571)270-0515. The examiner can normally be reached M-F, 12pm-10pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kambiz Abdi can be reached on (571) 272-6702. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Bion A Shelden/Primary Examiner, Art Unit 3685 2025-03-03
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