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 .
This office action is in response to the claimed amendment, filed on December 11, 2023, in which claims 1-45, 51, 55-62, 65-67, 71-77, 79, 81-82, 84 and 87 were canceled; and claims 46-50, 52-54, 63-64, 68-70, 78, 80, 83, 85-86 and 88-89 are presented for examination.
Information Disclosure Statement
The information disclosure statement filed on August 16, 2023 and January 14, 2026 complies with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609. It has been placed in the application file. The information referred to therein has been considered as to the merits.
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 46-50, 52-54, 63-64, 68-70, 78, 80, 83, 85-86 and 88-89 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract without significantly more.
At Step 1:
With respect to subject matter eligibility under 35 USC 101, it is determined that the claims are directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
At Step 2A, Prong One:
The limitation “identifying, using at least some of the DNA-based machine learning classifier outputs including the first output and the second output, at least one candidate molecular category for the biological sample” in claims 46, 88 and 89, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement, but for the recitation of generic computer components. That is, other than reciting “identifying, using at least some of the DNA-based machine learning classifier outputs including the first output and the second output, at least one candidate molecular category for the biological sample”, nothing in the claim element precludes the steps from practically being performed in a human mind. For example, the limitation “identifying”, in the context of these claims encompasses one can mentally, or manually with the aid of pen and paper identify at least one candidate molecular category for the biological sample.
If a claim limitation, under its broadest reasonable interpretation, covers mental processes but for the recitation of generic computer components, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgement, and opinion). Accordingly, the claim recites an abstract idea.
At Step 2A, Prong Two:
This judicial exception is not integrated into a practical application. In particular, the claims recite the following additional elements:
That the method is "implemented by a computing system” is a high-level recitation of a generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
“a machine learning classifier by performing…" the above identified mental processes and the "identifying" above being performed "using the machine learning model" is at best generally linking the abstract idea to the particular field of use or technological environment of machine learning (see MPEP 2106.05(h), and/or akin to using machine learning as a mere tool (2106.05(f)). No specific type of machine learning processing or techniques are recited in the claim itself.
The limitation “obtaining DNA expression data previously obtained by processing the biological sample obtained from the subject, wherein the DNA expression data comprises first DNA expression data and second DNA expression data” amount to data-gathering steps which is considered to be insignificant extra-solution activity, (See MPEP 2106.05(g)).
The limitation “processing the DNA expression data using a hierarchy of DNA-based machine learning classifiers corresponding to a hierarchy of molecular categories to obtain DNA- based machine learning classifier outputs including a first output and a second output, the hierarchy of molecular categories including a parent molecular category and first and second molecular categories that are children of the parent molecular category in the hierarchy of molecular categories, the hierarchy of DNA-based machine learning classifiers comprising first and second DNA-based machine learning classifiers corresponding to the first and second molecular categories” recites insignificant extra-solution activity such as mere outputting of the result and does not meaningfully limit the abstract idea. Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
The limitation “processing the first DNA expression data using the first DNA-based machine learning classifier to obtain the first output indicative of whether the first molecular category is a candidate molecular category for the biological sample” recites insignificant extra-solution activity such as mere outputting of the result and does not meaningfully limit the abstract idea. Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
The limitation “processing the second DNA expression data using the second DNA-based machine learning classifier to obtain the second output indicative of whether the second molecular category is a candidate molecular category for the biological sample” recites insignificant extra-solution activity such as mere outputting of the result and does not meaningfully limit the abstract idea. Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
The limitation “at least one computer hardware and non-transitory computer-readable storage medium” are recited at a high level of generality such that they amount to on more than mere instructions to apply the exception using a generic component. (see MPEP 2106.05(f)). These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer (see MPEP 2106.05(h)).
At Step 2B:
The conclusions for the mere implementation using a computer, mere field of use, and using generic computer components (machine learning i.e. ML) as a tool are carried over and do not provide significantly more.
With respect to the " obtaining ….." identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); 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);" and thus remains insignificant extra-solution activity that does not provide significantly more.
With respect to the " processing …" identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334; i. … transmitting data over a network, …Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); 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)".
The “at least one computer hardware and non-transitory computer-readable storage medium” amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrate by: Relevant court decision: the followings are examples of court decisions demonstrating well-understood, routine and conventional activities, see e.g., MPEP 2106.05(d)(II) and MPEP 2106.05(f)(2): Computer readable storage media comprising instructions to implement a method, e.g., see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Looking at the claim as a whole does not change this conclusion and the claim appears to be ineligible.
Accordingly, claim 46 is directed to an abstract idea. The remaining independent claim 88 and 89 fall short the 35 USC 101 requirement under the same rationale.
The dependent claims 47-50, 52-54, 63-64, 68-70, 78, 80, 83 and 85-86 when analyzed and each taken as a whole are held to be patent ineligible under 35 USC 101 because the additional recited limitations fail to establish that the claims are not directed to an abstract idea.
Claim 47 recites “wherein the DNA expression data further comprises third DNA expression data, wherein the hierarchy of molecular categories further comprises a third molecular category that is a child of the parent molecular category in the hierarchy of molecular categories, wherein the hierarchy of DNA-based machine learning classifiers further comprises a third DNA-based machine learning classifier corresponding to the third molecular category, wherein the processing further comprises processing the third DNA expression data using the third DNA-based machine learning classifier to obtain a third output indicative of whether the third molecular category is a candidate molecular category for the biological sample, and wherein identifying the at least one candidate molecular category for the biological sample is performed using the third output”. This additional element is recited at a high level of generality and would function in its ordinary capacity for processing the third DNA expression data using the third DNA-based machine learning classifier to obtain a third output indicative of whether the third molecular category is a candidate molecular category for the biological sample, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more.
Claim 48 recites “wherein the DNA expression data further comprises fourth DNA expression data, wherein the hierarchy of molecular categories further comprises a fourth molecular category that is a child of the first molecular category in the hierarchy of molecular categories, wherein the hierarchy of DNA-based machine learning classifiers further comprises a fourth DNA-based machine learning classifier corresponding to the fourth molecular category, wherein the processing further comprises processing the fourth DNA expression data using the fourth DNA-based machine learning classifier to obtain a fourth output indicative of whether the fourth molecular category is a candidate molecular category for the biological sample, and wherein identifying the at least one candidate molecular category for the biological sample is performed using the fourth output”. This additional element is recited at a high level of generality and would function in its ordinary capacity for processing the fourth DNA expression data using the fourth DNA-based machine learning classifier to obtain a fourth output indicative of whether the fourth molecular category is a candidate molecular category for the biological sample, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more.
Claim 49 recites “fifth DNA expression data, wherein the hierarchy of molecular categories further comprises a fifth molecular category that is a child of the first molecular category in the hierarchy of molecular categories, wherein the hierarchy of DNA-based machine learning classifiers further comprises a fifth DNA-based machine learning classifier corresponding to the fifth molecular category, wherein the processing further comprises processing the fifth DNA expression data using the fifth DNA-based machine learning classifier to obtain a fifth output indicative of whether the fifth molecular category is a candidate molecular category for the biological sample, and wherein identifying the at least one candidate molecular category for the biological sample is performed using the fifth output”. This additional element is recited at a high level of generality and would function in its ordinary capacity for processing the fifth DNA expression data using the fifth DNA-based machine learning classifier to obtain a fifth output indicative of whether the fifth molecular category is a candidate molecular category for the biological sample, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more.
Claim 50 recites “wherein the parent molecular category is a solid neoplasm molecular category, the first molecular category is an adenocarcinoma molecular category, and the second molecular category is a sarcoma molecular category”. There is no additional elements recited so the claim does not provide a practical application and is not considered to be significantly.
Claim 52 recites “wherein the parent molecular category is a molecular category selected from, and the first and second molecular categories are children of the parent molecular category in the hierarchy of categories”. There is no additional elements recited so the claim does not provide a practical application and is not considered to be significantly.
Claim 53 recites “wherein processing the first DNA expression data using the first DNA-based machine learning classifier comprises: obtaining one or more first DNA features from the first DNA expression data; and applying the first DNA-based machine learning classifier to the first DNA features to obtain the first output”. This additional element is recited at a high level of generality and would function in its ordinary capacity for obtaining one or more first DNA features from the first DNA expression data; and applying the first DNA-based machine learning classifier to the first DNA features to obtain the first output, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more.
Claim 54 recites “wherein the one or more first DNA features comprise: one or more features indicating, for each gene of a respective set of one or more genes, whether the DNA expression data indicates presence of a pathogenic mutation for the gene; one or more features indicating, for each gene of a respective set of one or more genes, whether the DNA expression data indicates presence of a hotspot mutation for the gene; a feature indicating tumor mutational burden for the biological sample; one or more features indicating a normalized copy number for each chromosome segment of a respective set of one or more chromosome segments for which expression data is included in the DNA expression data; one or more features indicating loss of heterozygosity (LOH) for each chromosome segment of a respective set of one or more chromosome segments for which expression data is included in the DNA expression data; one or more features indicating whether the DNA expression data indicates presence of one or more protein coding genes; one or more features indicating, for each gene of a respective set of one or more genes, whether the DNA expression data indicates presence of a fusion with another gene of the respective plurality of genes; a feature indicating ploidy for the biological sample; and/or a feature indicating whether the DNA expression data indicates presence of microsatellite instability (MSI)”. This additional element is recited at a high level of generality and would function in its ordinary capacity for a feature indicating ploidy for the biological sample; and/or a feature indicating whether the DNA expression data indicates presence of microsatellite instability (MSI), this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more.
Claim 63 recites “wherein the one or more first DNA features comprise at least 10 features listed corresponding to the first molecular category”. There is no additional elements recited so the claim does not provide a practical application and is not considered to be significantly.
Claim 64 recites “wherein processing the first DNA expression data using the first DNA-based machine learning classifier to obtain the first output comprises processing the first DNA expression data to obtain a first probability that the first molecular category is a first candidate molecular category for the biological sample, wherein processing the second DNA expression data using the second DNA-based machine learning classifier to obtain the second output comprises processing the second DNA expression data to obtain a second probability that the second molecular category is a second candidate molecular category for the biological sample, wherein dentifying the at least one candidate molecular category for the biological sample comprises: comparing the first probability to a threshold; and including the first molecular category in the at least one candidate molecular category identified for the biological sample when the first probability exceeds the threshold”. This additional element is recited at a high level of generality and would function in its ordinary capacity for processing the first DNA expression data using the first DNA-based machine learning classifier to obtain the first output, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more.
Claim 68 recites “wherein the first molecular category is a molecular category selected from molecular categories listed”. There is no additional elements recited so the claim does not provide a practical application and is not considered to be significantly.
Claim 69 recites “wherein the first molecular category is associated with at least one DNA classification of diseases (ICD) code”. There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception.
Claim 70 recites “obtaining DNA expression data previously obtained by processing the biological sample obtained from the subject; and processing the DNA expression data using a hierarchy of DNA-based machine learning classifiers corresponding to the hierarchy of molecular categories to obtain DNA-based machine learning classifier outputs, wherein the hierarchy of DNA-based machine learning classifiers is different from the hierarchy of DNA-based machine learning classifiers, wherein the identifying of the at least one candidate molecular category for the biological sample is performed also using at least some of the DNA-based machine learning classifier outputs”. This additional element is recited at a high level of generality and would function in its ordinary capacity for obtaining DNA expression data previously obtained, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more.
Claim 78 recites “receiving an indication of a clinical diagnosis of the biological sample; and determining an accuracy of the clinical diagnosis based on the at least one candidate molecular category identified for the biological sample”. There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception.
Claim 80 recites “wherein the first molecular category of the hierarchy of molecular categories is one of neoplasm, hematologic neoplasm, melanoma, sarcoma, mesothelioma, neuroendocrine, squamous cell carcinoma, adenocarcinoma, glioma, testicular germ cell tumor, pheochromocytoma, cervical squamous cell carcinoma, liver neoplasm, lung adenocarcinoma, high grade glioma isocitrate dehydrogenase (IDH) mutant, thyroid neoplasm, squamous cell lung adenocarcinoma, thymoma, prostate adenocarcinoma, urinary bladder urothelial carcinoma, oligodendroglioma, squamous cell carcinoma of the head and neck, gastrointestinal adenocarcinoma, gynecological cancer, renal cell carcinoma, astrocytoma, pancreatic adenocarcinoma, stomach adenocarcinoma, pancreatic adenocarcinoma, breast cancer, ovarian cancer, uterine corpus endometrial carcinoma, non-clear cell carcinoma, clear cell carcinoma, basal breast cancer, non-basal breast cancer, papillary renal cell carcinoma, and chromophobe renal cell carcinoma”. This additional element is recited at a high level of generality and would function in its ordinary capacity for the first molecular category of the hierarchy of molecular categories is one of neoplasm, hematologic neoplasm, melanoma, sarcoma, mesothelioma, neuroendocrine, squamous cell carcinoma, adenocarcinoma, glioma, testicular germ cell tumor, pheochromocytoma, cervical squamous cell carcinoma, liver neoplasm, lung adenocarcinoma, high grade glioma isocitrate dehydrogenase (IDH) mutant, thyroid neoplasm, squamous cell lung adenocarcinoma, thymoma, prostate adenocarcinoma, urinary bladder urothelial carcinoma, oligodendroglioma, squamous cell carcinoma of the head and neck, gastrointestinal adenocarcinoma, gynecological cancer, renal cell carcinoma, astrocytoma, pancreatic adenocarcinoma, stomach adenocarcinoma, pancreatic adenocarcinoma, breast cancer, ovarian cancer, uterine corpus endometrial carcinoma, non-clear cell carcinoma, clear cell carcinoma, basal breast cancer, non-basal breast cancer, papillary renal cell carcinoma, and chromophobe renal cell carcinoma, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more.
Claim 83 recites “wherein each DNA-based machine learning classifier of the hierarchy of DNA-based machine learning classifiers is a gradient-boosted decision tree classifier, a neural network classifier, or a logistic regression classifier”. This additional element is recited at a high level of generality and would function in its ordinary capacity for each DNA-based machine learning classifier of the hierarchy of DNA-based machine learning classifiers is a gradient-boosted decision tree classifier, a neural network classifier, or a logistic regression classifier, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more.
Claim 85 recites “wherein the biological sample is a sample of a cancer of unknown primary (CUP) tumor”. There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception.
Claim 86 recites “identifying at least one anti-cancer therapy for the subject based on the identified at least one molecular category; and administering the at least one anti-cancer therapy”. This additional element is recited at a high level of generality and would function in parts ordinary capacity for administering the at least one anti-cancer therapy, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 52, 63 and 68 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. The claims recite “wherein the parent molecular category is a molecular category selected from Table 2, and the first and second molecular categories are children of the parent molecular category in the hierarchy of categories shown in Figures 7A-1, 7A-2, and 7A-3”; “wherein the one or more first DNA features comprise at least 10 features listed Table 5 corresponding to the first molecular category”; and “wherein the first molecular category is a molecular category selected from molecular categories listed in Table 2”. It is not clear as what the applicant meant by the underlined limitations. Such underlined limitations are not acceptable in a claimed language. Applicant is advised to amend the claims to solve such ambiguity set forth in the claims.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 46-50, 52-54, 63-64, 68-70, 78, 80, 83, 85-86 and 88-89 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Pan et al., (hereinafter “Pan”) US 2020/0199671.
As to claim 46, Pan discloses a method for identifying at least one candidate molecular category for a biological sample obtained from a subject (see e.g. " in par. 11 12, 13; or claim 7), the method comprising: using at least one computer hardware processor to perform:
obtaining DNA expression data previously obtained by processing the biological sample obtained from the subject (" in par. 11 12, 13 and claim 7),
wherein the DNA expression data comprises first DNA expression data for a first set of genes and second DNA expression data for a second set of genes different from the first set of genes (see i.e. in par. 13: "determine an expression level of a plurality of target DNA molecules in the biological test sample; compare the expression level of each of the plurality of target DNA molecules to an DNA tissue score matrix to determine a cancer indicator score for each of the plurality of target DNA molecule");
processing the DNA expression data using a hierarchy of DNA-based machine learning classifiers corresponding to a hierarchy of molecular categories to obtain DNA-based machine learning classifier outputs including a first output and a second output (see in par. 13 above; see " in par. 157-161 for the machine learning classifiers based on a predicted "score or probability"), the hierarchy of molecular categories including a parent molecular category and first and second molecular categories that are children of the parent molecular category in the hierarchy of molecular categories (see e.g. the cancer "type or subtype" in par. 12), the hierarchy of DNA-based machine learning classifiers comprising first and second DNA-based machine learning classifiers corresponding to the first and second molecular categories, the processing comprising:
processing the first DNA expression data using the first DNA-based machine learning classifier to obtain the first output indicative of whether the first molecular category is a candidate molecular category for the biological sample ("the cancer indicator score for the biological test sample exceeds a threshold value" in par. 13);
processing the second DNA expression data using the second DNA-based machine learning classifier to obtain the second output indicative of whether the second molecular category is a candidate molecular category for the biological sample (see " in par. 13 above, "for each of the plurality of target DNA molecules"); and
identifying, using at least some of the DNA-based machine learning classifier outputs including the first output and the second output, at least one candidate molecular category for the biological sample (" in par. 13: "detecting the presence of the cancer"; see also " in par. 18).
As to claim 88, claim 88 is a system for performing the method of claim 46 above. It is rejected under the same rationale.
As to claim 89, claim 89 is a non-transitory computer readable storage medium having stored thereof instructions for executing the method of claim 46 above. It is rejected under the same rationale.
As to claims 47-50, Pan discusses utilizing a multiplicity of RNA datasets each comprising differing RNA expression data, which can include 2, 3, 4, 5 or more. (pp0010-0021).
As to claims 52-54, Pan discloses using DNA expression data, at pp0167-0170; and the classification model at paragraphs 0157-0161
As to claims 63-64, Pan discloses the sequencing techniques set forth at 0162-0170 discuss DNA features. The classification model is disclosed at 0157-0161.
As to claims 68-70, Pan discloses that each feature is set forth in an ultimate and/or, such that only one feature type is required. Pan discloses "one or more features indicating whether the NA expression data indicates the presence of one or more protein coding genes." (throughout, embodiments where the expressed gene transcript only comprises exons, known annotated genes etc.
As to claims 78 and 80, Pan discloses that purity of the samples is discussed at [0073-0080] in the Examples, and the following materials and methods. [0270-0324].
As to claim 83, Pan discloses a GUI is presented in the figures, and description of the computing elements. Fig 13 shows one way to visualize categories (Cancer type); paragraphs 0171-0189 discuss computing elements, including display and GUI elements.
As to claim 85, Pan discloses that gradient boosting, logistic regression, random forest, neural networks and/or multinomial regression are each disclosed. [end of 0222, embodiment 86, 0253 et al.]
As to claim 86, Pan discloses that each table comprises at least 2-500 genes, including at least 10 and fewer than 300, paragraph 0139, 0140, et al..
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US20170083825A (involved in a device has a controller that receives data. The data is classified into a first class or a second class using a first machine learning classifier. The data is classified into one of a third class and a fourth class using a second machine learning classifier if the data is classified into the second class. The first and second machine learning classifiers have their own predefined sets of rules for classifying data.)
US 20110243426 (involved in a classifier generating apparatus and a classifier generating method, for generating classifiers having tree structures for performing multi class classification of objects. The present invention is also related to a program that causes a computer to execute the classifier generating method.)
US20070133857 (involved in providing a training set of gene expression profiles of known tissue samples; constructing a probabilistic boosting tree classifier using a learning framework; and outputting the probabilistic boosting tree classifier for tissue sample classification, is new.)
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/JEAN M CORRIELUS/Primary Examiner, Art Unit 2159 January 18, 2026