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 .
Redocketing Status
This application has been redocketed to Examiner Meredith Vassell of Art Unit 1687.
Election/Restrictions
Response to election with traverse:
Applicant's election with traverse of Invention I (claims 1-15 and 36) in the reply filed on 02/26/2026 is acknowledged. The traversal is on the ground(s) that Invention I (claims 1-15 and 36) is basically the same as that of Invention II (claim 16) and of invention III (claims 20-22), and any prior art searched for one invention is applicable to the other invention. Hence, there would not be a serious search and/or examination burden. This traversal is not found persuasive because it cannot be agreed the inventions are the same, as discussed in the restriction requirement, and it cannot be agreed that the art overlaps. The requirement is still deemed proper, however, see rejoinder of claim 16 directly below.
Rejoinder of claim 16:
Upon further consideration, claim 16 (Invention II) is rejoined into the elected group, such that the Invention I (the elected invention) now consists of: Claims 1-16 and 36.
Withdrawal of claims 20-22:
Claims 20-22 are withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a nonelected invention, there being no allowable generic or linking claim. Applicant timely traversed the restriction (election) requirement in the reply filed on 02/26/2026.
Claim Status
Claims 1-16, 20-22, and 36 are pending.
Claims 20-22 are directed to non-elected species and therefore withdrawn.
Claims 17-19 and 23-35 are canceled.
Claims 1-16 and 36 are under examination.
Claims 1-16 and 36 are rejected.
Claims 1, 3, 15, and 36 are objected to.
Claims 1, 16, and 20 are independent.
Claim 3 is amended.
Claim 36 is new.
Office Action Outline
Rejections applied
Abbreviations
x
112/b Indefiniteness
PHOSITA
"a Person Having Ordinary Skill In The Art before the effective filing date of the claimed invention"
112/b "Means for"
BRI
Broadest Reasonable Interpretation
x
112/a Enablement,
Written description
CRM
"Computer-Readable Media" and equivalent language
112 Other
IDS
Information Disclosure Statement
x
102, 103
JE
Judicial Exception
x
101 JE(s)
112/a
35 USC 112(a) and similarly for 112/b, etc.
101 Other
N:N
page:line
Double Patenting
MM/DD/YYYY
date format
Priority
As detailed in the 05/14/2025 filing receipt, this application is a 371 of PCT/US22/29270, filed 05/13/2022, which claims benefit of U.S. Provisional application 63/188,157, filed 05/13/2021.
Claim Objections
Claims 1, 3, 15, and 36 are objected to because of the following informalities:
Claim 1 recites "inputting into a computer system quantitative data" which should be amended to add two commas as follows: "inputting, into a computer system, quantitative data".
Claim 1 step (d), claim 3, and claim 36 each recite the term "biomarkers panel" in which the "s" should be deleted so the term is corrected to "biomarker[[s]] panel."
Claim 15 recites "wherein the machine deep learning classifier," which should be amended to "wherein the trained machine deep learning classifier."
Appropriate correction is required.
Claim Interpretation
The recited "biomarker multi-panel previously determined by using a selection of biomarkers executed on a plurality of clinical parameters" is interpreted as a product-by-process element, i.e. the recited "biomarker multi-panel" limited according to any structure clearly required by the recited product-by-process limitation of having been "determined." The recited process or step of having been "determined" is not itself claimed and is limiting only to the extent that the structure of the "biomarker multi-panel" is clearly required to be limited by that process or step. Regarding product-by process limitations within a claim, MPEP 2113 pertains, as well as, for example, Biogen MA, Inc. v. EMD Serono, Inc. (Fed. Cir. 9-28-2020, precedential).
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-15 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims depending from rejected claims are rejected similarly, unless otherwise noted, and any amendments in response to the following rejections should be applied throughout the claims, as appropriate.
Claim 1 step (c) is not clearly supported by sufficient detail in the specification as to how the recitation may be performed. The claim recites "c) using a selection of biomarkers executed on a plurality of clinical parameters." Specification [0009] discloses "biomarkers selection based on 1)..., 2)... and 3)... executed on a plurality of clinical parameters," but the Specification does not disclose how biomarkers are "executed on a plurality of clinical parameters." The meaning and interpretation of "executed on" are not clearly supported, and the phrase does not have a sufficiently well-known meaning in the recited context. The written description provided is not clearly commensurate with the above recitation. (There is an associated 112(b) rejection below and a product-by-process interpretation above.)
Claim 1 step (d) recites "a second-tier biomarkers panel that can implement... algorithms... to sub-phenotype the one or more diseases..." Specification paragraph [0026] discloses "a second-tier biomarkers panel that can implement...to sub-phenotype the one or more diseases...", but the Specification does not detail how a panel implements. The written description provided is not clearly commensurate with the recited "second-tier biomarkers panel that can implement..." As the limitation appears structural, it may be better to amend the phrase "can implement," to "configured to," however, details of the structure are needed in the claim. (See associated 112(b) rejection below.)
Claim 3 recites "a third-tier biomarkers panel that can implement... to identify specific etiology or comorbidities..." Specification paragraph [0026] discloses "a third-tier biomarkers panel that can implement... to identify specific etiology or comorbidities...", but the Specification does not detail how it a panel implements. The written description provided is not clearly commensurate with the recited "third-tier biomarkers panel that can implement..." As the limitation appears structural, it may be better to amend the phrase "can implement," to "configured to," however, details of the structure are needed in the claim. (See associated 112(b) rejection below.)
Claim 11 step (b) recites "applying a plurality of characteristics of the patient to the quantitative data." Specification paragraph [0027] discloses "applying a plurality of characteristics of the patient to the quantitative data," however, the Specification does not detail or disclose how a plurality of characteristics are applied to the quantitative data. The written description provided is not clearly commensurate with the recited "applying a plurality of characteristics of the patient to the quantitative data." (See associated 112(b) rejection below).
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.
Claims 1-15 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 depending from rejected claims are rejected similarly, unless otherwise noted, and any amendments in response to the following rejections should be applied throughout the claims, as appropriate.
In claim 1, and in particular in step (c), it is unclear what is required by recitations such as "determined by" and "executed on." These recitations appear to require that some action is or was taken or that some information is or was calculated, however it is neither clear what action or information is required nor is it clear how any such requirements relate to the other recited steps and elements of the claim. For example, regarding "...panel... determined by," it is unclear what properties of the "panel" must be "determined" such that it is unclear under what conditions the recited "determined" has been successfully performed. (There also is an associated 112(a) rejection above and a product-by-process interpretation above.)
In claim 1 step (e), the relationship is unclear between "diagnosing" and "prognosing," and what one requires versus the other. [26] of the specification pertains. The preamble of claim 1 has the same issue of the relationship being unclear between diagnosing and prognosing, and what one requires versus the other.
In claim 1 step (e), it is unclear whether the "if..." conditional applies to the entirety of performing the recited "diagnosing or prognosing," i.e. all of step e), vs. whether "diagnosing or prognosing" is always required and the "if..." conditional applies only to define the condition in which the result is positive, e.g. diagnosed with the disease.For compact examination, the claims will be interpreted as skipping all of step (e) if the conditional of step (e) fails.
In claim 1 step (d), the relationship between the structural requirements and the "second-tier biomarkers panel that can implement" is unclear because there is a lack of written description (see associated 112(a) rejection above).
In claim 3, the relationship is unclear between the structural requirements and the "third-tier biomarkers panel that can implement" because there is a lack of written description (see associated 112(a) rejection above).
In claim 5, the relationship is unclear between a "quantity" and a "panel of biomarkers" in the element "a quantity of a panel of biomarkers from subjects having the disease and from control subjects that do not have disease." It is unclear if the quantity refers to a panel or to biomarkers. If it is Applicant' intention that quantity refers to "panel", it is suggested to amend panel to the plural "panels," and the element will be interpreted as suggested to amend.
Further in claim 5, the relationship is unclear between "having the disease" and "do not have disease" in the element "from subjects having the disease and from control subjects that do not have disease" (emphasis added). First, it is not clear if "the disease" is instantiated in "a disease" of the preamble of claim 1, or in "one or more disease" of claim 1 step (c). Second, in the phrase "subjects that do not have disease," it is unclear if these subjects do not have the disease or do not have diseases. If it is Applicant's intention, it is suggested to amend the claim to recite "subjects that do not have the disease;" the claim will be interpreted as such.
In claim 11 step (b), the relationship is unclear between "applying...characteristics" and "the quantitative data" in the element "applying a plurality of characteristics of the patient to the quantitative data." (See associated 112(a) rejection above).
The claims recite the following elements which require but lack clear antecedent. If these recitations refer to previously instantiated instances, then it is not clear which instances those are. If these recitations instantiate the claim elements, this is not clear. These rejections might be overcome by for example amending to recite the article "a" instead of "the." The following elements lack antecedent basis (designated by bolded instances of "the" below):
• "the one or more diseases of the organ or the cell type" (claim 1, step (d))
• "of the organ or the cell type" (claim 1, step (d); this rejection might be remedied by making both "organ" and "type" plural)
• "the acquisition of the quantitative data" (claim 2)
• "the organ or the cell type affected identified in step c" (in both claim 1 step (d) and in claim 3; note: nothing has been identified in claim 1 step (c))
• "the techniques" (claim 7)
• "the trained machine learning and deep learning algorithms" (claim 9)
• "the metabolites" (claim 10; "metabolites" will be interpreted as "biomarkers")
• "the panel of metabolic biomarkers" (claim 11)
• "the dataset" (claim 11 step (c))
• "the excluded data" (claim 13)
• "the efficacy of the treatment" (claim 36; this rejection can be remedied by amending to "the efficacy of the therapy " to reflect the preamble of claim 1)
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 1-16 and 36 are rejected under 35 U.S.C. 101 because the claimed invention is directed to one or more judicial exceptions without significantly more.
MPEP 2106 details the following framework to analyze Subject Matter Eligibility:
• Step 1: Are the claims directed to a category of statutory subject matter (a process, machine, manufacture, or composition of matter)? (see MPEP § 2106.03)
• Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e. an abstract idea, a law of nature, or a natural phenomenon? (see MPEP § 2106.04(a), 2106.04(a)(2) & 2106.04(b)).
• Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application? (see MPEP § 2106.04(d))
• Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept? (see MPEP § 2106.05)
Step 1:
Claims 1-15 and 36 are directed to a 101 process, here a method. Claim 16 is directed to a 101 machine or manufacture, here a non-transitory, computer-readable medium having computer executable instructions. As such, claims 1-15 and 36 are directed to a related method and CRM, which fall under categories of statutory subject matter. (See MPEP § 2106.03). (Step 1: Yes.)
Step 2A, Prong One:
The claims are found to recite abstract ideas and natural laws in the form of mental processes, mathematical concepts and natural laws, as follows:
• analyzing the quantitative data (claim 1 step (b))
• using a first-tier biomarker multi-panel to distinguish healthy subjects (claim 1 step (c))
• determining and using a second/third-tier biomarkers panel (claim 1 step (d), claim 3)
• diagnosing or prognosing the subject (claim 1 step (e))
• biomarker selection is based on statistical significance, pathology of disease by an expert-in-the-loop, feature selection optimization using machine learning, deep learning algorithms, (claim 4 and 5)
• labeling quantitative data (claim 11 step (a))
• applying characteristics of the patient to the quantitative data (claim 11 step (b))
• balancing the dataset (claim 11 step (c))
• scaling the dataset (claim 11 step (d))
• determining whether quantitative data is indicative of the disease using trained machine learning deep classifier (claim 16 step (a))
• diagnosing the subject if the quantitative data is determined by the machine
deep learning classifier to be indicative of the disease (claim 16 step (b))
• tracking disease progression over time to determine the efficacy of treatment (claim 36)
The following claims further limit the abstract idea(s) of base claims:
• claim 8 further limits the clinical parameters of claim 1 step (c)
• claim 9 further limits the algorithms of claim 1 step (d)
• claim 10 further limits the biomarkers of claim 1 ("metabolites" are interpreted as "biomarkers")
• claim 12 further limits the characteristics of claim 11
• claim 13 further limits the excluded data of claim 11
• claim 14 further limits the multiple-use data points of claim 11
• claim 15 further limits the dataset of claim 11
Step 2A Prong One Summary: The claims recite mental processes, mathematical concepts and a natural law.
When considering the broadest reasonable interpretation (BRI) of the claims, the mental processes recited in independent claim 1 (e.g., "analyzing quantitative data;" "using a panels to distinguish healthy subjects;" "diagnosing or prognosing the subject; " etc.) are directed to processes that may be performed in the human mind, or with pen and paper, as there are no particular limitations recited in claim 1 which would prevent the mental processes from being performed in the human mind or with pen and paper.
The claims recite inherent mathematical processes in e.g., implementing machine learning, deep learning algorithms, while details of which are not explicitly shown, are discussed in the Specification, e.g., at [0022, 0057, 0085] etc. Such analysis performed mentally, or with paper and pencil, may take considerable time and effort, and although a general-purpose computer can perform these calculations at a rate and accuracy that can far exceed the mental performance of a skilled artisan, the nature of the activity is essentially the same, and therefore constitutes an abstract idea.
The claims recite a natural law as the relationship between the input biomarkers and the output disease diagnosis.
Therefore, the claims recite elements that constitute judicial exceptions in the form of abstract ideas and natural laws. (Step 2A, Prong One: Yes.)
Step 2A, Prong Two:
In Step 2A, Prong One above, claim steps and/or elements were identified as part of one or more judicial exceptions (JEs). Here at Step 2A, Prong Two, any remaining steps and/or elements not identified as JEs are therefore in addition to the identified JE(s), and are considered additional elements. Because the claims have been interpreted as being directed to judicial exceptions (abstract ideas in this instance) then Step 2A, Prong Two provides that the claims be examined further to determine whether the judicial exception is integrated into a practical application [see MPEP § 2106.04(d)]. A claim can be said to integrate a judicial exception into a practical application when it applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception.
MPEP § 2106.04(d)(I) lists the following five example considerations for evaluating whether a judicial exception is integrated into a practical application:
(1) An improvement in the functioning of a computer or an improvement to other technology or another technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a).
(2) Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2).
(3) Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b).
(4) Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c).
(5) Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
The claims recite additional elements as follows:
Additional elements of data gathering and inputting: Inputting data (claim 1), acquisition of data (claim 2), and techniques for determining biomarkers (claims 6-7). Data gathering steps are additional elements which perform functions of inputting, collecting, and outputting the data needed to carry out the abstract idea. These steps are considered insignificant extra-solution activity, and are not sufficient to integrate an abstract idea into a practical application as they do not impose any meaningful limitation on the abstract idea or how it is performed, nor do they provide an improvement to technology (see MPEP § 2106.04(d)(I)).
Additional elements of computer components: A computer (claim 1); and a CRM and a processor (claim 16). The claims require only generic computer components, which do not improve computer technology, and do not integrate the recited judicial exception into a practical application (see MPEP § 2106.04(d)(1) and MPEP § 2106.05(f)).
Step 2A Prong Two summary: The claims have been further analyzed with respect to Step 2A, Prong Two, and no additional elements have been found, alone or in combination, that would integrate the judicial exception into a practical application. At this point in examination, it is not yet the case that any of the Step 2A Prong Two considerations enumerated above clearly demonstrates integration of the identified JE(s) into a practical application. Referring to the considerations above, none of: (1) an improvement, (2) a treatment, (3) a particular machine, or (4) a transformation is clear in the record. For example, regarding the first consideration for improvement at MPEP 2106.04(d)(1), the record, including the Specification, does not yet clearly disclose an explanation of improvement over the previous state of the technology field, and the claims do not yet clearly result in such an improvement. (Step 2A, Prong Two: No).
Step 2B:
Because the additional claim elements do not integrate the abstract idea into a practical application, the claims are further examined under Step 2B, which evaluates whether the additional elements, individually and in combination, amount to significantly more than the judicial exception itself by providing an inventive concept. An inventive concept is furnished by an element or combination of elements that is recited in the claim in addition to the judicial exception, and is sufficient to ensure that the claim, as a whole, amounts to significantly more than the judicial exception itself (see MPEP § 2106.05).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that are well-understood, routine, and conventional. Those additional elements are as follows:
Additional elements of data gathering, inputting, and outputting steps: Inputting data (claim 1), acquisition of data (claim 2), and techniques for determining biomarkers (claims 6-7) do not cause the claims to rise to the level of significantly more than the judicial exception. The courts have recognized receiving or transmitting data over a network; storing and retrieving information in memory; and determining the level of a biomarker in blood by any means [see MPEP§2106.05(d)(II)], as well-understood, routine, conventional activity when they are claimed in a merely generic manner (e.g., at a high level of generality) or as extra-solution activity.
Additionally, the techniques for determining biomarkers are shown to be conventional by the following review:
Griffiths (Angewandte Chemie International Edition, vol. 49(32), pp.5426-5445 (2010); cited on the attached form PTO-892) presents a review on biomarker analysis using metabolomics, and shows HPLC and mass spectrometry techniques (p.5428-5432).
Additional elements of computer components: The computer (claim 1); and CRM and processor (claim 16) do not cause the claims to rise to the level of significantly more than the judicial exception, and as such do not provide an inventive concept; these are conventional computers and computer components.
All limitations of claims 1-16 and 36 have been analyzed with respect to Step 2B, and none provides a specific inventive concept, as they all fail to rise to the level of significantly more than the identified judicial exception, and thus do not transform the judicial exception into a patent eligible application of the exceptions. Step2B: NO.
Therefore, the claims, when the limitations are considered individually and as a whole, are rejected under 35 U.S.C. § 101 as being directed to non patent-eligible subject matter.
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.
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.
Claims 1-16 and 36 are rejected under 35 U.S.C. 103 as being unpatentable over Cohen (US 2020/0005901 A1, published 01/02/2020; cited on the attached form PTO-892), in view of Blume (US 2016/0299144 A1, published 10/13/2016; cited on the attached form PTO-892).
Regarding inputting quantitative data of a panel of biomarkers in a biological sample obtained from a subject (claim 1 step (a)); analyzing the quantitative data using machine or deep learning (claim 1 step (b)); and machine learning, and/or deep learning algorithms (claim 1 step (d)), Cohen shows a method, in a computer-implemented system comprising a processor and a memory, for using classifier models to predict an increased risk of having or developing cancer, for an asymptomatic patient, which comprises obtaining measured values of a panel of biomarkers in a sample from the patient, wherein a value of a biomarker corresponds to a level of the biomarker in the sample [0014].
Regarding using a first-tier biomarker multi-panel to distinguish healthy subjects from subjects with one or more diseases that affect different organs or cell types (claim 1 step (c)), Cohen shows the cancer classifier model assigns the organ system class membership [0038]. Cohen shows the classifier model was able to distinguish cancers from noncancers [0047] and [0049].
Regarding sub-phenotyping the one or more diseases of the organ or the cell type (claim 1 step (d)), Cohen shows eight asymptomatic patients (5 male and 3 female) were first screened using the pan cancer test according to Example 1, and then those categorized as moderate or high risk were further screened using the organ system-based malignancy test according to Example 2 [0189].
Regarding diagnosing or prognosing the subject if the quantitative data is correlated by the computer system using tiered panels, machine learning, and/or deep learning algorithms to produce risk scores or other values that are indicative of the one or more diseases (claim 1 step (e)), Cohen shows machine learning system using training data comprising values from a panel of at least two biomarkers, age, and a diagnostic indicator for a population of patients [0059]. Cohen shows the first classifier model yields a numerical risk score for each patient tested, which can be used by physicians to further inform screening procedures to better predict and diagnose early stage cancer [0060].
Regarding internal or external standards in acquiring quantitative data (claim 2), Cohen shows comparing the measured value to a standard [0110].
Regarding selecting biomarkers based on statistical significance and feature selection optimization uses machine and/or deep learning (claim 4), Cohen shows the development of the classifier model, and the selection of markers (both blood and clinical parameters) may be based on a combination of accuracy, area under the curve (AUC), sensitivity, specificity values, and/or Youden index (Sensitivity+Specificity-1) that provide a measure of the performance of the classifier model [0159].
Regarding the feature selection optimization has been trained using biomarkers from subjects and controls respectively having and not having the disease (claim 5), Cohen shows the training data comprises a group of data from a group of patients with no cancer diagnosis three or more months after providing; and the training data comprises a group of data from a group of patients with a cancer diagnosis three or more months after providing a sample [0016]. (Note: Blume also shows samples selected for analysis were divided into discovery and validation sets, with 75 control and 75 advanced colorectal adenoma samples in the discovery set, and 76 control and 76 advanced colorectal adenomas samples in the validation set (Blume-[0269]).)
Regarding the techniques for determining the biomarkers (claim 6) and the mass spectrometry techniques (claim 7), Cohen shows use of mass spectroscopy in analyzing markers [0111].
Regarding the clinical parameters of sex, redox level, or cytokine level (claim 8), Cohen shows clinical parameters from the patient including gender [0102].
Regarding linear regression, logistic regression, decision tree, support vector machine, Naive Bayes, K nearest neighbors, K-Means, random forest, and/or artificial neural networks (claim 9), Cohen shows decision trees, artificial neural networks, deep learning neural network, support vector machines, random forest, logistic regression, linear regression [0070].
Regarding metabolites comprise carbohydrates, amino acids, fatty acids, and/or nucleotides (claim 10), Cohen shows biomarkers include nucleotides, amino acids, fatty acids, carbohydrates [0073].
Regarding labeling the quantitative data with a confirmed diagnosis (claim 11 step (a)), Cohen shows a panel of eight sera biomarkers were measured, and for each patient, the following information was obtained: General Information (age, gender, height, weight, race, ethnicity, current health status, fitness level), Health History [0190-0192].
Regarding applying characteristics of the patient to the quantitative data (claim 11 step (b)) and the characteristics gender, age, race, ethnicity, time/ date of sample collection and patient condition (claim 12), Cohen shows obtaining clinical parameters corresponding to the patient including at least age and gender [0025]. Cohen shows the first classifier model classifies the patient in an increased risk category using input variables of age and the measured values of a panel of biomarkers from the patient [0027].
Regarding the computer readable media and processor (claim 16), determining whether quantitative data of a panel of metabolic biomarkers is indicative of the disease using a trained machine deep learning classifier for distinguishing subjects with different diseases and without disease (claim 16 step (a)), and diagnosing the subject if the quantitative data is determined by the machine deep learning classifier to be indicative of the disease (claim 16 step (b)), Cohen shows this application pertains generally to classifier models generated by a machine learning system, trained with longitudinal data, for identifying asymptomatic patients with an increased risk for developing cancer and the type of cancer [0002]. Cohen shows a method, in a computer-implemented system comprising a processor and a memory, for using classifier models to predict an increased risk of having or developing cancer, for an asymptomatic patient, which comprises obtaining measured values of a panel of biomarkers in a sample from the patient, wherein a value of a biomarker corresponds to a level of the biomarker in the sample [0014].
While Cohen predicts risk for cancer, and shows biomarkers panels used in machine learning models for identifying asymptomatic patients with an increased risk for developing cancer, Cohen does not explicitly show diagnosing cancer of claim 1 and 16. (shown by Blume).
Cohen does not show using a third-tier biomarkers panel to identify specific etiology or comorbidities of the disease(s) of the organ or the cell type affected of claim 3 (shown by Blume).
Cohen does not show balancing the dataset through addition of multiple-use data points (claim 11 step (c)), scaling the data ((claim 11 step (d)); multiple-use data points comprising randomly picked data points (claim 14); and the data is scaled to a range of [0,1] (claim 15) (shown by Blume).
Cohen does not show tracking, by the third-tier biomarkers panel, a progression of the one or more diseases over time to determine the efficacy of therapy of claim 36 (shown by Blume).
Also: see note below regarding claim 13.
Regarding diagnosing cancer of claim 1 and 16, Blume shows biomarker panels and assays useful for the diagnosis and/or treatment of at least one of advanced colorectal adenoma and colorectal cancer [0023].
Regarding balancing the dataset through addition of multiple-use data points (claim 11 step (c)), scaling the data ((claim 11 step (d)); multiple-use data points comprising randomly picked data points (claim 14); and the data is scaled to a range of [0,1] (claim 15), Blume shows normalization of data [0191-0193]. Blume shows classifier models and the associated classification performance were assessed using a 10 by 10-fold cross validation procedure. The 10 by 10-fold cross validation was performed using the discovery set only, and incorporated feature selection and classification model assembly. In the cross validation procedure, feature selection was first applied to reduce the number of features used, followed by development of the classifier model and subsequent classification performance evaluation; feature selection and model assembly was performed using the training set only, and these models were then applied to the testing set to evaluate classifier performance, typically via the area under the curve (AUC) (i.e. at a range of [0,1]) from the receiver operating characteristic (ROC) plot [0233]. Blume shows FIG. 20 depicts AUC values for randomly generated panels from a biomarker set enriched to be predictive of colorectal cancer (CRC) [0297].
Regarding using a third-tier biomarkers panel to identify specific etiology or comorbidities of the disease(s) of the organ or the cell type affected of claim 3; and tracking, by the third-tier biomarkers panel, a progression of the one or more diseases over time to determine the efficacy of therapy (claim 36), Blume shows machine learning algorithms can aid in selection of important biomarker features and transform the underlying measurements into a score or probability relating to, for example, clinical outcome, disease risk, disease likelihood, presence or absence of disease, treatment response, and/or classification of disease status [0194]. Blume shows a clinical outcome score is determined by comparing a level of at least two biomarkers (i.e., a third tier biomarkers panel) in the biological sample obtained from the subject to a reference level of the at least two biomarkers [0195]. Blume shows in some cases, an increase in a score indicates an increased likelihood of complete response, partial response, or non-response [0196].
Note about claim 13:
With respect to claim 13 (for the excluded data includes metabolites associated with consumption of certain food or drugs, redundant metabolites, or noise), Cohen in view of Blume is applied to claim 13 as Cohen in view of Blume is applied to claim 11, because claim 11 is rejected over its embodiment that does not include the excluded data of claim 11, due to the alternative embodiments in claim 11 for "balancing the dataset through exclusion of data that does not correspond to a disease biomarker, addition of multiple-use data points, or a combination thereof."
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the machine learning models for predicting risk of cancer using biomarker panels of Cohen with the diagnostic and treatment response analysis machine learning model of Blume. Blume provides motivation to modify by showing models that include panels of markers which result in high levels of specificity and/or sensitivity, and algorithms that aid in selection and transformation of important biomarker feature measurements into a score or probability relating to clinical outcome, disease risk, disease likelihood, presence or absence of disease, treatment response, and/or classification of disease status. One of ordinary skill in the art would have understood how to and been motivated to modify Cohen with Blume and would have had a reasonable expectation of success in doing so because Cohen with Blume are generally drawn to related teaching of machine learning models for analysis and modeling of cancer biomarkers, as such, the combination would have been obvious.
Conclusion
No claims are allowed.
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/M.A.V./Examiner, Art Unit 1687
/G. STEVEN VANNI/Primary patents examiner, Art Unit 1686
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