DETAILED ACTION
Notices to Applicant
This communication is a First Action Non-Final on the merits. Claims 1-3, 5-6, 8-13, 16-18, 20-24 and 28 as filed 10/06/2025, are currently pending and have been considered below.
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 the benefit of priority of Israeli Patent Application No. 301945 filed on 04/04/2023.
Claim Objections
Claims 1-2 are objected to because of the following informalities:
Claim 1 recites “said procedure,” in lines 5 and 6; however, the limitation should read, “said machine learning procedure.” Claim 2 recites the same limitation and should be similarly corrected.
Appropriate correction is required.
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 1-3, 5-6, 8-13, 16-18, 20-24 and 28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Claims 1-3 and 20-21 are drawn to a system for predicting probability for developing a liver associated disease, which is within the four statutory categories (i.e. machine).
Independent Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 recites
1. (Original) A system for predicting probability for developing a liver associated disease, the system comprising a data processor having a circuit configured to obtain a plurality of parameters extracted from a body liquid test applied to a healthy subject,
to access a computer readable medium storing a machine learning procedure trained for predicting probabilities for liver associated disease,
to feed said procedure with said plurality of parameters, and
to receive from said procedure an output indicative of a probability that said subject is expected to develop a liver associated disease.
The claim limitations, as drafted, is a machine that, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people through rules or instructions but for the recitation of generic computer components. That is, other than reciting the above bolded language, nothing in the claim precludes the steps from performing the abstract idea. For example, but for the above bolded language, obtain a plurality of parameters extracted from a body liquid test applied to a healthy subject, predicting probabilities for liver associated disease, to receive an output indicative of a probability that said subject is expected to develop a liver associated disease in the context of this claim encompasses rule or instructions for managing personal behavior or interactions between people for predicting probability of developing a liver associated disease. If a claim limitation, under its broadest reasonable interpretation, covers rule or instructions for managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Further, the limitation of predicting probabilities for liver associated disease is covers performance of the limitation in the mind but for recitation of generic computer components, which falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites the above bolded additional elements of using, for example, “the system comprising a data processor having a circuit configured,” and “to access a computer readable medium storing a machine learning procedure trained,” to perform the claim limitations. The elements in each of these steps are recited at a high-level of generality (i.e., a processor and memory hardware as they relate to general purpose computer components and machine learning in any suitable algorithm suitable without limitation e.g. clustering, association rule algorithms, feature evaluation algorithms, subset selection algorithms, support vector machines, classification rules, cost-sensitive classifiers, vote algorithms, stacking algorithms, Bayesian networks, decision trees, neural networks, instance-based algorithms, linear modeling algorithms, k-nearest neighbors (KNN) analysis, ensemble learning algorithms, probabilistic models, graphical models, logistic regression methods (including multinomial logistic regression methods), gradient ascent methods, singular value decomposition methods and principle component analysis (Application Specification [0038], [0056]-[0057], [0071], [0114])). As such, the limitations amount to no more than mere instructions to implement an abstract idea on a computer or other machinery in its ordinary capacity, or merely uses a computer or other machinery in its ordinary capacity as a tool to perform an abstract idea. See MPEP 2106.05(f)(2). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the above bolded additional elements of using, for example, “the system comprising a data processor having a circuit configured,” and “to access a computer readable medium storing a machine learning procedure trained,” to perform the claim limitations amounts to no more than mere instructions to apply the exception using generic computer components (i.e., a processor and memory hardware as they relate to general purpose computer components and machine learning in any suitable algorithm suitable without limitation e.g. clustering, association rule algorithms, feature evaluation algorithms, subset selection algorithms, support vector machines, classification rules, cost-sensitive classifiers, vote algorithms, stacking algorithms, Bayesian networks, decision trees, neural networks, instance-based algorithms, linear modeling algorithms, k-nearest neighbors (KNN) analysis, ensemble learning algorithms, probabilistic models, graphical models, logistic regression methods (including multinomial logistic regression methods), gradient ascent methods, singular value decomposition methods and principle component analysis (Application Specification [0038], [0056]-[0057], [0071], [0114])). Mere instructions to apply an exception using a generic computer component other machinery in its ordinary capacity as a tool cannot provide an inventive concept. See MPEP 2106.05(f)(2). The claim is not patent eligible.
Dependent claims 2-3 and 20-21 include limitations of the independent claim and are directed to the same abstract idea as discussed above and incorporated herein. The dependent claims are rejected under 35 U.S.C. § 101 because they are directed to non-statutory subject matter. These additional claims recite what the data is and how it is analyzed. These information characteristics do not integrate the judicial exception into a practical application, and, when viewed individually or as a whole, they do not add anything substantial beyond the abstract ideas. Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore the dependent claims are rejected under 35 U.S.C. § 101.
Claims 5-6, 8-13, 16-17, 22-24, and 28 are drawn to a system for predicting probability for developing a liver associated disease, which is within the four statutory categories (i.e. method).
Independent Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 5 recites
5. (Original) A method of predicting probability for developing a liver associated disease, comprising: obtaining a plurality of parameters extracted from a body liquid test applied to a healthy subject;
accessing a computer readable medium storing a machine learning procedure trained for predicting probabilities for liver associated disease
feeding said procedure with said plurality of parameters and
receiving from said procedure an output indicative of a probability that said subject is expected to develop a liver associated disease.
The claim limitations, as drafted, is a method that, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people through rules or instructions but for the recitation of generic computer components. That is, other than reciting the above bolded language, nothing in the claim precludes the steps from performing the abstract idea. For example, but for the above bolded language, obtaining a plurality of parameters extracted from a body liquid test applied to a healthy subject, predicting probabilities for liver associated disease, and receiving an output indicative of a probability that said subject is expected to develop a liver associated disease in the context of this claim encompasses rule or instructions for managing personal behavior or interactions between people for predicting probability of developing a liver associated disease. If a claim limitation, under its broadest reasonable interpretation, covers rule or instructions for managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Further, the limitation of predicting probabilities for liver associated disease is covers performance of the limitation in the mind but for recitation of generic computer components, which falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites the above bolded additional elements of using, for example, “accessing a computer readable medium storing a machine learning procedure trained,” to perform the claim limitations. The elements in each of these steps are recited at a high-level of generality (i.e., a processor and memory hardware as they relate to general purpose computer components and machine learning in any suitable algorithm suitable without limitation e.g. clustering, association rule algorithms, feature evaluation algorithms, subset selection algorithms, support vector machines, classification rules, cost-sensitive classifiers, vote algorithms, stacking algorithms, Bayesian networks, decision trees, neural networks, instance-based algorithms, linear modeling algorithms, k-nearest neighbors (KNN) analysis, ensemble learning algorithms, probabilistic models, graphical models, logistic regression methods (including multinomial logistic regression methods), gradient ascent methods, singular value decomposition methods and principle component analysis (Application Specification [0038], [0056]-[0057], [0071], [0114])). As such, the limitations amount to no more than mere instructions to implement an abstract idea on a computer or other machinery in its ordinary capacity, or merely uses a computer or other machinery in its ordinary capacity as a tool to perform an abstract idea. See MPEP 2106.05(f)(2). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the above bolded additional elements of using, for example, ““accessing a computer readable medium storing a machine learning procedure trained,” to perform the claim limitations amounts to no more than mere instructions to apply the exception using generic computer components (i.e., a processor and memory hardware as they relate to general purpose computer components and machine learning in any suitable algorithm suitable without limitation e.g. clustering, association rule algorithms, feature evaluation algorithms, subset selection algorithms, support vector machines, classification rules, cost-sensitive classifiers, vote algorithms, stacking algorithms, Bayesian networks, decision trees, neural networks, instance-based algorithms, linear modeling algorithms, k-nearest neighbors (KNN) analysis, ensemble learning algorithms, probabilistic models, graphical models, logistic regression methods (including multinomial logistic regression methods), gradient ascent methods, singular value decomposition methods and principle component analysis (Application Specification [0038], [0056]-[0057], [0071], [0114])). Mere instructions to apply an exception using a generic computer component other machinery in its ordinary capacity as a tool cannot provide an inventive concept. See MPEP 2106.05(f)(2). The claim is not patent eligible.
Dependent claims 6, 8-13, 16-17, 22-24, and 28 include limitations of the independent claim and are directed to the same abstract idea as discussed above and incorporated herein. The dependent claims are rejected under 35 U.S.C. § 101 because they are directed to non-statutory subject matter. These additional claims recite what the data is and how it is analyzed. These information characteristics do not integrate the judicial exception into a practical application, and, when viewed individually or as a whole, they do not add anything substantial beyond the abstract ideas. Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore the dependent claims are rejected under 35 U.S.C. § 101.
Claim 18 is drawn to a method for evaluating efficacy of a treatment for a liver associated disease, which is within the four statutory categories (i.e. method).
Independent Claim 18 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 18 recites
18. (Original) A method of evaluating efficacy of a treatment for a liver associated disease, comprising: obtaining a plurality of parameters extracted from a body liquid test applied to a subject undiagnosed with any liver associated condition;
accessing a computer readable medium storing a machine learning procedure trained for predicting probabilities for liver associated disease;
feeding said procedure with said plurality of parameters;
receiving from said procedure an output indicative of a probability that said subject is expected to develop a liver associated disease;
treating said subject by said treatment to evaluate the efficacy thereof only if said probability is above a predetermined threshold.
The claim limitations, as drafted, is a method that, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people through rules or instructions but for the recitation of generic computer components. That is, other than reciting the above bolded language, nothing in the claim precludes the steps from performing the abstract idea. For example, but for the above bolded language, obtaining a plurality of parameters extracted from a body liquid test applied to a healthy subject, predicting probabilities for liver associated disease, and receiving an output indicative of a probability that said subject is expected to develop a liver associated disease and treating said subject by said treatment to evaluate the efficacy thereof only if said probability is above a predetermined threshold in the context of this claim encompasses rule or instructions for managing personal behavior or interactions between people for predicting probability of developing a liver associated disease and evaluating the efficacy of a treatment thereof. If a claim limitation, under its broadest reasonable interpretation, covers rule or instructions for managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Further, the limitation of predicting probabilities for liver associated disease is covers performance of the limitation in the mind but for recitation of generic computer components, which falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites the above bolded additional elements of using, for example, “accessing a computer readable medium storing a machine learning procedure trained,” to perform the claim limitations. The elements in each of these steps are recited at a high-level of generality (i.e., a processor and memory hardware as they relate to general purpose computer components and machine learning in any suitable algorithm suitable without limitation e.g. clustering, association rule algorithms, feature evaluation algorithms, subset selection algorithms, support vector machines, classification rules, cost-sensitive classifiers, vote algorithms, stacking algorithms, Bayesian networks, decision trees, neural networks, instance-based algorithms, linear modeling algorithms, k-nearest neighbors (KNN) analysis, ensemble learning algorithms, probabilistic models, graphical models, logistic regression methods (including multinomial logistic regression methods), gradient ascent methods, singular value decomposition methods and principle component analysis (Application Specification [0038], [0056]-[0057], [0071], [0114])). As such, the limitations amount to no more than mere instructions to implement an abstract idea on a computer or other machinery in its ordinary capacity, or merely uses a computer or other machinery in its ordinary capacity as a tool to perform an abstract idea. See MPEP 2106.05(f)(2). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the above bolded additional elements of using, for example, “accessing a computer readable medium storing a machine learning procedure trained,” to perform the claim limitations amounts to no more than mere instructions to apply the exception using generic computer components (i.e., a processor and memory hardware as they relate to general purpose computer components and machine learning in any suitable algorithm suitable without limitation e.g. clustering, association rule algorithms, feature evaluation algorithms, subset selection algorithms, support vector machines, classification rules, cost-sensitive classifiers, vote algorithms, stacking algorithms, Bayesian networks, decision trees, neural networks, instance-based algorithms, linear modeling algorithms, k-nearest neighbors (KNN) analysis, ensemble learning algorithms, probabilistic models, graphical models, logistic regression methods (including multinomial logistic regression methods), gradient ascent methods, singular value decomposition methods and principle component analysis (Application Specification [0038], [0056]-[0057], [0071], [0114])). Mere instructions to apply an exception using a generic computer component other machinery in its ordinary capacity as a tool cannot provide an inventive concept. See MPEP 2106.05(f)(2). The claim is not patent eligible.
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 1, 5, 20-24, and 28 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. 2023/0274840 A1 (hereinafter “Okanoue”).
RE: Claim 1 Okanoue teaches the claimed:
1. (Original) A system for predicting probability for developing a liver associated disease, the system comprising a data processor having a circuit configured to obtain a plurality of parameters extracted from a body liquid test applied to a healthy subject ((Okanoue, [0113], [0114]) (The fatty liver disease evaluation device 10 is constructed by installing a program for implementing these configurations in a computer. The fatty liver disease evaluation device 10 can be used by directly operating the computer or accessing the computer through a network such as an Internet or an Intranet. biochemical data including AST (GOT), ALT (GPT), gamma GTP, PLT, T-Cho, TG, and (d) diagnostic results from a doctor));
to access a computer readable medium storing a machine learning procedure trained for predicting probabilities for liver associated disease ((Okanoue, [0114]]) (The estimation model generation circuitry generates, by machine learning, an estimation model for estimating the risk level of fatty liver disease));
to feed said procedure with said plurality of parameters, and to receive from said procedure an output indicative of a probability that said subject is expected to develop a liver associated disease ((Okanoue, [0113], [0116]) (using, as a learning model, (a) attribute data including gender and age, (b) body observation data related to body findings including height and weight, (c) biochemical data including AST (GOT), ALT (GPT), gamma GTP, PLT, T-Cho, TG, and (d) diagnostic results from a doctor. The risk level prediction circuitry 40 inputs the data acquired in the data acquisition circuit to by the estimation model generated in the estimation model generation step and predicts the risk level of the fatty liver disease of the subject)).
RE: Claim 5 Okanoue teaches the claimed:
5. (Original) A method of predicting probability for developing a liver associated disease, comprising: obtaining a plurality of parameters extracted from a body liquid test applied to a healthy subject ((Okanoue, [0113], [0114]) (The fatty liver disease evaluation device 10 is constructed by installing a program for implementing these configurations in a computer. The fatty liver disease evaluation device 10 can be used by directly operating the computer or accessing the computer through a network such as an Internet or an Intranet. biochemical data including AST (GOT), ALT (GPT), gamma GTP, PLT, T-Cho, TG, and (d) diagnostic results from a doctor));
accessing a computer readable medium storing a machine learning procedure trained for predicting probabilities for liver associated disease ((Okanoue, [0114]]) (The estimation model generation circuitry generates, by machine learning, an estimation model for estimating the risk level of fatty liver disease));
feeding said procedure with said plurality of parameters and receiving from said procedure an output indicative of a probability that said subject is expected to develop a liver associated disease ((Okanoue, [0113], [0116]) (using, as a learning model, (a) attribute data including gender and age, (b) body observation data related to body findings including height and weight, (c) biochemical data including AST (GOT), ALT (GPT), gamma GTP, PLT, T-Cho, TG, and (d) diagnostic results from a doctor. The risk level prediction circuitry 40 inputs the data acquired in the data acquisition circuit to by the estimation model generated in the estimation model generation step and predicts the risk level of the fatty liver disease of the subject)).
RE: Claim 20 Okanoue teaches the claimed:
20. (Currently Amended) The system or method according claim 1, wherein said plurality of parameters comprises at least one parameter extracted from an electronic health record associated with said subject ((Okanoue, [0049]) (As the attribute data used in the estimated model generation step and the data acquisition step, it can be used such as gender, age, medical history, presence/absence of basic disease, nationality, and the like)).
RE: Claim 21 Okanoue teaches the claimed:
21. (Currently Amended) The system or method according to claim 1, wherein said plurality of parameters comprises at least one parameter extracted from a body liquid test applied to said subject ((Okanoue, [0113], [0116]) (using, as a learning model, (a) attribute data including gender and age, (b) body observation data related to body findings including height and weight, (c) biochemical data including AST (GOT), ALT (GPT), gamma GTP, PLT, T-Cho, TG, and (d) diagnostic results from a doctor)).
RE: Claim 22 Okanoue teaches the claimed:
22. (Currently Amended) The system or method according to claim 5, wherein said plurality of parameters comprises at least five blood levels or counts selected from the group consisting of: hemoglobin, platelet, white blood count, AST (GOT), ALT (GPT), albumin, bilirubin total, PT-INR, vitamin B12, glucose, hemoglobin Alc, cholesterol, cholesterol - HDL, cholesterol - LDL calc, triglycerides, and protein – total ((Okanoue, [0052], [0057], [0070]) (Among body measurements and routine biochemical tests, AST, ALT, GGT, cholesterol, triglyceride, and PLT; Cholesterol, triglyceride, PLT, hemoglobin Ale (HbAlc), and immunoreactive insulin (IRI) levels were analyzed. The distributions of these factors were analyzed, and criteria were set from the distribution of 11 values, excluding HbAlc and IRI; NASH-Scope was based on 11 items including age, sex, height, weight, waist circumference, aspartate aminotransferase, alanine aminotransferase, gamma-glutamyl transferase, cholesterol, triglyceride, and platelet count; the blood examination data including Type 4 collagen and at least one selected from AST (GOT), ALT (GPT), gamma-GTP, PLT, T-Cho and TG )).
RE: Claim 23 Okanoue teaches the claimed:
23. (Currently Amended) The system or method according to claim 5, wherein said plurality of parameters comprises blood level of aspartate aminotransferase, platelet score, an age of said subject, blood level of albumin, and blood level of glucose ((Okanoue, [0077], [0113], [0116]) (using, as a learning model, (a) attribute data including gender and age, (b) body observation data related to body findings including height and weight, (c) biochemical data including AST (GOT), ALT (GPT), gamma GTP, PLT, T-Cho, TG, and (d) diagnostic results from a doctor; determine the clinical significance and accuracy of Fibro-Scope with other NITs of liver fibrosis, we examined the following parameters: albumin (g / dL); impaired fasting glycemia/diabetes (yes= 1, no= 0) + 0.99)).
RE: Claim 24 Okanoue teaches the claimed:
24. (Currently Amended) The system or method according to claim 5, wherein said machine learning procedure comprises at least one procedure selected from the group consisting of decision trees, clustering, support vector machine, linear modeling, k-nearest neighbors analysis, a set of decision trees, ensemble learning procedure, neural networks, probabilistic model, graphical model, Bayesian network, and association rule learning ((Okanoue [0104]-[0105]) (The seven newly created indexes are defined as physical score (two data) and functional score (four data). It is assigned as an independent variable (X) in the decision tree of the machine learning field; neural network step and post treatment step)).
RE: Claim 28 Okanoue teaches the claimed:
28. (Currently Amended) The system or method according to claim 5, wherein said liver associated disease comprises at least one of: livercirrhosis, liver fibrosis, nonalcoholic fatty liver disease, non-alcoholic steatohepatitis, alcoholic fatty liver disease, alcoholic steatohepatitis, autoimmune hepatitis, hepatocarcinoma ((Okanoue, [0056]) (The present specification provides noble noninvasive tests using artificial intelligence (AI)/neural network (NN) algorithms to screen nonalcoholic fatty liver disease (NAFLD; NASH-Scope) and to diagnose fibrosis stages in nonalcoholic steatohepatitis (NASH; Fibro-Scope))).
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.
Claims 2 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2023/0274840 A1 (hereinafter “Okanoue”) in view of U.S. 2022/0028503 A1 (hereinafter “Bull et al.”)
RE: Claim 2 Okanoue teaches the claimed:
2. (Original) The system according to claim 1,
Okanoue fails to explicitly teach, but Bull et al. teaches the claimed:
wherein said circuit is configured to receive from said procedure an output indicative of an expected onset time of said liver associated disease in said subject ((Bull et al., [0047]) (Another prediction could be new patient is 25% likely to develop fatty liver disease in 10 years based on the percentage of patients in the patient cohort that developed this disease in the time slice age grouping))
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the dynamic patient outcome prediction for a patient likely to develop fatty liver disease over time as taught by Bull et al. within the method and system for evaluating risk of liver disease as taught by Okanoue with the motivation of predicting patient outcomes from patient health record data sets (Bull et al. at [0002]).
RE: Claim 10 Okanoue teaches the claimed:
10. (Currently Amended) The method according to claim 5
Okanoue fails to explicitly teach, but Bull et al. teaches the claimed:
wherein said output is indicative of an expected onset time of said liver associated disease in said subject ((Bull et al., [0047]) (Another prediction could be new patient is 25% likely to develop fatty liver disease in 10 years based on the percentage of patients in the patient cohort that developed this disease in the time slice age grouping))
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the dynamic patient outcome prediction for a patient likely to develop fatty liver disease over time as taught by Bull et al. within the method and system for evaluating risk of liver disease as taught by Okanoue with the motivation of predicting patient outcomes from patient health record data sets (Bull et al. at [0002]).
Claims 3 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2023/0274840 A1 (hereinafter “Okanoue”) in view of U.S. 2021/0208147 A1 (hereinafter “Poynard et al.”)
RE: Claim 3 Okanoue teaches the claimed:
3. (Currently Amended) The system according to claim 1, wherein said plurality of parameters comprises a blood level of aspartate aminotransferase and a platelet score ((Okanoue, [0057]) (NASH-Scope was based on 11 items including age, sex, height, weight, waist circumference, aspartate aminotransferase, alanine aminotransferase, gamma-glutamyl transferase, cholesterol, triglyceride, and platelet count)).
Okanoue fails to explicitly teach, but Poynard et al. teaches the claimed:
and is devoid of blood hyaluronic acid concentration, and blood procollagen III-NP concentration ((Poynard, [0050]) (The markers can thus be selected from the group consisting of α2-macroglobulin (A2M), GGT (gammaglutamyl transpeptidase), haptoglobin, apolipoprotein A-I (apoA1), bilirubin, alanine transaminases (ALT), aspartate transaminases (AST), triglycerides, total cholesterol, fasting glucose, γ-globulin, albumin, α1-globulin, α2-globulin, β-globulin, IL10, TGF-β1, apoA2, apoB, cytokeratin 18, platelets number, prothrombin level, hyaluronic acid, urea, N-terminal of type III pro-collagen)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the biomarkers of blood considered including hyaluronic acid and N-terminal of type III pro-collagen as taught by Poynard et al. within the method and system for evaluating risk of liver disease as taught by Okanoue with the motivation of providing non-invasive quantitative test (Poynard et al. at [0002]).
RE: Claim 12 Okanoue teaches the claimed:
12. (Currently Amended) The method according to claim 5, wherein said plurality of parameters comprises a blood level of aspartate aminotransferase and a platelet score ((Okanoue, [0057]) (NASH-Scope was based on 11 items including age, sex, height, weight, waist circumference, aspartate aminotransferase, alanine aminotransferase, gamma-glutamyl transferase, cholesterol, triglyceride, and platelet count));
Okanoue fails to explicitly teach, but Poynard et al. teaches the claimed:
and is devoid of blood hyaluronic acid concentration, and blood procollagen III-NP concentration ((Poynard, [0050]) (The markers can thus be selected from the group consisting of α2-macroglobulin (A2M), GGT (gammaglutamyl transpeptidase), haptoglobin, apolipoprotein A-I (apoA1), bilirubin, alanine transaminases (ALT), aspartate transaminases (AST), triglycerides, total cholesterol, fasting glucose, γ-globulin, albumin, α1-globulin, α2-globulin, β-globulin, IL10, TGF-β1, apoA2, apoB, cytokeratin 18, platelets number, prothrombin level, hyaluronic acid, urea, N-terminal of type III pro-collagen)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the biomarkers of blood considered including hyaluronic acid and N-terminal of type III pro-collagen as taught by Poynard et al. within the method and system for evaluating risk of liver disease as taught by Okanoue with the motivation of providing non-invasive quantitative test (Poynard et al. at [0002]).
Claims 6 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2023/0274840 A1 (hereinafter “Okanoue”) in view of U.S. 2022/0028503 A1 (hereinafter “Bull et al.”) and U.S. 2021/0208147 A1 (hereinafter “Everson et al.”)
RE: Claim 6 Okanoue teaches the claimed:
6. (Original) The method according to claim 5,
Okanoue fails to explicitly teach, but Bull et al. teaches the claimed:
comprising when said probability is above a predetermined threshold ((Bull et al., [0047]) (Another prediction could be new patient is 25% likely to develop fatty liver disease in 10 years based on the percentage of patients in the patient cohort that developed this disease in the time slice age grouping))
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the dynamic patient outcome prediction for a patient likely to develop fatty liver disease over time as taught by Bull et al. within the method and system for evaluating risk of liver disease as taught by Okanoue with the motivation of predicting patient outcomes from patient health record data sets (Bull et al. at [0002]).
Okanoue and Bull et al. fail to explicitly teach, but Everson et al. teaches the claimed:
performing at least one imaging test directed to identify the liver associated disease ((Everson et al., [0635]) (Consistent surveillance was defined as having an ultrasound and alpha-fetoprotein every 12 months. 83 patients developed HCC; and 23 (27.7%) were detected beyond Milan criteria (advanced HCC). Definite HCC was defined by (a) imaging demonstrating a mass with AFP levels > 1,000 ng/ml or (b) histological confirmation)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the consistent surveillance including ultrasound every 12 months as taught by Everson et al. within the method and system for evaluating risk of liver disease as taught by Okanoue and the dynamic patient outcome prediction for a patient likely to develop fatty liver disease over time as taught by Bull et al. with the motivation of monitoring chronic lever disease progression, which is typically slow and silent (Everson et al. at [0002]).
RE: Claim 8 Okanoue teaches the claimed:
8. (Currently Amended) The method according to claim 5, […] by at least one technique selected from the group consisting of biopsy, MRI elastography, transient elastography, ultrasound elastography, and a fibrosis test ((Okanoue, [0052], [0077]) (the evaluation device, or the evaluation program for evaluating the degree of risk of liver fibrosis may be referred to as Fibro-Scope; Fib-4 Index; We compared the diagnostic properties of FibroScope with various NITs. The AUROC values for the various NITs including AAR, APRI, FIB-4, NSF, CA index fibrosis, FM-fibro index, and Fibro-Scope)).
Okanoue fails to explicitly teach, but Bull et al. teaches the claimed:
comprising when said probability is above a predetermined threshold ((Bull et al., [0047]) (Another prediction could be new patient is 25% likely to develop fatty liver disease in 10 years based on the percentage of patients in the patient cohort that developed this disease in the time slice age grouping))
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the dynamic patient outcome prediction for a patient likely to develop fatty liver disease over time as taught by Bull et al. within the method and system for evaluating risk of liver disease as taught by Okanoue with the motivation of predicting patient outcomes from patient health record data sets (Bull et al. at [0002]).
Okanoue and Bull et al. fail to explicitly teach, but Everson et al. teaches the claimed:
periodically monitoring said subject for onset of the liver associated disease ((Everson et al., the STAT test may be used to monitor a patient periodically for improvement or liver disease progression. The patient can be monitored periodically for improvement or disease progression. For example, depending on the STAT test result, the patient can be followed for quantitative improvement with annual STAT, FLOW, SHUNT and/or DSI tests)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the consistent surveillance including ultrasound every 12 months as taught by Everson et al. within the method and system for evaluating risk of liver disease as taught by Okanoue and the dynamic patient outcome prediction for a patient likely to develop fatty liver disease over time as taught by Bull et al. with the motivation of monitoring chronic lever disease progression, which is typically slow and silent (Everson et al. at [0002]).
RE: Claim 9 Okanoue, Bull et al., and Everson et al. teach the claimed:
9. (Currently Amended) The method according to claim 8, wherein said fibrosis test comprises at least one of: fibrosis-4 (FIB-4), aspartate aminotransferase to platelet ratio index (APRI), and aspartate aminotransferase to alanine aminotransferase ratio ((Okanoue, [0052], [0077]) (the evaluation device, or the evaluation program for evaluating the degree of risk of liver fibrosis may be referred to as Fibro-Scope; Fib-4 Index; We compared the diagnostic properties of FibroScope with various NITs. The AUROC values for the various NITs including AAR, APRI, FIB-4, NSF, CA index fibrosis, FM-fibro index, and Fibro-Scope)).
RE: Claim 11 Okanoue teaches the claimed:
11. (Original) The method according to claim 10, further comprising, a predetermined time ahead of said expected onset time, periodically monitoring said subject for onset of the liver associated disease, by at least one technique selected from the group consisting of biopsy, MRI elastography, transient elastography, ultrasound elastography, and a fibrosis test ((Everson et al., [0394], [0635]) (the STAT test may be used to monitor a patient periodically for improvement or liver disease progression. The patient can be monitored periodically for improvement or disease progression. For example, depending on the STAT test result, the patient can be followed for quantitative improvement with annual STAT, FLOW, SHUNT and/or DSI tests; Consistent surveillance was defined as having an ultrasound and alpha-fetoprotein every 12 months. 83 patients developed HCC; and 23 (27.7%) were detected beyond Milan criteria (advanced HCC). Definite HCC was defined by (a) imaging demonstrating a mass with AFP levels > 1,000 ng/ml or (b) histological confirmation)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the consistent surveillance including ultrasound every 12 months as taught by Everson et al. within the method and system for evaluating risk of liver disease as taught by Okanoue and the dynamic patient outcome prediction for a patient likely to develop fatty liver disease over time as taught by Bull et al. with the motivation of monitoring chronic lever disease progression, which is typically slow and silent (Everson et al. at [0002]).
Claims 13 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2023/0274840 A1 (hereinafter “Okanoue”) in view of U.S. 2023/0240589 A1 (hereinafter “Ray et al.”)
RE: Claim 13 Okanoue teaches the claimed:
13. (Currently Amended) The method according to claim 5,
Okanoue fails to explicitly teach, but Ray et al. teaches the claimed:
further comprising applying measures to prevent a development of the liver associated disease or to reduce said probability ((Ray et al., [0066]) (perform analytics thereon for determining the probability of the presence and/or severity of liver disease for the user and providing one or more recommendations for treatment based, at least in part, on the determination)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the recommended treatment following the determining the probability of the presence and/or severity of liver disease for a user as taught by Ray et al. within the method and system for evaluating risk of liver disease as taught by Okanoue with the motivation of preventing a user's liver condition may significantly deteriorating, preventing additional health issues from arising, and, in some cases, preventing an increased risk or likelihood of mortality (Ray et al. at [0007]).
RE: Claim 16 Okanoue teaches the claimed:
16. (Currently Amended) The method according to claim 13,
Okanoue fails to explicitly teach, but Ray et al. teaches the claimed:
wherein said measures comprise at least one of: administering a pharmaceutical agent, administering nutritional supplements or a dietary formulation, and providing recommendations for a change of life style and/or dietary habits ((Ray et al., [0074, [0132]) (make patient-specific treatment decisions or recommendations for liver disease, 6) provide information on the effect of an intervention ( e.g., an effect of a lifestyle change of the patient, an effect of a surgical procedure, an effect of the patient taking new medication, etc.))).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the consideration of lifestyle effect and food and how different types of food impact the liver’s function to make lifestyle and food recommendations as taught by Ray et al. within the method and system for evaluating risk of liver disease as taught by Okanoue with the motivation of preventing a user's liver condition may significantly deteriorating, preventing additional health issues from arising, and, in some cases, preventing an increased risk or likelihood of mortality (Ray et al. at [0007]).
RE: Claim 17 Okanoue teaches the claimed:
17. (Currently Amended) A computer software product, comprising a computer-readable medium in which program instructions are stored, which instructions, when read by a data processor, cause the data processor to receive a food to which a response of a subject is unknown, and to execute the method according to claim 5 ((Ray et al., [0074]) (the model may be trained to provide food, exercise, therapeutic intervention, medication type and dosage, and other types of decision support recommendations to help the user improve their liver function based on the user's historical data, including how different types of food and/or exercise impacted the user's liver function in the past. For example, where the model is trained to provide food and/or exercise recommendations, the model may be trained by training server system based at least partially on historical glucose and lactate measurements after meals)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the consideration of food and how different types of food impact the liver’s function to make food recommendations as taught by Ray et al. within the method and system for evaluating risk of liver disease as taught by Okanoue with the motivation of preventing a user's liver condition may significantly deteriorating, preventing additional health issues from arising, and, in some cases, preventing an increased risk or likelihood of mortality (Ray et al. at [0007]).
RE: Claim 18 Okanoue teaches the claimed:
18. (Original) A method of evaluating efficacy of a treatment for a liver associated disease, comprising: obtaining a plurality of parameters extracted from a body liquid test applied to a subject undiagnosed with any liver associated condition ((Okanoue, [0113], [0114]) (The fatty liver disease evaluation device 10 is constructed by installing a program for implementing these configurations in a computer. The fatty liver disease evaluation device 10 can be used by directly operating the computer or accessing the computer through a network such as an Internet or an Intranet. biochemical data including AST (GOT), ALT (GPT), gamma GTP, PLT, T-Cho, TG, and (d) diagnostic results from a doctor));
accessing a computer readable medium storing a machine learning procedure trained for predicting probabilities for liver associated disease ((Okanoue, [0114]]) (The estimation model generation circuitry generates, by machine learning, an estimation model for estimating the risk level of fatty liver disease));
feeding said procedure with said plurality of parameters; receiving from said procedure an output indicative of a probability that said subject is expected to develop a liver associated disease ((Okanoue, [0113], [0116]) (using, as a learning model, (a) attribute data including gender and age, (b) body observation data related to body findings including height and weight, (c) biochemical data including AST (GOT), ALT (GPT), gamma GTP, PLT, T-Cho, TG, and (d) diagnostic results from a doctor. The risk level prediction circuitry 40 inputs the data acquired in the data acquisition circuit to by the estimation model generated in the estimation model generation step and predicts the risk level of the fatty liver disease of the subject; In post-treatment, the fibrosis stage is assigned based on the threshold of the output data. According to the learning result of 324 cases, the output result is calculated by 0.0 <negative answer <0.5 :S positive answer< 1.0; the fatty liver disease risk evaluation device 10 evaluates the risk level of the fatty liver disease by the risk degree prediction unit 40 based on the estimation model generated by the estimation model generation unit 20 and the data of the subject acquired by the data acquisition unit 30)).
Okanoue fails to explicitly teach, but Ray et al. teaches the claimed:
treating said subject by said treatment to evaluate the efficacy thereof only if said probability is above a predetermined threshold ((Ray et al., [0066]) (perform analytics thereon for determining the probability of the presence and/or severity of liver disease for the user and providing one or more recommendations for treatment based, at least in part, on the determination)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the recommended treatment following the determining the probability of the presence and/or severity of liver disease for a user as taught by Ray et al. within the method and system for evaluating risk of liver disease as taught by Okanoue with the motivation of preventing a user's liver condition may significantly deteriorating, preventing additional health issues from arising, and, in some cases, preventing an increased risk or likelihood of mortality (Ray et al. at [0007]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20210398676 A1 teaches preparing, training, and deploying a machine learning algorithm for making medical condition state determinations (Abstract);
US 20260120865 A1 teaches Model for End-Stage Liver Disease for prediction of survival in patients with various forms of liver failure ([0141]); and
US 20170325749 A1 teaches determine a meaningful risk score of developing sepsis, and the following chronic disease states listed in decreasing order of risk, can contribute to an increased risk of developing sepsis: AIDS, liver failure, cancer, and generally immune compromised patients ([0073]).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANTHONY BALAJ whose telephone number is (571)272-8181. The examiner can normally be reached 8:00 - 4:00 M-F.
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, Fonya Long can be reached at (571) 270-5096. 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.
/A.M.B./Examiner, Art Unit 3682
/FONYA M LONG/Supervisory Patent Examiner, Art Unit 3682
