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 .
DETAILED ACTION
This action is responsive to the Application filed on 08/04/2025
Claims 1-7, 10 and 21-22 are pending in the case. Claims 1 and 10 are independent claims. Claims 8-9 and 11-20 have been canceled. Claims 21-22 have been newly added.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 08/04/2025 has been entered.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-7, 10 and 21-22 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself. Applicant is advised to consult the 2019 PEG for more details of the analysis.
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Claims 1-7 and 21-22 are drawn to a method and claim 10 is drawn to an electronic apparatus, therefore each of these claim groups falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater; Step 1). Nonetheless, the claims are directed to a judicially recognized exception of an abstract idea without significant more (Step 2A, see below). Independent claims 1 and 10 are nonverbatim but similar in claim construction, hence share the same rationale that the claimed inventions are directed to non-statutory subject matter as follows:
Regarding claim 1:
Claim 1 recites: A method for automatically predicting a disease type, executed by an electronic apparatus, the method comprising the following steps:
detecting global mutant information of several mutant genes of a tested sample;
converting the global mutant information of several mutant genes of a tested sample into concerted effect (CE) parameters or concerted effect burden (CEB) parameters with a quantitative model that converts discrete qualitative data into continuous space, wherein the quantitative model is a multivariate correlation model between the global mutant information of several mutant genes of a tested sample and gene expression activity, and wherein the concerted effect (CE) parameters or concerted effect burden (CEB) parameters represent the comprehensive influence parameters of several mutant genes on the expression activity of any gene in the predetermined genome;
identifying characteristic difference of the concerted effect (CE) parameters or concerted effect burden (CEB) parameters of several mutant genes of a tested sample on expression activity of each gene in a predetermined genome; and
predicting a disease type label corresponding to the tested sample based on the characteristic difference of the concerted effect (CE) parameters or concerted effect burden (CEB) parameters of the several mutant genes on the expression activity of each gene in the predetermined genome
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Claim 1 is directed to an abstract idea, specifically, a mental process – concepts performed in the human mind or by a human using a pen and paper" (including an observation, evaluation, judgement, opinion). As well as, a mathematical concept, when the claim recites," a mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number." See MPEP § 2106.04(a)(2)(I)(C).
Independent claim 1 recites in part:
“detecting global mutant information of several mutant genes of a tested sample”
The limitation above is broadly and reasonably interpreted as a mental process, as a form of mental evaluation or judgement, and or by a human using a pen and paper. For example, one can find information about different mutant genes in a tested sample. See MPEP § 2106.04(a)(2)(I)(C).
“converting the global mutant information of several mutant genes of a tested sample into concerted effect (CE) parameters or concerted effect burden (CEB) parameters with a quantitative model that converts discrete qualitative data into continuous space, wherein the quantitative model is a multivariate correlation model between the global mutant information of several mutant genes of a tested sample and gene expression activity, and wherein the concerted effect (CE) parameters or concerted effect burden (CEB) parameters represent the comprehensive influence parameters of several mutant genes on the expression activity of any gene in the predetermined genome”
The limitation above is broadly and reasonably interpreted as a mathematical concept, when the claim recites," a mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number." See MPEP § 2106.04(a)(2)(I)(C). It describes a quantitative model that converts discrete qualitative data into a continuous space. The limitation mentions a multivariate correlation model, which is a mathematical/statistical framework. The goal is to numerically represent and analyze gene mutation effects.
“identifying characteristic difference of the concerted effect (CE) parameters or concerted effect burden (CEB) parameters of several mutant genes of a tested sample on expression activity of each gene in a predetermined genome”
The limitation above is broadly and reasonably interpreted as a mental process, as a form of mental evaluation or judgement, and or by a human using a pen and paper. For example, one can find different features of the concerted effect (CE) or concerted effect burden (CEB) of several mutant genes in a sample and how they affect the activity of each gene in a specific genome. See MPEP § 2106.04(a)(2)(I)(C).
“predicting a disease type label corresponding to the tested sample based on the characteristic difference of the concerted effect (CE) parameters or concerted effect burden (CEB) parameters of the several mutant genes on the expression activity of each gene in the predetermined genome”
The limitation above is broadly and reasonably interpreted as a mental process, as a form of mental evaluation or judgement, and or by a human using a pen and paper. For example, a scientist and/or pathologists can find out what type of disease a tested sample has by looking at how different mutant genes affect the activity of genes in the chosen genome. See MPEP § 2106.04(a)(2)(I)(C).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
Independent claim 1 recites in part:
“A method for automatically predicting a disease type, executed by an electronic apparatus, the method comprising the following steps” as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “electronic apparatus” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and §2106.04(d).
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
First, the additional elements directed to generally linking the use of a judicial exception to a particular technological environment or field of use are deemed insufficient to transform the judicial exception to a patentable invention because the claimed limitations generally link the judicial exception to the technology environment, see MPEP 2106.05(h). However, they are included below for the sake of completeness.
Second, the additional elements mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception. See MPEP 2106.05(f). However, they are included below for the sake of completeness.
Independent claim 1 recites in part:
“A method for automatically predicting a disease type, executed by an electronic apparatus, the method comprising the following steps” as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “electronic apparatus” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and §2106.04(d).
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. The claims are not eligible subject matter.
Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole the independent claim limitations do not recite what have the courts have identified as “significantly more”.
Regarding claim 10:
Claim 10 recites: An electronic apparatus, comprising: a memory, a processor and a program stored in the memory, wherein the program is configured to be executed by the processor, and when the processor executes the program, a method for automatically predicting a disease type is implemented, and the processor is configured for:
detecting global mutant information of several mutant genes of a tested sample;
converting the global mutant information of several mutant genes of a tested sample with a quantitative model that converts discrete qualitative data into continuous space, so that converting the global mutant information of several mutant genes of a tested sample into concerted effect (CE) parameters or concerted effect burden (CEB) parameters, wherein the quantitative model is a multivariate correlation model between the global mutant information of several mutant genes of a tested sample and gene expression activity, wherein the concerted effect (CE) parameters or concerted effect burden (CEB) parameters represent the comprehensive influence parameters of several mutant genes on the expression activity of any gene in the predetermined genome;
identifying characteristic difference of the concerted effect (CE) parameters or concerted effect burden (CEB) parameters of several mutant genes of a tested sample on expression activity of each gene in a predetermined genome; and
predicting a disease type corresponding to the tested sample based on the characteristic difference of the concerted effect (CE) parameters or concerted effect burden (CEB) parameters of the several mutant genes on the expression activity of each gene in the predetermined genome
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Claim 10 is directed to an abstract idea, specifically, a mental process – concepts performed in the human mind or by a human using a pen and paper" (including an observation, evaluation, judgement, opinion). As well as, a mathematical concept, when the claim recites," a mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number." See MPEP § 2106.04(a)(2)(I)(C).
Independent claim 10 recites in part:
“detecting global mutant information of several mutant genes of a tested sample”
The limitation above is broadly and reasonably interpreted as a mental process, as a form of mental evaluation or judgement, and or by a human using a pen and paper. For example, one can find information about different mutant genes in a tested sample. See MPEP § 2106.04(a)(2)(I)(C).
“converting the global mutant information of several mutant genes of a tested sample into concerted effect (CE) parameters or concerted effect burden (CEB) parameters with a quantitative model that converts discrete qualitative data into continuous space, wherein the quantitative model is a multivariate correlation model between the global mutant information of several mutant genes of a tested sample and gene expression activity, and wherein the concerted effect (CE) parameters or concerted effect burden (CEB) parameters represent the comprehensive influence parameters of several mutant genes on the expression activity of any gene in the predetermined genome”
The limitation above is broadly and reasonably interpreted as a mathematical concept, when the claim recites," a mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number." See MPEP § 2106.04(a)(2)(I)(C). It describes a quantitative model that converts discrete qualitative data into a continuous space. The limitation mentions a multivariate correlation model, which is a mathematical/statistical framework. The goal is to numerically represent and analyze gene mutation effects.
“identifying characteristic difference of the concerted effect (CE) parameters or concerted effect burden (CEB) parameters of several mutant genes of a tested sample on expression activity of each gene in a predetermined genome”
The limitation above is broadly and reasonably interpreted as a mental process, as a form of mental evaluation or judgement, and or by a human using a pen and paper. For example, one can find different features of the concerted effect (CE) or concerted effect burden (CEB) of several mutant genes in a sample and how they affect the activity of each gene in a specific genome. See MPEP § 2106.04(a)(2)(I)(C).
“predicting a disease type label corresponding to the tested sample based on the characteristic difference of the concerted effect (CE) parameters or concerted effect burden (CEB) parameters of the several mutant genes on the expression activity of each gene in the predetermined genome”
The limitation above is broadly and reasonably interpreted as a mental process, as a form of mental evaluation or judgement, and or by a human using a pen and paper. For example, a scientist and/or pathologists can find out what type of disease a tested sample has by looking at how different mutant genes affect the activity of genes in the chosen genome. See MPEP § 2106.04(a)(2)(I)(C).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
Independent claim 10 recites in part:
“An electronic apparatus, comprising: a memory, a processor and a program stored in the memory, wherein the program is configured to be executed by the processor, and when the processor executes the program, a method for automatically predicting a disease type is implemented, and the processor is configured for” as drafted, amount to additional elements that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, such generic computing components recited at a high-level of generality (i.e., as a generic processor performing data gathering and mathematical calculations) MPEP §§ 2106.04(d), 2106.05(f)(2).
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
First, the additional elements directed to generally linking the use of a judicial exception to a particular technological environment or field of use are deemed insufficient to transform the judicial exception to a patentable invention because the claimed limitations generally link the judicial exception to the technology environment, see MPEP 2106.05(h). However, they are included below for the sake of completeness.
Second, the additional elements mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception. See MPEP 2106.05(f). However, they are included below for the sake of completeness.
Independent claim 10 recites in part:
“An electronic apparatus, comprising: a memory, a processor and a program stored in the memory, wherein the program is configured to be executed by the processor, and when the processor executes the program, a method for automatically predicting a disease type is implemented, and the processor is configured for” as drafted, amount to additional elements that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, such generic computing components recited at a high-level of generality (i.e., as a generic processor performing data gathering and mathematical calculations) MPEP §§ 2106.04(d), 2106.05(f)(2).
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. The claims are not eligible subject matter.
Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole the independent claim limitations do not recite what have the courts have identified as “significantly more”.
Furthermore, regarding dependent claims 2-7 and 21-22 are dependent on claim 1, the claims are directed to a judicial exception without significantly more as highlighted below in the claim limitations by evaluating the claim limitations under Step 2A and 2B:
Claim 2 incorporates the rejection of independent claim 1 and does not integrate the judicial exception into a practical application. Involves, a mental process, as a form of mental evaluation or judgement, and or by a human using a pen and paper. See MPEP § 2106.04(a)(2)(I)(C).
Claim 3 incorporates the rejection of independent claim 1 and does not integrate the judicial exception into a practical application.
Claim 4 incorporates the rejection of claim 3 and does not integrate the judicial exception into a practical application.
Claim 5 incorporates the rejection of claim 4 and does not integrate the judicial exception into a practical application.
Claim 6 incorporates the rejection of claim 5 and does not integrate the judicial exception into a practical application.
Claim 7 incorporates the rejection of claim 3 and does not integrate the judicial exception into a practical application.
Claim 21 incorporates the rejection of independent claim 1 and does not integrate the judicial exception into a practical application.
Claim 22 incorporates the rejection of independent claim 1 and does not integrate the judicial exception into a practical application.
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.
Claim(s) 1-2, 10 and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (US Patent No. 10,665,347 B1), hereinafter referred to as Chen, in view of Sirohey et al (Pub No.: 20090292557 A1), hereinafter referred to as Sirohey.
With respect to claim 1, Chen disclose:
A method for automatically predicting a disease type, executed by an electronic apparatus, the method comprising the following steps: detecting global mutant information of several mutant genes of a tested sample (In Fig. 1 and Col. 4, lines 1–31, Chen discloses a method for predicting health outcomes. This target set includes information about a specific patient. For instance, it can have data on a patient with a disease. The data can include genetic information, medical records, or both. For example, genetic data can consist of tumor gene details, changes in genes from advanced sequencing, and other genetic variations.)
Converting the global mutant information of several mutant genes of a tested sample into concerted effect (CE) parameters or concerted effect burden (CEB) parameters with a quantitative model that converts discrete qualitative data into continuous space, wherein the quantitative model is a multivariate correlation model between the global mutant information of several mutant genes of a tested sample and gene expression activity, and wherein the concerted effect (CE) parameters or concerted effect burden (CEB) parameters represent the comprehensive influence parameters of several mutant genes on the expression activity of any gene in the predetermined genome ("Based on examiners' broadest reasonable interpretation (BRI) and the lack of details in the specification, the concerted effect (CE) parameters is interpreted as normalizing" data by assigning it a value. Examiner selects: concerted effect (CE) parameters. In Fig. 1 and Cols. 5–6, lines 62–25, Chen discloses weighting the normalized common comparison feature data, assigning different levels of importance to each genomic data and clinical data. The genomic data can be weighted prior to weighting the clinical data.)
Predicting a disease type label corresponding to the tested sample based on the characteristic difference of the concerted effect (CE) parameters or concerted effect burden (CEB) parameters of the several mutant genes on the expression activity of each gene in the predetermined genome (In Cols. 9-10, lines 41-3, Chen discloses that patient data can be sorted using a trained system. This sorting can involve preparing a reference set so that important features are given a value before sorting the actual patient data. The patient data is then sorted based on this valued reference set. To sort the patient data, the system can look at already sorted patient data and find important features from a comparison set.)
With respect to claim 1, Chen do not explicitly disclose:
identifying characteristic difference of the concerted effect (CE) parameters or concerted effect burden (CEB) parameters of several mutant genes of a tested sample on expression activity of each gene in a predetermined genome
However, Sirohey is known to disclose:
Identifying characteristic difference of the concerted effect (CE) parameters or concerted effect burden (CEB) parameters of several mutant genes of a tested sample on expression activity of each gene in a predetermined genome (In paragraph [0062], Sirohey discloses automatically identifying a potential patient's disease type based at least in part on the comparison. For instance, the data processing system 32 may identify various potential disease types or severity levels and present the identified disease types or severity levels to a user for diagnosis. )
Chen and Sirohey are analogous pieces of art because all references concern predicting the prognosis of the patient. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Chen, with determining one or more best matches by applying a classification method to the weighted normalized common comparison feature data, and predicting the prognosis of the patient in the target set as taught by Chen, with medical diagnosis and, more particularly, to the diagnosis of medical conditions from patient deviation data as taught by Sirohey. The motivation for doing so would have been to improve the accuracy of a patient's prognosis or prediction of response to therapy (see (Col. 1, lines 18-20) of Chen).
Regarding claim 2, Chen in view Sirohey disclose the elements of claim 1. In addition, Chen disclose:
The method of claim 1, wherein the step of predicting the disease type corresponding to the tested sample comprises: predicting the disease type label corresponding to the tested sample from at least two disease type labels having evolutionary correlation (In Col. 4, lines 32–61, Chen discloses the method can automatically compare similar data, making it easier to gather more information. The reference set may also use DNA sequencing data from (1) past medical records, (2) future clinical trials, or (3) larger biobanking projects. The reference set can include data from many patients who share at least one feature with the target patient, such as a disease, a drug treatment, or a physical trait. The data can be genetic, clinical, or both. Genetic data may include information on tumor gene expression, single nucleotide changes from advanced sequencing, and mutation data. More data in the reference set can lead to better predictions of health outcomes.)
and the predetermined genome corresponds to the at least two diseases having evolutionary correlation (In Col. 4, lines 32–61, Chen discloses the reference set can include data from many patients who share at least one feature with the target patient, such as a disease, a drug treatment, or a physical trait. The data can be genetic, clinical, or both. )
With respect to claim 10, Chen disclose:
An electronic apparatus, comprising: a memory, a processor and a program stored in the memory, wherein the program is configured to be executed by the processor, and when the processor executes the program, a method for automatically predicting a disease type is implemented, and the processor is configured for (In Fig. 1 and Col. 4, lines 1–31, Chen discloses a method for predicting health outcomes. This target set includes information about a specific patient. For instance, it can have data on a patient with a disease. The data can include genetic information, medical records, or both. For example, genetic data can consist of tumor gene details, changes in genes from advanced sequencing, and other genetic variations. In Col. 11, lines 8-15, Chen disclose the components of the computer 401 can comprise, but are not limited to, one or more processors or processing units 403, a system memory 412, and a system bus 413 that couples various system components including the processor 403 to the system memory 412. In the case of multiple processing units 403, the system can utilize parallel computing.)
Converting the global mutant information of several mutant genes of a tested sample into concerted effect (CE) parameters or concerted effect burden (CEB) parameters with a quantitative model that converts discrete qualitative data into continuous space, wherein the quantitative model is a multivariate correlation model between the global mutant information of several mutant genes of a tested sample and gene expression activity, and wherein the concerted effect (CE) parameters or concerted effect burden (CEB) parameters represent the comprehensive influence parameters of several mutant genes on the expression activity of any gene in the predetermined genome ("Based on examiners' broadest reasonable interpretation (BRI) and the lack of details in the specification, the concerted effect (CE) parameters is interpreted as normalizing" data by assigning it a value. Examiner selects: concerted effect (CE) parameters. In Fig. 1 and Cols. 5–6, lines 62–25, Chen discloses weighting the normalized common comparison feature data, assigning different levels of importance to each genomic data and clinical data. The genomic data can be weighted prior to weighting the clinical data.)
Predicting a disease type label corresponding to the tested sample based on the characteristic difference of the concerted effect (CE) parameters or concerted effect burden (CEB) parameters of the several mutant genes on the expression activity of each gene in the predetermined genome (In Cols. 9-10, lines 41-3, Chen discloses that patient data can be sorted using a trained system. This sorting can involve preparing a reference set so that important features are given a value before sorting the actual patient data. The patient data is then sorted based on this valued reference set. To sort the patient data, the system can look at already sorted patient data and find important features from a comparison set.)
With respect to claim 1, Chen do not explicitly disclose:
identifying characteristic difference of the concerted effect (CE) parameters or concerted effect burden (CEB) parameters of several mutant genes of a tested sample on expression activity of each gene in a predetermined genome
However, Sirohey is known to disclose:
Identifying characteristic difference of the concerted effect (CE) parameters or concerted effect burden (CEB) parameters of several mutant genes of a tested sample on expression activity of each gene in a predetermined genome (In paragraph [0062], Sirohey discloses automatically identifying a potential patient's disease type based at least in part on the comparison. For instance, the data processing system 32 may identify various potential disease types or severity levels and present the identified disease types or severity levels to a user for diagnosis. )
Chen and Sirohey are analogous pieces of art because all references concern predicting the prognosis of the patient. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Chen, with determining one or more best matches by applying a classification method to the weighted normalized common comparison feature data, and predicting the prognosis of the patient in the target set as taught by Chen, with medical diagnosis and, more particularly, to the diagnosis of medical conditions from patient deviation data as taught by Sirohey. The motivation for doing so would have been to improve the accuracy of a patient's prognosis or prediction of response to therapy (see (Col. 1, lines 18-20) of Chen).
Regarding claim 21, Chen in view Sirohey disclose the elements of claim 1. In addition, Chen disclose:
The method of claim 1, wherein the global mutant information of several mutant genes of a tested sample is detected by high-throughput data technologies which comprise: whole- exome sequencing technologies, whole-genome sequencing technologies, gene chip technologies, expression chip technologies, and/or genotyping data technologies (In Col. 4, lines 32–62, Chen discloses a reference set can be determined. The reference set can comprise data reflecting the prior diagnosis of one or more patients. The reference set can be determined using DNA sequencing data performed as part of (1) retrospective chart review, (2) prospective clinical trials, and/or (3) larger biobanking initiatives.)
Regarding claim 22, Chen in view Sirohey disclose the elements of claim 1. In addition, Chen disclose:
The method of claim 1, wherein the quantitative model converting the global mutant information of several mutant genes of a tested sample into concerted effect (CE) parameters or concerted effect burden (CEB) parameters though statistical algorithms, or a simple network analysis method, or machine learning methods, or deep learning network methods (In Col. 13, lines 17–27, Chen discloses the methods and systems that can employ Artificial Intelligence techniques such as machine learning and iterative learning. Examples of such techniques include, but are not limited to, expert systems, case-based reasoning, Bayesian networks, behavior-based AI, neural networks, fuzzy systems, evolutionary computation (e.g. genetic algorithms), swarm intelligence (e.g. ant algorithms), and hybrid intelligent systems (e.g. Expert inference rules generated through a neural network or production rules from statistical learning).)
Claim(s) 3-7 are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Sirohey and further in view of Avinash et al (Pub No.: 20110129130 A1), hereinafter referred to as Avinash.
Regarding claim 3, Chen in view of Sirohey disclose elements of claim 1. Chen in view of Sirohey do not explicitly disclose:
The method of claim 1, wherein the step of predicting a disease type corresponding to the tested sample based on the data of the comprehensive influence parameters of the several mutant genes on the expression activity of each gene in the predetermined genome comprises: inputting the data of the comprehensive influence parameters of the tested sample into a preset classifier
and running the preset classifier, and outputting the disease type label corresponding to the tested sample from of the first disease type and the second disease type through the preset classifier
However, Avinash disclose the limitation:
The method of claim 1, wherein the step of predicting a disease type corresponding to the tested sample based on the data of the comprehensive influence parameters of the several mutant genes on the expression activity of each gene in the predetermined genome comprises: inputting the data of the comprehensive influence parameters of the tested sample into a preset classifier (In paragraph [0100], Avinash discloses reference data that may be classified and sorted into standardized databases, such as through an exemplary method 118 generally depicted in FIG. 7 in accordance with one embodiment of the present invention. The method 118 may include accessing reference data 120, which may include known population image data, and classifying such data in a step 122. )
and running the preset classifier, and outputting the disease type label corresponding to the tested sample from of the first disease type and the second disease type through the preset classifier (In paragraph [0100], Avinash discloses the reference data. 120 may be classified into various groups, such as data 124 for normal patients; data 126 for patients clinically diagnosed with a first condition, such as Alzheimer's disease (AD); data 128 for patients diagnosed with a second condition, such as frontotemporal dementia (FTD))
Chen and Sirohey are analogous pieces of art because all references concern predicting the prognosis of the patient. Accordingly, it would have been obvious to a person having ordinary skills in the art before the effective filling date of the claimed invention, having the teaching of Chen in view of Sirohey to include Avinash, with medical diagnosis and, more particularly, to the diagnosis of medical conditions from patient deviation data as taught by Avinash. The motivation for doing so would have been to automatically compare the patient deviation map to reference deviation maps in the library of reference deviation maps and to automatically select the closest matches (See [0139] of Avinash).
Regarding claim 4, Chen in view of Sirohey and Avinash disclose elements of claim 3. In addition, Avinash disclose:
The method of claim 3, wherein the preset classifier is trained by at least a first modeling data set of a first modeling sample group and a second modeling data set of a second modeling sample group, wherein first modeling samples are from a patient of the first disease type, and second modeling samples are from a patient of the second disease type (In paragraph [0112], Avinash discloses a first plurality of time-dependent metrics derived from a first data set of longitudinal medical diagnosis test results corresponding to an identified patient population of interest and a second plurality of time-dependent metrics derived from a second data set of longitudinal medical diagnosis test results corresponding to a reference population)
wherein the first modeling data set comprises the label of the first disease type and the data of the comprehensive influence parameters of several mutant genes of each first modeling sample on the expression activity of each gene in the first predetermined genome, and the second modeling data set comprises the label of the second disease type and the data of the comprehensive influence parameters of several mutant genes of each second modeling sample on the expression activity of each gene in the second predetermined genome, and the first predetermined genome corresponds to the first disease type, and the second predetermined genome corresponds to the second disease type (In paragraph [0112], Avinash discloses the first and second data sets preferably further comprising a disease signature corresponding to the differences therebetween. The visual representation further comprises at least one representation of a medical image. Each of the first and second data sets preferably include data from more than one medical diagnosis test, a plurality of different tests, or a single test type taken repetitively over time.)
or the first modeling data set comprises the label of the first disease type and the data of the comprehensive influence parameters of several mutant genes of each first modeling sample on the expression activity of each gene in the third predetermined genome, and the second modeling data set comprises the label of the second disease type and the data of the comprehensive influence parameters of several mutant genes of each second modeling sample on the expression activity of each gene in the third predetermined genome, wherein the third predetermined genome is a genome corresponding to the first disease and the second disease (In paragraph [0112], Avinash discloses a first imaging modality, while the patient's functional deviation map is generated from image data (of both the patient and standardized reference sources) obtained through a second imaging modality different than the first.)
Regarding claim 5, Chen in view of Sirohey and Avinash disclose elements of claim 4. In addition, Chen disclose:
The method of claim 4, wherein the preset classifier is established by followings: inputting the first modeling data set and the second modeling data set into a plurality of candidate classifier models respectively, and performing training to acquire a plurality of candidate classifier and parameter values of predetermined evaluation parameters of each of the candidate classifiers (In Cols. 9-10, lines 41-3, Chen discloses that when the patient data comprise both genomic data and clinical data, the genomic data can be classified using a first trained classifier prior to classifying the clinical data using a second trained classifier. In an aspect, the first trained classifier can be different from the second trained classifier.)
and selecting the candidate classifier with a best parameter value of the predetermined evaluation parameters from the plurality of candidate classifiers as the preset classifier (In Cols. 9-10, lines 41-3, Chen discloses that when the patient data comprise both genomic data and clinical data, the genomic data can be classified using a first trained classifier prior to classifying the clinical data using a second trained classifier. In an aspect, the first trained classifier can be different from the second trained classifier.)
Regarding claim 6, Chen in view of Sirohey and Avinash disclose elements of claim 5. In addition, Chen disclose:
The method of claim 5, wherein each of the candidate classifier models is selected from classifier models based on stochastic gradient boosting, support vector machines, random forests, and neural networks (In Col. 6, lines 47–60, Chen discloses a support vector machine classification method, determined by applying a classification method to the weighted normalized common comparison feature data.)
Regarding claim 7, Chen in view of Sirohey and Avinash disclose elements of claim 3. In addition, Sirohey disclose:
The method of claim 3, wherein the tested sample is from a patient having both all or a part of lesion characteristics of the first disease type, and all or a part of the lesion characteristics of the second disease type, and the first disease type and the second disease type are evolutionarily related (In paragraph [0066], Sirohey discloses that the patient's structural deviation map may be generated through comparison of patient image data and standardized image data each of a first imaging modality, while the patient's functional deviation map is generated from image data (of both the patient and standardized reference sources) obtained through a second imaging modality different than the first. For example, structural deviations identified through comparison of MR images may be combined with functional deviations obtained from PET image data to generate a single composite patient deviation map indicative of both functional and structural deviations.)
Response to Arguments
Applicant's arguments filed 08/04/2025 have been fully considered but were not persuasive.
Pertaining to the rejection under 101
Arguments are not persuasive and a full 101 analysis is set forth above.
Pertaining to Rejection under 103
Applicant’s arguments in regard to the examiner’s rejections under 35 USC 103 are moot in view of the new grounds of rejection.
Conclusion
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EVEL HONORE
Examiner
Art Unit 2142
/Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142