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 04/10/2026
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 currently amended. Claims 23-24 have been newly added.
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 21-24 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 taken from a target object by high-throughput data technologies;
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).
“[…] taken from a target object by high-throughput data technologies” 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 “high-throughput data technologies” 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).
“[…] taken from a target object by high-throughput data technologies” as drafted, amounts to generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
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).
“[…] taken from a target object by high-throughput data technologies” 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 “high-throughput data technologies” 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).
“[…] taken from a target object by high-throughput data technologies” as drafted, amounts to generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
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 taken from a target object by high-throughput data technologies;
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).
“[…] taken from a target object by high-throughput data technologies” 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 “high-throughput data technologies” 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).
“[…] taken from a target object by high-throughput data technologies” as drafted, amounts to generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
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).
“[…] taken from a target object by high-throughput data technologies” 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 “high-throughput data technologies” 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).
“[…] taken from a target object by high-throughput data technologies” as drafted, amounts to generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
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 23 incorporates the rejection of independent claim 1 and does not integrate the judicial exception into a practical application.
Claim 24 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, 7, 10 and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over “Systematic analysis of somatic mutations impacting gene expression in 12 tumour types”, https://www.nature.com/articles/ncomms9554, Ding et al, 10/05/2015, hereinafter referred to as Ding and further in view of Venn et al. (Pub No.: 20180203974 A1), hereinafter referred to as Venn.
With respect to claim 1, Ding disclose:
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 (In Figure 1 and Page 3, Ding discloses a mutation matrix containing mutation information for many genes across patients. This contains expression values measured from RNA sequencing or microarrays. Also, on page 2, we discuss the hierarchical Bayes statistical model, to systematically quantify the impact of somatic mutations on expression profiles. In addition, Ding disclose to address the central question of whether somatic mutationsIn a patient’s tumor impact gene expression, we developed a Generative probabilistic model, seq. On page 4-5, Ding disclose computing probabilities that a mutated gene influences expressions, probabilities that an individual mutation influences expression. These are numerical outputs generated from mutation data.)
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 (On Page 3, Ding computes quantitative outputs (e.g., P (F) and P (D), identifying which mutations significantly influence expression. Distinguishing upregulation, neutral regulation, and downregulation, identifies recurrent mutation effects across patients and determines which mutated genes influence the expression network.)
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 (In Figure 1 and Page 3, Ding discloses a model that predicts the probability of each. Mutation (P(F)) and the probability of each. Mutated gene (P(D)) influencing expression. )
With respect to claim 1, Ding does not explicitly 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 taken from a target object by high-throughput data technologies
However, it is known by Venn to 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 taken from a target object by high-throughput data technologies (Under the broadest reasonable interpretation, the recited "high-throughput data technologies" encompass any technology capable of obtaining patient test samples used for identification of somatic mutations. In Fig. 1 and paragraph [0079], Venn discloses methods and systems for identifying somatic mutational signatures for detecting, diagnosing, monitoring and/or classifying cancer in a patient known to have, or suspected of having cancer.)
Ding and Venn are analogous pieces of art because both references concern analysis of somatic mutations. 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 Ding with a systematically, quantifying the impact of somatic mutations on expression profiles as taught by Ding, while identifying somatic mutational signatures for detecting, diagnosing, monitoring and/or classifying cancer in a patient as taught by Venn. The motivation for doing so would have been to improve computational interpretation of genomic mutation data to make disease prediction more accurate (See (Page 1) of Ding)
Regarding claim 2, Ding in view of Venn disclose the elements of claim 1. In addition, Venn disclose:
The method of claim 1, wherein the step of predicting the disease type corresponding to the tested sample comprises: predicting the disease type corresponding to the tested sample from at least two disease type having evolutionary correlation (In paragraph [0194], Venn discloses different tissue types exhibit different somatic mutation profiles. The white blood cell (WBC) DNA and cell-free DNA (DNA) from three individuals to determine whether cfDNA contains mutation patterns that distinguish cancerous from non-cancerous subjects.)
Predetermined genome corresponds to the at least two diseases having evolutionary correlation (In paragraph [0194], Venn disclose comparison of Sequence Context of WBC and cfDNA)
Regarding claim 3, Ding in view of Venn disclose the elements of claim 1. In addition, Venn 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: imputing the data of the comprehensive influence parameters of the tested sample into a preset classifier (In paragraph [0196], Venn discloses mutational signatures identified in a patient's samples to perform molecular classification of disease, which can aid diagnosis and treatment decisions.)
Running the preset classifier, and outputting the disease type label corresponding to the tested sample from the first disease type and the second disease type through the preset classifier (In paragraph [0106], Venn discloses that sequencing reads are obtained from test samples obtained from a patient at two or more time points (e.g., a first time point and a second time point) and used for identification of one or more mutational signatures.)
Regarding claim 7, Ding in view of Venn disclose the elements of claim 3. In addition, Venn 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 [0194], Venn disclose different tissues types exhibit different somatic mutation profiles. The white blood cell (WBC) DNA and cell-free DNA (cfDNA) from three individual to determine whether cfDNA contains mutation patterns that distinguish cancerous from non-cancerous subject. Venn disclose comparison of Sequence Context of WBC and cfDNA.)
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Ding in view of Venn and further in view of Avinash et al (Pub No.: 20110129130 A1), hereinafter referred to as Avinash.
Regarding claim 4, Ding in view of Venn disclose elements of claim 3. Ding in view of Venn does not explicitly 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
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
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
However, Avinash disclose the limitation:
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.)
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.)
Ding and Venn 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 Ding in view of Venn 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).
Claim(s) 5-6 is rejected under 35 U.S.C. 103 as being unpatentable over Ding in view of Venn, Avinash and further in view of Chen et al. (US Patent No. 10,665,347 B2), hereinafter referred to as Chen.
Regarding claim 5, Ding in view of Venn and Avinash disclose elements of claim 4. Ding in view of Venn and Avinash does not explicitly 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
Selecting the candidate classifier with a best parameter value of the predetermined evaluation parameters from the plurality of candidate classifiers as the preset classifier
However, Chen disclose the limitation:
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.)
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.)
Ding and Venn 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 Ding in view of Venn to include Chen, with predicting the prognosis of a patient or their response to therapy as taught by Chen. 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 19-20) of Chen).
Regarding claim 6, Ding in view of Venn 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.)
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Ding in view of Venn and further in view of Harris et al (Pub No.: 20160019341 A1), hereinafter referred to as Harris.
Regarding claim 21, Ding in view of Venn disclose elements of claim 1. Ding in view of Venn does not explicitly 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
However, Harris disclose the limitation:
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 paragraph [0038], Harris systems for sample processing and data analysis such as genome sequencing or other types of sequencing. The system comprises whole genome sequencing.)
Ding and Venn 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 Ding in view of Venn to include Harris, with variants in the whole genome can be identified using untargeted sequencing as taught by Harris. The motivation for doing so would have been to generate an output that indicates the presence or absence of one or more polymorphisms in the sample of the subject (See [0003] of Harris.)
Claims 23-24 are rejected under 35 U.S.C. 103 as being unpatentable over Ding in view of Venn and further in view of Scott et al (Pub No.: 20170116379 A1), hereinafter referred to as Scott.
Regarding claim 22, Ding in view of Venn disclose elements of claim 1. Ding in view of Venn does not explicitly disclose:
The method of claim 1, further comprising a step: according to the predicted disease type corresponding to the tested sample, applying clinical intervention directed to the predicted disease type to the target object
However, Scott disclose the limitation:
The method of claim 1, further comprising a step: according to the predicted disease type corresponding to the tested sample, applying clinical intervention directed to the predicted disease type to the target object (In paragraph [0040], Scott discloses that the server compares the genotypes of the smaller sample of individuals against the genotype of the patient for specific genotypes of interest. The server provides a therapeutic recommendation.)
Ding and Venn 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 Ding in view of Venn to include Scott, with a first patient in the plurality of patients, generate one or more clusters of patients that have similar characteristics to the first patient as taught by Scott. The motivation for doing so would have been compare effectiveness based on genome (See [0066] of Scott.)
Regarding claim 24, Ding in view of Venn disclose elements of claim 10. Ding in view of Venn does not explicitly disclose:
The electronic apparatus of claim 10, wherein the electronic apparatus is further configured for: according to the predicted disease type corresponding to the tested sample, applying clinical intervention directed to the predicted disease type to the target object
However, Scott disclose the limitation:
The electronic apparatus of claim 10, wherein the electronic apparatus is further configured for: according to the predicted disease type corresponding to the tested sample, applying clinical intervention directed to the predicted disease type to the target object (In paragraph [0040], Scott discloses that the server compares the genotypes of the smaller sample of individuals against the genotype of the patient for specific genotypes of interest. The server provides a therapeutic recommendation.)
Ding and Venn 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 Ding in view of Venn to include Scott, with a first patient in the plurality of patients, generate one or more clusters of patients that have similar characteristics to the first patient as taught by Scott. The motivation for doing so would have been compare effectiveness based on genome (See [0066] of Scott.)
Response to Arguments
Applicant's arguments filed 08/04/2025 have been fully considered but were not persuasive.
Pertaining to the rejection under 101
On page 7 of applicant remarks, the applicant's discuss the step of “detecting global mutant information of several mutant genes of a tested sample taken from a target object by high-throughput data technologies." Therefore, the step cannot be performed in the human mind or by a human using a pen and paper. However, examiner firmly believes, “high-throughput data technologies” is recited in a manner that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h). As well as, mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and §2106.04(d). Although the claim recites “detecting global mutant information of several mutant genes of a tested sample taken from a target object by high-throughput data technologies”, the claim does not affirmatively recite performing any specific high-throughput sequencing or data acquisition operation. Rather, the claim broadly recites detecting global mutant information, while the reference to “high-throughput data technologies “merely identifies the source or environment from which the information is obtained and does not positively require the application of any particular sequencing technique or technological process.
Furthermore, nowhere in the claims/specification discuss “high-throughput data technologies” in detailed as recited in the applicants' remarks/arguments.
On pages 8-9, the applicant discuss claim 1 is designed to improve the technological field of genomic data analysis and predictive diagnostics. However, the examiner, when considered as a whole, does not recite a technological improvement to those field with the meaning of See MPEP § 2106.05(a). The claim does not recite a technological improvement to genomic data analysis or predictive diagnostics, but instead applies a quantitative model to analyze genomic information and generate a diagnostic conclusion. Therefore, the additional element do not integrate the recited judicial exception into a practical application.
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
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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EVEL HONORE
Examiner
Art Unit 2142
/Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142
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