DETAILED ACTION
Applicant’s response, filed 12 May 2026, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
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 .
Status of Claims
It is noted that the claim amendments filed 12 May 2026 are not made with respect to the previous claim set filed 02 March 2022, and instead were made with respect to the non-entered claim set filed 22 Jan. 2026. Applicant should make future claim amendments with respect to the immediate prior version of the claims in accordance with 37 CFR. 1.121(c)(2). However, in the interest of compact prosecution, the claims are being examined herein.
Claims 2, 6, 11, 15, and 17 are canceled.
Claims 22-24 are newly added.
Claims 1, 3-5, 7-10, 12-14, 16, and 18-24 are pending.
Claims 1, 3-5, 7-10, 12-14, 16, and 18-24 are rejected.
Claims 1, 7-8, 10, and 18-20 are objected to.
Priority
Applicant’s claim for the benefit of a prior-filed application, PCT/CN2019/104005 filed 02 Sept. 2019 under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Accordingly, the effective filing date of the claimed invention is 02 Sept. 2019.
Specification
References to Applicant’s specification throughout the Office action are made with respect to the published version of the specification.
The objection to the abstract in the Office action mailed 27 Oct. 2026 has been withdrawn in view of the amendment to the abstract received 22 Jan. 2026.
Claim Objections
The objection to claims 3-4, 7, and 10 in the Office action mailed 27 Oc.t 2025 has been withdrawn in view of claim amendments filed 12 May 2026.
Claims 1, 7-8, 10, and 18-20 are objected to because of the following informalities. This objection is newly recited and necessitated by claim amendment.
Claims 1 and 10 recites “predicting at least one treatment management factor characteristics” in the preamble, which is a grammatical error and should recite “..at least one treatment management factor characteristic[[s]]”.
Claims 1 and 10 recite “wherein the CEB parameter is a result…of overall characteristic of…”, which is a grammatical error and should recite “…of an overall characteristic of…”.
Claim 7 recites “at least one treatment management factor characteristics” in lien4-5, which is a grammatical error and should recite “..at least one treatment management factor characteristic[[s]]”.
Claims 8 and 18-20 recite “…and acquired composite data of statistical characteristic based on at least two data of statistical characteristic”, which is a grammatical error and should recite “…data of statistical characteristic parameters” in each instance, or another amendment that fixes the grammar.
Appropriate correction is required.
Response to Arguments
Applicant's arguments filed 22 Jan. 2026 regarding the claim objections have been fully considered but they do not pertain to the newly recited objections set forth above.
Claim Interpretation
Claim 3 recites “…a survival model label” in the last line of the claim. Applicant’s specification at pg. 22, lines 8-11 disclose the survival model label may include, but is not limited to, data (such a 1) indicating long survival period or data (such as 0) indicating short survival period, and/or data including survival years and corresponding survival probability, and/or confidence coefficient parameter prediction results, etc. Therefore, the broadest reasonable interpretation of a “survival model label” in light of the specification is any data labeling survival information (not limited to survival labels relating to a statistical “mode”).
Claim 5 recites “…determining the preset threshold….based on CEB parameter data of several modeling samples, and wherein the several modeling samples are acquired from several patients suffering from the disease and at a specified evolutionary stage of the disease”. The limitation regarding how the several modeling samples were previously acquired is interpreted to be a product by process limitation that describes how the several modeling samples were previously acquired, and ultimately how the CEB parameter data of several modeling samples was previously acquired. See MPEP 2113 I, stating "[E]ven though product-by-process claims are limited by and defined by the process, determination of patentability is based on the product itself. The patentability of a product does not depend on its method of production. If the product in the product-by-process claim is the same as or obvious from a product of the prior art, the claim is unpatentable even though the prior product was made by a different process." In re Thorpe, 777 F.2d 695, 698, 227 USPQ 964, 966 (Fed. Cir. 1985).
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1, 3-5, 7-10, 12-14, 16, and 18-24 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor had possession of the claimed invention. This rejection is newly recited and necessitated by claim amendment.
Claims 1 and 10, and claims dependent therefrom, recite “processing the global mutant information by a multi-level deep learning neural network framework to acquire concreted effect (CE) parameter data…”.
MPEP 2161.01 I states original claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. See MPEP §§ 2163.02 and 2181, subsection IV.
In the instant case, the claims specify a desired result of acquiring concerted effect (CE) parameter data using a multi-level deep learning neural network framework, but the disclosure does not sufficiently describe how the CE parameter data is acquired using the neural network framework. Applicant’s specification at para. [0196] discloses that “a multi-level deep learning neural network framework can be used to process global mutant information, and a data-knowledge hybrid-driven approach can be used to establish a transformation function between different types of intracellular deterministic event set features for projections suitable for different tumor types.”. Applicant’s specification at para. [0197] discloses the concerted effect parameters may be acquired by calculating through a simple network analysis method, or different types of deep learning network methods. Applicant’s specification does not provide any details regarding the multi-level deep learning neural network and how it is used to process the global mutant information and acquire concerted effect parameters, and instead merely restates the function recited in the claims.
Therefore, the specification does not provide a sufficient disclosure of the limitation above recited in claims 1 and 10, and claims dependent therefrom, to demonstrate to one of ordinary skill in the art that the inventor possessed the invention at the time the application was filed. For more information regarding the written description requirement, see MPEP §2161.01- §2163.07(b).
Claim 10, and claims dependent therefrom, recite “An electronic apparatus comprising: a memory, a processor, and a program stored in the memory…the processor is configured for: detecting a tested sample of a target object by high-throughput data technologies to acquire global mutant information…”. Thus claim 10 encompasses a processor being configured to acquire mutant information from a tested sample of a target object.
Applicant’s specification at para. [0196] discloses high-throughput data technologies may include whole-exome sequencing, whole-genome sequencing, and gene chip data. However, Applicant’s specification does not provide support for an electronic apparatus comprising a memory and processor configured to perform these high-throughput data technologies on a sample of a target object as claimed.
For the reasons discussed above, the specification does not provide a sufficient disclosure of the limitation above recited in claims 10, 22, and 24 to demonstrate to one of ordinary skill in the art that the inventor possessed the invention at the time the application was filed. THS IS A NEW MATTER REJECTION. For more information regarding the written description requirement, see MPEP §2161.01- §2163.07(b).
Claim Rejections - 35 USC § 112(b)
The rejection of claims 11, 15, and 17 under 35 U.S.C. 112(b) in the Office action mailed 27 Oct. 2025 has been withdrawn in view of the cancellation of these claims received 12 May 2026.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1, 3-5, 7-10, 12-14, 16, and 18-24 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Any newly recited portion is necessitated by claim amendment.
Claims 1 and 10, and claims dependent therefrom, are indefinite for recitation of “detecting a tested sample of a target object by high-throughput data technologies”. The term “high-throughput” is a relative term which renders the claim indefinite. The term “high-throughput” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Applicant’s specification at para. [0196] provides examples of high-throughput data technologies, but does not define the term such that the metes and bounds are clear.
Claims 1 and 10, and claims dependent therefrom, are indefinite for recitation of “global mutant information of the several mutant genes”. It is unclear what type of mutant information is considered “global” versus local or non-global mutant information. A review of Applicant’s specification discloses, at para. [0059] that the several mutant genes can be global mutant information, for example, whole exome sequencing data. As a result, it is not clear if the claims only require analyzing mutant information of “several mutant genes” (i.e. only two genes), if the claims intend to require acquiring mutant information over the whole exome, or if the claims intend to require mutant information over some other subset of genome that would be considered “global”. Clarification is requested via claim amendment. For purpose of examination, the limitation is interpreted to mean mutant information is acquired for several mutant genes.
Claims 1 and 10, and claims dependent therefrom, are indefinite for recitation of “detecting…the several mutants genes of the target object”. There is insufficient antecedent basis for “the several mutant genes” in the claims because the claims do not previously recite any mutant genes of the target object.
Claims 1 and 10, and claims dependent therefrom, are indefinite for recitation of “wherein the CE parameter represents the comprehensive influence of the several mutant genes on the expression activity of any gene…”. There is insufficient antecedent basis for “the CEP parameter” and “the comprehensive influence” in the claims, because claims 1 and 10 do not previously recite any particular CE parameters or a comprehensive influence. While claim 1 does previously recite “concerted effect (CE) parameter data”, which may comprise multiple CE parameters, it is not clear which parameter of the CE parameter data, “the CEP parameter” is referring to. Similarly, it is not clear what influence is being referred to.
Claims 1 and 10, and claims dependent therefrom, are indefinite for recitation of “…where in the CE parameter represents…in the predetermined genome”. There is insufficient antecedent basis for “the predetermined genome” because the claims do not previously recite a predetermined genome.
Claims 1 and 10, and claims dependent therefrom, are indefinite for recitation of “wherein the CEB parameter”. There is insufficient antecedent basis for “the CEB parameter” in the claims because while claims 1 and 10 previously recite “concerted effect burden (CEB) parameter data”, which encompasses data of multiple CEB parameters, the claim does not recite a particular CEB parameter. As a result, it is not clear if the CEB parameter data is data of a single CEB parameter, for which “the CEB parameter” refers, or if “the CEB parameter” is intended to refer to the CEB parameter data”. Clarification is requested via claim amendment.
Claim 5 is indefinite for recitation of “the CEB parameter data of several modeling samples”. There is insufficient antecedent basis for this limitation in the claim because claims 1 and 4, from which claim 5 depend, do not recite CEB parameter data of several modeling samples. Instead, claim 1 only recites “(CEB) parameter data” that was obtained from a tested sample of a target object. As a result, it is further unclear if “the CEB parameter data of several modeling samples” is intended to be the same CEB parameter of claim 1, such that the CEB parameter data of claim 1 is also of several modeling samples, or if this is a different set of CEB parameter data. For purpose of examination, the claim is interpreted to mean “based on CEB parameter data of several modeling samples”, such that this is a different set of CEB parameter data.
Claim 7, and claims dependent therefrom, are indefinite for recitation of “the at least one predetermined treatment management factor characteristics” in lines 5 and 7. Claim 1, from which claim 7 depends, recites “outputting…data of at least one treatment management factor characteristics” in the body of the claim and also recites “A method for…predicting at least one treatment management factor characteristics” in the preamble, but these are not required to be outputted in claim 1. Therefore, it is unclear if “the at least one predetermined treatment management factor characteristics” in claim 7 are referring to the ”at least one treatment management factor” recited in the body of claim 1, the “at least one treatment management factor characteristic” in the preamble of claim 1, or both the at least one treatment management factor and the treatment management factor characteristics. For purpose of examination, claim 7 is interpreted to refer to the at least one treatment management factor characteristic of the target object (in the body of claim 1).
Claims 9, 12-14, and 16 are indefinite for recitation of “processing on the CE parameter data of the several mutant genes on expression activity of each gene”. Claim 1, from which claims 9, 12-14, and 16 depends, as amended recites “…the several mutant genes on the expression activity of any gene in the predetermine genome” in lines 8-9 and later recites “all mutant genes of the target object” in lines 15-16. As a result, it is unclear if “each gene” is referring to the each gene of the several mutant genes, each gene of the several mutant genes and the “any gene” the several mutant genes influence expression activity, and/or each gene of all the mutant genes of the target object. Clarification is requested regarding which set of genes, “each gene” is referring to. For purpose of examination, the claim will be interpreted to refer to any of the recited gene sets.
Claims 18-20 recite “…meets a preset conditions…” It is unclear if claims 1-20 intend to require the number of genes meets a preset condition or meets preset conditions, due to the grammatical error that uses the singular article “a” with a plural “conditions”. Clarification is requested via claim amendment. For purpose of examination, claims 18-20 are interpreted to mean “meets a preset condition”.
Response to Arguments
Applicant's arguments filed 22 Jan. 2026 regarding 35 U.S.C. 112(b) have been fully considered but they are not persuasive.
Applicant remarks Applicant has amended the claims to address the previous 112(b) rejections (Applicant’s remarks at pg. 13, para. 6).
This argument is not persuasive, because the claim amendments do not address all previous 112(b) rejections to claims 7 and 18-20, and furthermore, the arguments do not address the newly recited rejections set forth above.
Claim Rejections - 35 USC § 101
The rejection of claims 11, 15, and 17 under 35 U.S.C. 101 in the Office action mailed 27 Oct. 2025 has been withdrawn in view of the cancellation of these claims received 12 May 2026.
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-5, 7-10, 12-14, 16, and 18-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea and law of nature without significantly more. Any newly recited portion is necessitated by claim amendment.
The Supreme Court has established a two-step framework for this analysis, wherein a claim does not satisfy § 101 if (1) it is “directed to” a patent-ineligible concept, i.e., a law of nature, natural phenomenon, or abstract idea, and (2), if so, the particular elements of the claim, considered “both individually and as an ordered combination,” do not add enough to “transform the nature of the claim into a patent-eligible application.” Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353 (Fed. Cir. 2016) (quoting Alice, 134 S. Ct. at 2355). Applicant is also directed to MPEP 2106.
Step 1: The instantly claimed invention (claims 1 and 10 being representative) is directed to a method and system. Therefore, the instantly claimed invention falls into one of the four statutory categories. [Step 1: YES]
Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in in Prong Two if the recited judicial exception is integrated into a practical application of that exception.
Step 2A, Prong 1: Under the MPEP § 2106.04, the Step 2A (Prong 1) analysis requires determining whether a claim recites an abstract idea, law of nature, or natural phenomenon.
Claims 1 and 10 recite the following steps which fall under the mental processes and/or mathematical concepts groupings of abstract ideas:
processing the global mutant information…to acquire concerted effect (CE) parameter data, wherein the CE parameter represents the comprehensive influence of the several mutant genes on the expression activity of any gene in the predetermined genome and the predetermined genome corresponds to the disease;
performing noise reduction processing on the CE parameter data to acquire concerted effect burden (CEB) parameter data, wherein the CEB parameter is a result of inductive simplification of an overall characteristic of the CE parameter of all mutant genes of the target object; and
outputting predictive data of at least one treatment management factor characteristic of the target object relative to the disease based on the CEB parameter data, wherein at least one treatment management factor characteristic of the target object relative to the disease comprises survival characteristics, pathophysiological characteristics, and/or clinical intervention effects of the target object with the disease.
The identified claim limitations falls into one of the groups of abstract ideas of mental processes for the following reasons. First, processing global mutant information to acquire effect burden parameter data of mutant genes on expression activity of any gene involves analyzing expression data of genes in a genome and determining how the expression level changes in these genes in the presence of the several mutant genes compared to in the presence of unmutated genes, which is a mental process of performing data evaluations and comparisons. Furthermore, performing noise reduction processing on the CE parameter data to acquire CE burden (CEB) parameter data, can be practically performed in the mind by analyzing average expression values of each gene in different groups, and performing a normalization process by dividing a mean by the standard deviation, as discussed in Applicant’s specification at para. [0100]-[0101]. Claiming the CEB parameter is a result of inductive simplification of a characteristic of the CE parameter data similarly amounts to a mere analysis of information (inductive reasoning), which is a mental process.
Last, outputting predictive data of a treatment management factor based on the concerted effect burden parameter data can be practically performed in the mind by comparing the concerted effect burden parameter data of a target object with a control concerted effect burden survival mode model of a disease, as recited in claim 3, to determine a survival of the target object based on the burden parameter data. Overall, the claims analogous to a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016). Other than reciting the steps are carried out by an electronic apparatus, nothing in the claims precludes the steps from being practically performed in the mind. See MPEP 2106.04(a)(2) III.
The limitation of performing noise reduction processing on the CE parameter data to acquire CEB parameter data further recites a mathematical concept. As discussed above, performing noise reduction process amounts to a textual equivalent to performing data normalization including multiplication and division, in light of Applicant’s specification at para. [0100]-[0101]. Therefore this limitation recites a mathematical calculation. See MPEP 2106.04(a)(2) I.
Dependent claims 3-5, 7-9, 12-14, 16, and 18-24 further recite an abstract idea and/or are part of the abstract idea identified above. Dependent claims 9, 12-14, and 16 further limit the mental process of acquiring concerted effect parameter data to comprise acquiring concerted effect parameter data, performing noise reduction processing on the data, and acquiring the data as a result of performing the noise reduction processing. Dependent claim 3 further limits the mental process of outputting the predictive data to comprise performing data comparisons between the concerted effect burden data with a preset concerted effect burden-survival mode model of the disease, and acquiring and outputting a survival model label indicating long/short survival period, survival years and probability and/or confidence coefficient parameter results. Furthermore, the comparison using a survival mode model (e.g. a Cox proportional hazards model) of the disease further recites a mathematical concept in light of Applicant’s specification at pg. 23, lines 23 to 30. Dependent claim 4 further limits the abstract idea of the concerted effect burden-survival mode model to comprise a first and second survival model label and a preset threshold, and further limits the comparing to comprise assigning the first label or the second label depending on whether the concerted effect burden data reaches or is less than the preset threshold, respectively. Dependent claim 5 further recites the mental process of determining a preset threshold based on CEB parameter data of several modeling samples. Dependent claim 7 further limits the mental process of outputting of predictive data to be based on the concerted effect burden parameter data of the target object, concerted effect burden data of pre-acquired modeling samples, and measured data. Dependent claims 8 and 18-20 further limit the mental process of acquiring concerted effect burden parameter data to involve determining a number of genes with influenced expression and/or a sum of absolute values, a median, a maximum value, and/or a statistical value (also reciting a mathematical concept) and/or acquiring at least two data of statistical characteristic parameters for describing CE parameter data and acquiring composite data of statistical characteristic. Dependent claims 9, 12-14, 16 also further recite a mathematical concept of performing noise reduction processing (i.e. normalization) in light of Applicant’s specification at pg. 16, lines 4-11. Dependent claims 21-22 further recite the mental process of determining the disease type corresponding to the texted sample from at least two disease types. Dependent claims 23-24 further recite the mental process of providing intervention treatment according to the outputted predictive data, which encompasses determining and writing/providing an intervention treatment.
The claims further recite a law of nature of a natural correlation between expression levels of mutated genes and treatment outcomes for a disease, analogous to the natural relationship between a patient’s CYP2D6 metabolizer genotype and the risk that the patient will suffer QTc prolongation after administration of a medication called iloperidone, Vanda Pharmaceuticals Inc. v. West-Ward Pharmaceuticals, 887 F.3d 1117, 1135-36, 126 USPQ2d 1266, 1281 (Fed. Cir. 2018). See MPEP 2106.04(b) I.
Therefore, claims 1, 3-5, 7-10, 12-14, 16, and 18-24 recite an abstract idea and law of nature. [Step 2A, Prong 1: YES]
Step 2A: Prong 2: Under the MPEP § 2106.04, the Step 2A, Prong 2 analysis requires identifying whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluating those additional elements to determine whether they integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application for the following reasons.
Dependent claims 3-5, 7-9, 12-14, 16, and 18-24 do not recite any elements in addition to the judicial exception and thus are part of the judicial exception.
The additional elements of claims 1 and 10 include:
an electronic apparatus (i.e. a computer);
by a multi-level deep learning neural network framework
a processor (claim 10);
a memory (claim 10); and
detecting a tested sample of a target object by high-throughput data technologies to acquire global mutant information of the several mutant genes of the target object.
The additional elements of a computer comprising a processor and memory, and a deep learning neural network, are generic computer components and/or functions merely used to carry out the abstract idea. The courts have found the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f). Further regarding the multi-level deep learning neural network framework, the additional element only serves to generally link the abstract idea of processing global mutant information to acquire concerted effect parameter data to the technological environment of neural networks, which does not provide integration. See MPEP 2106.05(h).
The additional element of detecting a texted sample by high-throughput data technologies (e.g. sequencing) to acquire global mutant information of the several mutant genes only serves to collect information for use by the abstract idea, which amounts to insignificant extra-solution activity that is not sufficient to integrate the recited judicial exception into a practical application.
Therefore, the additionally recited elements merely invoke computers as a tool to perform the abstract idea, generally link the abstract idea to a particular technological environment and/or amount to insignificant extra-solution activity, and as such, the claims as a whole do no integrate the abstract idea into practical application. Thus, claims 1, 3-5, 7-10, 12-14, 16, and 18-24 are directed to an abstract idea and law of nature. [Step 2A, Prong 2: NO]
Step 2B: In the second step it is determined whether the claimed subject matter includes additional elements that amount to significantly more than the judicial exception. See MPEP § 2106.05.
The claims do not include any additional steps appended to the judicial exception that are sufficient to amount to significantly more than the judicial exception.
Dependent claims 3-5, 7-9, 12-14, 16, and 18-24 do not recite any elements in addition to the judicial exception and thus are part of the judicial exception.
The additional elements of claims 1 and 10 include:
an electronic apparatus (i.e. a computer);
by a multi-level deep learning neural network framework
a processor (claim 10);
a memory (claim 10); and
detecting a tested sample of a target object by high-throughput data technologies to acquire global mutant information of the several mutant genes of the target object.
The additional elements of a computer comprising a processor and memory and a deep learning neural network are conventional computer components merely used to carry out the abstract idea. The courts have found the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Furthermore, regarding the neural network, as explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. See MPEP 2106.05(h).
The additional element of detecting a tested sample of a target object by high-throughput data technologies to acquire global mutant information of the several mutant genes of the target object encompasses performing sequencing on several genes of a DNA sample of a subject. The courts have recognized the following laboratory techniques as well-understood, routine, conventional activity in the life science arts when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: Analyzing DNA to provide sequence information or detect allelic variants, Genetic Techs. Ltd., 818 F.3d at 1377; 118 USPQ2d at 1546; and Amplifying and sequencing nucleic acid sequences, University of Utah Research Foundation v. Ambry Genetics, 774 F.3d 755, 764, 113 USPQ2d 1241, 1247 (Fed. Cir. 2014).
Therefore, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claims as a whole do not amount to significantly more than the exception itself. [Step 2B: NO]
Therefore, the instantly rejected claims are not drawn to eligible subject matter as they are directed to an abstract idea and law of nature without significantly more. For additional guidance, applicant is directed generally to applicant is directed generally to the MPEP § 2106.
Response to Arguments
Applicant's arguments filed 22 Jan. 2026 regarding 35 U.S.C. 101 have been fully considered but they are not persuasive.
Applicant remarks to overcome the 101 rejection, the claims have been amended to recite a step of “detecting a tested sample of a target object by high-throughput data technologies…”, and this whole process requires enormous computational storage and processing capabilities and a typical product of “technical means” rather than a simple act of observation or identification”, and further remarks the limitation clarifies the claims are based on specific data types obtained through particular means, thereby anchoring the claims from an abstract concept to a specific biotechnology field such that the detection step involve a practical application (Applicant’s remarks at pg. 14, para. 1 to pg. 15, para. 2).
This argument is not persuasive. It is agreed that the “detecting” step is not part of the abstract idea, but is an additional element of the claim analyzed under Step 2A, Prong 2 regarding whether the additional element integrates the judicial exception into a practical application. MPEP 2106.04(d) states It is notable that mere physicality or tangibility of an additional element or elements is not a relevant consideration in Step 2A Prong Two. As the Supreme Court explained in Alice Corp., mere physical or tangible implementation of an exception does not guarantee eligibility. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1983-84 (2014). See also Genetic Technologies Ltd. v. Merial LLC, 818 F.3d 1369, 1377, 118 USPQ2d 1541, 1547 (Fed. Cir. 2016) (steps of DNA amplification and analysis are not "sufficient" to render claim 1 patent eligible merely because they are physical steps).
In other words, simply because the claims were amended to recite a physical step of analyzing a sample to determine mutant information does not guarantee eligibility. In the instant case, the step of detecting only serves to collect the global mutation information necessary for use by the abstract idea, which does not provide integration as discussed in MPEP 2106.05(g).
Applicant remarks independent claims 1 and 10 are amended to include “processing the global mutant information by a multi-level deep learning neural network framework…”, that the CEP parameters are used to describe the characteristic difference between different types of diseases/tumors and represent an influence of mutant genes on expression of any gene, and therefore, the CE parameter is directed to a practical application (Applicant’s remarks at pg. 15, para. 3). Applicant further remarks that a multi-level deep learning neural network can be used to process the global mutant information, which describes a practical application of the global mutant information, rather than an abstract step (Applicant’s remarks at pg. 16, para. 1).
This argument is not persuasive. As discussed in the above rejection, the step of “processing the global mutant information…to acquire concerted effect (CE) burden parameter data” is part of the abstract idea, and is not an additional element analyzed under Step 2A, Prong 2. Therefore, the CE parameter cannot integrate the judicial exception (i.e. itself) into a practical application.
While, the deep learning neural network is considered an additional element, the neural network is generically recited, is merely used as a tool to carry out the abstract idea, and generally links the abstract idea to the technological environment of neural networks, as discussed in the above rejection. See MPEP 2106.05(f) and 2106.05(h). Applicant is also directed to example 47, claim 2 of the 2024 AI subject matter eligibility examples, which explains the trained ANN is used to generally apply the abstract idea without placing any limits on how the trained ANN functions, and furthermore, the recitation of “using a trained ANN” in limitations (d) and (e) also merely indicates a field of use or technological environment in which the judicial exception is performed (pg. 9, para. 1-2).
Applicant’s remarks at pg. 16, para. 2 have been fully considered but they do not pertain to the currently amended claims filed 12 May 2026.
Applicant remarks the step of “outputting predictive data…” reflects the practical application of the global mutant information for disease predicting (Applicant’s remarks at pg. 16, para. 3).
This argument is not persuasive. Under step 2A, Prong 2, the additional elements are evaluated to determine whether they integrate a judicial exception into a practical application. Determining predictive data of a treatment management factor based on CEB parameter data is part of the abstract idea, and therefore, cannot provide integration under Step 2A, Prong 2.
Applicant remarks claims 1 and 10 as a whole have a clear practical application, and the ordered combination of steps address a specific technical problem by solving the solution how to integrate global mutant information, how to establish a comprehensive quantitative index from the genomic mutation, and how to describe intracellular deterministic event characteristics associated with gene expression in complex diseases, by the technical solution of high-throughput sequencing, deep learning, and CE/CEB parameter quantification to achieve the technical effect of accurately predicting treatment management factors, which is a technical improvement (Applicant’s remarks at pg. 17, para. 1-2).
This argument is not persuasive. MPEP 2106.05(a) states it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. In the instant case, an improvement in predicting treatment management factors is an improved abstract idea (i.e. making predictions), which is not an improvement to technology. The claims do not appear to improve any specific technology, such as improvements in high-throughput sequencing or improvements to neural networks. Instead, for the reasons discussed in the above rejection, the additional elements when considered in combination with the judicial exception are not sufficient to provide integration.
Applicant’s remarks at pg. 17, para. 3 have been fully considered but they do not present an argument.
Applicant remarks that like Desjardins, the claimed invention improves upon existing genomic analysis techniques by introducing a specific quantitative model for converting discrete mutation data into CE parameters, thus distinguishing from abstract ideas (Applicant’s remarks at pg. 17, para. 4 to pg. 18, para. 2).
This argument is not persuasive. The claims in Desjardins reflected an improvement in machine learning by reciting a specific training process that solved the technological problem of “catastrophic forgetting” in machine learning. The instant claims recite a generic “multi-level deep learning neural network framework” without any details of how this neural network is trained, and/or how the neural networks is actually used to process the mutant information into the CE parameter data, such that the claims may reflect an improvement to neural networks.
Applicant remarks that dependent claims 21-22 recite “determining the disease type…”, which defines the causal relationship between the prediction results and disease type, and the technical solution processes practical application value (Applicant’s remarks at pg. 18, para. 3).
This argument is not persuasive. The limitation of determining the disease type is part of the abstract idea, and therefore cannot provide integration under Step 2A, Prong 2 for the reasons already discussed above.
Applicant remarks that dependent claims 23-24 recite “providing intervention treatment….”, which integrates the recited judicial exception into the practical application of effecting a particular treatment (Applicant’s remarks at pg. 18, para. 4 to pg. 19, para. 1).
This argument is not persuasive. MPEP 2106.04(d)(2) states examiners should keep in mind that in order to qualify as a "treatment" or "prophylaxis" limitation for purposes of this consideration, the claim limitation in question must affirmatively recite an action that effects a particular treatment or prophylaxis for a disease or medical condition. For example, a step of "prescribing a topical steroid to a patient with eczema" is not a positive limitation because it does not require that the steroid actually be used by or on the patient.
In the instant case, the claims “provide intervention treatment….”, which encompasses determining and providing a determined intervention treatment (i.e. amental process) and/or prescribing an intervention treatment, which do not require administering the treatment to a subject. Regardless, “intervention treatment” is not a particular treatment. See MPEP 2106.05(d)(2) a.
Applicant's arguments filed 12 May 2026 regarding 35 U.S.C. 101 have been fully considered but they are not persuasive.
Applicant remarks the noise reduction processing is not an abstract concept, and includes performing 10,00 random simulations as discussed in the specification at para. [0100]-[0101], and the solution constitutes an improvement to computer technology and a practical application for disease prediction (Applicant’s remarks at pg. 10, para. 1 and pg. 11, para. 3).
This argument is not persuasive. The argument is not commensurate with the scope of the claims. The claims do not require performing any number of random simulations, and instead encompass performing a simple normalization technique to perform noise reduction processing, as discussed in the above rejection. Therefore, this limitation recites a mental process and mathematical concept.
Furthermore, given the noise reduction processing is part of the abstract idea, the noise reduction processing cannot provide integration under Step 2A, Prong 2. Last, any improvement in noise reduction processing is an improvement in the abstract idea, rather than a technology. See MPEP 2106.05(a).
Applicant remarks the invention reliably extracts stable signals capable of predicting clinical outcomes from noise and complex CE data, which is a concrete technical problem that can be solved by existing bioinformatics analysis methods (Applicant’s remarks at pg. 10, para. 2). Applicant remarks the “noise reduction processing” is not general or routine data cleaning, but is a computation-intensive resampling technique that assess stability and achieves robust induction (Applicant’s remarks at pg. 10, para. 3 to pg. 11, para. 1 and 3).
This argument is not persuasive. MPEP 2106.05(a) states it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. In the instant case, an improvement in predicting clinical outcomes is an improved abstract idea (i.e. making predictions), which is not an improvement to technology. The claims do not appear to improve any specific technology, such as improvements in high-throughput sequencing or improvements to neural networks. Instead, for the reasons discussed in the above rejection, the additional elements when considered in combination with the judicial exception are not sufficient to provide integration.
Furthermore, Applicants arguments regarding the noise reduction processing being a resampling technique is not commensurate with the scope of the claims.
Applicant remarks that applying this noise reduction processing achieves improved technical effects of improved prediction accuracy and robustness and improved computer functions because the method defines a novel processing pipeline that converts CE data into CEB data (Applicant’s remarks at pg. 11, para. 2 and 3).
This argument is not persuasive. As discussed, above, an improvement in the abstract idea (i.e. improved prediction accuracy) is not an improvement to technology. MPEP 2106.05(a) I. further states, in computer-related technologies, the examiner should determine whether the claim purports to improve computer capabilities or, instead, invokes computers merely as a tool. Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336, 118 USPQ2d 1684, 1689 (Fed. Cir. 2016).
In the instant case, the claims merely invoke a computer as a tool to carry out the abstract idea, but the capabilities of the computer are not being improved. Applicant’s explanation regarding the alleged improvement to computer technology is merely the use of a computer to carry out an allegedly novel abstract idea of deriving clinical scores from genomic data, which is not an improvement to computer capabilities.
Claim Rejections - 35 USC § 102
The rejection of claims 1-20 under 35 U.S.C. 102(a)(1) as being anticipated by Pongor (2015) in the Office action mailed 27 Oct. 2025 has been withdrawn in view of claim amendments and cancellations received 12 May 2026.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3-5, 7-10, 12-14, 16, and 18-24 are rejected under 35 U.S.C. 103 as being unpatentable over Pongor (2015) in view of Xie (2016). This rejection is newly recited and necessitated by claim amendment.
Cited references:
Pongor et al., A genome-wide approach to link genotype to clinical outcome by utilizing next generation sequencing and gene chip data of 6,697 breast cancer patients, 2015, Genome Medicine, 7:104, pg. 1-11 (previously cited); and
Xie et al. A Predictive Model of Gene Expression Using a Deep Learning Framework, 2016, IEEE International Conference on Bioinformatics and Biomedicine, A Predictive Model of Gene Expression Using a Deep Learning Framework, pg. 676-681; (newly cited).
Regarding claims 1 and 10, Pongor discloses a method for predicting clinical outcome in breast cancer (i.e. a disease) using expression data and mutational status (Abstract), implemented using software stored on a computer, which inherently requires a processor for executing the software and memory storing the software (Abstract, see conclusion), which comprises the following:
Pongor discloses performing RNA-sequencing (i.e. high-throughput data technologies) comprising exome sequencing data, on a sample from a breast cancer patient to detect mutations of genes in the patient (i.e. global mutant information of several mutant genes) (pg. 4, col. 1, para. 4-5 and col. 2, para. 3-4; pg. 5, col. 2, para. 5; Table 2, e.g. driver genes with mutations)
Pongor discloses analyzing expression values of each gene of RNAseq and microarray data in breast cancer genomes of samples of cancer patients (i.e. a predetermined genome corresponding to the disease) and acquiring a set of genes whose expression is significantly up- or down-regulated between cohorts of patients having a mutation status in genes (i.e. several mutation genes) (pg. 2, col.2 , para. 3 and pg. 4, col. 1, para. 2, e.g. mutations in multiple genes can be used; Fig. 2, e.g. 3 genes together) compared to patients not having a mutation in the genes (i.e. concerted effect parameter data representing the comprehensive influence of the several mutant genes on the expression activity of a gene in the predetermined genome) (pg. 4, col. 1, para. 4-5 and col. 2, para. 1-3 to pg. 5, col. 1, para. 1 and col. 2, para. 3; Table). The significantly up- or down-regulated gene in patients having a mutation status in multiple genes compared to patients without the mutations, as shown by Pongor, represents a comprehensive influence of the mutated genes on the expression of the respective up or down-regulated gene, as claimed.
Pongor further discloses calculating the average expression of the set of identified significant up-regulated or down-regulated genes for given driver genes having mutants, designating the average a “metagene” (pg. 5, col. 1, para. 2). Taking the average expression of a set of up-regulated genes is considered “noise-reduction processing” under the broadest reasonable interpretation of the claim, given taking an average reduces noise. Similarly, taking the average is considered “inductive simplification of an overall characteristic of the CE parameter”, given the average reduces the set of expression values into a single average expression value (i.e. an overall characteristic).
Pongor discloses calculating and outputting a survival curve of probability of survival over time of a breast cancer patient (i.e. predictive data of at least one treatment management characteristic) using the expression data of the identified metagenes (i.e. based on the concerted effect burden parameter data) (pg. 2, col. 2, para. 3, e.g. predict probability of patient survival; pg. 5, col. 2, para. 3-4; FIG. 3, e.g. see survival prediction using expression signature of genes affected by AKT1 mutation; FIG. 2, e.g. survival prediction using signature). Pongor discloses the at least one treatment management factor characteristics comprises predicted probabilities of survival over time of a patient with breast cancer (i.e. survival characteristics of the target object relative to the disease) (Fig. 3).
Further regarding claim 1, Pongor does not disclose the following:
Pongor does not disclose the CE parameter data is acquired by processing the global mutant information by a multi-level deep learning neural framework.
However, Xie discloses a multilayer perceptron (MLP) and auto-encoder deep learning model (i.e. a deep multi-level deep learning neural network) for predicting gene expression from genotypes of genetic variation (Abstract). Xie discloses the neural network takes as input SNP genotypes of genes (i.e. global mutant information) in the form of a matrix, and outputs a matrix of predicted gene expression values of the genes (pg. 677, col. 1, para. 3 to col. 2, para. 1; Figure 3-4, e.g. estimated gene expression of genes). Xie discloses the prediction of expression values from SNP data implies the contribution of genetic variants to gene expression (pg. 680, col. 1, para. 3), and further discloses the deep learning method facilitates the understanding of biological systems (Abstract).
It would have been prima facie obvious, to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified the method of Pongor to have processed the global mutant information to acquire the genes up-regulated and down-regulated expression (i.e. the CE parameter data) using the multi-level deep learning neural network method of Pongor, as shown above. One of ordinary skill in the art would have been motivated to combine the methods of Pongor and Xie in order to facilitate the understanding of biological systems (Abstract), given Pongor similarly uses relationship between mutations and gene expression to predict survival in patients (i.e. to understand biological systems) (pg. 2, col. 2, para. 3; pg. 5, col. 2, para. 3-4; FIG. 3), and furthermore, to have allowed for the identification of up-regulated and down-regulated genes without the need for generating gene expression via an assay, as was required in Pongor (pg. 4, col. 1, para. 4-5). This modification would have had a reasonable expectation of success given Pongor generates the CE parameter data by comparing gene expression in genes from patients with and without certain mutations, such that the neural network generating expression data based on mutations of Xie is applicable to the method of Pongor.
Regarding the dependent claims:
Regarding claim 3, Pongor discloses comparing the over-expressed and under-expressed genes of the metagenes (i.e. concerted effect burden data) in a Cox Proportional hazards model of survival in breast cancer that takes the metagene expression data as input (i.e. a preset-concerted effect burden-survival mode model of the disease) (Fig. 3; pg. 5, col. 1, para. 3-4) and outputting probabilities of survival and corresponding survival years for the cancer patient with breast cancer (i.e. a survival model label of the target object relative to the disease comprising data indicating survival years and corresponding survival probability) (Fig. 3).
Regarding claim 4, Pongor discloses the concerted effect burden-survival mode model comprises probabilities of survival over time for each of patients exhibiting high expression of up-regulated genes and low expression of up-regulated genes (i.e. a first and second survival model label) (Fig. 3, e.g. two different curves).
Pongor discloses the expressions of the metagenes (i.e. the concerted effect burden data) of each cancer patient (i.e. target object) in the cohort of patients carrying the mutation are compared to a preset threshold representing a median expression level of the cohort for the survival model (pg. 5, col. 1, para. 3), and outputting a “high” survival curve probabilities for patients with concerted effect burden data higher than the median (i.e. reaching the preset threshold), or including the patients in the “low” survival curve probabilities having concerted effect burden data lower than the median (i.e. less than the preset threshold).
Regarding claim 5, Pongor discloses determining the preset threshold as a median expression of the concerted effect burden data (e.g. metagenes) of a cohort of patients with breast cancer (i.e. determining the preset threshold based on concerted effect burden data of modeling samples suffering from the disease) (pg. 5, col. 1, para. 3).
While Pongor does not explicitly state which stage of breast cancer the patients include, as discussed above in claim interpretation, the process in which the CEB parameter data was determined from model samples acquired from patients at a specified evolutionary stage of the disease is a product by process limitation. "[E]ven though product-by-process claims are limited by and defined by the process, determination of patentability is based on the product itself. The patentability of a product does not depend on its method of production. If the product in the product-by-process claim is the same as or obvious from a product of the prior art, the claim is unpatentable even though the prior product was made by a different process. See MPEP 2113 I. Here, the samples obtained from breast cancer patients necessarily are at a stage of the disease, and given claim 5 encompasses the modeling stages having any stage of disease (i.e. a specified…stage), and thus the CEB parameter data of modeling samples of Pongor reads on the CEB parameter data of modeling samples of the claim.
Regarding claim 7, Pongor discloses outputting the survival curve based on the concerted burden effect parameter data comprises outputting predicted probabilities of survival at various time points (i.e. predictive data) relative to expression data of the metagenes of a cohort of patients having breast cancer in a training set with known survival (i.e. concerted effect burden data of pre-acquired modeling samples and measured data of the predetermined treatment management factor characteristics) in a fitted Cox proportional hazards model (pg. 5, col. 1, para. 3-4 and col. 2, para. 2, e.g. survival data for separate large cohort of breast cancer patients).
Regarding claims 8 and 18-20, Pongor discloses the concerted effect burden parameter data comprises a number of genes with statistically significantly increased or decreased expression in patients with mutations in the several mutant genes compared to patients without the mutations (i.e. a number of genes whose expression activity is influenced by the several mutant genes among the genes in the predetermined genome) (pg. 4, col. 2, para. 3 to pg. 5, col. 1, para. 2; Fig. 2, e.g. 3 genes together). Pongor discloses the determination of statistically significant genes uses threshold of 0.01 for the p-value and a minimal ROC value of 0.65 (i.e. the genes meet preset conditions) (pg. 4, col. 2, para. 5 to pg. 5, col. 1, para. 1; pg. 5, col. 1, para. 3).
Regarding claims 9, 12-14, and 16, Pongor discloses performing the noise reduction comprises calculating an average on expression levels the CE parameter data for each mutant gene (pg. 5, col. 1, para. 2; Table 2).
Regarding claims 21-22, Pongor further discloses determining a tested sample from a breast cancer patient has a mutation in BRCA rather than being wild-type for BRCA (i.e. determining the sample is BRCA+ breast cancer from BRCA+ breast cancer and BRCA- breast cancer) (pg. 2, col. 2, para. 2; pg. 4, col. 2, para. 3; Table 2, e.g. Gene is BRCA2). The progression from the BRCA- type to BRCA+ type is considered an “evolutionary correlation”.
Regarding claims 23-24, Pongor does not explicitly disclose providing intervention treatment according to the outputted predictive data of at least one treatment management factor characteristic (i.e. the predicted probabilities of survival over time of Pongor).
However, Pongor does discloses that there is a need to identify clinically relevant mutations to spot candidate genes for targeted therapy (pg. 10, col. 2, para. 3), and discloses the expression of genes associated with driver genes having a mutation status can be evaluated for correlation with clinical outcome including therapy response (pg. 2, col. 2 para. 2). Pongor further discloses an example in which a KRAS status is considered an indicator of a patient’s response to EGFR inhibitors and provides other examples in which a mutation status in a gene indicates a patient response to a specific therapy (pg. 2, col. 1, para. 1 to col. 2, para. 1). Pongor further discloses evaluating the correlation of the CEB data with the clinical outcome of survival (i.e. a therapy response based on the survival probabilities) (Fig. 3).
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the method of Pongor to have provided a recommended intervention treatment according to the outputted survival probabilities, as suggested by Pongor above, which teaches that the expression of genes associated with driver genes having a mutation status can be evaluated for correlation with therapy response (pg. 2, col. 2 para. 2). One of ordinary skill in the art would have been motivated to modify the method of Pongor in order to provide a therapy recommendation based on a mutation status specific to a patient, given there is a need to identify clinically relevant mutations to spot candidate genes for targeted therapy, as shown by Pongor above (pg. 2, col. 1, para. 1 to col. 2, para. 1; pg. 10, col. 2, para. 3)
Therefore, the invention is prima facie obvious.
Response to Arguments
Applicant's arguments filed 22 Jan. 2026 regarding 35 U.S.C. 102, as applicable to the 35 U.S.C. 103 above, have been fully considered but they are not persuasive.
Applicant remarks that Pongor fails to teach (1) processing global mutant information into CE parameters via a multi-level deep learning neural network framework, (2) further deriving CEB parameters via machine learning or a deep learning network, and (3) outputting predictive data of at least one treatment management factor characteristic of the target object relative to the disease, and therefore, Pongor does not disclose the amended features of independent claim 1 alone or in combination, and furthermore Pongor provides no motivation to convert global mutant data into CE and CEB parameter for disease type differentiation, and instead focuses on breast-cancer specific transcriptomic fingerprints, teaching away from the claimed invention (Applicant’s remarks at pg. 19, para. 5 to pg. 20, para. 1).
This argument is not persuasive. Regarding (1), Pongor is not relied upon to teach processing the global mutant information into CE parameters using a neural network, and instead, this limitation is made obvious by Xie as newly recited and applied in the above rejection. Further regarding Applicant’s arguments regarding “global mutant information”, claim 1 recites “global mutant information of the several genes”, which is indefinite as discussed in the 112(b) rejection above, given it is not clear if mutant information is only required for several genes or across the exome or genome (i.e. globally). Regardless, Pongor does teach analyzing mutations in the exome (i.e. globally) (pg. 5, col. 2, para. 5 to pg. 6, col. 1, para. 1). Furthermore, the claim 1 only requires processing the global mutant information by the neural network to acquire CE parameter data, wherein “CE parameter represents the…influence of the several genes on the expression activity of any gene”. In other words, the CE parameter data only represents the influence of at least two genes on expression, and while the CE parameter data is derived from “global mutant information”, there is no requirement that the CE parameter data encompasses all genes in an exome.
Regarding (2), this argument is not commensurate with the scope of the claims given step (2) requires performing noise reduction rather than performing machine learning or deep learning network method.
Regarding (3), the previously cited claims also required the step of outputting predictive data of at least one treatment management factor characteristic, which Pongor discloses as applied in the above rejection. Furthermore, Applicant’s arguments do not discuss why Pongor does not disclose this particular limitation, and therefore the argument is not persuasive.
Applicant remarks that claim 10 and dependent claims 3-9, 12-14, 16, and 19-20 are patentable over Pongor for the same reason as claim 1 and their dependency on claim 1 respectively (Applicant’s remarks at pg. 20, para. 2-3).
This argument is not persuasive for the same reasons discussed above for claim 1.
Applicant remarks new claims 21-22 define “disease type shaving evolutionary correlation” and a preset classifier achieves technical effects of distinguishing evolutionarily related diseases, which is not achieved by Pongor (Applicant’s remarks at pg. 20, para. 4).
This argument is not persuasive because it is not commensurate with the scope of the claims. Claims 21-22 only require “determining the disease type…from at least two disease types having evolutionary correlation”, but does not require a preset classifier that distinguishes between these disease types. As newly discussed in the above rejection, Pongor discloses determining a tested sample from a breast cancer patient has a mutation in BRCA rather than being wild-type for BRCA (i.e. determining the sample is BRCA+ breast cancer from BRCA+ breast cancer and BRCA- breast cancer) (pg. 2, col. 2, para. 2; pg. 4, col. 2, para. 3; Table 2, e.g. Gene is BRCA2). The progression from the BRCA- type to BRCA+ type is considered an “evolutionary correlation”, particularly in light of Applicant’s remarks stating disease types having evolutionary correlation share genetic evolutionary relationship.
Applicant remarks that Pongor does not involve providing intervention treatment as claimed (Applicant’s remarks at pg. 20, para. 5).
This argument is not persuasive. While Pongor does not explicitly recite this limitation, providing an intervention treatment is obvious in view of Pongor as newly applied in the above rejection.
Applicant's arguments filed 12 May 2026 have been fully considered but they are not persuasive because they do not present any arguments regarding the 35 U.S.C. 102 rejection.
It is noted that Applicant remarks that in view of replacing “a machine learning method…” with “noise reduction processing”, the Applicant adopts a similar approach to respond to the 102 rejection set forth in the Office action mailed 27 Oct. 2025 (Applicant’s remarks at pg. 9, para. 6). However, the remarks filed do not actually contain any arguments regarding the 102 rejection, and instead only address the 101 rejection (which is responded to above).
Conclusion
No claims are allowed.
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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/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685
