DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claim 7 is cancelled.
Claims 1-6 and 8-20 are pending and are examined on the merits.
Priority.
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365(c) is acknowledged. Priority of REPUBLIC OF KOREA application 10-2024-0008609 filed 1/19/2024 is acknowledged.
Withdrawn Rejections/Objections
The rejection to claims 1-6 and 8-20 are rejected under 35 U.S.C. 112(a) in the Office action posted 17 February 2026 are withdrawn in view of claim amendments filed 15 May 2026.
The rejection to claims 1-6 and 8-20 are rejected under 35 U.S.C. 112(b) in the Office action posted 17 February 2026 are withdrawn in view of claim amendments filed 15 May 2026.
The rejection to claims 1-6 and 8-20 are rejected under 35 U.S.C. 103 in the Office action posted 17 February 2026 are withdrawn in view of claim amendments filed 15 May 2026.
Regarding the 35 U.S.C. 103
The claims are free of the analogous art at least because close art, e.g. as cited on the 2/17/2026 Office Action and those cited on the 11/12/2024, 5/27/2025 IDSs as well as art found in the search histories, either individually or in obvious combination, does not teach:
Filtering, based on the classification result indicating that the first variant candidate is an artifact, the first variant candidate from a variant candidate list to generate a filtered variant list comprising variant candidates determined to be true positive variants; and
Identifying, based on the classification result and based on a prediction of how the individual will respond to treatment using a drug prescribed to the individual and associated with one or more tumors of the individual, an adjusted prescription of the drug, wherein the adjusted prescription is predicted to increase drug efficacy.
The combination of these claim limitations is not taught by any art, and is not obvious. Additionally, Applicant's Remarks filed 05/15/2026 at pages 16 supports the withdrawal of the 102/103 rejections.
Claim Rejections - 35 USC § 101
This rejection is maintained from a previous Office action. Modifications are necessitated by claim amendments.
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 1-6 and 8-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step 1: Process, Machine, Manufacture or Composition
Claims 1-6 and 8-16 are directed to a process, here a "method," for training a machine learning model, with process steps "receiving”, “detecting", “generating”, and “training”.
Claims 17-18 are directed to another process, here another “method”, with process steps “receiving”, “determining”, and “performing”.
Claim 19 is directed to a 101 machine, here “a non-transitory computer-readable storage medium”, with known structure.
Claim 20 is directed to another 101 machine, here “an apparatus”, with structural components like “at least one processor”, and “a memory”.
Step 2A Prong One: Identification of Abstract Ideas
Claims 1, 17 and 20 recite:
Detecting, based on the non-tumorous sequencing data and the tumorous sequencing data, a reference variant candidate in a reference sample comprising the non-tumor-derived sample and the tumor-derived sample;
----This step recites a data analysis process that leads to a reference variant candidate. Under a broadest reasonable interpretation (BRI), this step requires sequence comparison and decision-making. Hence this step equates to an abstract idea of mental processes.
Generating, based on the POF and the reference variant candidate, annotation information comprising one or more of:
A number of samples, of the plurality of FFPE samples, associated with a variant allele frequency (VAF), at a position in a base sequence in the samples, less than a predetermined threshold; or
A number of samples, among the plurality of FFPE samples, having a predetermined number of variant reads at a predetermined position;
----This step recites data manipulation activities of adding annotation information (to the reference variant candidate) through cross-referencing, which equates to an abstract idea of mental processes.
Generating training data based on:
the reference variant candidate;
the annotation information;
second non-tumorous sequencing data of the individual; and
second tumorous sequencing data, of the individual, that corresponds to the second sample type, wherein labels of the training data comprise classification information corresponding to the reference variant candidate;
----This step recites data manipulation activities that extract and re-organize data in the way required by the machine learning model. Therefore, this step equates to an abstract idea of mental processes.
Training, based on the training data, the machine learning model by adjusting one or more weights of one or more nodes of an artificial neural network.
----This step recites training a machine learning model, which has weighted adjustment of nodes. Under a BRI, the machine learning model is a regression model, which will require mathematical operations. Therefore, this step equates to an abstract idea of mathematical concepts.
Filtering, based on the classification result indicating that the first variant candidate is an artifact, the first variant candidate from a variant candidate list to generate a filtered variant list comprising variant candidates determined to be positive variants.
----This step recites data manipulation activities that remove artifact candidates based on the classification result. Therefore, this step equates to an abstract idea of mental processes.
Identifying, based on the classification result and based on a prediction of how the individual will respond to treatment using a drug prescribed to the individual and associated with one or more tumors of the individual, an adjusted prescription of the drug, wherein the adjusted prescription is predicted to increase drug efficacy;
----This step recites an judgement/decision-making activity based on the data observation or data analysis on the predicted output. Therefore, this step equates to an abstract idea of mental processes.
Therefore, this step equates to an abstract idea of mental processes.
Step 2A Prong Two: Consideration of Practical Application
The claims result in a process of:
“identifying, based on the predicting how the individual will respond to treatment using the drug, a quantity of the drug”; and
“causing the quantity of the drug to be administered to the individual.”
The first step reads on a judgement/decision-making process based on data (ML prediction output, more specifically validated variants) observation, which equates to an abstract idea of mental processes. The second step is interpreted as merely an intended application of the claimed invention or a field of use limitation, it cannot integrate a judicial exception under the "treatment or prophylaxis" consideration (MPEP §2106.04(d)(2)), because it is not a particular treatment or prophylaxis (no disease, no drug is specified).
The claims do not recite any additional elements that integrate the abstract idea/judicial exception into a practical application.
This judicial exception is not integrated into a practical application because the claims do not meet any of the following criteria:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than
a drafting effort designed to monopolize the exception.
Step 2B: Consideration of Additional Elements and Significantly More
The claimed method also recites "additional elements" that are not limitations drawn to an abstract idea. The recited additional elements are drawn to:
Claims 1, 17 and 20 recite:
receiving: non-tumorous sequencing data based on a non-tumorous sample of an individual; and tumorous sequencing data based on a tumorous sample, of the individual, that corresponds to a first sample type processed differently from a second sample type, wherein a plurality of artifacts are associated with the first sample type;
----This step recites receiving two datasets, which equates to an additional element.
receiving a Panel of FFPEs (POF) generated based on sequencing data associated with a plurality of Formalin-Fixed, Paraffin-Embedded (FFPE) samples, wherein the plurality of FFPE samples corresponds to the first sample type;
----This step recites receiving one dataset, which equates to an additional element.
Executing the trained machine learning model by:
providing, as input to an input node of the artificial neural network of the trained machine learning model, input comprising:
information corresponding to a first variant candidate of a first sample, and a feature of the first variant candidate in the first sample; and
receiving, as output from an output node of the artificial neural network of the trained machine learning model and based on the input, a classification result indicating whether the first variant candidate is a true positive or an artifact;
----This step recites prediction input/output, which equates to an additional element.
Outputting an indication of the adjusted prescription of the drug;
----This step recites generating predictive/analytical output, which equates to an additional element;
Claim 19 recites:
A non-transitory computer-readable storage medium.
----this step recites a storage medium, which equates to an additional element.
Claim 20 recites:
An apparatus, comprising: at least one processor;
----this step recites one processor, which equates to an additional element.
and a memory storing instructions that, when executed, configure the at least one processor to:
----this step recites a memory, which equates to an additional element.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because it is routine and conventional to perform the acts of acquiring sequencing data for further analysis. Other elements of the method include processor, storage memory and an ANN model, which are recitations of generic computer components that serve to perform generic computing functions that are well-understood, routine, and conventional activities previously known to the pertinent industry.
Particularly, 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 (MPEP 2106.05(d).II):
v. Analyzing DNA to provide sequence information or detect allelic variants, Genetic Techs. Ltd., 818 F.3d at 1377; 118 USPQ2d at 1546;
Similarly, the current claims are about analyzing DNA to provide sequence information and detect allelic variants.
Viewed as a whole, applying machine learning technology to variant calling is taught by at least the following listed art, as discussed in the previously Office action:
Heo, Dong-hyuk, et al. "Reducing artifactual somatic variant calls from formalin-fixed paraffin-embedded specimens by using DEEPOMICSⓇ FFPE, a bioinformatic approach based on deep neural networks." (2023). Previously cited.
Wu, Chao, et al. "Using machine learning to identify true somatic variants from next-generation sequencing." Clinical chemistry 66.1 (2020): 239-246. Previously cited.
Dodani, et al. ("Combinatorial and machine learning approaches for improved somatic variant calling from formalin-fixed paraffin-embedded genome sequence data." Frontiers in Genetics 13 (2022): 834764. Previously cited).
Reference A, B and C applied different machine learning models to call variants from next generation sequencing data. Reference A specifically tried to improve variant calls from FFPE samples and reference B specifically tried to avoid FFPE samples (Wu: last para, col 1, pg. 240) as it is known that FFPE samples harbor artifacts of variants. Reference C performed an in-depth comparison of somatic SNVs called on matching FF and FFPE Whole Genome Sequence (WGS) samples extracted from the same tumor, and illustrates that when using the correct variant calling strategy, the majority of clonal SNVs can be recovered in an FFPE sample with high precision and sensitivity (De Schaetzen van Brienen: section Abstract, pg. 1).
Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea recited in the instantly presented claims into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Response to Applicant’s Argument
Applicant's arguments filed 5/15/2026 have been fully considered but they are not persuasive.. Particularly:
In the Remarks filed 15 May 2026, Applicant argued (page 12, last para through page 13, 2nd para) that “The Action's approach is not a legally supported way to determine whether the claims are directed to an abstract idea… the Examining Corps should recognize the difference between "claims that recite a judicial exception" and "claims that merely involve a judicial exception" and that Examiners must both analyze claims "as a whole" and consider "whether a claim is directed to an improvement in 'any other technology or technical field. … a claim is not directed to an abstract idea merely because the Office can point to a few steps that have some distant relationship to an abstract idea.”
In response, Applicant’s argument refers to Step 2A/Prong one in the 101 analysis, relating to whether claims recite abstract ideas or not. Applicant’s argument is not persuasive. As discussed above in the 101 rejection, elements are not just distantly related to abstract ideas, but recite abstract ideas. For example, element “detecting, based on the non-tumorous sequencing data and the tumorous sequencing data, a reference variant candidate in a reference sample comprising the non-tumor-derived sample and the tumor-derived sample” recites a data analysis process that is based on two sequence comparison. Under a BRI, such a process can be achieved in the human mind, with or without the help of a pen and paper. The human judgement/decision-making then acted upon the sequence comparison result.
Element “generating, based on the POF and the reference variant candidate, annotation information comprising one or more of: a number of samples, of the plurality of FFPE samples, associated with a variant allele frequency (VAF), at a position in a base sequence in the samples, less than a predetermined threshold; or a number of samples, among the plurality of FFPE samples, having a predetermined number of variant reads at a predetermined position” recites data manipulation activities of adding annotation information (to the reference variant candidate) through cross-referencing, which equates to an abstract idea of mental processes.
Claims 1, 17 and 20 end at generating new information of adjusted prescription. As a whole, the claims are directed to abstract ideas without significantly more.
In the Remarks, Applicant argued (page 13, 3rd para) that “the Action's approach is inherently faulty. Even if it were the case that the claimed machine learning model involves math, that does not mean that the claims are directed to math: after all, all computers could be argued to involve bitwise arithmetic. Similarly, virtually all computing processes could be hand-waved as similar to human mental processes, but all computing processes are not directed to human mental processes. The inquiry is whether the claims are directed to abstract ideas, and it is manifestly clear that they are not”.
In response, Applicant’s argument still refers to Step 2A/Prong one in the 101 analysis, relating to whether claims recite abstract ideas or not. Applicant’s argument is not persuasive. As discussed above in the 101 analysis, element like “training, based on the training data, the machine learning model by adjusting one or more weights of one or more nodes of an artificial neural network” recites training a machine learning model, which recites weighted adjustment of nodes explicitly. Under a BRI, the machine learning model is a regression model, which will require mathematical operations. Therefore, this step equates to an abstract idea of mathematical concepts. Claims do recite elements that are directed to math.
In the Remarks, Applicant argued (page 13, last para through page 14, 2nd para) the element “filtering, based on the classification result indicating that the first variant candidate is an artifact, the first variant candidate from a variant candidate list to generate a filtered variant list comprising variant candidates determined to be true positive variants" “comprises a concrete data transformation step that comprises far more than any alleged abstract idea and that recites a practical application of such an
abstract idea. In this manner, the claims generally recite a specific technical solution that produces a concrete technical improvement-for instance, generating a filtered variant list by removing FFPE-induced artifacts, and using this filtered list to enable accurate drug prescription adjustments. This is not merely an intended use or field of use limitation, but rather a specific application that produces a tangible output."
In response, Applicant’s argument refers to Step 2A/Prong two in the 101 analysis, relating to whether claims are integrated into a practical application or not. Applicant’s argument is not persuasive. The argued example, is directed to abstract idea of mental processes. Because filtering variant list reads on a human judgement, opinion and decision-making on the generated data (here the variant list). Such a process can be achieved in the human mind. The instant claims might have recited a better data analysis, but the technical merit in data analysis should be realized in additional elements.
In the Remarks, Applicant argued (page 14, 3rd para) that “processing both non-tumorous and tumorous sequencing data to identify reference variant candidates and generate annotation information which can be used to specifically train a machine learning model to perform classification tasks and ultimately adjust prescriptions in a manner "predicted to increase drug efficacy." As such, Applicant is not purporting to claim the idea of data processing, but is instead describing a particular series of steps for enabling a computing device to perform classification tasks for input samples that ultimately enables improvement of drug prescriptions.”
In response, Applicant’s argument still refers to Step 2A/Prong two in the 101 analysis, relating to whether claims are integrated into a practical application or not. Applicant’s argument is not persuasive. The argued example, “predicted to increase drug efficacy”, is directed to abstract idea of mental processes. Because generating a prescription reads on a human opinion, or judgement on the patient, based on generated data. Such a process can be achieved in the human mind. The instant claims might recite a better data analysis, and a better analytical output, but the technical merits should be realized in additional elements.
In the Remarks, Applicant argued (page 14, last para through page 15, 1st para) that “the Action's rejection is flawed because the piecemeal approach to dividing up the claims into different portions is the direct consequence of its alleged inability to find ‘additional elements that are sufficient to amount to significantly more’”; and "[c]onsideration of the elements in combination is particularly important."
In response, Applicant’s argument refers to Step 2B in the 101 analysis, relating to whether claims amount to significantly more or not. Applicant’s argument is not persuasive. Examiner’s examination followed the MPEP standards. “Considers all claim elements” has to be based on individual analysis to elements.
In the Remarks, Applicant argued (page 15, 2nd para) that “combination of specific features recited in the present claims amount to significantly more than the alleged abstract idea. The claims use specific machine learning processes and other steps to ultimately adjust prescriptions in a manner "predicted to increase drug efficacy." The claims are not attempting to monopolize human mental processes, the organization of humans, or the like.”
In response, Applicant’s argument refers to Step 2B in the 101 analysis. Applicant’s argument is not persuasive. Whether claims recite “significantly more” is based on analysis of additional elements. The identified additional elements, when considered as a whole, do not amount to be “significantly more” as they are common and conventional. “Predicted to increase drug efficacy” is not an additional element, but data and information, which reads on an opinion or judgement regarding the patients. Such an opinion or judgement is common in the clinical industry.
In the Remarks, Applicant argued (page 15, 3rd para through last para) that “This rejection is particularly faulty because it purports to identify the alleged relevance of art for the purposes of a § 101 rejection. This is, flatly, improper. The MPEP is clear: "the search for an inventive concept should not be confused with a novelty or non-obviousness determination." MPEP § 2106.05(I). In fact, the Federal Circuit has explicitly criticized this approach to rejecting the claims, stating that the "novelty' of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the §101 categories of possibly patentable subject matter."
In response, Applicant’s argument is not persuasive. Applicant ignores that analyzing tumor sequence data for variants is one of the most explored technical area. Using ML to remove FFPE-induced artifact is directed to data analysis, and a data analysis, even if improved, is not sufficient to make the claims statutory. The 101 analysis is looking for additional elements, that steps away from laboratory data analysis, that use the JEs, capture JEs and effect JEs in a meaningful way.
For the above reason, the 101 rejection is maintained.
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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/GL/
Patent Examiner
Art Unit 1686
/Anna Skibinsky/
Primary Examiner, AU 1635
Prosecution Insights