DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10/21/2025 has been entered.
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 .
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.
Response to Amendment
In the submission dated 10/21/2025, the following has occurred: Claims 17, 21, 26, and 29 have been amended
Claims 1 – 16 have been previously canceled.
Claims 33 - 36 were previously added in the amendment dated 11/13/2024
Claims 17 – 36 are pending.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 17 – 36 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
The claim(s) recite(s) subject matter within a statutory category as a process (claims 17 – 25, 32, 35, and 36), machine (claims 26 – 28 and 33), and manufacture (claims 29 – 31, 34) which recite steps of
analyzing a plurality of types of omics data to predict a first set of potential biomarkers for each type of omics data comprising genomics data, transcriptomics data, and proteomics data, wherein:
the genomics data comprise DNA, the transcriptomics data comprise RNA, and the proteomics data comprise one of peptides and protein; and
the first set of potential biomarkers are defined as quantitative features obtained from omics or clinical measurements that are used individually or in combination to perform at least one of diagnosis of a disease, prognosis of a disease, and prediction of a response to treatments;
constructing an integrative biological network by integrating at least one individual biological network using the first set of potential biomarkers, wherein:
each of the at least one individual biological network maps different types of omics data onto nodes and connects the nodes with edges by exploiting overlapping information from individual biological networks; and
each individual biological network maps only one type of omics data;
clustering the biological network into clusters of the first set of potential biomarkers;
creating a second set of potential biomarkers by electing from each cluster a single biomarker that conveys the most information about the cluster;
creating a third set of potential biomarkers by applying to the second set of potential biomarkers an algorithm using clinical data as additional potential biomarkers and using the algorithm to (i) produce a plurality of solutions, each of the plurality of solutions corresponding to a different selection from among the second set of biomarkers and the clinical data, and (ii) produce the third set of potential biomarkers by applying selection, crossover, and mutation to the plurality of solutions to generate new solutions that select only one biomarker from each one of the network clusters, wherein:
the algorithm optimizes the second set of biomarkers by exploring the search space of potential biomarkers and by using an evaluation function to assess a solution of the algorithm, wherein the optimization algorithm reduces a bias caused by data that is not relevant within the dimensionality reduction process; and
the evaluation function uses as parameters a patient's clinical data and associated clinical knowledge;
iterating the steps of: (a) selection by using algorithms; (b) crossover by producing a single crossover biomarker set, the single crossover biomarker set consisting of a part of a first biomarker set and a part of a second biomarker set; and (c) mutation for creating a new biomarker set from the crossover biomarker set by the algorithm until a threshold representative of the predictive performance of the final optimized biomarkers being obtained is reached to produce a final optimized set of biomarkers;
annotating the final optimized set of biomarkers with gene ontology terms and molecular pathways by identifying, in both the gene ontology terms and the molecular pathways, data associated with the obtained optimized biomarkers;
identifying cellular functions of the biological condition by comparing the annotated final optimized set of biomarkers with an existing set of known biological function contained in the large set of cellular functions with similar but not same functionality from the gene ontology and molecular pathways, where the functionalities are affected by the annotated final optimized set of biomarkers;
determining any cellular functions that are adversely affected: and
predicting a health condition based on the determining
It should be emphasized here that the result of the instant invention is “identify.” How or what “identify” means is not disclosed. Even if the result was outputted, although not currently claimed, it would represent extra-solution activity.
These steps of claims 17 – 36, as drafted, under the broadest reasonable interpretation, includes mathematical concepts. The Examiner makes this conclusion by understanding the claims in light of the specification. For example, paragraph 2 states, “The present invention relates to the computational prediction of biomarkers by integrating data from various biological experiments.” Paragraph 6 goes on to describe how this is done, “[0006] The computational prediction of biomarkers uses genetic experimental data and applies statistics, clustering, optimization and other types of algorithms to identify correlations between seemingly unrelated data and uncover biomarkers that cannot be easily detected by experimental techniques.” This idea of using algorithms to analyze data is repeated many times more within the Specification. For example, the Summary paragraph 8 states, “Biomarker prediction is performed on disparate omics data by mixing various types of algorithms, including clustering, feature selection and optimization.” The Summary concludes with paragraph 10 stating, “Finally, the current invention proposes an automated solution for optimizing the ordering of the algorithmic steps and their internal parameters.”
Paragraph 44 provides an example of the invention
[0044] The innovative nature of the proposed solution lies in (i) the use of a wide variety of available data, far wider than any known prior art technique, by appropriate handling and integrating disparate data from distributed sources, (ii) the use of existing mathematical algorithms in a novel way by first combining optimized "pipelines" of multiple algorithms executed serially and in parallel, and then reducing dimensionality in order to minimize bias caused by data conveying no new information to the analysis, (iii) the automated optimization of the algorithmic parameters and order of their execution in specific diseases and medical conditions, and (iv) the use of non-coding RNA in biomarker identification.
Applying known data and known algorithms to computers to achieve all the benefits of applying the known data and known algorithms to computers. The result of the invention is data that only has a potential usage. Therefore, there is no technological improvement and no practical application.
It should be emphasized here that the Specification does not describe the invention as a technological improvement to overcome a technological problem. Rather, the invention as described in paragraph 10, “Finally, the current invention proposes an automated solution for optimizing the ordering of the algorithmic steps and their internal parameters.” Or stating this differently, the invention applies technology to an abstract idea to obtain the benefits of applying that technology to an abstract idea.
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 2 – 25, 27, 28, 30 – 34, and 36 reciting particular aspects of how optimizing may be performed but for recitation of generic computer components).
This judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception (such as recitation of using the information handling system, the processor configured to, and computer program product that causes… amount to invoking computers as a tool to perform the abstract idea, see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea (such as recitation of a plurality of types of omics data amounts to mere data gathering, see MPEP 2106.05(g))
It should be emphasized again here that the invention is directed towards the creation of better data. That is shown in the specification:
[0090] The processing ends with reporting (step 580) the final biomarker set for the examined biological condition ( e.g. a syndrome or a disease) together with the relevant prediction models and the affected cellular functions.
[00141] Processing continues m step 870 by ranking the combined biomarkers according to the calculated confidence scores and the processing ends with step 890 by reporting the ranked biomarkers.
The result of the invention is the publication of data. That data has only a potential usage and therefore there is no practical application. There are only potential applications.
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2 – 25, 27, 28, 30 – 34, and 36, additional limitations which amount to invoking computers as a tool to perform the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields (such as claims 17 – 36; iterate the steps of…, e.g., performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii))
Additional elements
information handling system – paragraph 50
optimization algorithm – paragraph 82 Any optimization algorithm can be used
genetic algorithm – paragraphs 91, 92, and 117, also Hermawanto, “Genetic Algorithm for Solving Simple Mathematical Equality Problem”
classification models – paragraph 117
FAST-Q/ BAM – Bihary, “Reference genomes and common file formats”
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2 – 25, 27, 28, 30 – 34, and 36, additional limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, e.g., performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii)). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
It is the Examiner’s concern that the Applicant may go down the improvements to technology route. The Examiner reminds the Applicant that if the combination of features is performed in a novel way, then it is required to describe how these features are combined. It is noted that the Specification is written at a high level using functional terms. It may be challenging to find 35 USC § 112a, written description, support. For example, paragraph 44 is a description of the invention. (Emphasis added)
[00044] The innovative nature of the proposed solution lies in (i) the use of a wide variety of available data, far wider than any known prior art technique, by appropriate handling and integrating disparate data from distributed sources, (ii) the use of existing mathematical algorithms in a novel way by first combining optimized "pipelines" of multiple algorithms executed serially and in parallel, and then reducing dimensionality in order to minimize bias caused by data conveying no new information to the analysis, (iii) the automated optimization of the algorithmic parameters and order of their execution in specific diseases and medical conditions, and (iv) the use of non-coding RNA in biomarker identification.
The written description describes how these steps are performed. It is not enough to state that functions occur but must be described per MPEP 2161.01.
Response to Arguments
Applicant’s arguments, see Claim rejections under 35 U.S.C. §112, filed claims 18 - 36, with respect to every data set have been fully considered and are persuasive. The 35 U.S.C. §112 rejection of claims 17 - 36 has been withdrawn.
Applicant's arguments filed 10/21/2025 have been fully considered but they are not persuasive.
Claim rejections under 35 U.S.C. §101
The Applicant states, “Applicant respectfully submits that this limitation goes beyond adding insignificant extra-solution activity to an abstract idea. Determining adversely-affected cellular functions and subsequently predicting a health condition is a practical end-solution.” The Applicant’s opinion about the potential use of a number is understood. Like the curing time of rubber, that value in and of itself has not value unless combined with a practical application.
The Applicant states, “This limitation indicates that the associated optimization process is not just limited to data gathering and processing, as it represents a technical benefit of the optimization.” The Applicant’s Specification contradicts this assertion.
[0078] The importance of reducing dimensionality of the biomarker optimization problem goes well beyond the mere reduction of computational complexity and the increased calculation speed. Dimensionality reduction gives results that are more accurate and avoids bias introduced by the manual operation of the processing steps.
Note that the improvement, as disclosed, is over a manual operation. This is exactly as one would suspect after applying technology to an abstract idea to achieve the benefits of applying that technology to the abstract idea.
The Applicant states, “Furthermore, a list of references is provided herein. These references are related to existing art that describes the necessity of using computing systems to achieve the results of the claimed subject matter.” The Examiner has no comment upon the “list of references.” They are not included as being part of a declaration. They are also not included within an Information Disclosure Statement. Whether or not a solution uses a different technique does not automatically make one a technological improvement.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hare et al Pub. No.: US 2012/0142544 Molecular signatures that function as very sensitive diagnostic biomarker for myocarditis, heart disease and disorders thereof, are identified.
Dimitrova et al Pub. No.: US 2020/0020421 A clinical genomic data processing device includes at least one microprocessor (10) and a non-transitory storage
medium (12) storing instructions to implement functions of the device.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Neal R Sereboff whose telephone number is (571)270-1373. The examiner can normally be reached M - T, M - F 8AM - 6PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Morgan can be reached at (571)272-6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NEAL SEREBOFF/
Primary Examiner
Art Unit 3626