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 .
Applicant's response filed 1/21/2026 has been fully considered. The following rejections
and/or objections are either reiterated or newly applied.
Status of Claims
Claims 1, 3-17, and 19-20 pending and examined on the merits.
Claims 2 and 18 canceled.
Priority
The instant application filed on 5/4/2022 claims the benefit of priority to U.S. Provisional Patent Application No. 63/313,743 filed on 2/25/2022. Thus, the effective filing date of the claims is 2/25/2022. The applicant is reminded that amendments to the claims and specification must comply with 35 U.S.C. § 120 and 37 C.F.R. § 1.121 to maintain priority to an earlier-filed application. Claim amendments may impact the effective filing date if new subject matter is introduced that lacks support in the originally filed disclosure. If an amendment adds limitations that were not adequately described in the parent application, the claim may no longer be entitled to the priority date of the earlier filing.
Information Disclosure Statement
The IDS filed on 1/21/2026 has been entered and considered. A signed copy of the corresponding 1449 form has been included with this Office action.
Claim Objections
The objection to claims 1 and 5 withdrawn in view of Applicant's claim amendments filed on 1/21/2026.
Withdrawn Rejections
35 USC § 112(b)
The rejection of claims 1, 5, 7, and 17 under 35 USC 112(b) withdrawn in view of Applicant's claim amendments filed on 1/21/2026.
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 1, 3-17, and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of a mental process, a mathematical concept, organizing human activity, or a law of nature or natural phenomenon without significantly more. In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claims 1 and 17: “using a mathematical formula to calculate a prognostic index, and a risk level of prognostic change of the test patient is evaluated according to the prognostic index, the mathematical formula as follows:
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; wherein U1 is the reference pathological eigenvalue; X1 is the reference radiomics; U2 is the test pathological eigenvalue; X2 is the test radiomics” provides a mathematical calculation (calculating an index) that is considered a mathematical concept, which is an abstract idea.
“when the prognostic index is greater than or equal to 1, it is evaluated that the risk of prognostic change of the test patient is higher than or equal to that of the reference patient; when the prognostic index is less than 1, it is evaluated that the risk of prognostic change of the test patient is lower than that of the reference patient” provides an evaluation or logical reasoning (comparing the index to a threshold) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
These recitations are similar to the concepts of collecting information, analyzing it, and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind or are mathematical relationships. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. Additionally, while claims 17-20 recite performing some aspects of the analysis on a system with “a backbone comprising [] computing layer[s]”, there are no additional limitations that indicate that this requires anything other than carrying out the recited mental processes or mathematical concepts in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental processes” grouping of abstract ideas. As such, claims 1, 3-17, and 19-20 recite an abstract idea (Step 2A, Prong 1: YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). The judicial exceptions listed above are not integrated into a practical application because the claims do not recite an additional element or elements that reflects an improvement to technology. Specifically, the claims recite the following additional elements:
Claims 1 and 17: “capturing a reference radiomics, wherein the reference radiomics is based on a reference image and the reference image is a lesion medical image of a reference patient; obtaining a reference pathological eigenvalue, wherein the reference pathological eigenvalue is based on pathological features of the reference patient, the pathological features comprising genomic features, gene expression, test values or a combination of two or more thereof; capturing a test radiomics, wherein the test radiomics is based on a test image and the test image is a lesion medical image of a test patient; obtaining a test pathological eigenvalue, wherein the test pathological eigenvalue is based on pathological features of a test patient, the pathological features comprising genomic features, gene expression, test values or a combination of two or more thereof” provides insignificant extra-solution activities (generating radiomics formatted data and obtaining eigenvalues are a pre-solution activities involving data gathering and manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
The steps for generating and obtaining data are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application because they are pre-solution activities involving data gathering and manipulation steps (see MPEP 2106.04(d)(2)). Furthermore, the limitations regarding implementing the method on a computing system do not indicate that they require anything other than mere instructions to implement the abstract idea in a generic way or in a generic computing environment. As such, this limitation equates to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. Therefore, claims 1, 3-17, and 19-20 are directed to an abstract idea (Step 2A, Prong 2: NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application, or equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment.
As discussed above, there are no additional elements to indicate that the claimed system with “a backbone comprising [] computing layer[s]” requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims. Additionally, the limitations for generating and obtaining data are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application. Furthermore, no inventive concept is claimed by these limitations as they are well-understood, routine, and conventional.
The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1, 3-17, and 19-20 are not patent eligible.
Response to Arguments under 35 USC § 101
Applicant’s arguments filed 1/21/2026 are fully considered but they are not persuasive.
Applicant asserts that “the claimed process CANNOT be practically performed in the human mind" because "evaluation of joint probability density functions based on high-dimensional pathological and radiomic data necessarily requires computer-based processing of quantitatively extracted medical features, which exceeds the capability of mental observation, evaluation, or judgement" (Remarks 1/21/2026 Pages 5-6). Examiner notes that simply claiming performing a complex mathematical function that is easier to implement in a generic computing environment does not render said function incalculable by the human mind or with pen and paper. Therefore, because the recited index is used as part of a computer-implemented technical procedure does not render said procedure outside the realm of a mental process and abstract idea. Applicant also asserts that "no person could do this entirely mentally, as one would need to obtain measured pathological eigenvalues and radiomic features from medical image data [and] the invention requires concrete instrument to serve the aforesaid purpose", which has been identified above as an additional element and not a judicial exception that is in question (Remarks 1/21/2026 Page 6).
Applicant asserts that claims 1 and 17 "imposes a limit on the exception" and therefore "is integrated into a practical application [via] computer-based processing of quantitatively extract medical features" to calculate a prognostic index (Remarks 1/21/2026 Page 7). Examiner notes that there are no additional limitations that indicate that this requires anything other than carrying out the recited mental processes or mathematical concepts in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental processes” grouping of abstract ideas.
Applicant asserts that the 2019 PEG explicitly calls out an improvement in the function of a computer or other technology or technological field indicates integration into a practical application. However, Examiner notes that there is no part of the claimed method that explicitly improves "the functioning of a computer, or other technology or technological field", and rather, in Applicant's own words, it is "improving the performance of method for predicting cancer prognosis" (Remarks 1/21/2026 Page 8). This simply restricts use to a particular environment or application without adding significant innovation that does not serve to integrate the judicial exceptions into a practical application because they are post-solution activities involving a mere field of use (see MPEP 2106.04(d)(2) - Integration of a Judicial Exception Into A Practical Application; MPEP 2106.05(g) - Insignificant Extra-Solution Activity; and MPEP 2106.05(h) - Field of Use and Technological Environment).
The Examiner also notes that MPEP 2106(I) states that if the claims are directed to a judicial exception, the second part of the Mayo test is to determine whether the claim recites additional elements that amount to significantly more than the judicial exception. Id. citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966). In the “search for an ‘inventive concept’” (the second part of the Alice/Mayo test), the additional elements identified do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception because generating radiomics formatted data and obtaining eigenvalues (data gathering and manipulation steps) are all well-understood, routine, and conventional techniques that are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application. Therefore, combining insignificant extra-solution activities with any of the identified judicial exceptions would not result in patent eligible subject matter because integrating well-understood, routine, and conventional techniques does not yield “significantly more” to a mental process, a mathematical concept, organizing human activity, or a law of nature or natural phenomenon.
Therefore, the rejection of claims 1 and 17 under 35 USC 101 is maintained. All other claims depend from these independent claims; therefore, their rejection is likewise maintained.
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.
Claims 1, 3-17, and 19-20 rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (Chen et al., "Histopathological image and gene expression pattern analysis for predicting molecular features and prognosis of head and neck squamous cell carcinoma." Cancer medicine 10.13 (2021): 4615-4628) in view of Drake et al. (WO-2019200410).
Regarding claims 1 and 17, Chen teaches A method for predicting cancer prognosis (Page 3 Figure 1 description "First, we performed the histopathological image processing and feature extraction. Secondly, three classifiers were constructed by feature selection and 5-fold cross-validation, and applied to classify the somatic mutations, transcriptional, and methylation subtypes. Subsequently, we selected the prognostic image features, used bioinformatics analyses to identify correlated gene modules, and established an integrative prognostic model to improve prognosis prediction").
Chen also teaches capturing reference and test radiomics, wherein the reference radiomics is based on a reference image and the reference image is a lesion medical image of a reference patient; as interpreted above, the image feature variables are extracted from medical images, outputting a reference image format data, and normalizing the reference image format data to obtain a reference radiomics (Page 3 Section 2.2 first paragraph "The size of whole-slide image made it difficult to recognize features, thus we first used the Openslide Python library 22 to crop 395 whole-slide images into 342,086 sub-images. We divided the 40×images into sub-images of 1000 × 1000 pixels, and divided the 20× images into sub-images of 500 × 500 pixels to get a same perspective. Then we re-sized the 500 × 500 pixel sub-images to 1000 × 1000 pixels for further analysis. According to previous studies,19, 23 we excluded the sub-images with less information, which had more than 50% white background. To decrease the computational cost, 20 sub-images were randomly selected from the remaining images.14, 19 An eligible sub-image contained about 500 cells, and each TMA sample contained about 1500 cells to extract the objective cellular attributes").
Chen also teaches obtaining reference and test pathological eigenvalue, wherein the reference pathological eigenvalue is based on pathological features of the reference patient, the pathological features comprising genomic features, gene expression, test values or a combination of two or more thereof (Page 4 Section 2.4 first paragraph "The module eigengene (ME) was the first principal component, which was the representative of module to explain the maximum variation of expression level. We estimated the correlation between MEs and image features to identify the key module", as "the first principal component" of expression level is an eigenvalue).
Chen also teaches using a mathematical formula to calculate a prognostic index, and a risk level of prognostic change of the test patient is evaluated according to the prognostic index, the mathematical formula; as interpreted above, a prognostic index that is a function of reference and test pathological eigenvalues and radiomics (Pages 4-5 Section 2.6 paragraph 2 "the risk score calculated by this model was [the] histopathological transcriptomics risk score (HTRS)" which is calculated by combining image feature data and gene expression eigenvalue data).
Chen also teaches when the prognostic index is greater than or equal to 1, it is evaluated that the risk of prognostic change of the test patient is higher than or equal to that of the reference patient; when the prognostic index is less than 1, it is evaluated that the risk of prognostic change of the test patient is lower than that of the reference patient (Page 8 col 1 paragraph 1 "We called the risk score estimated by the integrative model as histopathological transcriptomics risk score (HTRS). The differences of survival outcomes between high-HTRS and low-HTRS groups were significant in the training set" and Page 8 col 2 first paragraph "Furthermore, the prognostic features (p < 0.10) were enrolled in multivariate Cox analysis, which suggested that HTRS was an independent prognostic biomarker of OS (HR = 5.17, 95%CI: 2.82–9.41, p < 0.001)", and while Chen does not explicitly use a value of 1 as the cutoff for "higher risk of test patient compared to reference", the scale is arbitrary and could easily be adapted to the framework Chen has set forth by their HTRS).
Chen does not explicitly teach an explicit calculation involving joint probability density functions, correcting the linear relationship between tensors and covariates, nor classification of more than two types.
However, Drake teaches machine learning models employing maximizing joint probabilities of feature vectors, probabilistic models that include modeling with covariates, and outputting more than two types of classifications for cancer prognosis (Para.0020 "the classifying of the biological sample is performed by a classifier trained and constructed according to one or more of: linear discriminant analysis (LDA)", para.0032 "In a fifth aspect, the present disclosure provides a method of determining the prognosis of an individual with cancer", para.0086 "The term ‘'machine learning model” (or“model”) refers to a collection of parameters and functions, where the parameters are trained on a set of training samples. The parameters and functions may be a collection of linear algebra operations, non-linear algebra operations, and tensor algebra operations. The parameters and functions may include statistical functions, tests, and probability models. The training samples can correspond to samples having measured properties of the sample (e.g., genomic data and other subject data, such as images or health records), as well as known classifications/labels (e.g., phenotypes or treatments) for the subject", para.0154 "PGMs [probabilistic graphical models] can include varied information, measurements, and mathematical objects that contribute to a model that can be made more accurate. These objects can include other measured covariates such as the biological context of the data and the lab process conditions of the sample", and para.0246 "Features can also be engineered, e.g., by setting up a multi-task unsupervised learning problem where the joint probability' of all feature vectors given a set of parameters and latent vectors is maximized").
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Chen as taught by Drake in order to optimize parameters/functions for classification accuracy of a cancer prognosis (Para.0086 "The model can learn from the training samples in a training process that optimizes the parameters (and potentially the functions) to provide an optimal quality metric (e.g., accuracy) for classifying new samples. The training function can include expectation maximization, maximum likelihood, Bayesian parameter estimation methods such as markov chain monte carlo, gibbs sampling, hamiltonian monte carlo, and variational inference, or gradient based methods such as stochastic gradient descent and the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm", para.0032 "In a fifth aspect, the present disclosure provides a method of determining the prognosis of an individual with cancer", and para.0092 "The term“prognosis” as used herein refers to the likelihood of the clinical outcome for a subject afflicted with a specific disease or disorder. With regard to cancer, the prognosis is a representation of the likelihood (probability) that the subject will survive (such as for one, two, three, four or five years) and/or the likelihood (probability) that the tumor will metastasize"). One skilled in the art would have a reasonable expectation of success because both methods are concerned with classifying cancer prognosis using gene expression and medical image data.
Regarding claims 3-4, Chen teaches the methods of Claim 1 on which this claim depends/these claims depend. Chen also teaches the gene expression comprises RNA sequencing expression or protein expression, and the genomic features comprise gene copy number, gene mutant site, and single nucleotide polymorphisms (Page 2 col 2 last paragraph "The 212 HNSCC patients with clinical characteristics, somatic mutation, and mRNA sequencing data were acquired from The Cancer Genome Atlas (TCGA)", RNA-Seq data inherently includes data regarding gene copy number and mutations such as SNPs).
Regarding claim 5, Chen teaches the methods of Claim 1 on which this claim depends/these claims depend. Chen also teaches normalizing a raw read count per gene; as interpreted above, to encompass RPKM (Chen uses RNA-Seq data sourced by TCGA, which is under NIH (cancer.gov), and encompasses the Office of Cancer Genomics (OCG). Data is generally made available to the public once OCG researchers have published an initial overview and analysis of the data. The majority of genomics data generated by OCG programs is available through the Genomic Data Commons (GDC) (https://www.cancer.gov/ccg/access-data); "The GDC mRNA quantification analysis pipeline measures gene level expression with STAR as raw read counts. Subsequently the counts are augmented with several transformations including Fragments per Kilobase of transcript per Million mapped reads (FPKM), upper quartile normalized FPKM (FPKM-UQ), and Transcripts per Million (TPM) (https://docs.gdc.cancer.gov/Data/Bioinformatics_Pipelines/Expression_mRNA_Pipeline/)).
Regarding claim 6-11, Chen teaches the methods of Claim 1 on which this claim depends/these claims depend. While Chen does not explicitly teach “the reference and test image is one of a CT image, an fMRI image, an X-ray image, an ultrasound image or a pathological tomography image”, it would have been obvious to use other kinds of medical images such as CT or MRI in place of histopathological images because histopathological studies are used to validate them, as evidenced by Schillaci (Page 2 col 1 last paragraph, "Still, histopathological studies are essential to validate the results of PET-CT and MRI exams [13]"). One skilled in the art would have a reasonable expectation of success because both approaches are using image analysis, and the methods used for extracting features are applicable to both image types.
Regarding claim 12-16 and 19-20, Chen teaches the methods of Claim 1 on which this claim depends/these claims depend. Chen also teaches the cancer is a solid carcinoma (Head and neck squamous cell carcinoma is a type of solid carcinoma, as Chen also discusses T-cell response in solid tumors, page 12 col 2 paragraph 2 "The T cell-mediated immune response has been widely researched in solid tumors, and applied in immunotherapies such as checkpoint inhibitors").
Response to Arguments under 35 USC § 103
Applicant’s arguments filed 1/21/2026 are fully considered but they are not persuasive.
Applicant argues that Chen "completely fails to disclose/tech/suggest a specific calculating" along with the amendments to claim 1 (Remarks 1/21/2026 pages 13-14). Amendments necessitated new grounds for rejection: specifically the now explicitly claimed "joint probability density" functions, and, while not explicitly claimed, "correcting the linear relationship between tensors and covariates [and] classification of more than two types" (Remarks 1/21/2026 pages 13-14). Examiner notes above that it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Chen as taught by Drake in order to optimize parameters/functions for classification accuracy of a cancer prognosis (Para.0086 "The model can learn from the training samples in a training process that optimizes the parameters (and potentially the functions) to provide an optimal quality metric (e.g., accuracy) for classifying new samples. The training function can include expectation maximization, maximum likelihood, Bayesian parameter estimation methods such as markov chain monte carlo, gibbs sampling, hamiltonian monte carlo, and variational inference, or gradient based methods such as stochastic gradient descent and the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm", para.0032 "In a fifth aspect, the present disclosure provides a method of determining the prognosis of an individual with cancer", and para.0092 "The term“prognosis” as used herein refers to the likelihood of the clinical outcome for a subject afflicted with a specific disease or disorder. With regard to cancer, the prognosis is a representation of the likelihood (probability) that the subject will survive (such as for one, two, three, four or five years) and/or the likelihood (probability) that the tumor will metastasize"). One skilled in the art would have a reasonable expectation of success because both methods are concerned with classifying cancer prognosis using gene expression and medical image data.
Therefore, the rejection of claims 1 and 17 under 35 USC 103 is maintained. All other claims depend from these independent claims; therefore, their rejection is likewise 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 TH REE-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 finaI action.
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Robert A. Player whose telephone number is (571)272-6350. The examiner can normally be reached Mon-Fri, 8am-5pm.
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/R.A.P./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686