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
This Nonfinal Office Action is in response to the Application filed 05/06/2025. Claims 1-20 are currently pending and considered herein.
Claim Rejections - 35 USC § 112
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 4 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 4, claim 4 recites the concept of “the probability scores of each of the first and second machine learning models.” This limitation lacks antecedent basis and is not found in claim 1.
Appropriate correction is required.
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 1-20 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.
Claim 1 recites, wherein the abstract elements are not emboldened:
A method of diagnosing or determining the prognosis of cancer in a patient, the method comprising: receiving genomic data of a patient; receiving biopsy image data of the patient; processing, using RNA-sequencing and a first machine learning model, the genomic data of the patient to determine at least one of a first cancer type or degree of cancer; processing, using histopathology and a second machine learning model, the biopsy image data of the patient to determine at least one of a second cancer type or degree of cancer; comparing the determined first type or degree of cancer with the determined second type or degree of cancer; in response to determining a level of correlation between the determined first cancer type or degree and the second determined cancer type or degree, generating an output diagnosing or determining the prognosis of cancer in the patient as the first cancer type or degree; in response to determining that the determined first cancer type or degree and the determined second cancer type or degree do not have the level of correlation, generating an output indicating that the diagnosing or determining the prognosis is undetermined.
Independent claims 11 and 12 recite substantially similar limitations. The claimed invention is directed to the abstract idea of collecting patient genomic and biopsy information, analyzing the information, and generating a cancer diagnosis or prognosis based on the analyses.
The limitations of “receiving genomic data of a patient; receiving biopsy image data of the patient; processing, using RNA-sequencing, the genomic data of the patient to determine at least one of a first cancer type or degree of cancer; processing, using histopathology, the biopsy image data of the patient to determine at least one of a second cancer type or degree of cancer; comparing the determined first type or degree of cancer with the determined second type or degree of cancer; in response to determining a level of correlation between the determined first cancer type or degree and the second determined cancer type or degree, generating an output diagnosing or determining the prognosis of cancer in the patient as the first cancer type or degree; in response to determining that the determined first cancer type or degree and the determined second cancer type or degree do not have the level of correlation, generating an output indicating that the diagnosing or determining the prognosis is undetermined,” as drafted is a process that, under its broadest reasonable interpretation, is an abstract idea that covers performance of the limitation as organizing human activity. For example, but for the generic computer system reciting a processor and memory and executable instructions (claims 11 and 12) and the first and second machine learning models, the claim recites an abstract idea that covers performance of the limitation as organizing human activity including following rules or instructions. The claim recites as a whole a method of organizing human activity because the limitations include a method that allows users to access patient data and biopsy images, analyze those data and determine whether certain cancers are present or not based on the analyses. This is a method of managing interactions between people. The nominal recitation of a generic computer system and machine learning models does not take the claims out of the method of organizing human interactions grouping. The additional limitations amount to computer methods for further implementing the abstract idea of organizing human activity. Thus, the claims recite an abstract idea.
The claims also recited an abstract idea including mental processes. But for the generic computer system and machine learning models, nothing in the claims is precluded from being performed in the mind. For example, a physician can collect the patient biopsy and genomic data and analyze them and then manually formulate insights about a cancer and appropriate recommendations/treatments. Thus, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of the generic computer system and machine learning models. The computer and machine learning models in these steps are recited at a high-level of generality (i.e., as a generic processor/server/storage/display performing a generic computer function of receiving inputs, analyzing the inputs, and displaying selected information) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The limitations seem to monopolize the abstract idea of patient analysis and diagnoses and general techniques between a physician and her patient. Furthermore, there is no clear improvement to the underlying computer technology in the claim. The claim is thus directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of the generic computer system and machine learning models amounts to no more than mere instructions to apply the exception using a computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements when considered separately and as an ordered combination do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computing environment.
The dependent claims do not remedy the deficiencies of the independent claims with respect to patent eligible subject matter. The dependent claims further limit the abstract idea. Claims 2-4 and 13-15 describe various machine learning model techniques, which are recited at a high level of generality such that it amounts to no more than mere instructions to apply the judicial exception using a generic computer component and cannot provide an inventive concept. Even in combination, the machine learning models in the dependent claims do not integrate the abstract idea into a practical application and does not amount to significantly more than the abstract idea itself. Claims 5-6 and 16-17 describe the genomic data and further limits the abstract idea. Claims 7 and 18 detail a cancer type or degree and limits the abstract idea. Claim 8 details a survival rate and limits the abstract idea. Claims 9-10 and 19-20 describe a threshold for the machine learning models and further limits the abstract idea. Therefore, the claims are not patent eligible.
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 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 4-13 and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2025/0250636 A1 to Landau et al., hereinafter “Landau,” in view of U.S. 2021/0090694 A1 to Colley et al., hereinafter “Colley” and further in view of U.S. 2024/0249798 A1 to Fillipova et al., hereinafter “Fillipova.”
Regarding claim 1, Landau discloses A method of diagnosing or determining the prognosis of cancer in a patient, the method comprising: receiving genomic data of a patient; receiving biopsy image data of the patient (See Landau at least at Abstract (“Classifying sequence fragments and labelling sequence fragments that represent tumor markers. A plurality of reference sequences are read. A plurality of sequence fragments obtained from a biological sample of a patient are read.”); Paras. [0025]; Figs. 1-4, 9); processing, using RNA-sequencing and a first machine learning model, the genomic data of the patient to determine at least one of a first cancer type or degree of cancer (See id. at least at Paras. [0025]-[0026] (machine learning models and sequencing), [0031]-[0032] (“SNV classification performance for different machine learning models. F1 score was assessed on tumor-confirmed melanoma ctDNA SNV fragments vs. cfDNA artifacts from healthy controls.”), [0043]-[0054] (machine learning, sequencing machines), [0075]-[0077] (RNA sequencing), [0090]-[0093] (deep sequencing, machine learning); Figs. 1-7).
Landau may not specifically describe but Colley teaches in response to determining a level of correlation between the determined first cancer type or degree and the second determined cancer type or degree, generating an output diagnosing or determining the prognosis of cancer in the patient as the first cancer type or degree (See Colley at least at Paras. [0157]-[0161] (“Transcriptome profiling of tumor samples by standard RNA (ribonucleic acid) sequencing methods measures the average gene expression of the cell types present in the sample at the time of sampling, the samples generally including both tumor (target) and non-tumor (non-target) cells. The expression profile is largely shaped by the sample's tumor architecture. Tumor purity, i.e., the proportion of cancerous cells in the sample, can directly influence the sequencing results, genomic interpretation, and any consequent proposed associations with clinical outcomes […] RNA expression from normal adjacent cells to the tumor could increase or wash out the relevant expression signal for a given gene and result in the erroneous interpretation of over or under expression and subsequent treatment recommendations […] The present disclosure relates to generating and applying RNA profiles to identify cell types and their proportions in patient samples, to improve precision of treatment selection and monitoring.”)); processing, using histopathology and a second machine learning model, the biopsy image data of the patient to determine at least one of a second cancer type or degree of cancer (See Colley at least at Paras. [0134]-[0135] (“A Generalizable and Interpretable Deep Learning Framework for Predicting MSI from Histopathology Slide Images.”), [0175], [0316]-[0327]); comparing the determined first type or degree of cancer with the determined second type or degree of cancer (See id. at least at Paras. [0175], [0316]-[0337] (“[T]he set of stained histopathology images having a first cancer type-specific bias; store in a database, using the one or more computing devices, an association between the histopathology slide images and the plurality of MSI classification labels; apply a statistical model to analyze the set of stained histopathology images and predict an initial baseline MSI status, the initial baseline MSI prediction status exhibiting cancer type-specific bias […] mapping a first plurality of genomic sequencing reads from a tumor specimen to the locus; mapping a second plurality of genomic sequencing reads from a matched-normal specimen to the locus; comparing the mapping of the first plurality to the mapping of the second plurality and determining the likelihood of microsatellite instability based on the comparison […] mapping a second plurality of genomic sequencing reads from a matched-normal specimen to the locus; comparing the mapping of the first plurality to the mapping of the second plurality and determining the likelihood of microsatellite instability based on the comparison.”)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Landau to incorporate the teachings of Colley and provide histopathology and using a machine learning model to classify different cancers. Colley is directed to data based cancer research and gene sequencing. Incorporating the cancer research and models as in Colley with the classifying of sequence fragments that represent tumor markers as in Landau would thereby increase the applicability, utility, and efficacy of the claimed multi-modal machine learning approaches for predicting cancer type.
The references may not specifically describe but Fillipova teaches in response to determining that the determined first cancer type or degree and the determined second cancer type or degree do not have the level of correlation, generating an output indicating that the diagnosing or determining the prognosis is undetermined (See Fillipova at least at Paras. [0019] (“[C]lassifying the subject by applying the identification of the relative copy number at each respective genomic location to a classifier, thereby determining a cancer class of the subject, where the cancer class of the subject is a presence or absence of a cancer selected from a set of cancers.” Determining whether undiagnosed subject has cancer or not), [0126]-[0128] (machine learning and training disease state models), [0244]-[0245] (“Sequencing reactions were also performed against genomic DNA preparations of tumor samples from each of the 35 cancer subjects. The fraction of cancer-derived sequence reads was then estimated by comparing cancer-derived variant alleles identified in the tumor sample with sequence reads of the cell-free DNA either from the size-selected sample or following in silico size selection.”), [0258]-[0261]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Landau and Colley to incorporate the teachings of Fillipova and provide determining the presence or absence of a cancer by comparison of different cancer types or degrees. Fillipova is directed to determining a cancer class using a patient biological sample and sequencing. Incorporating the cancer class determinations and type/degree comparisons as in Fillipova with the cancer research and models as in Colley and the classifying of sequence fragments that represent tumor markers as in Landau would thereby increase the applicability, utility, and efficacy of the claimed multi-modal machine learning approaches for predicting cancer type.
Regarding claim 2, Landau as modified by Colley and Fillipova disclose the limitations of claim 1and Landau further discloses wherein the first machine learning model comprises at least one of a support vector machine (SVM) or gradient boosting decision tree (GBDT) (See Landau at least at Paras. [0042], [0053]-[0054], [0092]).
Regarding claim 4, Landau as modified by Colley and Fillipova disclose the limitations of claim 1 and Landau further discloses wherein the first machine learning model comprises a linear SVM model ((See Landau at least at Paras. [0042], [0053]-[0054], [0092]). Colley further teaches the second machine learning model comprises a Resnet 18 model (See Colley at least at Paras. [1843]-[1846], [1860]-[1861]), wherein generating an output diagnosis or determining the prognosis of a cancer comprises multiplying the probability scores of each of the first and second machine learning models (See id. at least at Paras. [2304], [2702]-2704]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Landau and Fillipova to incorporate the teachings of Colley and provide various machine learning models and multiplying probability scores. Colley is directed to cancer research and treatment systems. Incorporating the cancer research and models as in Colley with the cancer class determinations and type/degree comparisons as in Fillipova and the classifying of sequence fragments that represent tumor markers as in Landau would thereby increase the applicability, utility, and efficacy of the claimed multi-modal machine learning approaches for predicting cancer type.
Regarding claim 5, Landau as modified by Colley and Fillipova disclose the limitations of claim 1 and Landau further discloses wherein the genomic data is RNA sequence data (See Landau at least at Paras. [0047], [0075], [0090], ).
Regarding claim 6, Landau as modified by Colley and Fillipova disclose the limitations of claim 5 and Colley further teaches wherein the RNA sequence data is derived from protein-encoding genes (See Colley at least at Paras. [0238]-[0248], [1063]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Landau and Fillipova to incorporate the teachings of Colley and provide RNA sequence data from protein encoding genes. Colley is directed to cancer research and treatment systems. Incorporating the cancer research and models as in Colley with the cancer class determinations and type/degree comparisons as in Fillipova and the classifying of sequence fragments that represent tumor markers as in Landau would thereby increase the applicability, utility, and efficacy of the claimed multi-modal machine learning approaches for predicting cancer type.
Regarding claim 7, Landau as modified by Colley and Fillipova disclose the limitations of claim 1 and Landau further discloses wherein the first and second cancer type or degree comprises at least one of cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), cholangiocarcinoma (CHOL), uterine carcinosarcoma (UCS), or Gleason score (See Landau at least at Paras. [0033]; Fig. 17).
Regarding claim 8, Landau as modified by Colley and Fillipova disclose the limitations of claim 1 and Landau further discloses wherein the method is used to predict Luad/Lusc overall survival rate (See id. at least at Paras. [0025]-[0027], [0032]-[0033]; Fig. 17).
Regarding claim 9, Landau as modified by Colley and Fillipova disclose the limitations of claim 1 and Landau further discloses wherein determining the level of correlation comprises determining that the first and second types or degrees of cancer are the same and that an F1 score for the first machine learning model with respect to the first type or degree of cancer exceeds a first predetermined threshold and that an F1 score for the second machine learning model with respect the second type or degree of cancer exceeds a second predetermined threshold (See Landau at least at Paras. [0025]-[0026] (machine learning models and sequencing), [0031]-[0032] (“SNV classification performance for different machine learning models. F1 score was assessed on tumor-confirmed melanoma ctDNA SNV fragments vs. cfDNA artifacts from healthy controls […] Fragment-level ROC analysis for MRD-EDGE SNV classifier for different cancer types. Performance is assessed on post-quality filtered fragments (˜90% of low-quality cfDNA artifacts are excluded by quality filters).”), [0054], [0075]).
Regarding claim 10, Landau as modified by Colley and Fillipova disclose the limitations of claim 9 and Vladimirova further discloses wherein the F1 score threshold is at least 90% (See id.).
Regarding claims 11 and 12, claims 11 and 12 recite substantially the same limitations as included in independent claim 1. Thus, the claims are rejected under the same grounds of rejection and for the same reasoning applied to claim 1, above.
Regarding claim 13, claim 13 recites substantially the same limitations as included in claim 2. Thus, claim 13 is rejected under the same grounds of rejection and for the same reasoning applied to claim 2, above.
Regarding claims 15-18 and 19-20, claims 15-18 and 19-20 recite substantially the same limitations as included in claims 4-7 and 9-10, respectively. Thus, the claims are rejected under the same grounds of rejection and for the same reasoning applied to claims 4-7 and 9-10, above.
Claims 3 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Landau, in view of Colley, in view of Fillipova and further in view of U.S. 2020/0258223 A1 to Yip et al., hereinafter “Yip.”
Regarding claim 3, Landau as modified by Colley and Fillipova disclose the limitations of claim 1. The references may not specifically describe but Yip teaches wherein the second machine learning model comprises attention- based multiple instance learning (Attention MIL) or Resnet 18 (See Yip at least at Paras. [0017]-[0019] (“the imaging-based biomarker prediction systems are formed of deep learning frameworks having a single-scale configuration designed to perform classifications on (labeled or unlabeled) histopathology images using classifiers trained using multiple instance learning (MIL) techniques. In some examples, the single-scale configurations contain slide-level classifiers trained using gene sequencing data, such as RNA sequencing data.”)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Landau, Colley and Fillipova to incorporate the teachings of Yip and provide MIS for cancer determinations. Yip is directed to determining biomarkers from histopathology slide images. Incorporating the histopathology analysis techniques as in Yip with the cancer class determinations and type/degree comparisons of Fillipova, the cancer research and models as in Colley and the classifying of sequence fragments that represent tumor markers as in Landau would thereby increase the applicability, utility, and efficacy of the claimed multi-modal machine learning approaches for predicting cancer type.
Regarding claim 14, claim 14 recites substantially the same limitations as included in claim 3. Thus, claim 14 is rejected under the same grounds of rejection and for the same reasoning applied to claim 3, above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: U.S. 2020/0105413 A1 to Vladimirova, U.S. 2024/0368700 A1 to Tuller, U.S. 2025/0104827 A1 to Esteva
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/WILLIAM T. MONTICELLO/Examiner, Art Unit 3682
/FONYA M LONG/Supervisory Patent Examiner, Art Unit 3682
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