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 .
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, 365(c), or 386(c) is acknowledged.
Applicant’s Response
In Applicant’s Response dated 1/19/26, the Applicant argued Claims 1-12 previously rejected in the Office Action dated 7/30/25. Claims 1-12 are pending examination.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-5, 8-12 are rejected under 35 U.S.C. 102(a)(2) as being anticipate by Rathore et al., “Radiopathomics: Integration of radiographic and histologic characteristics for prognostication in glioblastoma” (hereinafter “Rathore”).
Claim 1:
Rathore discloses:
A method, comprising:
using a first machine learning model to generate a first medical prediction associated with a lesion in a medical scan using one or more intra-lesional radiomic features associated with the lesion and one or more peri-lesional radiomic features associated with a peri-lesional region around the lesion (see page 2-3 “Introduction” and page 4 “2.4. Feature Extraction”). Rathore teaches using an algorithm to extract features and make prediction based on the medical images of tumors using the radiomic features;
using a second machine learning model to generate a second medical prediction associated with the lesion using one or more pathomic features associated with the lesion (see page 2-3 “Introduction” and page 4 “2.4. Feature Extraction”). Rathore teaches using an algorithm to extract features and make prediction based on the medical images of tumors using the pathomic features. Both are independently analyzed to make independent predictions; and
generating a combined medical prediction associated with the lesion using the first medical prediction and the second medical prediction as inputs to a third model (see page 6 “3.2 performance of the radiopathomic classification model”). Rathore teaches generating a combined medical prediction using a third model could facilitate characterization of the heterogeneity of brain tumors across the entire breadth of the tissue specimen or radiographic appearance of the tumor. These tools can help in patient stratification into appropriate treatments, and identification of patients with relatively highly heterogeneous tumors, who would benefit from more extensive histopathological and molecular analysis through multiple samples, as well as by combination treatments.
Claim 2:
Rathore discloses:
A method, comprising:
inputting, to a first machine learning model, one or more intra-lesional radiomic features associated with a lesion in a medical scan and one or more peri-lesional radiomic features associated with a peri-lesional region around the lesion, the first machine learning model having been pre-trained to make a medical prediction based on the one or more intra-lesional radiomic features and the one or more peri-lesional radiomic features (see page 2-3 “Introduction” and page 4 “2.4. Feature Extraction”). Rathore teaches using an algorithm to extract features and make prediction based on the medical images of tumors using the radiomic features. Apply supervised learning algorithm on multiple data streams to exploit the complementary information provided by these imaging sequences for improved prognostication.;
receiving a first medical prediction associated with the lesion from the first machine learning model in response to said inputting (see page 2-3 “Introduction” and page 4 “2.4. Feature Extraction”). Rathore teaches applying supervised learning algorithm on multiple data streams to exploit the complementary information provided by these imaging sequences for improved prognostication.;
inputting, to a second machine learning model, one or more pathomic features associated with the lesion, the second machine learning model having been pre-trained to make a medical prediction based on the one or more pathomic features (see page 2-3 “Introduction” and page 4 “2.4. Feature Extraction”). Rathore teaches using an algorithm to extract features and make prediction based on the medical images of tumors using the pathomic features. Both are independently analyzed to make independent predictions;
receiving a second medical prediction associated with the lesion from the second machine learning model in response to said inputting (see page 2-3 “Introduction” and page 4 “2.4. Feature Extraction”). Rathore teaches applying supervised learning algorithm on multiple data streams to exploit the complementary information provided by these imaging sequences for improved prognostication; and
generating a combined medical prediction associated with the lesion using the first medical prediction and the second medical prediction as inputs to a third model (see page 6 “3.2 performance of the radiopathomic classification model”). Rathore teaches generating a combined medical prediction using a third model could facilitate characterization of the heterogeneity of brain tumors across the entire breadth of the tissue specimen or radiographic appearance of the tumor. These tools can help in patient stratification into appropriate treatments, and identification of patients with relatively highly heterogeneous tumors, who would benefit from more extensive histopathological and molecular analysis through multiple samples, as well as by combination treatments.
Claim 3:
Rathore discloses:
wherein the first machine learning model and the second machine learning model are different from one another (page 5 “2.5 Machine learning and correlation analysis”). Rathore teaches trained separate support vector regression (SVR) models in a LOOCV configuration for the prediction of survival with continuous values.
Claim 4:
Rathore discloses:
wherein the lesion comprises a solid tumor (see page 1 “Abstract”). Rathore teaches images captured from tissue samples are currently acquired as standard clinical practice for glioblastoma tumors.
Claim 5:
Rathore discloses:
wherein the combined medical prediction comprises a combined prognosis (see page 2 “Introduction” and page 6 “3.2 performance of the radiopathomic classification model”). Rathore teaches generating a combined medical prediction using a third model could facilitate characterization of the heterogeneity of brain tumors across the entire breadth of the tissue specimen or radiographic appearance of the tumor. The combined evaluation of Rad and Path images will even further improve prognostication, and will enhance our understanding of the disease.
Claim 8:
Rathore discloses:
wherein the third model comprises a machine learning model (see page 2 “Introduction” and page 6 “3.2 performance of the radiopathomic classification model”). Rathore teaches generating a combined medical prediction using a third model could facilitate characterization of the heterogeneity of brain tumors across the entire breadth of the tissue specimen or radiographic appearance of the tumor.
Claim 9:
Although Claim 9 is a non-transitory machine readable medium, it is interpreted and rejected for the same reasons as the method of Claim 2.
Claim 10:
Rathore discloses:
wherein the combined medical prediction concerns treatment of the lesion (see Page 1 “Abstract”, “Introduction” and Page 8 “Discussion”). Rathore teaches the combination of radiomic images/prediction and patholic images/predictions to diagnose and generate treatment plans.
Claim 11:
Rathore discloses:
wherein the combined medical prediction concerns diagnosis of the lesion (see Page 1 “Abstract”, “Introduction” and Page 8 “Discussion”). Rathore teaches the combination of radiomic images/prediction and patholic images/predictions to diagnose and generate treatment plans for the tumors.
Claim 12:
Rathore discloses:
wherein the combined medical prediction concerns monitoring of the lesion (Page 8 “Discussion”). Rathore teaches having a panel of computational tools combining histology and radiographic sequences is likely to improve our ability to target the right patients with the right treatments, and to monitor response over the whole course of the disease.
Claim Rejections - 35 USC § 103
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.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Rathore, in view of Lim et al., WO 2017014694 (hereinafter “Lim”).
Claim 6:
Rathore fails to expressly disclose disease-free survival (DFS), non-DFS, or a likelihood of DFS.
Lim discloses:
wherein the combined medical prediction is one of disease-free survival (DFS), non-DFS, or a likelihood of DFS (see paragraph [0087]). Lim teaches predicting a disease free survival prediction.
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention was made to modify the method disclosed by Rathore to include a DFS prediction for the purpose of efficiently determining characteristics of the tumor/disease, as taught by Lim.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Rathore, in view of Land et al., WO 2018098241 (hereinafter “Land”)
Claim 7:
Rathore fails to expressly the combined model being a nomogram.
Land discloses:
wherein the third model comprises a nomogram (see page 40 lines 13-19). Land teaches a combined nomogram model for the prediction.
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention was made to modify the method disclosed by Rathore to include a nomogram for the combined prediction model for the purpose of efficiently making more accurate predictions, as taught by Land.
Response to Arguments
Applicant's arguments filed 1/19/26 have been fully considered but they are not persuasive.
35 USC 102 Rejections
Applicant argues First, as a threshold issue, the Examiner asserts on the Form 892 that Rathore was published in 2018. However, the Examiner has proffered no evidence that this is the case. The copy of the article in the application file contains no publication data at all, i.e., no indication of where or when the article was published. Therefore, Applicant respectfully requests that the Examiner either substantiate the publication date of Rathore or withdraw the rejection.
The Applicant disagrees.
Although the Examiner inadvertently documents the wrong date of 2018 instead of 2019, Rathore is still prior art. The claims are still rejected under Rathore under 35 UC 102(a)(2). This is an oversight by the Examiner but the claims remain rejected under Rathore. The Examiner did include a date of submission for the article in the magazine. See attached.
Applicant argues Notably, each of these claims recites using three models: radiomic features are input to a first model, pathomic features are input to a second model, and a medical prediction is made by a third model, which receives the outputs of the other two models and generates a combined medical prediction. That is not the case in Rathore. Rather, in Rathore, the reference speaks of constructing a single regression model that receives all forms of data (e.g., both rad and path image types). See, e.g., 3.1-3.2 of Rathore (stating that "both the image types were used together in the regression model" (emphasis added)). Thus, Applicant respectfully submits that, for at least these reasons, Rathore does not render these claims obvious.
The Examiner disagrees.
Rathore teaches an algorithm to extract pathomic features for a prediction, and an algorithm to extract radiomic features for a prediction. The claims does not specify if the type of output from the model and using the broadest reasonable interpretation, the output could be features. Input an image and output features. The claim only uses the machine learning model as an algorithm to output predictions, which is what Rathore teaches. An algorithm to output pathomic and radiomic features. The claim does not impose any limits on how the data is output or require any particular components that are used to output the prediction data. The claims also does not train the machine learning models, therefore the trained machine learning models are simply used to output data, which is what Rathore algorithm does, it output features from pathomic images and outputs features from radiomic images uses those features from both types of images as input into the prediction model to produce another output. Thus, Rathore
Applicant argues Applicant respectfully submits that, whatever Lim may disclose, it does not remedy the deficiencies of Rathore. Accordingly, for at least the above reasons, Applicant respectfully requests that the rejections be withdrawn.
The Examiner agrees.
Lim is not used to teaches the argued limitations of Claim 1.
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TIONNA M BURKE whose telephone number is (571)270-7259. The examiner can normally be reached M-F 8a-4p.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Stephen Hong can be reached at (571)272-4124. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TIONNA M BURKE/Examiner, Art Unit 2178 4/8/26
/STEPHEN S HONG/Supervisory Patent Examiner, Art Unit 2178
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