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 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.
DETAILED ACTION
Response to Arguments
With regard to the 112 rejections, applicant has made amendments to some claims regarding the HLL, LLL, etc. However, due to the dependency of some of the claims, such clarifications do not carry over to other claims. Thus for those other claims, there is still the need to amend the claim to clarify the three letters. The examiner suggests that applicant amend all instances instead of only the first instance. Thus some 112 issues remain.
With regard to the art rejection, the examiner appreciates applicant’s explanation as to the differences between applicant’s invention and the references. Of note is applicant’s statement that the process first segments the metabolic scan and then extracts information from the anatomical scan. However, this is not reflected in the claims as in a method claim steps can occur in any order unless it is clear either explicitly or implicitly in the claim that one step must follow another. The examiner notes that the amended language merely maps both scans to each other and does not indicate the specific order applicant argues. The claims do not reflect any such step order and thus the argument is not persuasive. If applicant intends there to be an order, the examiner suggests that applicant explicitly provide the order of steps in a future amendment.
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.
Claims 12, 13, and 15 are 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. In particular the claims use HLL and LLL, LLH, and similar three letters without previously indicating what the letters stand for.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(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 1-8 and 18-19 are rejected under 35 USC 103 as being anticipated by US 2022/0398724, Anand (hereafter Anand)
1. A method of predicting propensity of metastasis of a tumor in a patient by at least one processor, the method comprising:
receiving, from a first scan modality, a first scan comprising a set of scan images depicting metabolic information; (Anand claim 1, functional modality, ¶4 PET/SPECT, PMSA scan – spec ¶82 illustrates this as metabolic information)
receiving, from a second scan modality, a second scan comprising a set of scan images depicting anatomical information; (Anand claim 1, scan on anatomical modality)
segmenting the first scan to identify a volumetric segment representing a suspected tumor, based on the depicted metabolic information; (Anand claim 2, voxels of the functional image)
registering the first scan with the second scan to map the volumetric segment identified in the first scan to a corresponding volumetric segment in the second scan (Anand ¶127 registering both images)
extracting one or more radiomics features from the second scan, corresponding to the volumetric segment, based on the depicted anatomical information; and (Anand claim 1, voxels of the anatomical image)
predicting propensity of metastasis of the suspected tumor, based on the one or more radiomics features. (Anand claim 1, predicting risk of disease status)
Claim 19 is similarly rejected.
2. The method of claim 1, further comprising determining at least one of a prognosis and a suggested course of treatment for the suspected tumor, based on the predicted propensity of metastasis. (Anand ¶3 recommended course of treatment)
3. The method of claim 1, wherein the second scan modality is a Positron Emission Tomography (PET) scan modality, and wherein the first scan modality is selected from a Computed Tomography (CT) scan modality, and a Magnetic Resonance Imaging (MRI) scan modality. (Anand ¶3 PET and SPECT scans (note SPECT is a type of CT scan))
4. The method of claim 1, wherein predicting propensity of metastasis of the suspected tumor comprises applying at least one machine-learning (ML) model on the extracted radiomics features, to produce a prediction of propensity of metastasis. (Anand claim 1, machine learning)
5. The method of claim 4, further comprising:
extracting, from the volumetric segment of the first scan, at least one metabolic feature representing the depicted metabolic information; and (Anand claim 2, volume corresponding to the functional image)
training the at least one ML model to produce a prediction of propensity of metastasis, based on at least one of: (a) the radiomics features and (b) the at least one metabolic feature. (Anand claim 1, ML voxels of the functional image)
6. The method of claim 5, further comprising:
applying a feature selection algorithm on a group of features comprising (a) the radiomics features and (b) the at least one metabolic feature, to select a subset of the group of features, based on the propensity of metastasis as predicted by the at least one ML model; and (Anand ¶113, voxels and clinical variables)
further training the at least one ML model based on the subset of the group of features, to produce a prediction of propensity of metastasis. (Anand ¶109, use of clinical data to train)
7. The method of claim 5, further comprising training the at least one ML model in an iterative process, wherein at least one iteration of the iterative process comprises:
receiving a first group of features selected from (a) the radiomics features and (b) the at least one metabolic feature; (Anand ¶113, voxels and clinical variables)
selecting a second group of features that is a subset of the first group; (Anand ¶116, secondary segmentation module operates on the initial VOI)
training the at least one ML model to produce a prediction of propensity of metastasis, based on the second group of features; and (Anand ¶116 training the ML model)
providing the second group of features as a first group of features for a subsequent iteration, based on propensity of metastasis as predicted by the at least one ML model. (Anand ¶116 using the features to predict)
8. The method of claim 4, further comprising:
extracting at least one metabolic feature, representing the metabolic information depicted in the volumetric segment of the first scan; (Anand claim 2, volume corresponding to the functional image)
receiving annotation data, representing propensity of metastasis, corresponding to at least one of the first scan and second scan; and (Anand ¶117 used known metastatic states)
training the at least one ML model to produce a prediction of propensity of metastasis, based on at least one of the extracted radiomics features and the at least one metabolic feature, according to the annotation data. (Anand ¶117 training the ML model)
18. The method of claim 1, wherein the volumetric segment is comprised within a prostate of the patient, and wherein the metabolic information represents a depicted intake of Prostate-Specific Membrane Antigen (PSMA) within the prostate. (Anand ¶4 targets PSMA)
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 of this title, 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 16-17 are rejected under 35 USC 103 as being unpatentable over Anand in view of Machine Learning Methods for Quantitative Radiomic Biomarkers, Parmar et al. (hereafter Paramar)
16. The method of claim 5, wherein the at least one ML model comprises a first ML model that is a binary classifier, and a second ML model that is a random forest ML model. Anand discloses using a binary classifier.
Anand does not disclose a random forest ML model. However Paramar page 3 discloses the use of well known classifiers including random forest. It would have been obvious to utilize one of a limited number of well-known ML models for purposes of being readily available for use.
17. The method of claim 16, wherein both the binary classifiers and the random forest ML models are trained to produce a prediction of propensity of metastasis, based on at least one of:
(a) the radiomics features and (b) the at least one metabolic feature, and wherein the method further comprises: (Anand ¶113-119 discusses using machine learning to predict likelihood of metastasis using two ML models)
arbitrating between prediction of propensity of metastasis of both ML models; and
producing a prediction of propensity of metastasis of the suspected tumor, based on the arbitration. (Anand ¶119 outputs a prediction and an AUC (expected accuracy) between the two models)
Claims 9-15 are rejected under 35 USC 103 as being unpatentable over Anand in view of Machine Learning Methods for Quantitative Radiomic Biomarkers, Parmar et al. (hereafter Paramar) in further view of Texture Analysis, SNAP, ESA available at https://seadas.gsfc.nasa.gov/help-9.0.0/operators/GLCM.html, hereafter (ESA)
Anand does not disclose
9. The method of claim 1, wherein extracting the radiomics features comprises:
computing a Grey Level Co-occurrence Matrix (GLCM), based on at least one image of the second scan;
and calculating at least one GLCM-based radiomics feature selected from a list consisting of a GLCM joint entropy feature, a GLCM joint energy feature, a GLCM difference entropy feature, a GLCM contrast feature, a GLCM sum squares feature, a GLCM difference average feature, a GLCM Inverse Difference feature, and a GLCM Inverse Difference Moment IDM feature.
Paramar page 2 discloses using GLCM as a common usage of extraction of radiomic features. Therefore, it would have been obvious to modify the system of Anand to utilize GLCM as a well-known and common method to extract radiomic features in images.
Anand and Paramar do not explicitly disclose:
and calculating at least one GLCM-based radiomics feature selected from a list consisting of a GLCM joint entropy feature, a GLCM joint energy feature, a GLCM difference entropy feature, a GLCM contrast feature, a GLCM sum squares feature, a GLCM difference average feature, a GLCM Inverse Difference feature, and a GLCM Inverse Difference Moment IDM feature.
However, ESA, discloses common functions of GLCM extraction, such as energy and entropy. As radiomics is concerned with the use of many features, it would have been obvious to utilize in the Anand/Paramar system many of the known GLCM extraction functions taught by ESA.
10. The method of claim 9, wherein extracting the radiomics features comprises applying one or more wavelet-based algorithms on the GLCM matrix, to produce at least one wavelet-based GLCM radiomics feature, selected from a list consisting of an HLL GLCM joint entropy feature, an HLL GLCM difference entropy feature, and an HLL GLCM sum entropy feature. (Paramar page 2 discloses wavelet based algorithms)
Anand does not disclose
11. The method of claim 1, wherein extracting the radiomics features comprises:
computing a Grey Level Run Length Matrix (GLRLM), based on at least one image of the second scan; and calculating a Normalized Gray Level Non-uniformity (GLNN) radiomics feature based on the GLRLM matrix.
Paramar page 2 discloses using GLRLM as a common usage of extraction of radiomic features. Therefore, it would have been obvious to modify the system of Anand to utilize GLCM as a well-known and common method to extract radiomic features in images.
Anand and Paramar do not explicitly disclose:
and calculating a Normalized Gray Level Non-uniformity (GLNN) radiomics feature based on the GLRLM matrix.
However, ESA, discloses common functions of GLCM extraction, such as variance. As radiomics is concerned with the use of many features, it would have been obvious to utilize in the Anand/Paramar system many of the known GLCM extraction functions taught by ESA.
12. The method of claim 11, wherein extracting the radiomics features comprises applying one or more wavelet-based algorithms on the GLRLM matrix, to produce at least one wavelet-based GLRLM radiomics feature, selected from a list consisting of an HLL GLRLM Normalized Gray Level Non-uniformity feature, an HLH GLRLM Short Run Emphasis feature, an HLH GLRLM Short Run High Gray Level Emphasis feature, an HLH GLRLM Long Run Low Gray Level Emphasis feature, and an HLH GLRLM Run Entropy feature. (ESA teaches variance)
Anand does not disclose
13. The method of claim 1, wherein extracting the radiomics features comprises computing a wavelet-based radiomics feature, based on at least one image of the second scan, wherein said wavelet-based radiomics feature is selected from a list consisting of an HLL first order median feature, an HLL first order Robust Mean Absolute Deviation feature, an HLL first order Mean Absolute Deviation feature, an HLL first order entropy feature, an HLL first order X-Percentile feature, and an LLL first-order uniformity feature.
Paramar page 2 discloses using GLCM as a common usage of extraction of radiomic features. Therefore, it would have been obvious to modify the system of Anand to utilize GLCM as a well-known and common method to extract radiomic features in images.
Anand and Paramar do not explicitly disclose:
wherein said wavelet-based radiomics feature is selected from a list consisting of an HLL first order median feature, an HLL first order Robust Mean Absolute Deviation feature, an HLL first order Mean Absolute Deviation feature, an HLL first order entropy feature, an HLL first order X-Percentile feature, and an LLL first-order uniformity feature.
However, ESA, discloses common functions of GLCM extraction, such as entropy. As radiomics is concerned with the use of many features, it would have been obvious to utilize in the Anand/Paramar system many of the known GLCM extraction functions taught by ESA.
Anand does not disclose
14. The method of claim 1, wherein extracting the radiomics features comprises computing a Grey Level Dependence Matrix (GLDM), based on at least one image of the second scan; and
calculating at least one GLDM-based radiomics feature based on the GLDM matrix, said GLDM-based radiomics feature selected from a list consisting of a GLDM grey level variance feature.
Paramar page 2 discloses using GLCM as a common usage of extraction of radiomic features. Therefore, it would have been obvious to modify the system of Anand to utilize GLCM as a well-known and common method to extract radiomic features in images.
Anand and Paramar do not explicitly disclose:
calculating at least one GLDM-based radiomics feature based on the GLDM matrix, said GLDM-based radiomics feature selected from a list consisting of a GLDM grey level variance feature.
However, ESA, discloses common functions of GLCM extraction, such as variance. As radiomics is concerned with the use of many features, it would have been obvious to utilize in the Anand/Paramar system many of the known GLCM extraction functions taught by ESA.
15. The method of claim 14, further comprising applying one or more wavelet-based algorithms on the calculated GLDM matrix, to produce corresponding wavelet-based GLDM radiomics features, selected from a list consisting of an LLH GLDM Small Dependence High Gray Level Emphasis feature, an HLH GLDM Small Dependence High Gray Level Emphasis feature, and an LLH GLDM Small Dependence Low Gray Level Emphasis feature. (ESA discloses contrast models/dissimilarity)
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ming Shui whose telephone number is (303)297-4247. The examiner can normally be reached on 7-5 Pacific Time, M-Th.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Greg Morse can be reached on 571-272-38383838. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Ming Shui/
Primary Examiner, Art Unit 2663