DETAILED ACTION
Notice of AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
2. Applicant’s remarks received on 11/10/2025 with respect to the amended independent claims have been acknowledged and are moot in view of a new ground of rejection necessitated by the corresponding amendment. Currently claims 1-3, 5-13, and 15-22 are rejected; and claim 4 and 14 are cancelled.
Response to Amendments
Objection
3. Claim 22 is objected to because of the following informalities. Appropriate correction is required.
Claim 22 identical to claim 21 should be a further limitation of claim 11 rather than claim 1.
Claim Rejections - 35 USC § 103
4. 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.
51066.. Claims 1-3, 5, 6, 10, 11-13, 15, 16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Avanaki et al (US Pub: 2020/0359887) and in further view of Zwanenburg et al (The Image Biomarker Standardization Initiative May, 2020) and Da-ano et al (Performance comparison of modified ComBat for harmonization of radiomic features for multicenter studies, 06/24/2020).
Regarding claim 1, Avanaki et al teaches: A method of radiomics standardization for patient scan data obtained by a particular imaging device, the method comprising: acquiring, using the particular imaging machine, the patient scan data; obtaining unstandardized radiomics for the patient scan data; recovering standardized radiomics for the patient scan data based on at least: the patient scan data, the unstandardized radiomics for the patient scan data, and outputting the standardized radiomics [p0051, p0054 (Generate raw/unstandardized optical radiomic features from OCT images and recover standardized/normalized radiomic features.)].
Avanaki et al does not recover standardized radiomics based on calibration phantom data for the particular machine obtained using at least one calibration phantom. In the same field of endeavor, Zwanenburg et al teaches: recover standardized radiomics based on calibration phantom data for the particular machine obtained using at least one calibration phantom [page 329: Study Design, Data sets; page 330: fig. 1 (Reference values for 174 base features derived from 3D digital phantom with a 74-voxel ROI mask is the calibrated phantom data.]. Zwanenburg compare and standardize radiomics feature extraction from patient scan data for constraining different software outputs by validating against phantom derived references. Therefore, it would have been obvious for an ordinary skilled in the art before the effective filing date of the claimed invention to combine the teaching of the two to include standardized radiomics based on calibration phantom data for improving output effect from different radiomics software/device.
Avanaki et al in view of Zwanenburg et al does not disclose inverting a model relating changes in imaging conditions to changes in radiomics. In the same field of endeavor, Da-ano et al teaches: wherein the recovering comprises inverting a model that relates changes in imaging conditions to changes in radiomics and outputting the standardized radiomics [page 2: ComBat approach description (Final center effect adjusted values. Removes learned center shift from standardization and maps back to adjusted radiomics value.)]. Therefore, it would have been obvious for an ordinary skilled in the art before the effective filing date of the claimed invention to combine the teaching of all to recover radiomics for better representation with common references.
Regarding claim 2, the rationale applied to the rejection of claim 1 has been incorporated herein. Zwanenburg et al teaches: The method of claim 1, wherein the particular imaging machine comprises at least one: x-ray machine, computed tomography machine, magnetic resonance imaging machine, or ultrasound machine [page 328: Material and Methods].
Regarding claim 3, the rationale applied to the rejection of claim 1 has been incorporated herein. Zwanenburg et al teaches: The method of claim 1, wherein the patient scan data comprises a two- dimensional slice of a three-dimensional volume constructed from raw patient scan data [page 331: table 1].
Regarding claim 5, the rationale applied to the rejection of claim 1 has been incorporated herein. Avanaki et al and Zwanenburg et al further teach: The method of claim 1, further comprising: providing the patient scan data and the calibration phantom data to a trained image property predictor [Avanaki: p0090-p0092]; and obtaining noise and resolution characteristics for the particular machine from the trained image property predictor; wherein the recovering the standardized radiomics comprises recovering the standardized radiomics based on the patient scan data, the unstandardized radiomics for the patient scan data, and the noise and resolution characteristics [Zwanenburg: page 332: fig. 2 (Denoising and interpolation involve noise and resolution characteristics.)].
Regarding claim 6, the rationale applied to the rejection of claim 1 has been incorporated herein. Avanaki et al in view of Zwanenburg et al further teaches: The method of claim 1, wherein the recovering the standardized radiomics comprises: providing the patient scan data, the unstandardized radiomics for the patient scan data, and the calibration phantom data for the particular machine to a machine learning model trained using a training corpus comprising radiomics in association with example scan data and calibration phantom data, whereby the machine learning model provides the standardized radiomics [Zwanenburg: p329: Materials and Methods, p330: fig. 1 (Standardization of radiomics takes place by incorporating calibrated digital phantom and derived radiomic features using CT image of a patient.)]. Therefore, given Avanaki et al’s prescription on providing patient scan data and optical radiomic features to neural network for identification and Zwanenburg et al’s process for standardizing radiomics features using radiomics from patient scan data and calibrated digital phantom, the combined teaching of the two would have made it obvious to a skilled in the art to apply a machine learning model to Zwanenburg et al’s data set to standardize radiomics for improving processing efficiency and accuracy.
Regarding claim 10, the rationale applied to the rejection of claim 1 has been incorporated herein. Avanaki et al further teaches: The method of claim 1, wherein the outputting comprises causing the standardized radiomics to be input to a radiomics model for clinical decision making [p0053].
Claims 11-13, 15, 16 and 20 have been analyzed and rejected with regard to claims 1-3, 5, 6 and 10 respectively.
61066.. Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Avanaki et al (US Pub: 2020/0359887), Zwanenburg et al (The Image Biomarker Standardization Initiative May, 2020), and Da-ano et al (Performance comparison of modified ComBat for harmonization of radiomic features for multicenter studies, 06/24/2020); and in further view of Zhou et al (US Pub: 20210035338) and Silverstein et al (US Pub: 2006/0279639).
Regarding claim 7, the rationale applied to the rejection of claim 1 has been incorporated herein. Zwanenburg deblurs a radiomics image [fig. 2]. Avanaki et al in view of Zwanenburg and Da-ano et al does not specify denoising or deconvolving. In the same field of endeavor, Zhou et al and Silverstein et al teaches: The method of claim 1, wherein the recovering comprises: deblurring an image corresponding to the patient scan data to produce a deblurred image; determining radiomics for the deblurred image; determining radiomics for noise of the deblurred image [Zhou: p0054, p0055]; and deconvolving the radiomics for the deblurred image with the radiomics for the noise of the deblurred image [Silverstein: p0029]. Therefore, it would have been obvious for an ordinary skilled in the art before the effective filing date of the claimed invention to combine the teaching of all to deconvolve to isolate radiomic signal.
Claim 17 has been analyzed and rejected with regard to claim 7.
71066.. Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Avanaki et al (US Pub: 2020/0359887), Zwanenburg et al (The Image Biomarker Standardization Initiative May, 2020), and Da-ano et al (Performance comparison of modified ComBat for harmonization of radiomic features for multicenter studies, 06/24/2020); and in further view of Zhou et al (US Pub: 20210035338).
Regarding claim 8, the rationale applied to the rejection of claim 1 has been incorporated herein. Zwanenburg deblurs a radiomics image in fig. 2. In the same field of endeavor, Zhou et al teaches: The method of claim 1, wherein the recovering comprises: passing an image corresponding to the patient scan data to a first machine learning model trained to deblur images to obtain a deblurred image; computing radiomics for the deblurred image; passing the radiomics for the deblurred image to a second machine learning model trained to remove noise, whereby the standardized radiomics are obtained [p0054, p0055]. Therefore, given Zhou et al’s prescription on deblur and denoise through DNN, it would have been obvious for an ordinary skilled in the art before the effective filing date of the claimed invention to combine the teaching of all to pass radiomics for deblur and denoise for improving output result.
Claim 18 has been analyzed and rejected with regard to claim 8.
81066.. Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Avanaki et al (US Pub: 2020/0359887), Zwanenburg et al (The Image Biomarker Standardization Initiative May, 2020), and Da-ano et al (Performance comparison of modified ComBat for harmonization of radiomic features for multicenter studies, 06/24/2020); and in further view of Madabhushi et al (US Pub: 2018/0342058) (Applicant submitted reference).
Regarding claim 9, the rationale applied to the rejection of claim 1 has been incorporated herein. Avanaki et al in view of Zwanenburg et al, and Da-ano et al does not specify a grey level co-occurrence matrix. In the same field of endeavor, Madabhushi et al teaches: The method of claim 1, wherein the radiomics comprise standardized radiomics comprise a grey-level co-occurrence matrix [p0052, p0053]. Therefore, it would have been obvious for an ordinary skilled in the art before the effective filing date of the claimed invention to combine the teaching of all to extract a grey level co-occurrence matrix feature as a target for standardization.
Claim 19 has been analyzed and rejected with regard to claim 9.
91066.. Claims 21 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Avanaki et al (US Pub: 2020/0359887), Zwanenburg et al (The Image Biomarker Standardization Initiative May, 2020), and Da-ano et al (Performance comparison of modified ComBat for harmonization of radiomic features for multicenter studies, 06/24/2020); and in further view of Ger et al (Comprehensive Investigation on Controlling for CT Imaging Variabilities in Radiomics Studies, August 29, 2018).
Regarding clam 21 (New), the rationale applied to the rejection of claim 1 has been incorporated herein. Avanaki et al in view of Zwanenburg et al and Da-ano et al does not disclose scanning a physical calibration phantom. In the same field of endeavor, Ger et al teaches: The method of claim 1, wherein the calibration phantom data for the particular machine is obtained by using the particular imaging machine to scan at least one physical calibration phantom [page 5: Result]. Therefore, given Ger et al’s prescription, it would have been obvious for an ordinary skilled in the art before the effective filing date of the claimed invention to combine the teaching of all to introduce scanning a physical calibration phantom for improving accuracy.
Regarding claim 22 (New), the rationale applied to the rejection of claim 11 has been incorporated herein. Claim 22 has been analyzed and rejected with regard to claim 21.
Conclusion
10. There is a new ground of rejection necessitated by the corresponding amendment 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 extension fee 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.
Contact
11. Any inquiry concerning this communication or earlier communications from the examiner should be directed to FAN ZHANG whose telephone number is (571)270-3751. The examiner can normally be reached on Mon-Fri 9:00-5:00.
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/Fan Zhang/
Patent Examiner, Art Unit 2682