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 .
Request for Continued Examination
2. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17© has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant’s submission filed on July 01, 2026 has been entered.
Response to Arguments
3. Applicant’s remarks received on 05/06/2026 with respect to the amended independent claims have been acknowledged and are moot in view of a new ground of rejected necessitated by the corresponding amendment. Currently claims 1-3, 5-13, and 15-22 are rejected and claims 4 and 14 are cancelled.
Response to Amendment
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, 2, 6, 11, 12, 16, 21, and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Zhovannik et al (Learning from scanners: Bias reduction and feature correction in radiomics, 2019) and in further view of Andrearczyk et al (Neural network training for cross protocol radiomic feature standardization in computed tomography, 2019) and Robins et al (Systematic analysis of bias and variability of texture measurements in computed tomography, 2019).
Regarding claim 1 (Currently Amended), Zhovannik et al teaches: A method of radiomics standardization for patient scan data obtained by a particular imaging device [abstract], the method comprising: acquiring, using the particular imaging machine, the patient scan data; obtaining unstandardized radiomics for the patient scan data [page 34: 2.2]; 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 calibration phantom data for the particular machine obtained using at least one calibration phantom [page 34: 2.1, 2.3], wherein the recovering comprises inverting an imaging model that relates changes in imaging conditions to changes in radiomics, and outputting the standardized radiomics [page 34: p02, 2.3 (apply a model derived correction to reverse predicted radiomic bias.)].
Zhovannik et al does not explicitly exemplify on a specific patient. In the same field of endeavor, Andrearczyk et al teaches phantom-trained standardized radiomics, basically obtaining unstandardized radiomics and using phantom scan data to learn standardization, and apply the standardization transformation to radiomics extracted from patient scan data in [page 2: p05; page 3: 2.1; page 12: conclusion]. 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 convert observed radiomics into scanner stabilized standardized radiomics.
Zhovannik et al in view of Andrearczyk et al does not quantify noise and resolution. In the same field of endeavor, Robins et al teaches: wherein the imaging model quantifies noise and resolution of the particular imaging machine [abstract, page 3: p01, p02]. Therefore, given Zhovannik et al in view of Andreaczyk et al’s teaching on modeling radiomic feature value dependency on a scanner condition and applying correction factor to measure d radiomics to correspond a corrected value to a reference condition; and Robins’ teaching on characterizing CT imaging system using NPS for noise and TTF for spatial resolution, 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 compensate radiomic changes caused by machine noise and resolution.
Regarding claim 2 (Original), rationale applied to the rejection of claim 1 has been incorporated herein. Zhovannik et al further 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 [abstract].
Regarding claim 6 (Original), the rationale applied to the rejection of claim 1 has been incorporated herein. Andreaczyk 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 [page 4: 2.5, page 12: conclusion, fig. 4 (providing observed radiomic features from patient data to a trained CNN to obtain standardized features.)].
Claims 11, 12, and 16 have been analyzed and rejected with regard to claims 1, 2, and 6 respectively.
Regarding clam 21 (previously presented), the rationale applied to the rejection of claim 1 has been incorporated herein. Zhovannik et al further 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 34: p03; page 36: p01].
Regarding claim 22 (currently amended), the rationale applied to the rejection of claim 1 has been incorporated herein. Claim 22 has been analyzed and rejected with regard to claim 21.
61066.. Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Zhovannik et al (Learning from scanners: Bias reduction and feature correction in radiomics, 2019), Andrearczyk et al (Neural network training for cross protocol radiomic feature standardization in computed tomography, 2019), and Robins et al (Systematic analysis of bias and variability of texture measurements in computed tomography, 2019); and in further view of Vasanawala et al (Practical parallel imaging compressed sensing MRI: summary of two years of experience in accelerating body MRI of pediatric patients) (Applicant supplied reference).
Regarding claim 3 (Original), rationale applied to the rejection of claim 1 has been incorporated herein. Zhovannik et al in view of Andrearczyk et al and Robins et al does not specify 2D or 3D from raw scan data. In the same field of endeavor, Vasanawala 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 [abstract, page 4: 3.3, page 10: fig. 3]. 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 feed 2D slice of 3D volume from patient scan data with reduced scanner related variation for improved response from the model.
Claim 13 has been analyzed and rejected with regard to claim 3.
71066.. Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Zhovannik et al (Learning from scanners: Bias reduction and feature correction in radiomics, 2019), Andrearczyk et al (Neural network training for cross protocol radiomic feature standardization in computed tomography, 2019), and Robins et al (Systematic analysis of bias and variability of texture measurements in computed tomography, 2019); and in further view of Patwari et al (Measuring CT Reconstruction Quality with Deep Convolutional Neural Networks, 2019).
Regarding claim 5 (Original), the rationale applied to the rejection of claim 1 has been incorporated herein. Zhovannik et al in view of Andreaczyk et al and Robins et al does not specify a trained image predictor. In the same field of endeavor, Patwari et al teaches: The method of claim 1, further comprising: providing the patient scan data and the calibration phantom data to a trained image property predictor; 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 [abstract, 2.3, 2.4]. Therefore, it would have been obvious to combine the teaching of all to apply known CNN prediction and phantom trained standardization techniques to Zhovannik et al’s scanner specific radiomics correction technique by using Robins et al’s NPS and TTF as properties to be predicted to improve correction for noise and resolution.
Claim 15 has been analyzed and rejected with regard to claim 5.
81066.. Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Zhovannik et al (Learning from scanners: Bias reduction and feature correction in radiomics, 2019), Andrearczyk et al (Neural network training for cross protocol radiomic feature standardization in computed tomography, 2019), and Robins et al (Systematic analysis of bias and variability of texture measurements in computed tomography, 2019); 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. Zhovannik et al in view of Andrearczyk et al and Robins 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.
91066.. Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Zhovannik et al (Learning from scanners: Bias reduction and feature correction in radiomics, 2019), Andrearczyk et al (Neural network training for cross protocol radiomic feature standardization in computed tomography, 2019), and Robins et al (Systematic analysis of bias and variability of texture measurements in computed tomography, 2019); 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. Zhovannik et al in view of Andrearczyk et al and Robins et al does not specify deblurring images. 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.
101066.. Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Zhovannik et al (Learning from scanners: Bias reduction and feature correction in radiomics, 2019), Andrearczyk et al (Neural network training for cross protocol radiomic feature standardization in computed tomography, 2019), and Robins et al (Systematic analysis of bias and variability of texture measurements in computed tomography, 2019); 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. Zhovannik et al in view of Andrearczyk et al and Robins 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.
11.1066. Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhovannik et al (Learning from scanners: Bias reduction and feature correction in radiomics, 2019), Andrearczyk et al (Neural network training for cross protocol radiomic feature standardization in computed tomography, 2019), and Robins et al (Systematic analysis of bias and variability of texture measurements in computed tomography, 2019); and in further view of Da-ano et al (Performance comparison of modified ComBat for harmonization of radiomic features for multicenter studies, 06/24/2020).
Regarding claim 10 (Original), the rationale applied to the rejection of claim 1 has been incorporated herein. Andreaczyk 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 [page 12: conclusion; page 2: p05]. In the same field of endeavor, Da-ano further teaches it in [page 4: Experiments and analysis]. Therefore, the combined teaching of all would have been obvious to a skilled in the art to apply standardized radiomics to clinical model for more accurate prediction.
Claim 20 (previously presented) has been analyzed and rejected with regard to claim 10.
Contact
12. 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
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