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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/26/2025 has/have been considered by the examiner.
Claim Objections
The claim 4 is objected to because they include reference characters which are not enclosed within parentheses.
Reference characters corresponding to elements recited in the detailed description of the drawings and used in conjunction with the recitation of the same element or group of elements in the claims should be enclosed within parentheses so as to avoid confusion with other numbers or characters which may appear in the claims. See MPEP § 608.01(m).
Claim 4 recites the abbreviation "DICOM" without its expanded text. The claims should be rewritten in such manner so as to recite "Digital Imaging and Communications in Medicine (DICOM)" in order to introduce the abbreviation along with its corresponding expanded text. Note the order of abbreviation with respect to its corresponding expanded text. Additionally, it is the abbreviation that should be enclosed by parentheses.
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.
Claim(s) 1, 3-5, 7, 10-12 and 19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bengtsson et al (US 20210401392 A1), hereinafter Bengtsson.
-Regarding claim 1, Bengtsson discloses a computer-implemented method for processing images, comprising (Abstract; FIGS. 1-14F; [0160]-[0161]): retrieving an input image to be processed (FIG. 2A, input image element 205; FIGS. 1, 2B-3, 6-8); determining a first value or set of values for an image metric associated with the input image ([0079], “geometric re-sampling … uniform pixel spacing (e.g., 1.0 mm)… intensity values of all images may be truncated to a specified range (e.g., −1000 to 3000 Hounsfield Unit) to remove noise and possible artifacts …”; [0092], “provide location labels for the corresponding anatomical regions …”); generating a first filter based on a relationship between the first value or set of values for the image metric and a target value or set of values for the image metric (FIG. 2A, Data Pre-processing 210; FIGS. 1, 2B-3, 6-8; [0079], “pre-processing subsystem 210 of the CNN architecture …”; [0063], “the CNN models can be trained using supervised training …”); applying the first filter to the input image to generate a working image (FIG. 2A, Data Pre-processing 210 (performing re-sampling (e.g., interpolating), uniform pixel spacing, truncating intensity values, removing noise and artifacts); FIGS. 1, 2B-3, 6-8; [0012]; [0050], “standardized images”; [0079]), the working image having a second value or set of values (for the image metric substantially similar to the target value or set of values for the image metric ( [0079], “… standardization of the spacing, slice thickness, and units ensures that each pixel has a consistent area and each voxel has a consistent volume across all images of the input image elements 205 …”); processing the working image using a standardized image processing methodology based on the target value or set of values for the image metric (FIG. 2A, output of block 210, blocks 215-230; FIGS. 1, 2B--3, 6-8); and generating an output based on the processed working image (FIG. 2A, image 235; FIGS. 1, 2B-3, 6-8).
-Regarding claim 3, Bengtsson discloses the method of claim 1. Bengtsson further discloses wherein the determination of the first value or set of values is based on acquisition parameters associated with the input image ([0079]).
-Regarding claim 4, Bengtsson discloses the method of claim 3. Bengtsson further discloses wherein the acquisition parameters are extracted from DICOM files associated with the retrieved input image ([0154], “the data set is comprised of a total of 1,139 pre-treatment PET/CT scans … stored in the DICOM format”; [0143]; [0141]).
-Regarding claim 5, Bengtsson discloses the method of claim 1. Bengtsson further discloses wherein the determination of the first value or set of values is based on visual characteristics of the retrieved input image ([0078]; [0079], “… image intensity …”).
-Regarding claim 7, Bengtsson discloses the method of claim 5. Bengtsson further discloses wherein the determination of the first value or set of values is based on a trained neural network independent of the standardized image processing methodology (FIG. 2A; [0079], “… pre-processing subsystem 210 of the CNN architecture … subsets of standardized images for slices (e.g., coronal, axial, and sagittal slices) or regions of the body ”; FIG. 6).
-Regarding claim 10, Bengtsson discloses the method of claim 1. Bengtsson further discloses wherein the standardized image processing methodology is a trained artificial intelligence based process, and wherein the target value or set of values for the image metric is an average value or set of values of the image metric calculated for training materials used to train the standardized image processing methodology (FIG. 2A; [0047]; [0048], “average SUV”; [0083]; [0093], “the classifier combines … averages pixel and/or voxel values … to generate a final masked image …”).
-Regarding claim 11, Bengtsson discloses the method of claim 10. Bengtsson further discloses wherein the standardized image processing methodology is a convolutional neural network, and wherein the standardized image processing methodology is used for denoising, segmenting, or classifying a target image (FIGS. 1, 2A-2B, 6; [0059]).
-Regarding claim 12, Bengtsson discloses the method of claim 11. Bengtsson further discloses wherein the standardized image processing methodology is tuned based on a hypothetical target image having the target value or set of values for the image metric ([0080], “… each CNN model is trained for processing images originating from different slices (coronal, axial, and sagittal slices) or regions of the body …”; [0143]-[0144]).
-Regarding claim 19, Bengtsson discloses a computer-implemented system for processing images, comprising (Abstract; FIGS. 1-14F; [0160]-[0161]): a memory that stores a plurality of instructions; and processor circuitry that couples to the memory and is configured to execute the plurality of instructions to (FIG. 1; [0023]-[0025]): retrieve an input image to be processed (FIG. 2A, input image element 205; FIGS. 1, 2B-3, 6-8); determine a first value or set of values for an image metric associated with the input image ([0079], “geometric re-sampling … uniform pixel spacing (e.g., 1.0 mm)… intensity values of all images may be truncated to a specified range (e.g., −1000 to 3000 Hounsfield Unit) to remove noise and possible artifacts …”; [0092], “provide location labels for the corresponding anatomical regions …”); generate a first filter based on a relationship between the first value or set of values for the image metric and a target value or set of values for the image metric (FIG. 2A, Data Pre-processing 210; FIGS. 1, 2B-3, 6-8; [0079], “pre-processing subsystem 210 of the CNN architecture …”; [0063], “the CNN models can be trained using supervised training …”); apply the first filter to the input image to generate a working image (FIG. 2A, Data Pre-processing 210 (performing re-sampling (e.g., interpolating), uniform pixel spacing, truncating intensity values, removing noise and artifacts); FIGS. 1, 2B-3, 6-8; [0012]; [0050], “standardized images”; [0079]), the working image having a second value or set of values (for the image metric substantially similar to the target value or set of values for the image metric ( [0079], “… standardization of the spacing, slice thickness, and units ensures that each pixel has a consistent area and each voxel has a consistent volume across all images of the input image elements 205 …”); process the working image using a standardized image processing methodology based on the target value or set of values for the image metric (FIG. 2A, output of block 210, blocks 215-230; FIGS. 1, 2B--3, 6-8); and generate an output based on the processed working image (FIG. 2A, image 235; FIGS. 1, 2B-3, 6-8).
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(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bengtsson et al (US 20210401392 A1), hereinafter Bengtsson in view of Aoki et al (US 20120121127 A1), hereinafter Aoki.
-Regarding claim 2, Bengtsson discloses the method of claim 1.
Bengtsson does not disclose generating the output image by applying a second filter to the working image after processing, the second filter being an inverse of the first filter.
In the same field of endeavor, Aoki teaches a calibration process implemented by capturing a marker disposed in a predetermined position within a camera capturing range with the camera and using the position of the marker on the captured image (Aoki: Abstract; FIGS. 1-16). Aoki further teaches generating the output image by applying a second filter to the working image after processing, the second filter being an inverse of the first filter (Aoki: FIG.1, inverting unit 236; [0046], “acquires the position having the peak response value of the filter on the standardization-converted image … inverts the standardization-converted image to the grayscale image … having the peak response value of the filter on the standardization-converted image …”; [0076]).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Bengtsson with the teaching of Aoki by applying a second filter to the working image after processing in order to obtain a position on the standardized image with a peak response value of the filter and determines the position as a feature for calibration process.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bengtsson et al (US 20210401392 A1), hereinafter Bengtsson in view of April et al (WO 2019113712 A1), hereinafter April.
-Regarding claim 9, Bengtsson discloses the method of claim 1.
Bengtsson does not disclose wherein the image metric is defined by one of sharpness of the image.
In the same field of endeavor, April teaches a method for processing a digital magnetic resonance (MR) image volume in an image data set using intensity standardization to provide standardized MR image slices. April further teaches wherein the image metric is defined by one of sharpness of the image (April: [00158]; [00159], “image features … image sharpness …”; [00188]).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Bengtsson with the teaching of April by using an image metric that is defined by one of sharpness of the image in order to obtain denoised and bias field corrected standardized image (April: [00159]).
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bengtsson et al (US 20210401392 A1), hereinafter Bengtsson in view of Hiasa et al (US 20240087086 A1), et al, hereinafter Hiasa.
-Regarding claim 13, Bengtsson discloses the method of claim 1.
Bengtsson does not disclose wherein the image metric is a modulation transfer function for the image, and the relationship between the first value or set of values and the target value or set of values is defined by the shape of the modulation transfer function relative to a Nyquist frequency of an image grid of the corresponding image.
In the same field of endeavor, Hiasa teaches a method to improve the accuracy of processing for reducing a sampling pitch of a captured image (Hiasa: Abstract; FIGS. 1A-13). Hiasa further teaches wherein the image metric is a modulation transfer function for the image, and the relationship between the first value or set of values and the target value or set of values is defined by the shape of the modulation transfer function relative to a Nyquist frequency of an image grid of the corresponding image (Hiasa: FIGS. 1A-1B; [0029]; [0050]; [0055]).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Bengtsson with the teaching of Hiasa by using the image metric that is a modulation transfer function for the image, and the relationship between the first value or set of values and the target value or set of values is defined by the shape of the modulation transfer function relative to a Nyquist frequency of an image grid of the corresponding image in order to obtained denoised standardized image with better resolution (Hiasa: FIG. 10; [0027]).
Claim(s) 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bengtsson et al (US 20210401392 A1), hereinafter Bengtsson in view of Kapoor et al (US 20230325982 A1), hereinafter Kapoor.
-Regarding claim 16, Bengtsson discloses the method of claim 1.
Bengtsson discloses wherein the standardized image processing methodology is a denoising process ([0079], “remove noise and possible artifacts …”).
Bengtsson does not disclose denoising based on noise-power spectrum (NPS) of corresponding image.
In the same field of endeavor, Kapoor teaches a method for processing image data and applying a filter on the image data to generate filtered image data, the filter being configured to suppress adversarial perturbations within the image data. The filtered image data is processed using a machine-learning model (Kapoor: FIG. 1a-3n). Kapoor further teaches denoising based on noise-power spectrum (NPS) of corresponding image (Kapoor: [0136], “real-world denoising applications is now to estimate the clean signal power spectrum and the noise power spectrum …” ).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Bengtsson with the teaching of Kapoor by using noise-power spectrum (NPS) in order to provide a metric for denoising.
-Regarding claim 17, Bengtsson discloses the method of claim 1.
Bengtsson discloses wherein the standardized image processing methodology is a segmentation process (FIG. 2a) and wherein the image metric is based on a resolution ([0066], “sharpen image resolution”; [0081]; [0088]).
Bengtsson does not disclose image segmentation based on a signal to noise ratio (SNR) of corresponding image.
In the same field of endeavor, Kapoor teaches a method for processing image data and applying a filter on the image data to generate filtered image data, the filter being configured to suppress adversarial perturbations within the image data. The filtered image data is processed using a machine-learning model (Kapoor: FIG. 1a-3n). Kapoor further teaches image segmentation based on a signal to noise ratio (SNR) of corresponding image (Kapoor: FIG. 3b; [0026]; [0123]; [0131]; [0132], “The image x is fed as an input to a semantic segmentation neural network …”; [0134]; [0136], “estimate the clean signal power spectrum and the noise power spectrum, or, alternatively, the signal-to-noise ratio …”; equations (11)-(13)).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Bengtsson with the teaching of Kapoor by using signal-to-noise ratio in order to provide a metric for denoising and segmentation.
-Regarding claim 18, Bengtsson discloses the method of claim 1.
Bengtsson discloses wherein the standardized image processing methodology is a classification process (FIGS. 1, 2a) and wherein the image metric is based on a resolution ([0066], “sharpen image resolution”; [0081]; [0088]).
Bengtsson does not disclose image classification based on a signal to noise ratio (SNR) of corresponding image and a field of view (FOV) of the corresponding image.
In the same field of endeavor, Kapoor teaches a method for processing image data and applying a filter on the image data to generate filtered image data, the filter being configured to suppress adversarial perturbations within the image data. The filtered image data is processed using a machine-learning model (Kapoor: FIG. 1a-3n). Kapoor further teaches image classification based on a signal to noise ratio (SNR) of corresponding image (Kapoor: FIG. 3b; [0026], “processing of image data … this kind of (Wiener) filter can also be used in other use cases … image classification tasks …”; [0086]; [0123]; [0136], “estimate the clean signal power spectrum and the noise power spectrum, or, alternatively, the signal-to-noise ratio …”; equations (11)-(13)) and a field of view (FOV) of the corresponding image (Kapoor: [0076], “image data may be … the adversarial perturbations over the field of view of the camera used for obtaining the image data”).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Bengtsson with the teaching of Kapoor by using signal-to-noise ratio in order to provide a metric for denoising and segmentation.
Allowable Subject Matter
Claims 6, 8 and 14-15 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
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/XIAO LIU/Primary Examiner, Art Unit 2664
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