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) filed on 11/13/2024 was considered and placed on the file of record by the examiner.
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, 10, 11, 15, 18, 26, 47, 49 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Golden et al. (US 2020/0380675).
Regarding claim 1, Golden teaches a computer-implemented method of processing magnetic resonance (MR) imaging data, the method being performed by a computer processing system and comprising:
receiving MR imaging data for a region of interest in a body of a human or animal subject (see para. 0422, 0441, Golden discusses receiving MRI image data of body parts);
inputting the MR imaging data to a trained machine learning model (see para. 0444-0445, Golden discusses machine learning algorithm may be trained on an annotated dataset of images);
operating the trained machine learning model to generate location data representative of a probability of cancer at a location in the region of interest (see para. 0413, Golden discusses trained CNN model for classification of cancerous lesions; see para. 0480, 0484, Golden discusses predicting the likelihood of lung cancer); and
processing the location data to generate a human-readable image of the region of interest indicative of a probability of cancer at the location (see para. 0030, Golden discusses each image of the image data, set the class of each pixel to a foreground cancerous anatomical structure class when the cancerous class probability for the pixel is at or above a determined threshold, and set the class of each pixel to a background class when the cancerous class probability for the pixel is below a determined threshold; and store the set classes as a label map; see para. 0480, 0484, Golden discusses predicting the likelihood of lung cancer).
Regarding claim 5, Golden teaches wherein the location data comprises, for each of one or more MR images in the MR imaging data, a respective probability map comprising, for each of a plurality of pixels in the MR image, data indicative of a probability of the region of the body containing cancer at a location corresponding to the respective pixel (see para. 0030, Golden discusses each image of the image data, set the class of each pixel to a foreground cancerous anatomical structure class when the cancerous class probability for the pixel is at or above a determined threshold, and set the class of each pixel to a background class when the cancerous class probability for the pixel is below a determined threshold; and store the set classes as a label map; see para. 0480, 0484, Golden discusses predicting the likelihood of lung cancer).
Regarding claim 10, Golden teaches further comprising processing the location data to generate, for at least one of the one or more MR images in the MR imaging data, a lesion detection map comprising, for each pixel in the MR image, a binary indicator having a first value when the respective pixel is determined to correspond to a location containing cancer, and having a second value when the respective pixel is determined to correspond to a location not containing cancer (see para. 0291, Golden discusses CNN can be trained as a binary classifier to classify images of lesions as benign or malignant. The final output of such a network typically has only a single scalar value: the probability that a lesion is malignant, from 0 to 1).
Regarding claim 11, Golden teaches wherein generating the lesion detection map comprises: for each of a plurality of voxels extracted from the one or more MR images in the MR imaging data, comparing data indicative of a probability of a location corresponding to said voxel containing cancer to a voxel-level threshold value; and determining one or more candidate lesions, each candidate lesion comprising a group of adjacent voxels having associated probabilities that exceed the voxel-level threshold value. (see para. 0030, Golden discusses each image of the image data, set the class of each pixel to a foreground cancerous anatomical structure class when the cancerous class probability for the pixel is at or above a determined threshold, and set the class of each pixel to a background class when the cancerous class probability for the pixel is below a determined threshold; and store the set classes as a label map).
Regarding claim 15, Golden teaches wherein generating the lesion detection map comprises: assigning a binary indicator having the first value to each voxel of each remaining candidate lesion; and for an MR image of the one or more MR images, determining which of the voxels associated with a binary indicator having the first value correspond to pixels in the MR image, and projecting the binary indicators associated with said voxels onto said corresponding pixels of the MR image (see para. 0291, Golden discusses a binary classifier to classify image of lesions as benign or malignant).
Regarding claim 18, Golden teaches wherein the trained machine learning model comprises a generative model trained using a generative adversarial network (see para. 0301, Golden discusses training performed using a generative adversarial network).
Regarding claim 26, Golden teaches a method of training a machine learning model for processing magnetic resonance (MR) imaging data, the method being performed by a computer processing system and comprising:
receiving training data comprising, for each of a plurality of human or animal subjects, respective MR imaging data for a region of interest in the body of the human or animal subject and associated diagnosis data comprising an indication of whether the MR imaging data represents clinically significant cancer in the region of interest (see para. 0413, Golden discusses trained CNN model for classification of cancerous lesions; see para. 0480, 0484, Golden discusses predicting the likelihood of lung cancer); and
using the training data to train a machine learning model that is arranged to receive, as input, MR imaging data of the region of interest in a body of a human or animal subject, and to generate, as output, location data representative of a probability of cancer at a location in the region of interest (see para. 0291, Golden discusses CNN can be trained as a binary classifier to classify images of lesions as benign or malignant. The final output of such a network typically has only a single scalar value: the probability that a lesion is malignant, from 0 to 1).
Claim 47 is rejected as applied to claim 1 as pertaining to a corresponding computer-readable storage medium.
Regarding claim 49, Golden teaches wherein the trained machine learning model comprises a plurality of individually-trained models, and wherein operating the trained machine learning model to generate location data comprises ensembling respective outputs from the plurality of individually-trained models (see para. 0147, Golden discusses multiple models trained in different ways are ensembled, as each model may pick up on different cancerous anatomical structures).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 2, 16, 34-36 are rejected under 35 U.S.C. 103 as being unpatentable over Golden et al. (US 2020/0380675) in view of Sunoqrot ”Automated reference tissue normalization of T2‑weighted MR images of the prostate using object recognition.”
Regarding claim 2, Golden does not expressly disclose wherein the region of interest comprises at least a portion of a prostate. However, Sunoqrot teaches wherein the region of interest comprises at least a portion of a prostate (see page 309, Sunoqrot discusses segmenting a prostate region to perform prostate cancer diagnosis).
Motivation to combine may be gleaned from the prior art considered. It would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to modify the invention of Golden with Sunoqrot to derive at the invention of claim 2. The result would have been expected, routine, and predictable in order to perform cancer classification.
The determination of obviousness is predicated upon the following: One skilled in the art would have been motivated to modify Golden in this manner in order to improve cancer diagnostic on a prostate region. Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in this manner explained using known engineering design, interface and/or programming techniques, without changing a fundamental operating principle of Golden, while the teaching of Sunoqrot continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result of analyzing a prostate to determine a probability of cancer. The Golden and Sunoqrot systems perform cancer analysis, therefore a person having ordinary skill in the art would have reasonable expectation of success in the combination yielding predictable results. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Regarding claim 16, Golden does not expressly disclose wherein the trained machine learning model comprises a trained radiomics-based classifier model. However, Sunoqrot teaches wherein the trained machine learning model comprises a trained radiomics-based classifier model (see page 310, 318, Sunoqrot discusses radiomic feature classifier).
Motivation to combine may be gleaned from the prior art considered. It would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to modify the invention of Golden with Sunoqrot to derive at the invention of claim 16. The result would have been expected, routine, and predictable in order to perform cancer classification.
The determination of obviousness is predicated upon the following: One skilled in the art would have been motivated to modify Golden in this manner in order to improve cancer diagnostic on a prostate region using a trained radiomics-based classifier. Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in this manner explained using known engineering design, interface and/or programming techniques, without changing a fundamental operating principle of Golden, while the teaching of Sunoqrot continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result of analyzing a prostate to determine a probability of cancer. The Golden and Sunoqrot systems perform cancer analysis, therefore a person having ordinary skill in the art would have reasonable expectation of success in the combination yielding predictable results. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Regarding claim 34, Golden does not expressly disclose further comprising processing the training data by performing intensity normalisation over each of one or more MR images in the training data. However, Sunoqrot teaches further comprising processing the training data by performing intensity normalisation over each of one or more MR images in the training data (see abstract, figure 1, page 310-311, Sunoqrot discusses dual-reference tissue-normalization of MRI training data of a prostate).
Motivation to combine may be gleaned from the prior art considered. It would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to modify the invention of Golden with Sunoqrot to derive at the invention of claim 34. The result would have been expected, routine, and predictable in order to perform cancer classification.
The determination of obviousness is predicated upon the following: One skilled in the art would have been motivated to modify Golden in this manner in order to improve cancer identification on a prostate region using a trained radiomics-based classifier. Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in this manner explained using known engineering design, interface and/or programming techniques, without changing a fundamental operating principle of Golden, while the teaching of Sunoqrot continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result of analyzing a prostate to determine a probability of cancer. The Golden and Sunoqrot systems perform cancer analysis, therefore a person having ordinary skill in the art would have reasonable expectation of success in the combination yielding predictable results. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Regarding claim 35, Sunoqrot teaches wherein the intensity normalisation is performed by performing dual-reference tissue-normalisation over each of one or more MR images in the training data (see abstract, figure 1, page 310-311, Sunoqrot discusses dual-reference tissue-normalization of MRI training data of a prostate).
The same motivation of claim 34 is applied to claim 35. Motivation to combine may be gleaned from the prior art considered. It would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to modify the invention of Golden with Sunoqrot to derive at the invention of claim 35. The result would have been expected, routine, and predictable in order to perform cancer classification.
Regarding claim 36, Sunoqrot teaches wherein the intensity normalisation is performed using an AutoRef normalisation method (see abstract, figure 1, page 310-311, Sunoqrot discusses dual-reference tissue-normalization of MRI training data of a prostate).
The same motivation of claim 34 is applied to claim 36. Motivation to combine may be gleaned from the prior art considered. It would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to modify the invention of Golden with Sunoqrot to derive at the invention of claim 36. The result would have been expected, routine, and predictable in order to perform cancer classification.
Claims 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over Golden et al. (US 2020/0380675) in view of Markova et al. (US 2011/0158491).
Regarding claim 12, Golden teaches Morphological operations may include dilation, erosion, opening and closing (see para. 0159). Golden does not expressly disclose wherein generating the lesion detection map comprises: determining a respective diameter and/or volume of each candidate lesion; comparing each respective diameter and/or volume to a size threshold value; and discarding each candidate lesion having a respective diameter and/or volume falling below the size threshold value.
However, Markova teaches wherein generating the lesion detection map comprises: determining a respective diameter and/or volume of each candidate lesion; comparing each respective diameter and/or volume to a size threshold value; and discarding each candidate lesion having a respective diameter and/or volume falling below the size threshold value (see figure 1, para. 0047, 0052, 0064, Markova discusses applying erosion filtering operation that removes regions outside the erosion size mask).
Motivation to combine may be gleaned from the prior art considered. It would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to modify the invention of Golden with Markova to derive at the invention of claim 12. The result would have been expected, routine, and predictable in order to perform cancer classification.
The determination of obviousness is predicated upon the following: One skilled in the art would have been motivated to modify Golden in this manner in order to improve cancer identification on a prostate region applying an erosion filter to each candidate lesion to properly perform segmentation. Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in this manner explained using known engineering design, interface and/or programming techniques, without changing a fundamental operating principle of Golden, while the teaching of Markova continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result of analyzing a prostate to determine a probability of cancer by applying an erosion filter to each candidate lesion. The Golden and Markova systems perform cancer analysis, therefore a person having ordinary skill in the art would have reasonable expectation of success in the combination yielding predictable results. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Regarding claim 13, Golden does not expressly disclose wherein generating the lesion detection map comprises applying an erosion filter to each candidate lesion, and discarding any candidate lesions which are substantially removed through application of the erosion filter. However, Markova teaches wherein generating the lesion detection map comprises applying an erosion filter to each candidate lesion, and discarding any candidate lesions which are substantially removed through application of the erosion filter (see figure 1, para. 0047, 0052, 0064, Markova discusses applying erosion filtering operation that removes regions outside the erosion size mask).
Motivation to combine may be gleaned from the prior art considered. It would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to modify the invention of Golden with Markova to derive at the invention of claim 13. The result would have been expected, routine, and predictable in order to perform cancer classification.
The determination of obviousness is predicated upon the following: One skilled in the art would have been motivated to modify Golden in this manner in order to improve cancer identification on a prostate region applying an erosion filter to each candidate lesion to properly perform segmentation. Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in this manner explained using known engineering design, interface and/or programming techniques, without changing a fundamental operating principle of Golden, while the teaching of Markova continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result of analyzing a prostate to determine a probability of cancer by applying an erosion filter to each candidate lesion. The Golden and Markova systems perform cancer analysis, therefore a person having ordinary skill in the art would have reasonable expectation of success in the combination yielding predictable results. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Regarding claim 14, Golden teaches wherein generating the lesion detection map comprises: calculating, for each candidate lesion, a respective probability indicative of a likelihood of the candidate lesion corresponding to a region of clinically significant cancer; calculating a lesion-level threshold value based on the respective probabilities associated with each candidate lesion; and discarding each candidate lesion having a respective probability falling below the lesion-level threshold value (see figure 1, para. 0159-0161, Golden discusses false positive candidate reduction; 0215, 0410, Golden discusses a CNN proposes locations of potential lesions with a focus on high sensitivity, and the second CNN sorts through these proposed lesions and discards results determined to be false positives).
The same motivation of claim 12 is applied to claim 14. Motivation to combine may be gleaned from the prior art considered. It would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to modify the invention of Golden with Sunoqrot to derive at the invention of claim 14. The result would have been expected, routine, and predictable in order to perform cancer classification.
Claim 44 is rejected under 35 U.S.C. 103 as being unpatentable over Golden et al. (US 2020/0380675) in view of Adeniji et al. “Volumetric Semantic Segmentation of Glioblastoma Tumors from MRI Studies.”
Regarding claim 44, Golden does not expressly disclose further comprising processing the training data by generating, for each of one or more MR images in the training data, a respective plurality of cropped images using a random or strided cropping process. However, Adeniji teaches further comprising processing the training data by generating, for each of one or more MR images in the training data, a respective plurality of cropped images using a random or strided cropping process (see section 4.2, 5.1, Adeniji discusses random crops per image in training data).
Motivation to combine may be gleaned from the prior art considered. It would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to modify the invention of Golden with Adeniji to derive at the invention of claim 44. The result would have been expected, routine, and predictable in order to perform cancer classification.
The determination of obviousness is predicated upon the following: One skilled in the art would have been motivated to modify Golden in this manner in order to improve cancer identification on a prostate region by random cropping candidate lesion regions to properly perform segmentation. Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in this manner explained using known engineering design, interface and/or programming techniques, without changing a fundamental operating principle of Golden, while the teaching of Adeniji continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result of analyzing a prostate to determine a probability of cancer by random cropping candidate lesion regions. The Golden and Adeniji systems perform cancer analysis, therefore a person having ordinary skill in the art would have reasonable expectation of success in the combination yielding predictable results. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Allowable Subject Matter
Claim 21 is 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.
The following is a statement of reasons for the indication of allowable subject matter: No prior art was found to claim “21. The method of claim 18, wherein operating the trained machine learning model comprises: generating, for each of one or more MR images in the MR imaging data, a respective synthetic MR image representative of a non-cancerous version of the region of interest; and
determining a pixel-wise difference between each MR image and the respective synthetic MR image to generate a respective difference image;
wherein processing the location data to generate a human-readable image comprises calculating, for each difference image, local maxima across the difference image.”
Claim 46 is 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.
The following is a statement of reasons for the indication of allowable subject matter: No prior art was found to claim “46. The method of claim 44, comprising generating the cropped images using a CroPRO cropping method.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Madabhushi et al. (US 2022/0401023) discusses radiomic feature classifier.
Lay et al. (US 2019/0370965) discusses prostate segmentation integrates holistically nested edge detection with fully convolutional networks.
El-Baz et al. (US 2020/0012761) discusses diagnostic probabilities for prostate cancer.
Sung et al. (US 2020/0278408) discusses trained neural network for segmenting and diagnosing prostate lesions in MRI image data.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KENNY A CESE whose telephone number is (571) 270-1896. The examiner can normally be reached on Monday – Friday, 9am – 4pm.
If attempts to reach the primary examiner by telephone are unsuccessful, the examiner’s supervisor, Gregory Morse can be reached on (571) 272-3838. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/Kenny A Cese/
Primary Examiner, Art Unit 2663
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