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 .
Comments
The Preliminary Amendments filed on December 22, 2023, and on January 1, 2024 have been entered and made of record.
Drawings
The drawings are objected to because Reference Numeral “112” shown in Figure 8 should be 818 (e.g., Paragraph 71 lines 7-9: “At block 818, the validation dataset is validated by an operator, which in turn may be used to select the medical images from the plurality of medical images to be included in the ground truth annotated dataset”).
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description:
Reference Numeral “520” shown in Figure 5.
Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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 5-7, 11, 16, 17, and 20 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.
Claim 5 recites the limitation “the containing medical image” in lines 4-5. There is insufficient antecedent basis for this limitation in the claim.
Claim 6 recites the limitation “the containing medical image” in lines 4-5. There is insufficient antecedent basis for this limitation in the claim.
Claim 7 recites the limitation “the containing medical image” in lines 4-5. There is insufficient antecedent basis for this limitation in the claim.
Claim 11 recites the limitation “the regression function” in line 2. There is insufficient antecedent basis for this limitation in the claim. However, claim 11 appears to be dependent upon claim 10 and has been treated as such. Affirmation of this is required by the appropriate amendment.
Claim 16 recites the limitation “the containing medical image” in lines 4-5. There is insufficient antecedent basis for this limitation in the claim.
Claim 17 recites the limitation “the containing medical image” in lines 4-5. There is insufficient antecedent basis for this limitation in the claim.
Claim 20 recites the limitation “the regression function” in line 2. There is insufficient antecedent basis for this limitation in the claim. However, claim 20 appears to be dependent upon claim 19 and has been treated as such. Affirmation of this is required by the appropriate amendment.
Appropriate correction is required.
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.
Claims 1, 3, 4, and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Fang et al. (U.S. Pub. No. 2022/0301156) in view of Liang et al. (U.S. Pub. No. 2010/0260390).
As to claims 1 and 14, Fang et al. teaches a method (i.e., “systems and methods for analyzing medical images using a learning model. The system receives a medical image acquired by an image acquisition device”, Abstract)/machine-readable, nontransitory memory (i.e., “storage device 304 may include a read-only memory (ROM), a flash memory, random access memory (RAM), a static memory, a volatile or non-volatile, magnetic, semiconductor, tape, optical, removable, nonremovable, or other types of storage device or tangible (e.g., non-transitory) computer-readable medium. In some embodiments, the storage device 304 may store computer-executable instructions of one or more processing programs and data generated when a computer program is executed”, Paragraph [0053]), having stored thereon one or more programs programmed to cause a processor to compute at least the following:
to images of a plurality of medical images, applying/to apply a trained segmentation model to generate first segmentation masks for lesions of the medical images (See for example, “Original image 402 is input into main model 404. Main model 404 is a learning model configured to perform the main medical image analysis task (e.g., classification, object detection or segmentation)”, Paragraph [0057]), a segmentation mask being a designation of an area of an image corresponding to a lesion shown in the image (See for example, “the learning model is designed to predict a segmentation mask of the medical image, e.g., a segmentation mask for a lesion in the lung region”, Paragraph [0098]), a segmentation model being an artificial intelligence model trained to identify segmentation masks corresponding to lesions in medical images (i.e., “The learning model is trained to perform an image analysis task”, Paragraph [0034]); and
combining/to combine into a training set (i.e., “training data 416”, Paragraph [0060]) for training of a segmentation model the evaluated first segmentation masks and their corresponding images based at least in part on meeting a filter (i.e., “If the predicted error is low, e.g., less than a predetermined threshold, unlabeled image 414 along with the main model result yielded by main model 404 is added to training data 416”, Paragraph [0060]).
However, Fang et al. does not explicitly disclose applying/to apply a geometric filter to evaluate the generated first segmentation masks based on geometric properties of the respective one or more lesions in the images, and the filter is a geometric filter.
Liang et al. teaches applying a geometric filter (i.e., “The extracted volume is then partitioned in step 125 in order to identify density texture features, morphological features and geometrical features of the candidate patches”, Paragraph [0049]) to evaluate the generated first segmentation masks (i.e., “candidate polyp patches … The set of identified features is then analyzed for each candidate patch to eliminate false positives 130”, Paragraph [0049]) based on geometric properties of the respective one or more lesions in the images (i.e., “The more closely this value approaches 1.0, the more likely this candidate will be a true polyp. Using an appropriate likelihood threshold, all the candidates can be classified and identified according to their likelihood values from the linear classifier as either polyps or false positives”, Paragraph [0106]), and the filter is a geometric filter (i.e., “Geometrical Features”, Paragraph [0090]).
Fang et al. and Liang et al. are analogous art because they are from the field of digital image processing for medical imaging analysis.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Fang et al. by incorporating the filter as a geometric filter, and applying the geometric filter to evaluate the generated first segmentation masks based on geometric properties of the respective one or more lesions in the images, as taught by Liang et al.
The suggestion/motivation for doing so would have been to reduce the number of false positives in image segmentation tasks.
Therefore, it would have been obvious to combine Liang et al. with Fang et al. to obtain the invention as specified in claims 1 and 14.
As to claim 3, Liang et al. teaches wherein in the applied geometric filter includes one or more of:
a relative area fraction based on a ratio of an area covered by each of the one or more lesions to a total area covered by a largest one component of other of one or more lesions for each of the plurality of medical images;
a convexity based on a ratio of an area covered by each of the one or more lesions to an area of a convex hull of the corresponding one of the one or more lesions;
an elliptical aspect ratio based on a ratio of major axis to minor axis of an ellipse corresponding to one or more lesions of the plurality of medical images (See for example, “Axis_Ratio”, Paragraphs [0090]-[0091]); and
an elliptical proximity based on a ratio of perimeter of an elliptical structure with same area as an ellipse corresponding to each of the one or more lesions with major axis and minor axis thereof being equal to length and width respectively of a minimum bounding rectangle thereto in the corresponding medical image, to a perimeter of the ellipse corresponding to each of the one or more lesions in the corresponding medical image.
As to claim 4, Liang et al. teaches wherein in the applied geometric filter includes one or more of:
a relative area fraction based on a ratio of an area covered by each of the one or more lesions to a total area covered by a largest one component of other of one or more lesions for each of the plurality of medical images;
a convexity based on a ratio of an area covered by each of the one or more lesions to an area of a convex hull of the corresponding one of the one or more lesions; and
an elliptical aspect ratio based on a ratio of major axis to minor axis of an ellipse corresponding to one or more lesions of the plurality of medical images (See for example, “Axis_Ratio”, Paragraphs [0090]-[0091]).
As to claim 13, Fang et al. teaches at a user interface, obtaining from a human operator a confirmation of segmentation masks to incorporate images and segmentation masks into an augmented training set for training of the segmentation model (See for example, “human annotation 418 may be requested and the annotated image may be added to training data 416”, Paragraph [0060]).
As to claim 15, Liang et al. teaches wherein in the applied geometric filter includes one or more of: determining an area fraction based on a ratio of an area covered by one or more lesions to a total area covered by other of one or more lesions. a relative area fraction based on a ratio of an area covered by each of the one or more lesions to a total area covered by a largest one component of other of one or more lesions for each of the plurality of medical images; a convexity based on a ratio of an area covered by each of the one or more lesions to an area of a convex hull of the corresponding one of the one or more lesions; an elliptical aspect ratio based on a ratio of major axis to minor axis of an ellipse corresponding to one or more lesions of the plurality of medical images (See for example, “Axis_Ratio”, Paragraphs [0090]-[0091]); and an elliptical proximity based on a ratio of perimeter of an elliptical structure with same area as an ellipse corresponding to each of the one or more lesions with major axis and minor axis thereof being equal to length and width respectively of a minimum bounding rectangle thereto in the corresponding medical image, to a perimeter of the ellipse corresponding to each of the one or more lesions in the corresponding medical image.
Claims 2, 5 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Fang et al. in view of Liang et al. as applied to claims 1 and 14 above, and further in view of Buelow et al. (U.S. Pub. No. 2011/0229004). The teachings of Fang et al. and Liang et al. have been discussed above.
As to claim 2, Fang et al. and Liang et al. do not explicitly disclose wherein in the applied geometric filter includes at least: determining an area fraction based on a ratio of an area covered by one or more lesions to a total area covered by other of one or more lesions.
Buelow et al. teaches an applied geometric that filter includes at least determining an area fraction based on a ratio of an area covered by one or more lesions to a total area covered by other of one or more lesions (See for example, “calculating a ratio of unenhanced lesion voxels (e.g., necrotic lesion voxels indicative of lesion tissue that does not absorb contrast agent) to total lesion voxels. The resulting “dark area rate” describes the ratio of necrotic lesion tissue to total lesion tissue, which is employed in assessing malignancy of the lesion”, Paragraph [0027]).
Fang et al. and Liang et al. are analogous art because they are from the field of digital image processing for medical imaging analysis.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to further modify Fang et al. and Liang et al. by incorporating the applied geometric filter includes at least: determining an area fraction based on a ratio of an area covered by one or more lesions to a total area covered by other of one or more lesions, as taught by Buelow et al.
The suggestion/motivation for doing so would have been to assess malignancy of a detected lesion among a plurality of lesions.
Therefore, it would have been obvious to combine Buelow et al. with Fang et al. and Liang et al. to obtain the invention as specified in claim 2.
As to claims 5 and 16, as best understood, Liang et al. teaches wherein in the applied geometric filter includes an elliptical aspect ratio based on a ratio of major axis to minor axis of an ellipse corresponding a lesion (See for example, “Axis_Ratio”, Paragraphs [0090]-[0091]).
However, Fang et al. and Liang et a. do not explicitly disclose wherein in the applied geometric filter includes at least one of: determining an area fraction based on a ratio of an area covered by one of the lesions to a total area covered by other of one or more lesions or all lesions of the containing medical image; a relative area fraction based on a ratio of an area covered by one of the lesions to a total area covered by a largest component of other of one or more lesions of the containing medical image; and a convexity based on a ratio of an area covered by a lesions to an area of a convex hull of the lesion.
Buelow et al. teaches an applied geometric filter that includes at least one of: determining an area fraction based on a ratio of an area covered by one of the lesions to a total area covered by other of one or more lesions or all lesions of the containing medical image (See for example, “calculating a ratio of unenhanced lesion voxels (e.g., necrotic lesion voxels indicative of lesion tissue that does not absorb contrast agent) to total lesion voxels. The resulting “dark area rate” describes the ratio of necrotic lesion tissue to total lesion tissue, which is employed in assessing malignancy of the lesion”, Paragraph [0027]); a relative area fraction based on a ratio of an area covered by one of the lesions to a total area covered by a largest component of other of one or more lesions of the containing medical image; and a convexity based on a ratio of an area covered by a lesions to an area of a convex hull of the lesion.
Therefore, in view of Buelow et al., it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify Fang et al. and Liang et al. by incorporating at least one of determining an area fraction based on a ratio of an area covered by one of the lesions to a total area covered by other of one or more lesions or all lesions of the containing medical image, a relative area fraction based on a ratio of an area covered by one of the lesions to a total area covered by a largest component of other of one or more lesions of the containing medical image, and a convexity based on a ratio of an area covered by a lesions to an area of a convex hull of the lesion to the applied geometric filter, as taught by Buelow et al., in order to assess malignancy of a detected lesion among a plurality of lesions.
Claims 6 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Fang et al. in view of Liang et al. as applied to claims 1 and 14 above, and further in view of Buelow et al. (U.S. Pub. No. 2011/0229004) and Serdjebi et al. (WO 2022/223922 A1 – Machine Translation). The teachings of Fang et al. and Liang et al. have been discussed above.
As to claims 6 and 17, as best understood, Liang et al. teaches wherein in the applied geometric filter includes an elliptical aspect ratio based on a ratio of major axis to minor axis of an ellipse corresponding a lesion (See for example, “Axis_Ratio”, Paragraphs [0090]-[0091]).
However, Fang et al. and Liang et al. do not explicitly disclose wherein in the applied geometric filter includes at least two of: determining an area fraction based on a ratio of an area covered by one of the lesions to a total area covered by other of one or more lesions or all lesions of the containing medical image; a relative area fraction based on a ratio of an area covered by one of the lesions to a total area covered by a largest component of other of one or more lesions of the containing medical image; and a convexity based on a ratio of an area covered by a lesions to an area of a convex hull of the lesion.
Buelow et al. teaches an applied geometric that filter includes at least determining an area fraction based on a ratio of an area covered by one or more lesions to a total area covered by other of one or more lesions or all lesions of the containing medical image (See for example, “calculating a ratio of unenhanced lesion voxels (e.g., necrotic lesion voxels indicative of lesion tissue that does not absorb contrast agent) to total lesion voxels. The resulting “dark area rate” describes the ratio of necrotic lesion tissue to total lesion tissue, which is employed in assessing malignancy of the lesion”, Paragraph [0027]).
The combination of Fang et al., Liang et al. and Buelow et al. do not explicitly disclose wherein in the applied geometric filter includes at least one of: a relative area fraction based on a ratio of an area covered by one of the lesions to a total area covered by a largest component of other of one or more lesions of the containing medical image; and a convexity based on a ratio of an area covered by a lesions to an area of a convex hull of the lesion.
Serdjebi et al. (Machine Translation) teaches an applied geometric filter that includes at least one of: a relative area fraction based on a ratio of an area covered by one of the lesions to a total area covered by a largest component of other of one or more lesions of the containing medical image; and a convexity based on a ratio of an area covered by a lesions to an area of a convex hull of the lesion (See for example, translation of page 54 line 21 through page 55 line 2, “quantifying the compactness of such a lesion polygon PLSz identified in step 510. Such a quantification of the compactness of a polygon carried out in step 520 can be the result of a calculation of a ratio of the area of said lesion polygon PLSz by the area of its convex envelope”, page 16, machine translation).
Fang et al., Liang et al., Buelow et al. and Serdjebi et al. are analogous art because they are from the field of digital image processing for medical imaging analysis.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to further modify Fang et al. and Liang et al. by incorporating at least two of determining an area fraction based on a ratio of an area covered by one of the lesions to a total area covered by other of one or more lesions or all lesions of the containing medical image, as taught by Buelow et al., a relative area fraction based on a ratio of an area covered by one of the lesions to a total area covered by a largest component of other of one or more lesions of the containing medical image, and a convexity based on a ratio of an area covered by a lesions to an area of a convex hull of the lesion, as taught by Serdjebi et al., in the applied geometric filter.
The suggestion/motivation for doing so would have been to assess malignancy of a detected lesion among a plurality of lesions, and to determine the compactness of a lesion for further analysis.
Therefore, it would have been obvious to combine Buelow et al. and Serdjebi et al. with Fang et al. and Liang et al. to obtain the invention as specified in claim 6.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Fang et al. in view of Liang et al. as applied to claim 1 above, and further in view of Zlotnick et al. (U.S. Pub. No. 2019/0172581). The teachings of Fang et al. and Liang et al. have been discussed above.
As to claim 8, Fang et al. and Liang et al. do not explicitly disclose at a user interface, receiving data from an operator to classify images of the plurality of medical images into one of: single lesion image with only one lesion, multiple lesion image with at least two lesions, normal image with no lesions, and unclear images.
Zlotnick et al. teaches at a user interface, receiving data from an operator to classify images of the plurality of medical images into one of: single lesion image with only one lesion, multiple lesion image with at least two lesions, normal image with no lesions, and unclear images (See for example, FIG. 2D, “A reviewing user can drag a medical image from the montage stack 204 for inclusion in a determined classification, thus rapidly riffling through the medical images. That is, the reviewing user can drag a top medical image included in the montage 204 onto, for example, Classification A 220 … classifications 220-224, included in user interface 202 may be related to a medical diagnosis associated with the BIRADS score. For example, the classifications can indicate whether the medical images represent cancer, e.g., highly likely, moderately likely, or not likely”, Paragraphs [0103]-[0104]).
Fang et al., Liang et al. and Zlotnick et al. are analogous art because they are from the field of digital image processing for medical imaging analysis.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to further modify Fang et al. and Liang et al. by incorporating the receiving of data from an operator to classify images of the plurality of medical images into one of: single lesion image with only one lesion, multiple lesion image with at least two lesions, normal image with no lesions, and unclear images at a user interface, as taught by Zlotnick et al.
The suggestion/motivation for doing so would have been to improve the classification of medical images.
Therefore, it would have been obvious to combine Zlotnick et al. with Fang et al. and Liang et al. to obtain the invention as specified in claim 8.
Claims 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Fang et al. in view of Liang et al. as applied to claims 1 and 14 above, and further in view of Ye et al. (U.S. Pub. No. 2011/0216951). The teachings of Fang et al. and Liang et al. have been discussed above.
As to claims 9 and 18, Fang et al. and Liang et al. do not explicitly disclose based on the evaluation of the geometric filter, discarding the medical image from the training set.
Ye et al. teaches based on the evaluation of the geometric filter (See for example, Paragraphs [0148]-[0151]), discarding the medical image from the training set (i.e., “step 450 reduces false positives in the set of initial lesion candidates by discarding lesion candidates that have a low probability of being lesions”, Paragraph [0152]).
Fang et al., Liang et al. and Ye et al. are analogous art because they are from the field of digital image processing for medical imaging analysis.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to further modify Fang et al. and Liang et al. by incorporating the discarding of the medical image from the training set based on the evaluation of the geometric filter, as taught by Ye et al.
The suggestion/motivation for doing so would have been to reduce the determination of false positive for further analysis.
Therefore, it would have been obvious to combine Ye et al. with Fang et al. and Liang et al. to obtain the invention as specified in claims 9 and 18.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Fang et al. in view of Liang et al. as applied to claim 1 above, and further in view of Song et al. (U.S. Pub. No. 2023/0281809). The teachings of Fang et al. and Liang et al. have been discussed above.
As to claim 12, Fang et al. and Liang et al. do not explicitly disclose augmenting a number of medical images in the plurality of medical images by using one or more techniques of: rotate, width-shift, horizontal-shift, horizontal-flip, vertical-flip, zoom, brightness and shear.
Song et al. teaches augmenting a number of medical images in the plurality of medical images (i.e., “augmenting the subset of training images 145a”, Paragraph [0048]) by using one or more techniques of: rotate, width-shift, horizontal-shift, horizontal-flip, vertical-flip, zoom, brightness and shear (i.e., “Image data augmentation may be performed by creating transformed versions of images in the datasets that belong to the same class as the original image. Transforms include a range of operations from the field of image manipulation, such as shifts, flips, zooms, and the like”, Paragraph [0050]).
Fang et al., Liang et al. and Song et al. are analogous art because they are from the field of digital image processing for medical imaging analysis.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to further modify Fang et al. and Liang et al. by incorporating the augmenting of a number of medical images in the plurality of medical images by using one or more techniques of: rotate, width-shift, horizontal-shift, horizontal-flip, vertical-flip, zoom, brightness and shear.
The suggestion/motivation for doing so would have been to artificially expand the size of the training set.
Therefore, it would have been obvious to combine Song et al. with Fang et al. and Liang et al. to obtain the invention as specified in claim 12.
Allowable Subject Matter
Claims 7, 10 and 19 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.
Claims 11 and 20, as best understood, would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include 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: the closest prior art made of record fails to disclose, teach, and/or suggest the method of claim 1, and further, wherein in the applied geometric filter includes at least four of: determining an area fraction based on a ratio of an area covered by one of the lesions to a total area covered by other of one or more lesions or all lesions of the containing medical image; a relative area fraction based on a ratio of an area covered by one of the lesions to a total area covered by a largest component of other of one or more lesions of the containing medical image; a convexity based on a ratio of an area covered by a lesions to an area of a convex hull of the lesion; and an elliptical aspect ratio based on a ratio of major axis to minor axis of an ellipse corresponding a lesion; or further comprising the step of: training a regression function based on ground truth values for relative area fraction, convexity, elliptical aspect ratio and elliptical proximity for lesions in medical images, to validate if the generated segmentation mask corresponds to the one or more lesions in the corresponding medical image.
Conclusion
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/JOSE M TORRES/Examiner, Art Unit 2664 01/02/2026