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 .
Claim Objections
Claim 1 is objected to because of the following informalities: the abbreviated term “3D” should be defined in its first occurrence in claim 1.
Claim 2 is objected to because of the following informalities: the abbreviated term “2D” should be defined in its first occurrence in claim 12.
Claim 10 is objected to because of the following informalities: the abbreviated term “DCE-MRI images” should be defined in its first occurrence in claim 10.
Claim 16 is objected to because of the following informalities: the abbreviated term “3D” should be defined in its first occurrence in claim 16.
Claim 17 is objected to because of the following informalities: the abbreviated term “3D” should be defined in its first occurrence in claim 17.
Claim 34 is objected to because of the following informalities: the abbreviated term “2D” should be defined in its first occurrence in claim 34.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-17 and 34-36 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite an abstract idea as discussed below. This abstract idea is not integrated into a practical application for the reasons discussed below. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the reasons discussed below.
Step 1 of the 2019 Guidance requires the examiner to determine if the claims are to one of the statutory categories of invention. Applied to the present application, the claims belong to one of the statutory classes of a process or product as a computer implemented method or a computer system/product.
Step 2A of the 2019 Guidance is divided into two Prongs. Prong 1 requires the examiner to determine if the claims recite an abstract idea, and further requires that the abstract idea belong to one of three enumerated groupings: mathematical concepts, mental processes, and certain methods of organizing human activity.
Regarding claim 1, the independent claim is directed to a computer-implemented method. The claim limitations of “applying a tumor localization procedure to the 3D image data, wherein applying the tumor localization procedure includes using a tumor localization neural network ensemble to predict 3D bounding boxes of tumor locations within the 3D image data; applying a tumor segmentation procedure to the 3D image data, wherein applying the tumor segmentation procedure includes using a tumor segmentation neural network to predict first probabilities that a first set of locations within the breast tissue contain tumor tissue; applying a multi-tissue segmentation procedure to the 3D image data, wherein applying the multi-tissue segmentation procedure includes using a tissue segmentation neural network ensemble to predict second probabilities that a second set of locations within the breast tissue contain one or more types of non-tumor tissue, and combining the second probabilities using a weighted average; and determining most likely tissue types for regions of the 3D image data based on the 3D bounding boxes, the first probabilities from the tumor segmentation procedure, the second probabilities as combined from the multi-tissue segmentation procedure, and anatomical feasibilities of the breast tissue” are directed to an abstract because the claim limitations can be performed via mathematical concepts and mental process, with assistance of basic physical aids or with pen and paper.
Furthermore, the claim does not include additional elements which are sufficient to amount to significantly more than the abstract idea. The additional elements of “obtaining 3D image data of breast tissue” is directed to extra solution activity of gathering data and does not include additional elements which are sufficient to amount to significantly more than the abstract idea.
In consideration of each of the relevant factors and the claim elements both individually and in combination, claim 1 is directed to an abstract ideas without sufficient integration into a practical application and without significantly more.
Regarding claims 2-15, the claims further recite claim limitations (e.g., flattening the 3D image data, predicting 2D tumor locations within the 2D image, merging the 2D tumor locations, eliminating or replacing predicted most likely tissue types of tumor located outside the of the 3D bounding boxes, using two neural networks, dividing the 3D image data into 3D windows, aligning the two or more DCE-MRI images, modifying the two or more DCE-MRI images to equalize pixel spacing in each dimension, determining a nipple location on a breast represented in 3D image, transforming the regions of the 3D image data into elements of a finite element model, assigning respective density and stiffness parameters, perform simulating and interpolating the elements in the supine position into further 3D image data) that are further directed to abstract idea because the claim limitations can be performed via mathematical concepts and mental process, with assistance of basic physical aids or with pen and paper.
Regarding claim 16, the independent claim is directed to a non-transitory computer-readable medium. The claim limitations of “applying a tumor localization procedure to the 3D image data, wherein applying the tumor localization procedure includes using a tumor localization neural network ensemble to predict 3D bounding boxes of tumor locations within the 3D image data; applying a tumor segmentation procedure to the 3D image data, wherein applying the tumor segmentation procedure includes using a tumor segmentation neural network to predict first probabilities that a first set of locations within the breast tissue contain tumor tissue; applying a multi-tissue segmentation procedure to the 3D image data, wherein applying the multi-tissue segmentation procedure includes using a tissue segmentation neural network ensemble to predict second probabilities that a second set of locations within the breast tissue contain one or more types of non-tumor tissue, and combining the second probabilities using a weighted average; and determining most likely tissue types for regions of the 3D image data based on the 3D bounding boxes, the first probabilities from the tumor segmentation procedure, the second probabilities as combined from the multi-tissue segmentation procedure, and anatomical feasibilities of the breast tissue” are directed to an abstract because the claim limitations can be performed via mathematical concepts and mental process, with assistance of basic physical aids or with pen and paper. Other than the computing system (which is represented simply as a part of a basic computer system), nothing identified in the claim is precluded from being practically performed in the mind, or with assistance of basic physical aids or with pen and paper. See MPEP § 2106.04(a)(2)(III)(B). Intellectual Ventures LLC v. Symantec Corp., 838 F.3d 1307, 1318 (Fed. Cir. 2016) established that mental processes encompass acts which, absent anything beyond generic computer components, may be “performed by a human, mentally or with pen and paper.” Intellectual Ventures additionally established that if a claim, under its broadest reasonable interpretation, covers performance in the mind but for the recitation of generic computer components, then it is still in the mental processes category of abstract ideas unless the claim cannot be practically performed in the mind.
The judicial exception is not integrated into a “practical application” as defined by the Subject Matter Eligibility Analysis documented in Federal Register 84(4), issued on 07 January 2019 and since documented in MPEP § 2106. While the claim recites that a “computing system” that performs the limitations encompassing mental processes, this simply represents implementing the abstract ideas with a computer. The additional limitations in relation to the computer, computer product, or computer system does not offer a meaningful limitation beyond generally linking the use of the method to a computer (see ALICE CORP. v. CLS BANK INT’L 573 U. S. ____ (2014)). The claim does not recite a particular machine applying or being used by the abstract idea. See also subsection I of the cited section and MPEP § 2106.05(f) which indicates that instructions to implement the abstract idea on a computer or that “using a computer as a tool to perform the abstract idea” are not sufficient to integrate a judicial exception into a “practical application” as interpreted by the courts.
Furthermore, the claim does not include additional elements which are sufficient to amount to significantly more than the abstract idea. As discussed above, the additional elements of a generic computer components (“computing system”) to execute the abstract ideas and does not add significantly more that the abstract idea because since the one or more processors are merely a generic computer component with the computer being used as a tool for performing the abstract idea
Furthermore, the claim does not include additional elements which are sufficient to amount to significantly more than the abstract idea. The additional elements of “obtaining 3D image data of breast tissue” is directed to extra solution activity of gathering data and does not include additional elements which are sufficient to amount to significantly more than the abstract idea.
In consideration of each of the relevant factors and the claim elements both individually and in combination, claim 16 is directed to an abstract ideas without sufficient integration into a practical application and without significantly more.
Regarding claim 17, the independent claim is directed to a system. The claim limitations of “applying a tumor localization procedure to the 3D image data, wherein applying the tumor localization procedure includes using a tumor localization neural network ensemble to predict 3D bounding boxes of tumor locations within the 3D image data; applying a tumor segmentation procedure to the 3D image data, wherein applying the tumor segmentation procedure includes using a tumor segmentation neural network to predict first probabilities that a first set of locations within the breast tissue contain tumor tissue; applying a multi-tissue segmentation procedure to the 3D image data, wherein applying the multi-tissue segmentation procedure includes using a tissue segmentation neural network ensemble to predict second probabilities that a second set of locations within the breast tissue contain one or more types of non-tumor tissue, and combining the second probabilities using a weighted average; and determining most likely tissue types for regions of the 3D image data based on the 3D bounding boxes, the first probabilities from the tumor segmentation procedure, the second probabilities as combined from the multi-tissue segmentation procedure, and anatomical feasibilities of the breast tissue” are directed to an abstract because the claim limitations can be performed via mathematical concepts and mental process, with assistance of basic physical aids or with pen and paper. Other than the one or more processor and memory (which is represented simply as a part of a basic computer system), nothing identified in the claim is precluded from being practically performed in the mind, or with assistance of basic physical aids or with pen and paper. See MPEP § 2106.04(a)(2)(III)(B). Intellectual Ventures LLC v. Symantec Corp., 838 F.3d 1307, 1318 (Fed. Cir. 2016) established that mental processes encompass acts which, absent anything beyond generic computer components, may be “performed by a human, mentally or with pen and paper.” Intellectual Ventures additionally established that if a claim, under its broadest reasonable interpretation, covers performance in the mind but for the recitation of generic computer components, then it is still in the mental processes category of abstract ideas unless the claim cannot be practically performed in the mind.
The judicial exception is not integrated into a “practical application” as defined by the Subject Matter Eligibility Analysis documented in Federal Register 84(4), issued on 07 January 2019 and since documented in MPEP § 2106. While the claim recites “one or more processor” that performs the limitations encompassing mental processes, this simply represents implementing the abstract ideas with a computer. The additional limitations in relation to the computer, computer product, or computer system does not offer a meaningful limitation beyond generally linking the use of the method to a computer (see ALICE CORP. v. CLS BANK INT’L 573 U. S. ____ (2014)). The claim does not recite a particular machine applying or being used by the abstract idea. See also subsection I of the cited section and MPEP § 2106.05(f) which indicates that instructions to implement the abstract idea on a computer or that “using a computer as a tool to perform the abstract idea” are not sufficient to integrate a judicial exception into a “practical application” as interpreted by the courts.
Furthermore, the claim does not include additional elements which are sufficient to amount to significantly more than the abstract idea. As discussed above, the additional elements of a generic computer components (“one or more processors” and “memory”) to execute the abstract ideas and does not add significantly more that the abstract idea because since the one or more processors are merely a generic computer component with the computer being used as a tool for performing the abstract idea
Furthermore, the claim does not include additional elements which are sufficient to amount to significantly more than the abstract idea. The additional elements of “obtaining 3D image data of breast tissue” is directed to extra solution activity of gathering data and does not include additional elements which are sufficient to amount to significantly more than the abstract idea.
In consideration of each of the relevant factors and the claim elements both individually and in combination, claim 17 is directed to an abstract ideas without sufficient integration into a practical application and without significantly more.
Regarding claims 34-36, the claims further recite claim limitations (e.g., flattening the 3D image data, eliminating or replacing predicted most likely tissue types of tumor located outside the of the 3D bounding boxes, transforming the regions of the 3D image data into elements of a finite element model, assigning respective density and stiffness parameters, perform simulating and interpolating the elements in the supine position into further 3D image data) that are further directed to abstract idea because the claim limitations can be performed via mathematical concepts and mental process, with assistance of basic physical aids or with pen and paper.
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 1-17 and 34-36 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.
Regarding claims 1, 4 and 15, the phrase “most likely tissue types” in line 15 of claim 1 is a relative which renders the claim indefinite. The phrase “most likely” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Therefore, the phrase “most likely” renders the claim indefinite. Claim 4 and 15 also recite the indefinite language.
Claims 2-3, 5-14 are rejected as they depend from rejected claim 1.
Regarding claims 16, 35 and 36, the phrase “most likely tissue types” in line 18 of claim 16 is a relative which renders the claim indefinite. The phrase “most likely” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Therefore, the phrase “most likely” renders the claim indefinite. Claim 35 and 36 also recite the indefinite language.
Claim 34 is rejected as the claim depends from rejected claim 16.
Regarding claims 17, the phrase “most likely tissue types” in line 21 of claim 17 is a relative which renders the claim indefinite. The phrase “most likely” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Therefore, the phrase “most likely” renders the claim indefinite.
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 1-14, 16-17 and 34-36 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US2021/0225027; hereinafter Wang), in view of Li (US 2022/0208355).
Regarding claim 1, Wang discloses an image region localization method. Wang shows a computer-implemented method (see abstract) comprising: obtaining 3D image data of breast tissue (see par. [0118], [0162]); applying a tumor localization procedure to the 3D image data, wherein applying the tumor localization procedure includes using a tumor localization neural network ensemble to predict tumor locations within the 3D image data (see par. [0118], [0171], [0225], [0226], [0324], [0378]; applying a tumor segmentation procedure to the 3D image data, wherein applying the tumor segmentation procedure includes using a tumor segmentation neural network to predict first probabilities that a first set of locations within the breast tissue contain tumor tissue (see par. [0268], [0269], [0290]-[0293], [0324], [0329]); applying a multi-tissue segmentation procedure to the 3D image data, wherein applying the multi-tissue segmentation procedure includes using a tissue segmentation neural network ensemble to predict second probabilities that a second set of locations within the breast tissue contain one or more types of non-tumor tissue (see par. [0268], [0269], [0290]-[0293]), and combining the second probabilities using a weighted average (see par. [0268], [0269], [0290]-[0293]); and determining most likely tissue types for regions of the 3D image data, the first probabilities from the tumor segmentation procedure (see par. [0227], [0290]-[0293]), the second probabilities as combined from the multi-tissue segmentation procedure (see par. [0227], [0290]-[0293]), and anatomical feasibilities of the breast tissue (see par. [0227], [0290]-[0293]).
But, Wang fails to explicitly state that 3D bounding boxes of tumor.
Li discloses contrast agent free medical diagnostic imaging. Li teaches 3D bounding boxes of tumor (see par. [0206]-[0208], [0279]-[0282).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing of the claimed invention, to have utilized the teaching of 3D bounding boxes of tumor in the invention of Wang, as taught by Li, to prove a more improved tumor detection and localization.
Regarding claim 2, Wang and Li disclose the invention substantially as described in the 103 rejection above, furthermore Wang shows wherein using the tumor localization neural network ensemble to predict tumor locations within the 3D image data comprises: flattening the 3D image data into 2D image data for two planes of the 3D image data (see par. [0155], [0165]-[0170], [0273], [0318]); using the tumor localization neural network ensemble to predict 2D tumor locations within the 2D image data (see par. [0155], [0165]-[0170], [0273], [0318]); and merging the 2D tumor locations (see par. [0155], [0165]-[0170], [0273], [0318]), and Li teaches the 3D bounding boxes of tumor (see par. [0206]-[0208], [0279]-[0280]).
Regarding claim 3, Wang and Li disclose the invention substantially as described in the 103 rejection above, furthermore Wang shows wherein merging the 2D tumor locations comprises determining intersections of the 2D tumor locations across the two planes (see par. [0375], [0378]-[0381], [0463]), and Li teaches the 3D bounding boxes of tumor (see par. [0206]-[0208], [0279]-[0280]).
Regarding claim 4, Wang and Li disclose the invention substantially as described in the 103 rejection above, furthermore Wang shows wherein determining most likely tissue types for regions of the 3D image data comprises one or more of: eliminating or replacing predicted most likely tissue types of tumor (see par. [0225]-[0227], [0318]-[0320]), and Li teaches the 3D bounding boxes of tumor (see par. [0206]-[0208], [0279]-[0280]).
Regarding claim 5, Wang and Li disclose the invention substantially as described in the 103 rejection above, furthermore Li teaches wherein the two planes of the 3D image data are selected from axial, sagittal, or coronal planes (see par. [0209]). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing of the claimed invention, to have utilized the teaching of wherein the two planes of the 3D image data are selected from axial, sagittal, or coronal planes in the invention of Wang, as taught by Li, to prove a more improved tumor detection and localization.
Regarding claim 6, Wang and Li disclose the invention substantially as described in the 103 rejection above, furthermore Wang shows wherein the tumor localization neural network ensemble comprises two neural networks that were respectively trained on labeled maximum intensity projections of tumor locations within the two planes (see par. [0206]-[0211], [0366]-[0371]).
Regarding claim 7, Wang and Li disclose the invention substantially as described in the 103 rejection above, furthermore Wang shows wherein applying the tumor segmentation procedure comprises dividing the 3D image data into 3D windows (see par. [0165]-[0175], [0272]-[0274]), and wherein the first set of locations comprises the 3D windows (see par. [0165]-[0175], [0272]-[0274]).
Regarding claim 8, Wang and Li disclose the invention substantially as described in the 103 rejection above, furthermore Wang shows wherein applying the multi-tissue segmentation procedure comprises dividing the 3D image data into further 3D windows (see par. [0165]-[0175], [0272]-[0274]), and wherein the second set of locations comprises the further 3D windows (see par. [0165]-[0175], [0272]-[0274]).
Regarding claim 9, Wang and Li disclose the invention substantially as described in the 103 rejection above, furthermore Wang shows wherein the 3D image data comprises two or more DCE-MRI images from different post-contrast injection time points or from two or more different MRI modalities (see par. [0162]).
Regarding claim 10, Wang and Li disclose the invention substantially as described in the 103 rejection above, furthermore Wang shows further comprising: aligning the two or more DCE-MRI images (see par. [0191], [0248]-[0256], [0332]); modifying the two or more DCE-MRI images to equalize pixel spacing in each dimension (see par. [0191], [0248]-[0256], [0332]); and standardizing intensities of pixels within the two or more DCE-MRI images (see par. [0191], [0248]-[0256], [0332]).
Regarding claim 11, Wang and Li disclose the invention substantially as described in the 103 rejection above, furthermore Wang shows wherein the tumor segmentation neural network was trained on randomly-selected labeled locations within 3D training images of breast tissue (see par. [0200]-[0205], [0207]-[0211] and [0288]-[0293]), wherein the randomly-selected labeled locations are biased toward tumor locations over non-tumor locations (see par. [0200]-[0205], [0207]-[0211] and [0288]-[0293]).
Regarding claim 12, Wang and Li disclose the invention substantially as described in the 103 rejection above, furthermore Wang shows wherein the tissue segmentation neural network ensemble comprises a plurality of tissue segmentation neural networks (see par. [0170]-[0173], [0198], [0269], [0292], [0293]), one for each of a plurality of non-tumor tissue types (see par. [0170]-[0173], [0198], [0269], [0292], [0293]), and wherein each of the plurality of tissue segmentation neural networks was trained on labeled locations within 3D training images of their respective non-tumor tissue types (see par. [0170]-[0173], [0198], [0269], [0292], [0293]).
Regarding claim 13, Wang and Li disclose the invention substantially as described in the 103 rejection above, furthermore Wang shows wherein the plurality of tissue segmentation neural networks include one for each of skin, adipose tissue, fibroglandular tissue, vasculature, and chest wall (see par. [0162], [0170], [0227], [0382] and fig. 3A).
Regarding claim 14, Wang and Li disclose the invention substantially as described in the 103 rejection above, furthermore Wang shows wherein tissue segmentation neural network ensemble also predicts probabilities that each of the first set of locations contain air (see par. [0162], [0170], [0227], [0378], [0382]), the method further comprising: determining a nipple location on a breast represented in 3D image data based on a confluence of physically adjacent or overlapping predictions of air (see par. [0162], [0170], [0227], [0378], [0382]), glandular tissue (see par. [0162], [0170], [0227], [0378], [0382]), and skin within the 3D image data (see par. [0162], [0170], [0227], [0378], [0382]).
Regarding claim 16, Wang discloses an image region localization method. Wang shows a non-transitory computer-readable medium, having stored thereon program instructions that, upon execution by a computing system (see abstract; par. [0126], [0127]), cause the computing system to perform operations comprising: obtaining 3D image data of breast tissue (see par. [0118], [0162]); applying a tumor localization procedure to the 3D image data, wherein applying the tumor localization procedure includes using a tumor localization neural network ensemble to predict tumor locations within the 3D image data (see par. [0118], [0171], [0225], [0226], [0324], [0378]; applying a tumor segmentation procedure to the 3D image data, wherein applying the tumor segmentation procedure includes using a tumor segmentation neural network to predict first probabilities that a first set of locations within the breast tissue contain tumor tissue (see par. [0268], [0269], [0290]-[0293], [0324], [0329]); applying a multi-tissue segmentation procedure to the 3D image data, wherein applying the multi-tissue segmentation procedure includes using a tissue segmentation neural network ensemble to predict second probabilities that a second set of locations within the breast tissue contain one or more types of non-tumor tissue (see par. [0268], [0269], [0290]-[0293]), and combining the second probabilities using a weighted average (see par. [0268], [0269], [0290]-[0293]); and determining most likely tissue types for regions of the 3D image data, the first probabilities from the tumor segmentation procedure (see par. [0227], [0290]-[0293]), the second probabilities as combined from the multi-tissue segmentation procedure (see par. [0227], [0290]-[0293]), and anatomical feasibilities of the breast tissue (see par. [0227], [0290]-[0293]).
But, Wang fails to explicitly state that 3D bounding boxes of tumor.
Li discloses contrast agent free medical diagnostic imaging. Li teaches 3D bounding boxes of tumor (see par. [0206]-[0208], [0279]-[0282).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing of the claimed invention, to have utilized the teaching of 3D bounding boxes of tumor in the invention of Wang, as taught by Li, to prove a more improved tumor detection and localization.
Regarding claim 17, Wang discloses an image region localization method. Wang shows a system (see fig. 5A) comprising one or more processors (see fig. 5A; par. [0126], [0127]); and memory (see fig. 5A), containing program instructions that, upon execution by the one or more processors, case the system to perform operations comprising: obtaining 3D image data of breast tissue (see par. [0118], [0162]); applying a tumor localization procedure to the 3D image data, wherein applying the tumor localization procedure includes using a tumor localization neural network ensemble to predict 3D bounding boxes of tumor locations within the 3D image data (see par. [0118], [0171], [0225], [0226], [0324], [0378]; applying a tumor segmentation procedure to the 3D image data, wherein applying the tumor segmentation procedure includes using a tumor segmentation neural network to predict first probabilities that a first set of locations within the breast tissue contain tumor tissue (see par. [0268], [0269], [0290]-[0293], [0324], [0329]); applying a multi-tissue segmentation procedure to the 3D image data, wherein applying the multi-tissue segmentation procedure includes using a tissue segmentation neural network ensemble to predict second probabilities that a second set of locations within the breast tissue contain one or more types of non-tumor tissue (see par. [0268], [0269], [0290]-[0293]), and combining the second probabilities using a weighted average (see par. [0268], [0269], [0290]-[0293]); and determining most likely tissue types for regions of the 3D image data, the first probabilities from the tumor segmentation procedure (see par. [0227], [0290]-[0293]), the second probabilities as combined from the multi-tissue segmentation procedure (see par. [0227], [0290]-[0293]), and anatomical feasibilities of the breast tissue (see par. [0227], [0290]-[0293]).
But, Wang fails to explicitly state that 3D bounding boxes of tumor.
Li discloses contrast agent free medical diagnostic imaging. Li teaches 3D bounding boxes of tumor (see par. [0206]-[0208], [0279]-[0282).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing of the claimed invention, to have utilized the teaching of 3D bounding boxes of tumor in the invention of Wang, as taught by Li, to prove a more improved tumor detection and localization.
Regarding claim 34, Wang and Li disclose the invention substantially as described in the 103 rejection above, furthermore Wang shows wherein using the tumor localization neural network ensemble to predict tumor locations within the 3D image data comprises: flattening the 3D image data into 2D image data for two planes of the 3D image data (see par. [0155], [0165]-[0170], [0273], [0318]); using the tumor localization neural network ensemble to predict 2D tumor locations within the 2D image data (see par. [0155], [0165]-[0170], [0273], [0318]); and merging the 2D tumor locations (see par. [0155], [0165]-[0170], [0273], [0318]), and Li teaches the 3D bounding boxes of tumor (see par. [0206]-[0208], [0279]-[0280]).
Regarding claim 35, Wang and Li disclose the invention substantially as described in the 103 rejection above, furthermore Wang shows wherein determining most likely tissue types for regions of the 3D image data comprises one or more of: eliminating or replacing predicted most likely tissue types of tumor (see par. [0225]-[0227], [0318]-[0320]), and Li teaches the 3D bounding boxes of tumor (see par. [0206]-[0208], [0279]-[0280]).
Claims 15 and 36 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US2021/0225027; hereinafter Wang), in view of Li (US 2022/0208355) as applied to claims 1 and 16 above, and further in view of Eiben et al. (US 2019/0066375; hereinafter Eiben).
Regarding claims 15 and 36, Wang and Li disclose the invention substantially as described in the 103 rejection above, furthermore Wang shows, furthermore, Wang shows wherein the 3D image data represents the breast tissue (see par. [0118], [0162]), the computer-implemented method further comprising: transforming the regions of the 3D image data into elements (see par. [0186], [0318]), the elements having their respective most likely tissue types (see par. [0275], [0276], [0366]-[0373]); based on their respective most likely tissue types (see par. [0275], [0276], [0366]-[0373]), assigning, to the elements, respective density (see par. [0250], [0268]-[0269]); simulating, by way of the model and based on the respective density (see par. [0250], [0268]-[0269]), but fails to explicitly state prone position, finite element model, and stiffness parameters, gravity in a posterior direction to translate the elements from the prone position to a gravity-unloaded position of the breast tissue; simulating, by way of the finite element model, gravity in the posterior direction to translate the elements from the gravity-unloaded position to a supine position of the breast tissue; and interpolating the elements in the supine position into further 3D image data with identified locations of the respective most likely tissue types.
Eiben discloses device, imaging system and method for correction of medical breast image. Eiben teaches prone position (see par. [0049]), finite element model (see par. [0045]), and stiffness parameters (see par. [0049]), gravity in a posterior direction to translate the elements from the prone position to a gravity-unloaded position of the breast tissue (see par. [0046], [0050], [0056], [0066]); simulating, by way of the finite element model (see par. [0046], [0050], [0056], [0066]), gravity in the posterior direction to translate the elements from the gravity-unloaded position to a supine position of the breast tissue (see par. [0046], [0050], [0056], [0066]); and interpolating the elements in the supine position into further 3D image data with identified locations of the respective most likely tissue types (see par. [0046], [0050], [0056], [0066]).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing of the claimed invention, to have utilized the teaching of prone position, finite element model, and stiffness parameters, gravity in a posterior direction to translate the elements from the prone position to a gravity-unloaded position of the breast tissue; simulating, by way of the finite element model, gravity in the posterior direction to translate the elements from the gravity-unloaded position to a supine position of the breast tissue; and interpolating the elements in the supine position into further 3D image data with identified locations of the respective most likely tissue types in the invention of Wang and Li, as taught by Eiben, to be able to provide an improved correction for medical breast image.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Lee et al. (US 2024/0090846) disclose autonomous healthcare visual system for real-time presentation and diagnostic.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHAHDEEP MOHAMMED whose telephone number is (571)270-3134. The examiner can normally be reached Monday to Friday, 9am to 5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anne M Kozak can be reached at (571)270-0552. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHAHDEEP MOHAMMED/Primary Examiner, Art Unit 3797
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