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 .
DETAILED ACTION
This action is responsive to amendment filed 8/10/2026.
This action is made Final.
Claims 1-4, 6-15, 17-22 are pending in the case. Claims 1, 10, and 20 are independent claims. Claims 1, 10 and 20 have been amended, 5 and 16 are canceled, new claims 21-22 have been added.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-4, 6-15, 17-22 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 112
The rejection of claims 1-20 under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, have been withdrawn as necessitated by amendment.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-3, 6-7, 10-14, 17 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tan (USPAT 10,354,377 B2) in view of Voronenko (USPUB 20240104767 A1) and further in view of Sun (USPUB 20180350078 A1).
Claim 1:
Tan teaches A method for image segmentation, implemented on a computing device having at least one processor and at least one storage device, the method comprising: determining, based on image data of a subject, an initial boundary of a target object inside the subject (Col 8 ln 25-47: Steps 211 through 225 are directed to determining an initial position for one or more boundaries of a corresponding one or more target objects. In step 211, it is determined whether the initial boundary is to be determined in a two dimensional slice or a higher dimensional subset of the image data. In the illustrated embodiment, it is determined whether the initial boundary is to be determined in three dimensions or two. If two, then in step 213 one or more initial boundary curves in two dimensions are determined. For example in some embodiments, a user draws an initial boundary in or near the target tissue of interest. In other embodiments, the initial boundary is based on an approximate segmentation technique, such as a watershed method with internal and external markers. The watershed transform with internal and external markers is well known in the art and described, for example, in L. Vincent and P. Soille, “Watersheds in digital spaces: an efficient algorithm based on immersion simulations,” IEEE Transactions On Pattern Analysis And Machine Intelligence, v. 13 (6), pp 583-598, June 1991. In some embodiments, the initial one or more boundaries are determined for a single subset of voxels in the image data, called a reference subset, such as on a reference slice at one axial position and moment in time); determining, based on the initial boundary of the target object, a closed boundary of the target object (Col 2 ln 48-50: determining a refined boundary based on active contouring of an initial boundary based on at least one of the center voxel and the center distance or the watershed boundary); and segmenting, based on the initial boundary and the closed boundary, a target portion corresponding to the target object from the image data (Fig 2 and Col 7 ln 18-Col 9 ln 21, Col 10 ln 41-56, Col 12 ln 38-40: In step 201, a region of interest (ROI) is obtained... In some embodiments, the ROI is defined by a user, such as a radiologist viewing the images on a display device, e.g., by drawing using a pointing device (such as a computer mouse or touchscreen), on a reference slice of the images such that the ROI encloses the target tissue (such as a an organ or tumor) to be segmented... an elliptical ROI, can be generated by providing the center of the ellipse and the lengths of its major and minor axes. ROI of other shapes can be generated in similar manner. In alternative embodiments, the ROI is automatically generated based on a number of selected points near a target boundary specified by the use... Steps 211 through 225 are directed to determining an initial position for one or more boundaries of a corresponding one or more target objects... For example in some embodiments, a user draws an initial boundary in or near the target tissue of interest... In the illustrated embodiment, the weighting is performed before applying the watershed transform or otherwise using gradients to segment a target object... the position of the one or more boundaries are refined using active contouring... Any active contouring may be used. Thus, during step 241, a refined boundary is determined based on performing active contouring of a first boundary... In step 271, it is determined whether some stop condition is satisfied. For example, it is determined whether the entire volume of interest has been segmented).
Tan, by itself, does not seem to completely teach wherein a difference between a first parameter value of at least one parameter of the target object and a second parameter value of the at least one parameter of the subject satisfies a condition.
The Examiner maintains that these features were previously well-known as taught by Voronenko.
Voronenko teaches wherein a difference between a first parameter value of at least one parameter of the target object and a second parameter value of the at least one parameter of the subject satisfies a condition (0069: he additional patient imaging data may be used to evaluate whether it is safe to continue delivering radiation to the target region(s). In some variations, metric values (e.g., tracer uptake values, dose values) derived from the acquired imaging data may be compared with the corresponding identification parameters of a target region to determine whether the location of the target region has changed and/or whether it is safe to continue delivering radiation to the target region).
Tan and Voronenko are analogous art because they are from the same problem-solving area, image segmentation.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Tan and Voronenko before him or her, to combine the teachings of Tan and Voronenko. The rationale for doing so would have been to accurately determine the target object.
Therefore, it would have been obvious to combine Tan and Voronenko to obtain the invention as specified in the instant claim(s).
Tran and Voronenko do not seem to completely teach the at least one parameter of the target object includes at least one of an attenuation parameter, a gradient parameter, or a grayscale parameter of the target object, the at least one parameter of the subject includes at least one of an attenuation parameter, a gradient parameter, or a grayscale parameter of the subject, and each of the at least one parameter of the target object corresponds to one of the at least one parameter of the subject.
The Examiner maintains that these features were previously well-known as taught by Sun.
Sun teaches the at least one parameter of the target object includes at least one of an attenuation parameter, a gradient parameter, or a grayscale parameter of the target object, the at least one parameter of the subject includes at least one of an attenuation parameter, a gradient parameter, or a grayscale parameter of the subject, and each of the at least one parameter of the target object corresponds to one of the at least one parameter of the subject (0067, 0079, 0088: CT data may indicate attenuation information of an imaged subject…the attenuation information of the second area may include attenuation information of the target object…the attenuation information of the second area may include attenuation information of the target object).
Tan and Sun are analogous art because they are from the same problem-solving area, medical image processing.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Tan and Sun before him or her, to combine the teachings of Tan and Sun. The rationale for doing so would have been to extract desired information from objects in a medical image.
Therefore, it would have been obvious to combine Tan and Sun to obtain the invention as specified in the instant claim(s).
Claim 2:
Tan teaches the determining, based on the initial boundary of the target object, a closed boundary of the target object includes: identifying a plurality of points on the initial boundary of the target object; generating a closed region by connecting each two points among the plurality of points; and determining, based on the closed region, the closed boundary of the target object (Col 7 ln 64 – 66: In alternative embodiments, the ROI is automatically generated based on a number of selected points near a target boundary specified by the user. If the ROI does not enclose the target, it can be dilated to enclose the target, in response to further user commands).
Claim 3:
Tan teaches the segmenting, based on the initial boundary and the closed boundary, a target portion corresponding to the target object from the image data includes: determining, based on the initial boundary and the closed boundary, a target boundary of the target object; and segmenting, based on the target boundary, the target portion corresponding to the target region from the image data (Fig 2 and Col 7 ln 18-Col 9 ln 21, Col 10 ln 41-56, Col 12 ln 38-40: In step 201, a region of interest (ROI) is obtained... In some embodiments, the ROI is defined by a user, such as a radiologist viewing the images on a display device, e.g., by drawing using a pointing device (such as a computer mouse or touchscreen), on a reference slice of the images such that the ROI encloses the target tissue (such as a an organ or tumor) to be segmented... an elliptical ROI, can be generated by providing the center of the ellipse and the lengths of its major and minor axes. ROI of other shapes can be generated in similar manner. In alternative embodiments, the ROI is automatically generated based on a number of selected points near a target boundary specified by the use... Steps 211 through 225 are directed to determining an initial position for one or more boundaries of a corresponding one or more target objects... For example in some embodiments, a user draws an initial boundary in or near the target tissue of interest... In the illustrated embodiment, the weighting is performed before applying the watershed transform or otherwise using gradients to segment a target object... the position of the one or more boundaries are refined using active contouring... Any active contouring may be used. Thus, during step 241, a refined boundary is determined based on performing active contouring of a first boundary... In step 271, it is determined whether some stop condition is satisfied. For example, it is determined whether the entire volume of interest has been segmented).
Claim 6:
Tan teaches wherein the determining, based on image data of a subject, an initial boundary of a target object inside the subject includes: segmenting an initial portion including the target object from the image data; and identifying, based on the initial portion, the initial boundary of the target object (Fig 2 and Col 7 ln 18-Col 9 ln 21, Col 10 ln 41-56, Col 12 ln 38-40: In step 201, a region of interest (ROI) is obtained... In some embodiments, the ROI is defined by a user, such as a radiologist viewing the images on a display device, e.g., by drawing using a pointing device (such as a computer mouse or touchscreen), on a reference slice of the images such that the ROI encloses the target tissue (such as a an organ or tumor) to be segmented... an elliptical ROI, can be generated by providing the center of the ellipse and the lengths of its major and minor axes. ROI of other shapes can be generated in similar manner. In alternative embodiments, the ROI is automatically generated based on a number of selected points near a target boundary specified by the use... Steps 211 through 225 are directed to determining an initial position for one or more boundaries of a corresponding one or more target objects... For example in some embodiments, a user draws an initial boundary in or near the target tissue of interest... In the illustrated embodiment, the weighting is performed before applying the watershed transform or otherwise using gradients to segment a target object... the position of the one or more boundaries are refined using active contouring... Any active contouring may be used. Thus, during step 241, a refined boundary is determined based on performing active contouring of a first boundary... In step 271, it is determined whether some stop condition is satisfied. For example, it is determined whether the entire volume of interest has been segmented).
Claim 7:
Tan teaches the image data includes at least one of projection data, a gradient image, at least one tomographic image, or a reconstruction image (Col 1 ln 26-32).
Claim 10:
Tan teaches A method for image segmentation, implemented on a computing device having at least one processor and at least one storage device, the method comprising: obtaining image data of a subject, the subject including a target object (Col 8 ln 25-47: Steps 211 through 225 are directed to determining an initial position for one or more boundaries of a corresponding one or more target objects. In step 211, it is determined whether the initial boundary is to be determined in a two dimensional slice or a higher dimensional subset of the image data. In the illustrated embodiment, it is determined whether the initial boundary is to be determined in three dimensions or two. If two, then in step 213 one or more initial boundary curves in two dimensions are determined. For example in some embodiments, a user draws an initial boundary in or near the target tissue of interest. In other embodiments, the initial boundary is based on an approximate segmentation technique, such as a watershed method with internal and external markers. The watershed transform with internal and external markers is well known in the art and described, for example, in L. Vincent and P. Soille, “Watersheds in digital spaces: an efficient algorithm based on immersion simulations,” IEEE Transactions On Pattern Analysis And Machine Intelligence, v. 13 (6), pp 583-598, June 1991. In some embodiments, the initial one or more boundaries are determined for a single subset of voxels in the image data, called a reference subset, such as on a reference slice at one axial position and moment in time); determining, based on the image data, an initial portion including the target object (Col 2 ln 48-50: determining a refined boundary based on active contouring of an initial boundary based on at least one of the center voxel and the center distance or the watershed boundary); and segmenting a target portion corresponding to the target object from the initial portion (Fig 2 and Col 7 ln 18-Col 9 ln 21, Col 10 ln 41-56, Col 12 ln 38-40: In step 201, a region of interest (ROI) is obtained... In some embodiments, the ROI is defined by a user, such as a radiologist viewing the images on a display device, e.g., by drawing using a pointing device (such as a computer mouse or touchscreen), on a reference slice of the images such that the ROI encloses the target tissue (such as a an organ or tumor) to be segmented... an elliptical ROI, can be generated by providing the center of the ellipse and the lengths of its major and minor axes. ROI of other shapes can be generated in similar manner. In alternative embodiments, the ROI is automatically generated based on a number of selected points near a target boundary specified by the use... Steps 211 through 225 are directed to determining an initial position for one or more boundaries of a corresponding one or more target objects... For example in some embodiments, a user draws an initial boundary in or near the target tissue of interest... In the illustrated embodiment, the weighting is performed before applying the watershed transform or otherwise using gradients to segment a target object... the position of the one or more boundaries are refined using active contouring... Any active contouring may be used. Thus, during step 241, a refined boundary is determined based on performing active contouring of a first boundary... In step 271, it is determined whether some stop condition is satisfied. For example, it is determined whether the entire volume of interest has been segmented)
Tan, by itself, does not seem to completely teach wherein a difference between a first parameter value of at least one parameter of the target object and a second parameter value of the at least one parameter of the subject satisfies a condition.
The Examiner maintains that these features were previously well-known as taught by Voronenko.
Voronenko teaches wherein a difference between a first parameter value of at least one parameter of the target object and a second parameter value of the at least one parameter of the subject satisfies a condition (0069: he additional patient imaging data may be used to evaluate whether it is safe to continue delivering radiation to the target region(s). In some variations, metric values (e.g., tracer uptake values, dose values) derived from the acquired imaging data may be compared with the corresponding identification parameters of a target region to determine whether the location of the target region has changed and/or whether it is safe to continue delivering radiation to the target region).
Tan and Voronenko are analogous art because they are from the same problem-solving area, image segmentation.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Tan and Voronenko before him or her, to combine the teachings of Tan and Voronenko. The rationale for doing so would have been to accurately determine the target object.
Therefore, it would have been obvious to combine Tan and Voronenko to obtain the invention as specified in the instant claim(s).
Tran and Voronenko do not seem to completely teach the at least one parameter of the target object includes at least one of an attenuation parameter, a gradient parameter, or a grayscale parameter of the target object, the at least one parameter of the subject includes at least one of an attenuation parameter, a gradient parameter, or a grayscale parameter of the subject, and each of the at least one parameter of the target object corresponds to one of the at least one parameter of the subject.
The Examiner maintains that these features were previously well-known as taught by Sun.
Sun teaches the at least one parameter of the target object includes at least one of an attenuation parameter, a gradient parameter, or a grayscale parameter of the target object, the at least one parameter of the subject includes at least one of an attenuation parameter, a gradient parameter, or a grayscale parameter of the subject, and each of the at least one parameter of the target object corresponds to one of the at least one parameter of the subject (0067, 0079, 0088: CT data may indicate attenuation information of an imaged subject…the attenuation information of the second area may include attenuation information of the target object…the attenuation information of the second area may include attenuation information of the target object).
Tan and Sun are analogous art because they are from the same problem-solving area, medical image processing.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Tan and Sun before him or her, to combine the teachings of Tan and Sun. The rationale for doing so would have been to extract desired information from objects in a medical image.
Therefore, it would have been obvious to combine Tan and Sun to obtain the invention as specified in the instant claim(s).
Claim 11:
Tan teaches the segmenting a target portion corresponding to the target object from the initial portion includes: obtaining an image segmentation model; determining the target portion corresponding to the target object by inputting the initial portion into the image segmentation model (Col 21 ln 61-65 and Col 22 ln 51-53: Part-solid objects often appear in image data, e.g., in CT image data as regions of ground glass opacities (GGO). GGO, or other part-solid, regions are included in the segmentation in some embodiments, using a Markov random field (MRF) model. This process can be understood through the following considerations, but the method is not limited by the accuracy or completeness of this treatment...Segmentation by the above MRF model can separate the VOI into background (e.g., low density lung parenchyma) and solid (e.g., high density regions).).
Claim 12:
Tan teaches the segmenting a target portion corresponding to the target object from the initial portion includes: determining, based on the initial portion, an initial boundary of the target object inside the subject (Col 8 ln 25-47: Steps 211 through 225 are directed to determining an initial position for one or more boundaries of a corresponding one or more target objects. In step 211, it is determined whether the initial boundary is to be determined in a two dimensional slice or a higher dimensional subset of the image data. In the illustrated embodiment, it is determined whether the initial boundary is to be determined in three dimensions or two. If two, then in step 213 one or more initial boundary curves in two dimensions are determined. For example in some embodiments, a user draws an initial boundary in or near the target tissue of interest. In other embodiments, the initial boundary is based on an approximate segmentation technique, such as a watershed method with internal and external markers. The watershed transform with internal and external markers is well known in the art and described, for example, in L. Vincent and P. Soille, “Watersheds in digital spaces: an efficient algorithm based on immersion simulations,” IEEE Transactions On Pattern Analysis And Machine Intelligence, v. 13 (6), pp 583-598, June 1991. In some embodiments, the initial one or more boundaries are determined for a single subset of voxels in the image data, called a reference subset, such as on a reference slice at one axial position and moment in time); determining, based on the initial boundary of the target object, a closed boundary of the target object (Col 2 ln 48-50: determining a refined boundary based on active contouring of an initial boundary based on at least one of the center voxel and the center distance or the watershed boundary); and segmenting, based on the initial boundary and the closed boundary, the target portion corresponding to the target object from the initial portion (Fig 2 and Col 7 ln 18-Col 9 ln 21, Col 10 ln 41-56, Col 12 ln 38-40: In step 201, a region of interest (ROI) is obtained... In some embodiments, the ROI is defined by a user, such as a radiologist viewing the images on a display device, e.g., by drawing using a pointing device (such as a computer mouse or touchscreen), on a reference slice of the images such that the ROI encloses the target tissue (such as a an organ or tumor) to be segmented... an elliptical ROI, can be generated by providing the center of the ellipse and the lengths of its major and minor axes. ROI of other shapes can be generated in similar manner. In alternative embodiments, the ROI is automatically generated based on a number of selected points near a target boundary specified by the use... Steps 211 through 225 are directed to determining an initial position for one or more boundaries of a corresponding one or more target objects... For example in some embodiments, a user draws an initial boundary in or near the target tissue of interest... In the illustrated embodiment, the weighting is performed before applying the watershed transform or otherwise using gradients to segment a target object... the position of the one or more boundaries are refined using active contouring... Any active contouring may be used. Thus, during step 241, a refined boundary is determined based on performing active contouring of a first boundary... In step 271, it is determined whether some stop condition is satisfied. For example, it is determined whether the entire volume of interest has been segmented).
Claim 13:
Tan teaches the determining, based on the initial boundary of the target object, a closed boundary of the target object includes: identifying a plurality of points on the initial boundary of the target object; generating a closed region by connecting each two points among the plurality of points; and determining, based on the closed region, the closed boundary of the target object (Col 7 ln 64 – 66: In alternative embodiments, the ROI is automatically generated based on a number of selected points near a target boundary specified by the user. If the ROI does not enclose the target, it can be dilated to enclose the target, in response to further user commands).
Claim 14:
Tan teaches the segmenting, based on the initial boundary and the closed boundary, the target portion corresponding to the target object from the initial portion includes: determining, based on the initial boundary and the closed boundary, a target boundary of the target object; and segmenting, based on the target boundary, the target portion corresponding to the target region from the initial portion (Fig 2 and Col 7 ln 18-Col 9 ln 21, Col 10 ln 41-56, Col 12 ln 38-40: In step 201, a region of interest (ROI) is obtained... In some embodiments, the ROI is defined by a user, such as a radiologist viewing the images on a display device, e.g., by drawing using a pointing device (such as a computer mouse or touchscreen), on a reference slice of the images such that the ROI encloses the target tissue (such as a an organ or tumor) to be segmented... an elliptical ROI, can be generated by providing the center of the ellipse and the lengths of its major and minor axes. ROI of other shapes can be generated in similar manner. In alternative embodiments, the ROI is automatically generated based on a number of selected points near a target boundary specified by the use... Steps 211 through 225 are directed to determining an initial position for one or more boundaries of a corresponding one or more target objects... For example in some embodiments, a user draws an initial boundary in or near the target tissue of interest... In the illustrated embodiment, the weighting is performed before applying the watershed transform or otherwise using gradients to segment a target object... the position of the one or more boundaries are refined using active contouring... Any active contouring may be used. Thus, during step 241, a refined boundary is determined based on performing active contouring of a first boundary... In step 271, it is determined whether some stop condition is satisfied. For example, it is determined whether the entire volume of interest has been segmented).
Claim 20:
Tan teaches A method for image correction, implemented on a computing device having at least one processor and at least one storage device, the method comprising: obtaining image data of a subject, the subject including a target object (Col 8 ln 25-47: Steps 211 through 225 are directed to determining an initial position for one or more boundaries of a corresponding one or more target objects. In step 211, it is determined whether the initial boundary is to be determined in a two dimensional slice or a higher dimensional subset of the image data. In the illustrated embodiment, it is determined whether the initial boundary is to be determined in three dimensions or two. If two, then in step 213 one or more initial boundary curves in two dimensions are determined. For example in some embodiments, a user draws an initial boundary in or near the target tissue of interest. In other embodiments, the initial boundary is based on an approximate segmentation technique, such as a watershed method with internal and external markers. The watershed transform with internal and external markers is well known in the art and described, for example, in L. Vincent and P. Soille, “Watersheds in digital spaces: an efficient algorithm based on immersion simulations,” IEEE Transactions On Pattern Analysis And Machine Intelligence, v. 13 (6), pp 583-598, June 1991. In some embodiments, the initial one or more boundaries are determined for a single subset of voxels in the image data, called a reference subset, such as on a reference slice at one axial position and moment in time), determining, based on the image data, an initial portion including the target object (Col 2 ln 48-50: determining a refined boundary based on active contouring of an initial boundary based on at least one of the center voxel and the center distance or the watershed boundary); segmenting a target portion corresponding to the target object from the initial portion (Fig 2 and Col 7 ln 18-Col 9 ln 21, Col 10 ln 41-56, Col 12 ln 38-40: In step 201, a region of interest (ROI) is obtained... In some embodiments, the ROI is defined by a user, such as a radiologist viewing the images on a display device, e.g., by drawing using a pointing device (such as a computer mouse or touchscreen), on a reference slice of the images such that the ROI encloses the target tissue (such as a an organ or tumor) to be segmented... an elliptical ROI, can be generated by providing the center of the ellipse and the lengths of its major and minor axes. ROI of other shapes can be generated in similar manner. In alternative embodiments, the ROI is automatically generated based on a number of selected points near a target boundary specified by the use... Steps 211 through 225 are directed to determining an initial position for one or more boundaries of a corresponding one or more target objects... For example in some embodiments, a user draws an initial boundary in or near the target tissue of interest... In the illustrated embodiment, the weighting is performed before applying the watershed transform or otherwise using gradients to segment a target object... the position of the one or more boundaries are refined using active contouring... Any active contouring may be used. Thus, during step 241, a refined boundary is determined based on performing active contouring of a first boundary... In step 271, it is determined whether some stop condition is satisfied. For example, it is determined whether the entire volume of interest has been segmented).
Tan, by itself, does not seem to completely teach wherein a difference between a first parameter value of at least one parameter of the target object and a second parameter value of the at least one parameter of the subject satisfies a condition; and correcting the image data of the subject based on the target portion corresponding to the target object.
The Examiner maintains that these features were previously well-known as taught by Voronenko.
Voronenko teaches wherein a difference between a first parameter value of at least one parameter of the target object and a second parameter value of the at least one parameter of the subject satisfies a condition (0069: he additional patient imaging data may be used to evaluate whether it is safe to continue delivering radiation to the target region(s). In some variations, metric values (e.g., tracer uptake values, dose values) derived from the acquired imaging data may be compared with the corresponding identification parameters of a target region to determine whether the location of the target region has changed and/or whether it is safe to continue delivering radiation to the target region); and correcting the image data of the subject based on the target portion corresponding to the target object (0062: The updated DVH and/or GI value may be compared with the tumor ID profile to determine whether the updated target region location is correct and/or whether it would be safe to proceed with radiation delivery).
Tan and Voronenko are analogous art because they are from the same problem-solving area, image segmentation.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Tan and Voronenko before him or her, to combine the teachings of Tan and Voronenko. The rationale for doing so would have been to accurately determine the target object.
Therefore, it would have been obvious to combine Tan and Voronenko to obtain the invention as specified in the instant claim(s).
Tran and Voronenko do not seem to completely teach the at least one parameter of the target object includes at least one of an attenuation parameter, a gradient parameter, or a grayscale parameter of the target object, the at least one parameter of the subject includes at least one of an attenuation parameter, a gradient parameter, or a grayscale parameter of the subject, and each of the at least one parameter of the target object corresponds to one of the at least one parameter of the subject.
The Examiner maintains that these features were previously well-known as taught by Sun.
Sun teaches the at least one parameter of the target object includes at least one of an attenuation parameter, a gradient parameter, or a grayscale parameter of the target object, the at least one parameter of the subject includes at least one of an attenuation parameter, a gradient parameter, or a grayscale parameter of the subject, and each of the at least one parameter of the target object corresponds to one of the at least one parameter of the subject (0067, 0079, 0088: CT data may indicate attenuation information of an imaged subject…the attenuation information of the second area may include attenuation information of the target object…the attenuation information of the second area may include attenuation information of the target object).
Tan and Sun are analogous art because they are from the same problem-solving area, medical image processing.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Tan and Sun before him or her, to combine the teachings of Tan and Sun. The rationale for doing so would have been to extract desired information from objects in a medical image.
Therefore, it would have been obvious to combine Tan and Sun to obtain the invention as specified in the instant claim(s).
Allowable Subject Matter
Claim 4, 8, 9, 15, 18, 19, 21 and 22 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: Tan and Voronenko, nor any of the references cited, disclose, teach, or suggest the combination of features of the claims listed. More specifically, Tan discusses techniques for segmentation including determining an edge of voxels in a range associated with a target object. A center voxel is determined. Target size is determined based on the center voxel. In some embodiments, edges near the center are suppressed, markers are determined based on the center, and an initial boundary is determined using a watershed transform. Some embodiments include determining multiple rays originating at the center in 3D, and determining adjacent rays for each. In some embodiments, a 2D field of amplitudes is determined on a first dimension for distance along a ray and a second dimension for successive rays in order. An initial boundary is determined based on a path of minimum cost to connect each ray. In some embodiments, active contouring is performed using a novel term to refine the initial boundary. In some embodiments, boundaries of part-solid target objects are refined using Markov models. Voronenko discusses techniques for identifying the location of a target region using a tumor identification (ID) profile. A tumor ID profile includes identification parameters that characterize the target region. The tumor ID profile may be used to facilitate the identification of multiple target regions and to evaluate whether it is safe to deliver radiation to the target regions at their updated locations. Also disclosed herein are methods for analyzing a dose distribution to a target region by generating a bounded dose volume histogram (bDVH) based on gamma criteria comprising a distance-to-agreement (DTA) criterion and a dose difference (DD) criterion. In one variation, a gamma-derived bDVH is used in a method for selecting gamma criteria values for evaluating a radiotherapy treatment plan. However Tan and Voronenko, neither alone nor in combination, teach the features of claims 4, 8, 9, 15, 18, 19, 21 or 22.
Note
The Examiner cites particular columns, line numbers and/or paragraph numbers in the references as applied to the claims below for the convenience of the Applicant(s). Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the Applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. See MPEP 2123.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMED H ZUBERI whose telephone number is (571)270-7761. The examiner can normally be reached Mon – Th 10AM-8PM.
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/MOHAMMED H ZUBERI/Primary Examiner, Art Unit 2178
