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 .
Priority
Acknowledgement is made to Applicant’s claim to priority to U.S. Provisional App. No. 62/409,040 filed October 17, 2016.
Status of Claims
This Office Action is responsive to the claims filed on 01/08/2026. Claims 1, 9, and 12 have been amended. Claims 1-20 are presently pending in this application.
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, 2, 5, 6, 8-10, 12, 13, 16, 17, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Stone (US 20150245825) in view of Kepner (Non-Patent Literature: Kepner, G. et al. 2010. Transperineal prostate biopsy: analysis of a uniform core sampling pattern that yields data on tumor volume limits in negative biopsies), Zhan (Non-Patent Literature: Zhan, Y. et al. 2007, Targeted Prostate Biopsy Using Statistical Image Analysis), and Kumar (US 20100172559 A1).
Regarding claim 1, Stone teaches a method of biopsy planning (Paragraphs [0015] and [0180]-[0186]; biopsy planning) comprising:
determining, using a processor (Paragraph [0221]; The device can include one or more computational elements, including processor(s)) and image data (Paragraph [0206]; images acquired from other imaging systems 1032 can be uploaded into the system 1102 allowing a user to perform targeted biopsies and/or treatments), a volume of a region of interest to be biopsied (Paragraph [0162]; the structures of the prostate are identified, including the prostate 1268; Paragraph [0170]; system 1102 calculates the target tissue volume 1254, here the volume of the prostate 1008; Paragraph [0183]; biopsy plan for the prostate; Selection of the prostate for biopsy is considered to read on the claimed limitation of determining a volume of the region to be biopsied as understood in its broadest reasonable interpretation and in view of Applicant Specification and Remarks);
determining, using the processor and the image data, a volume of biopsy tissue (Paragraph [0183]; biopsy sites are shown, as well as the length of the core, and the number of biopsy needles with a given core length needed to complete the biopsy plan; Paragraph [0015]; generate volume of tissue biopsied from the target tissue; Paragraph [0184]; the amount of tissue included in the biopsy plan; The calculated amount of biopsy tissue is considered to read on the claimed limitation as understood in its broadest reasonable interpretation and in view of Applicant specification and remarks) determined to detect tumors of a predetermined size (Paragraph [0183]; a lesion of a particular size);
determining, using the processor and the image data, a number, position, and length of biopsy cores (Paragraphs [0183]-[0184]; biopsy sites are shown, as well as the length of the core, and the number of biopsy needles with a given core length; Figs. 58 and 62), wherein the biopsy core comprises a cylindrical volume (Paragraph [0080]; needle assembly 100 is used to excise a tissue specimen from a target tissue site … the mandrel or inner needle includes an elongated cylindrical body);
determining, using the processor and the image data, needle paths for each of the biopsy cores (Paragraph [0185]; After generating a biopsy plan and making adjustments to the variables as necessary, a user can view the orientation of the biopsy needles 1480 in the three-dimensional prostate image 1472; Needle paths for the cores are determined in order to draw the paths in the image; Paragraph [0213]; A three-dimensional image of the prostate, urethra, and rectum with the biopsy needle paths is generated from the image slices and the biopsy site plan at step 1664); and
generating and displaying, using the processor, a three-dimensional biopsy plan representing the biopsy cores and the needle paths (Paragraph [0183]; FIG. 58, 56 biopsy sites are shown, as well as the length of the core, and the number of biopsy needles with a given core length needed to complete the biopsy plan; Paragraph [0185]; After generating a biopsy plan and making adjustments to the variables as necessary, a user can view the orientation of the biopsy needles 1480 in the three-dimensional prostate image 1472; In FIG. 62, the prostate with needles image 1518 is viewed; the 3-D image showing the biopsy plan with needle paths is considered to read on the claimed limitation of a three-dimensional biopsy plan representing the biopsy cores and the needle paths as understood in its broadest reasonable interpretation).
Stone does not explicitly teach that the sum of all of the cylindrical volumes of the biopsy cores yields the volume of biopsy tissue determined to detect tumors of a predetermined size; and
the needle path is determined in conjunction with an anatomical location for needle access; and
the needle path is determined in order to avoid anatomical constraints.
Kepner, however, teaches that the sum of all of the cylindrical volumes of the biopsy cores yields the volume of biopsy tissue determined to detect tumors of a predetermined size (Pg. 2, Para. 3; the mathematical analysis of this uniform transperineal core pattern calculates the probability that a spherical tumor of a given diameter and volume will be detected, based on the ratio of the volume of the locations where it would be detected to the total volume between the cores, Fig. 1B; A).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method of Stone to have included that the sum of all of the cylindrical volumes of the biopsy cores yields the volume of biopsy tissue determined to detect tumors of a predetermined size as taught by Kepner because it would have allowed one to more accurately determine the probability of detecting the tumor of the predetermined size for an arrangement of biopsy needles by comparing the total volume of biopsy tissue to the total size of the prostate volume and further offering a quantitative tool to help determine the template spacing options for placing the cores in a template-guided transperineal biopsy (Kepner, Pg. 7, Para. 2).
Together Stone and Kepner does not explicitly teach the needle path is determined in conjunction with an anatomical location for needle access; and
the needle path is determined in order to avoid anatomical constraints.
Zhan, however, teaches determining, using the processor and the image data, needle paths for each of the biopsy cores (Pg. 780, left col.; which proposed to optimize biopsy strategy based on a statistical atlas of spatial distribution of prostate cancer; Pg. 783, left col.; Given the prostate ultrasound image of a specific patient, the initial position of the deformable model is determined by transforming it to a pose which optimally matches with prostate boundary in the ultrasound image.), wherein the needle path is determined in conjunction with an anatomical location for needle access (Pg. 785; We also tested the optimized biopsy strategy for different physical constraints… for the optimal transperineal and trans-rectal biopsy cores; Fig. 9 show the optimized core distribution using the transperineal and transrectal biopsy operations. The placement of the cores based on the transperineal or transrectal constraints is considered to read on the claimed limitations of needle path is determined in conjunction with an anatomical location for needle access as understood in its broadest reasonable interpretation).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method of Stone in view of Kepner such that the needle path is determined in conjunction with an anatomical location for needle access as taught by Zhan because it would have ensured the placement of the biopsy needles are compatible with known biopsy operations and protocols, and thereby allowed realistic sampling of biopsy cores (Pg. 785; Right col.).
Together Stone, Kepner, and Zhan do not explicitly teach the needle path is determined in order to avoid anatomical constraints.
Kumar, however, teaches a method of biopsy planning (Paragraph [0005]; 3-D image guidance has been applied to prostate biopsy such that the prostate can be sampled in a desired fashion) comprising determining needle paths for each of the biopsy core (Paragraph [0054]; The presented invention does it automatically such that during planning, it computes which region the planned site lies in and the zone corresponding to the sampled core is also automatically computed) wherein the needle path is determined in order to avoid anatomical constraints (Paragraph [0064]; may automatically place planned sites on all grid elements lying inside the prostate that avoid certain regions; automatically load a system generated plan customized to the prostate. The custom plan may be computed such that the user-specified regions such as urethra and neighboring organs and nerve bundles may be avoided during plan generation).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have the method of Stone in view of Kepner and Zhan such that the needle path is determined in order to avoid anatomical constraints as taught by Kumar because it would have avoided accidental placement of needle for either biopsy or placing a bead at these sensitive locations during a procedure (Paragraph [0064]) thereby reducing the possibility of damage to sensitive locations and improving the safety of the procedure.
Regarding claim 2, together Stone, Kepner, Zhan, and Kumar teach all of the limitations of claim 1 as noted above. Stone further teaches implementing the method using a non-transitory computer readable medium (Paragraphs [0219] and [0225]; embodiments of the above-described systems and methods are implemented as software processes that are specified as a set of instructions recorded on a computer readable medium; non-transitory computer-readable media).
Regarding claim 5, together Stone, Kepner, Zhan, and Kumar teach all of the limitations of claim 1 as noted above. Stone further teaches detecting tumors in the prostate gland (Stone, Paragraphs [0002], [0145], and [0180]-[0186]; prostate gland).
Regarding claim 6, together Stone, Kepner, Zhan, and Kumar teach all of the limitations of claim 1 as noted above.
Stone does explicitly teach detecting tumors in any organ with a boundary that is segmentable as a surface.
Zhan, however, further teaches detecting tumors in any organ (prostate gland; Zhan, Title, Pg. 779-788; prostate biopsy) with a boundary (Pg. 782-784, Surface-Based Registration With Automatic Ultrasound Image Segmentation, texture characteristics to delineate prostate boundaries) that is segmentable (Pg. 780-784, Surface-Based Registration With Automatic Ultrasound Image Segmentation; an automatic segmentation method is designed to define the prostate capsule from ultrasound images) as a surface (Pg. 780-784; using the surface-based registration method).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have further included detecting tumors in any organ with a boundary that is segmentable as a surface into the method of Stone in view of Kepner, Zhan, and Kumar as it could be used to determine the prostate surface from the ultrasound image and guide the deformable registration with the atlas in clinical settings where ultrasound imaging is commonly used for biopsy guidance (Zhan, Pg. 780).
Regarding claim 8, together Stone, Kepner, Zhan, and Kumar teach all of the limitations of claim 1 as noted above.
Stone further teaches setting a tumor detection area (Paragraph [0182]; Selecting the inside capsule radio button causes the system to select needle lengths that are always inside the capsule. Selecting the outside capsule radio button causes the system to select needle lengths that may end up outside of the capsule, and would require the use of more biopsy needles, require the biopsy of longer core bed lengths, or both).
Regarding claim 9, together Stone, Kepner, Zhan, and Kumar teach all of the limitations of claim 1 as noted above.
Stone further teaches generating the three-dimensional biopsy plan for tumors of a predetermined size (Paragraphs [0182]-[0184]; The needle lengths text box allows a user to specify the length of the biopsy tissue specimen removed. The system calculates the specific length necessary depending upon the location of the biopsy in prostate. The system will calculate the probability of encountering a lesion of a particular size within the prostate using a given biopsy plan and an array of biopsy needle positions; Examiner notes that choosing the amount of tissue specimen removed will generate a biopsy plan that has a probability of detecting a lesion of a particular size, i.e. for significant tumors) for a predefined number of biopsy cores and lengths (Paragraphs [0182]-[0184]; the number of biopsy needles with a given core length needed to complete the biopsy plan).
Regarding claim 10, together Stone, Kepner, Zhan, and Kumar teach all of the limitations of claim 1 as noted above.
Stone further teaches generating the three-dimensional biopsy plan for tumors having a predetermined size threshold (Paragraphs [0182]-[0184]; The needle lengths text box allows a user to specify the length of the biopsy tissue specimen removed. The system calculates the specific length necessary depending upon the location of the biopsy in prostate. The system will calculate the probability of encountering a lesion of a particular size within the prostate using a given biopsy plan and an array of biopsy needle positions; Examiner notes that choosing the amount of tissue specimen removed will generate a biopsy plan that has a probability of detecting a lesion of a particular size, i.e. for insignificant tumors) for a predefined number of biopsy cores and lengths (Paragraphs [0182]-[0184]; the number of biopsy needles with a given core length needed to complete the biopsy plan).
Stone does not explicitly teach the tumor has a size smaller than a predetermined size threshold.
Kepner, however, further teaches the tumor has a size smaller than a predetermined size threshold (Pg. 10, Appendix; The case of point cores will be analyzed and compared to the case of finite cores, with biopsy needle radius of Rc. The point core case (Figure 2A) has a detection square with sides S cm. The inscribed circle tumor, and smaller tumors, are completely within this detection square. Tumors with larger diameters, up to the diameter of the circumscribed circle, are not completely within the square. Each of these conditions requires a different equation for calculating the Probability of Detection).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have further modified the method of Stone in view of Kepner, Zhan, and Kumar such that the tumor has a size smaller than a predetermined size threshold because it would allow adjusting the biopsy plan to improve the probability of detection for small tumors.
Regarding claim 12, Stone teaches a system (Paragraphs [0015] and [0180]-[0186]; system #1102) for biopsy planning (Paragraphs [0015] and [0180]-[0186]; biopsy planning) comprising:
a source (Paragraphs [0143]-[0147]; three-dimensional imaging system) of image data (Paragraphs [0143]-[0146] and [0154], images, image data #1118) capable of reconstructing a target organ (Paragraphs [0144] and [0145]; target tissues, prostate gland) in three-dimensions (Paragraphs [0144] and [0181]);
a non-transitory computer readable medium (Paragraphs [0219] and [0225]; non-transitory computer-readable media) programmed for (Paragraph [0219]; embodiments of the above-described systems and methods are implemented as software processes that are specified as a set of instructions recorded on a computer readable medium):
determining a volume of the region to be biopsied (Paragraph [0162]; the structures of the prostate are identified, including the prostate 1268; Paragraph [0170]; system 1102 calculates the target tissue volume 1254, here the volume of the prostate 1008; Paragraph [0183]; biopsy plan for the prostate; Selection of the prostate for biopsy is considered to read on the claimed limitation of determining a volume of the region to be biopsied as understood in its broadest reasonable interpretation and in view of Applicant Specification and Remarks);
determining a volume of biopsy tissue determined to detect tumors (Paragraph [0183]; biopsy sites are shown, as well as the length of the core, and the number of biopsy needles with a given core length needed to complete the biopsy plan; Paragraph [0015]; generate volume of tissue biopsied from the target tissue; Paragraph [0184]; the amount of tissue included in the biopsy plan; The calculated amount of biopsy tissue is considered to read on the claimed limitation as understood in its broadest reasonable interpretation and in view of Applicant specification and remarks) of a predetermined size (Paragraph [0183]; a lesion of a particular size);
determining a number, position, and length of biopsy cores (Paragraphs [0183]-[0184]; biopsy sites are shown, as well as the length of the core, and the number of biopsy needles with a given core length; Figs. 58 and 62), wherein the biopsy core comprises a cylindrical volume (Paragraph [0080]; needle assembly 100 is used to excise a tissue specimen from a target tissue site … the mandrel or inner needle includes an elongated cylindrical body), and the sum of all of the generally cylindrical volumes of the biopsy cores yields the volume of biopsy tissue necessary to detect tumors of a predetermined size;
determining needle paths for each of the biopsy cores (Paragraph [0185]; After generating a biopsy plan and making adjustments to the variables as necessary, a user can view the orientation of the biopsy needles 1480 in the three-dimensional prostate image 1472; Needle paths for the cores are determined in order to draw the paths in the image; Paragraph [0213]; A three-dimensional image of the prostate, urethra, and rectum with the biopsy needle paths is generated from the image slices and the biopsy site plan at step 1664), wherein the needle path is determined in conjunction with an anatomical location for needle access;
generating and displaying a three-dimensional biopsy plan representing the biopsy cores and the needle paths (Paragraph [0183]; FIG. 58, 56 biopsy sites are shown, as well as the length of the core, and the number of biopsy needles with a given core length needed to complete the biopsy plan; Paragraph [0185]; After generating a biopsy plan and making adjustments to the variables as necessary, a user can view the orientation of the biopsy needles 1480 in the three-dimensional prostate image 1472; In FIG. 62, the prostate with needles image 1518 is viewed; the 3-D image showing the biopsy plan with needle paths is considered to read on the claimed limitation of a three-dimensional biopsy plan representing the biopsy cores and the needle paths as understood in its broadest reasonable interpretation).
Stone does not explicitly teach that the sum of all of the cylindrical volumes of the biopsy cores yields the volume of biopsy tissue determined to detect tumors of a predetermined size; and
the needle path is determined in conjunction with an anatomical location for needle access; and
the needle path is determined in order to avoid anatomical constraints.
Kepner, however, teaches that the sum of all of the cylindrical volumes of the biopsy cores yields the volume of biopsy tissue determined to detect tumors of a predetermined size (Pg. 2, Para. 3; the mathematical analysis of this uniform transperineal core pattern calculates the probability that a spherical tumor of a given diameter and volume will be detected, based on the ratio of the volume of the locations where it would be detected to the total volume between the cores, Fig. 1B; A).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the system of Stone to have included that the sum of all of the cylindrical volumes of the biopsy cores yields the volume of biopsy tissue determined to detect tumors of a predetermined size as taught by Kepner because it would have allowed one to more accurately determine the probability of detecting the tumor of the predetermined size for an arrangement of biopsy needles by comparing the total volume of biopsy tissue to the total size of the prostate volume and further offering a quantitative tool to help determine the template spacing options for placing the cores in a template-guided transperineal biopsy (Kepner, Pg. 7, Para. 2).
Together Stone and Kepner does not explicitly teach the needle path is determined in conjunction with an anatomical location for needle access; and
the needle path is determined in order to avoid anatomical constraints.
Zhan, however, teaches determining, using the processor and the image data, needle paths for each of the biopsy cores (Pg. 780, left col.; which proposed to optimize biopsy strategy based on a statistical atlas of spatial distribution of prostate cancer; Pg. 783, left col.; Given the prostate ultrasound image of a specific patient, the initial position of the deformable model is determined by transforming it to a pose which optimally matches with prostate boundary in the ultrasound image.), wherein the needle path is determined in conjunction with an anatomical location for needle access (Pg. 785; We also tested the optimized biopsy strategy for different physical constraints… for the optimal transperineal and trans-rectal biopsy cores; Fig. 9 show the optimized core distribution using the transperineal and transrectal biopsy operations. The placement of the cores based on the transperineal or transrectal constraints is considered to read on the claimed limitations of needle path is determined in conjunction with an anatomical location for needle access as understood in its broadest reasonable interpretation).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the system of Stone in view of Kepner such that the needle path is determined in conjunction with an anatomical location for needle access as taught by Zhan because it would have ensured the placement of the biopsy needles are compatible with known biopsy operations and protocols, and thereby allowed realistic sampling of biopsy cores (Pg. 785; Right col.).
Together Stone, Kepner, and Zhan do not explicitly teach the needle path is determined in order to avoid anatomical constraints.
Kumar, however, teaches a method of biopsy planning (Paragraph [0005]; 3-D image guidance has been applied to prostate biopsy such that the prostate can be sampled in a desired fashion) comprising determining needle paths for each of the biopsy core (Paragraph [0054]; The presented invention does it automatically such that during planning, it computes which region the planned site lies in and the zone corresponding to the sampled core is also automatically computed) wherein the needle path is determined in order to avoid anatomical constraints (Paragraph [0064]; may automatically place planned sites on all grid elements lying inside the prostate that avoid certain regions; automatically load a system generated plan customized to the prostate. The custom plan may be computed such that the user-specified regions such as urethra and neighboring organs and nerve bundles may be avoided during plan generation).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have the system of Stone in view of Kepner and Zhan such that the needle path is determined in order to avoid anatomical constraints as taught by Kumar because it would have avoided accidental placement of needle for either biopsy or placing a bead at these sensitive locations during a procedure (Paragraph [0064]) thereby reducing the possibility of damage to sensitive locations and improving the safety of the procedure.
Regarding claim 13, together Stone, Kepner, Zhan, and Kumar teach all of the limitations of claim 12 as noted above. Stone further teaches the system comprises a computing device (Paragraphs [0221] and [0224]; the device can include computational elements, including processors).
Regarding claim 16, together Stone, Kepner, Zhan, and Kumar teach all of the limitations of claim 12 as noted above. Stone and further teaches detecting tumors in the prostate gland (Paragraphs [0002], [0145], and [0180]-[0186]).
Regarding claim 17, together Stone, Kepner, Zhan, and Kumar teach all of the limitations of claim 12 as noted above.
Stone does not explicitly teach detecting tumors in any organ with a boundary that is segmentable as a surface.
Zhan, however, teaches detecting tumors in any organ (prostate gland; Zhan, Title, Pg. 779-788; prostate biopsy) with a boundary (Pg. 782-784, Surface-Based Registration With Automatic Ultrasound Image Segmentation, texture characteristics to delineate prostate boundaries) that is segmentable (Pg. 780-784, Surface-Based Registration With Automatic Ultrasound Image Segmentation; an automatic segmentation method is designed to define the prostate capsule from ultrasound images) as a surface (Pg. 780-784; using the surface-based registration method).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have included detecting tumors in any organ with a boundary that is segmentable as a surface into the method of Stone in view of Kepner, Zhan, and Kumar as taught by Zhan as it could be used to determine the prostate surface from the ultrasound image and guide the deformable registration with the atlas in clinical settings where ultrasound imaging is commonly used for biopsy guidance (Zhan, Pg. 780).
Regarding claim 19, together Stone, Kepner, Zhan, and Kumar teach all of the limitations of claim 12 as noted above. Stone further teaches setting the tumor detection area (Paragraph [0182]; Selecting the inside capsule radio button causes the system to select needle lengths that are always inside the capsule. Selecting the outside capsule radio button causes the system to select needle lengths that may end up outside of the capsule, and would require the use of more biopsy needles, require the biopsy of longer core bed lengths, or both).
Regarding claim 20, together Stone, Kepner, Zhan, and Kumar teach all of the limitations of claim 12 as noted above. Stone further teaches that the system comprises a biopsy device (Abstract, Paragraphs [0002], [0079]-[0123]; biopsy needle, biopsy actuator, Figs. 3 and 10 #100 and 200).
Claims 3, 4, 14, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Stone in view of Kepner, Zhan, and Kumar as applied to claims 1 and 12 above, respectively, and further in view of Han (non-patent literature, Han et al. 2012, Geometric Evaluation of Systemic Transrectal Ultrasound Guided Prostate Biopsy) and Gazit (US 20160300351)..
Regarding claim 3, together Stone, Kepner, Zhan, and Kumar teach all of the limitations of claim 1 as noted above.
Stone does not teach a method further comprising: setting a bounding box for a tumor detection area and a voxel size to discretize the volume of biopsied tissue to detect tumors of a predetermined size at a predetermined level of resolution;
iterating through all voxels;
checking if a voxel center is within the tumor detection area, and if so add it to a set Γ;
iterating through all voxels of set Γ;
verifying if the voxel center falls within any of the biopsy cores of a set Π;
counting the voxel with a center that falls within any of the biopsy cores of set Π as sampled by adding it to a set Ω; and
calculating tumor prediction probability as the ratio of the number of elements of the Ω and Γ sets.
Han, however, teaches calculating tumor prediction probability as a ratio of nonoverlapping individual sampled volume to the total prostate volume (Pg. 3; Significant PCa Detection Rate Modeling).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have included the method of calculating tumor prediction probability of Han into the method of Stone in view of Kepner, Zhan, and Kumar as it is a simple substitution of the method to calculate tumor detection probability that would give obtain a predictable result of calculating the tumor detection probability.
The modified method of Stone in view of Han does not teach setting a bounding box for a tumor detection area and a voxel size to discretize the volume of biopsied tissue determined to detect tumors of a predetermined size at a predetermined level of resolution;
iterating through all voxels;
checking if a voxel center is within the tumor detection area, and if so add it to a set Γ;
iterating through all voxels of set Γ;
verifying if the voxel center falls within any of the biopsy cores of a set Π;
counting the voxel with a center that falls within any of the biopsy cores of set Π as sampled by adding it to a set Ω; and
calculating tumor prediction probability as the ratio of the number of elements of the Ω and Γ sets.
Gazit, however, teaches a method of calculating tumor detection probability (Paragraphs [0096], [0125], [0147]-[0150], [0211]-[0218] and [0236]-[0239]; Examiner notes the method teaches determining a probability that a structure is part of an organ, for example a lesion, which is the probability of detecting the tumor in the organ. See Paragraphs [0096], [0125], and [0236]-[0239]) with steps comprising: setting a bounding box (Paragraphs [0027] and [0129]-[0131]; bounding region, rectangular box) for a tumor detection area (Paragraph [0129]; target organ) and a voxel size (Paragraph [0148]; voxels of uniform size) to discretize this volume of biopsied tissue determined to detect tumors of a predetermined size (Paragraphs [0146]-[0148]; volume of the target organ, discrete set of locations) at a predetermined level of resolution (Paragraphs [0147] and [0148]; then the probabilistic atlas is optionally a cube of width 1, with each coordinate ranging from 0 to 1, and the width of the rectangular box in each principle direction, in each image, is optionally normalized to 1 when it is mapped to the probabilistic atlas. For example 100 voxels of uniform size across the width of the probabilistic atlas in each principle direction; Examiner notes that normalizing a volume to a set width and choosing a set number of voxels with uniform size would discretize the volume at a predetermined level of resolution);
iterating through all voxels (Paragraphs [0186]-[0188] and [0196]-[0200]; all of the voxels in the image are added to the mask);
checking if a voxel center is within the tumor detection area, and if so add it to a set Γ (Paragraph [0186]; all of the voxels of the target organ; Examiner notes these step are interpreted as steps to segment the tumor detection area into a discrete set of voxels. These steps together are equivalent to the steps of iterating through the voxels and obtaining the set of all voxels which include the target organ, as expressed in Gazit, Paragraphs [0185] and [0186]. Examiner notes that the total volume of the target organ would be proportional to the number of the elements of voxels including the target organ for some level of determined resolution);
iterating through all voxels of set Γ; verifying if the voxel center falls within any of the biopsy cores of a set Π; counting the voxel with a center that falls within any of the biopsy cores of set Π as sampled by adding it to a set Ω (Paragraph [0238]; the voxels that belong to any lesions; Examiner notes these steps are interpreted as steps to segment the biopsy volume into a discrete set of voxels. These steps together are equivalent to the steps of selecting voxels that belong to any lesion. Examiner notes that the biopsy volume would be proportional to the number of the elements of voxels selected belonging to any lesion for some level of determined resolution).
Examiner notes that one of ordinary skill in the art before the effective filing date of the invention would have realized that dividing the number of voxels selected belonging to any lesion by the number of all voxels which include the target organ would have been equivalent to taking a ratio of nonoverlapping individual sampling volume to the total organ volume, thereby calculating the tumor detection probability as taught by Han.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have used the method of Gazit to set a bounding box for a tumor detection area, set a discrete voxel size, segment the target organ into a set of voxels containing the target organ, and select a set of voxels selected belonging to any lesion with the method of calculating tumor detection probability as taught by the modified method of Stone in view of Kepner, Zhan, Han, and Kumar as it would be generic for many types of organs, and a new, unseen organ class may be added without making any changes to the code, simply by adding training examples of this organ (Gazit, Paragraphs [0223] and [0224]), and the method works well for images with widely variant fields of view, resolutions, contrast agent usage, noise levels, anomalies and pathologies, and other characteristics, as long as the training set is broad enough to encompass the desired range. Furthermore, the training set can always be broadened to better represent any class of test images that is found not to be segmented well (Gazit, Paragraph [0225]) and the method for segmenting images is very fast (Gazit, Paragraph [0226]).
Regarding claim 4, together Stone, Kepner, Zhan, and Kumar teaches all of the limitations of claim 1 as noted above.
Stone does not teach a method further comprising: setting the volume of the tumor detection area and a voxel size to discretize the volume of the tumor detection area at a predetermined level of resolution to a set of voxels Γ;
defining the tumor detection area of a biopsy core as a capsule surrounding the biopsy core with a cylindrical volume having hemispherical end caps of the diameter of the tumor to be detected;
iterating through all voxels of Γ and checking if a voxel center is within the tumor detection area of the biopsy cores of the plan;
adding the voxel center to a sampled voxel set Ω; and
calculating tumor prediction probability as the ratio of the number of elements of the detected voxel set Ω and tumor search area voxel set Γ.
Han, however, teaches a method of calculating tumor prediction with steps comprising: defining a tumor detection area (Pg. 3; Significant PCa Detection Rate Modeling; sampled volume is defined as the volume of the intersection between the respective capsule and the prostate) of a biopsy core (Pg. 3; Significant PCa Detection Rate Modeling; single core biopsy) as a capsule (Pg. 3; Significant PCa Detection Rate Modeling; the respective capsule) surrounding the biopsy core with a cylindrical volume having hemispherical end caps of the diameter (Pg. 3; Significant PCa Detection Rate Modeling; In the capsule modeling approach, we considered the tumor sampled and significant if its center was within a capsule with a radius equal to that of the tumor. See Fig. 2) of a tumor (Pg. 3; Significant PCa Detection Rate Modeling; a significant tumor) to be detected; and
Calculating tumor probability as a ratio of nonoverlapping individual sampled volume to the total prostate volume (Pg. 3; Significant PCa Detection Rate Modeling)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have included defining a tumor detection area of a biopsy core as a capsule surrounding the biopsy core with a cylindrical volume having hemispherical end caps of the diameter of a tumor to be detected in the method of Stone as this approach is equivalent to intersecting a line segment (biopsy core) with a sphere (tumor) but it simplifies the geometric calculations (Han, Pg. 3; Significant PCa Detection Rate Modeling); and to have included the method of calculating tumor prediction probability of Han into the method of Stone in view of Kepner, Zhan, and Kumar as it is a simple substitution of the method to calculate tumor detection probability that would give obtain a predictable result of calculating the tumor detection probability.
The modified method of Stone in view of Han does not teach setting the volume of the tumor detection area and a voxel size to discretize the volume of the tumor detection area at a predetermined level of resolution to a set of voxels Γ;
iterating through all voxels of Γ and checking if a voxel center is within the tumor detection area of the biopsy cores of the plan;
adding the voxel center to a sampled voxel set Ω; and
calculating tumor prediction probability as the ratio of the number of elements of the detected voxel set Ω and tumor search area voxel set Γ.
Gazit, however, teaches a method of calculating tumor detection probability (Paragraphs [0096], [0125], [0147]-[0150], [0211]-[0218] and [0236]-[0239]; Examiner notes the method teaches determining a probability that a structure is part of an organ, for example a lesion, which is the probability of detecting the tumor in the organ. See Paragraphs [0096], [0125], and [0236]-[0239]) with steps comprising: setting a volume (Paragraphs [0027] and [0129]-[0131]; bounding region including all of the voxels of the target organ) of a tumor detection area (Paragraph [0129]; target organ) and a voxel size (Paragraph [0148]; voxels of uniform size) to discretize the volume (Paragraphs [0146]-[0148]; volume of the target organ, discrete set of locations) of the tumor detection area at a predetermined level of resolution (Paragraphs [0147] and [0148]; then the probabilistic atlas is optionally a cube of width 1, with each coordinate ranging from 0 to 1, and the width of the rectangular box in each principle direction, in each image, is optionally normalized to 1 when it is mapped to the probabilistic atlas. For example 100 voxels of uniform size across the width of the probabilistic atlas in each principle direction; Examiner notes that normalizing a volume to a set width and choosing a set number of voxels with uniform size would discretize the volume at a predetermined level of resolution) to a set of voxels Γ (Paragraph [0186]; all of the voxels of the target organ; Examiner notes these step are interpreted as steps to segment the tumor detection area into a discrete set of voxels. These steps together are equivalent to the steps of iterating through the voxels and obtaining the set of all voxels which include the target organ, as expressed in Gazit, Paragraphs [0185] and [0186]. Examiner notes that the total volume of the target organ would be proportional to the number of the elements of voxels including the target organ for some level of determined resolution);
iterating through all voxels of Γ and checking if a voxel center is within the tumor detection area of the biopsy cores of the plan; and adding the voxel center to the sampled voxel set Ω (Paragraph [0238]; the voxels that belong to any lesions; Examiner notes these steps are interpreted as steps to selecting voxels that contain the biopsy cores. These steps together are equivalent to the steps of selecting voxels that belong to any lesion. Examiner notes that the biopsy volume would be proportional to the number of the elements of voxels selected belonging to any lesion for some level of determined resolution).
Examiner notes that one of ordinary skill in the art before the effective filing date of the invention would have realized that dividing the number of voxels selected belonging to any lesion by the number of all voxels which include the target organ would have been equivalent to taking a ratio of nonoverlapping individual sampling volume to the total organ volume, thereby calculating the tumor detection probability as taught by Han.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have used the method of Gazit to set a volume for a tumor detection area, set a discrete voxel size, segment the target organ into a set of voxels containing the target organ, and select a set of voxels including the biopsy cores with the method of calculating tumor detection probability as taught by the modified method of Stone in view of Kepner, Zhan, Han, and Kumar as it would be generic for many types of organs, and a new, unseen organ class may be added without making any changes to the code, simply by adding training examples of this organ (Gazit, Paragraphs [0223] and [0224]), and the method works well for images with widely variant fields of view, resolutions, contrast agent usage, noise levels, anomalies and pathologies, and other characteristics, as long as the training set is broad enough to encompass the desired range. Furthermore, the training set can always be broadened to better represent any class of test images that is found not to be segmented well (Gazit, Paragraph [0225]) and the method for segmenting images is very fast (Gazit, Paragraph [0226]).
Regarding claim 14, together Stone, Kepner, Zhan, and Kumar teach all of the limitations of claim 12 as noted above.
Stone does not teach a system further comprising: setting a bounding box for the tumor detection area and a voxel size to discretize the volume of biopsied tissue determined to detect tumors of a predetermined size at a predetermined level of resolution;
iterating through all voxels;
checking if a voxel center is within the tumor detection area, and if so add it to a set Γ;
iterating through all voxels of set Γ;
verifying if the voxel center falls within any of the biopsy cores of a set Π;
counting the voxel with a center that falls within any of the biopsy cores of set Π as sampled by adding it to a set Ω; and
calculating tumor prediction probability as the ratio of the number of elements of the Ω and Γ sets.
Han, however, teaches calculating tumor prediction probability as a ratio of nonoverlapping individual sampled volume to the total prostate volume (Pg. 3; Significant PCa Detection Rate Modeling).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have included the method of calculating tumor prediction probability of Han into the system of Stone in view of Kepner, Zhan, and Kumar as it is a simple substitution of the method to calculate tumor detection probability that would give obtain a predictable result of calculating the tumor detection probability.
The modified system of Stone in view of Han does not teach setting a bounding box for the tumor detection area and a voxel size to discretize the volume of biopsied tissue determined to detect tumors of a predetermined size at a predetermined level of resolution;
iterating through all voxels;
checking if a voxel center is within the tumor detection area, and if so add it to a set Γ;
iterating through all voxels of set Γ;
verifying if the voxel center falls within any of the biopsy cores of a set Π;
counting the voxel with a center that falls within any of the biopsy cores of set Π as sampled by adding it to a set Ω; and
calculating tumor prediction probability as the ratio of the number of elements of the Ω and Γ sets.
Gazit, however, teaches a method of calculating tumor detection probability (Paragraphs [0096], [0125], [0147]-[0150], [0211]-[0218] and [0236]-[0239]; Examiner notes the method teaches determining a probability that a structure is part of an organ, for example a lesion, which is the probability of detecting the tumor in the organ. See Paragraphs [0096], [0125], and [0236]-[0239]) with steps comprising: setting a bounding box (Paragraphs [0027] and [0129]-[0131]; bounding region, rectangular box) for the tumor detection area (Paragraph [0129]; target organ) and a voxel size (Paragraph [0148]; voxels of uniform size) to discretize the volume of biopsied tissue determined to detect tumors of a predetermined size (Paragraphs [0146]-[0148]; volume of the target organ, discrete set of locations) at a predetermined level of resolution (Paragraphs [0147] and [0148]; then the probabilistic atlas is optionally a cube of width 1, with each coordinate ranging from 0 to 1, and the width of the rectangular box in each principle direction, in each image, is optionally normalized to 1 when it is mapped to the probabilistic atlas. For example 100 voxels of uniform size across the width of the probabilistic atlas in each principle direction; Examiner notes that normalizing a volume to a set width and choosing a set number of voxels with uniform size would discretize the volume at a predetermined level of resolution);
iterating through all voxels (Paragraphs [0186]-[0188] and [0196]-[0200]; all of the voxels in the image are added to the mask);
checking if a voxel center is within the tumor detection area, and if so add it to a set Γ (Paragraph [0186]; all of the voxels of the target organ); iterating through all voxels of set Γ (Examiner notes these step are interpreted as steps to segment the tumor detection area into a discrete set of voxels. These steps together are equivalent to the steps of iterating through the voxels and obtaining the set of all voxels which include the target organ, as expressed in Gazit, Paragraphs [0185] and [0186]. Examiner notes that the total volume of the target organ would be proportional to the number of the elements of voxels including the target organ for some level of determined resolution);
verifying if the voxel center falls within any of the biopsy cores of a set Π; counting the voxel with a center that falls within any of the biopsy cores of set Π as sampled by adding it to a set Ω (Paragraph [0238]; the voxels that belong to any lesions; Examiner notes these steps are interpreted as steps to segment the biopsy volume into a discrete set of voxels. These steps together are equivalent to the steps of selecting voxels that belong to any lesion. Examiner notes that the biopsy volume would be proportional to the number of the elements of voxels selected belonging to any lesion for some level of determined resolution).
Examiner notes that one of ordinary skill in the art before the effective filing date of the invention would have realized that dividing the number of voxels selected belonging to any lesion by the number of all voxels which include the target organ would have been equivalent to taking a ratio of nonoverlapping individual sampling volume to the total organ volume, thereby calculating the tumor detection probability as taught by Han.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have used the method of Gazit to set a bounding box for a tumor detection area, set a discrete voxel size, segment the target organ into a set of voxels containing the target organ, and select a set of voxels selected belonging to any lesion with the system of calculating tumor detection probability as taught by the modified method of Stone in view of Kepner, Zhan, Kumar, and Han as it would be generic for many types of organs, and a new, unseen organ class may be added without making any changes to the code, simply by adding training examples of this organ (Gazit, Paragraphs [0223] and [0224]), and the method works well for images with widely variant fields of view, resolutions, contrast agent usage, noise levels, anomalies and pathologies, and other characteristics, as long as the training set is broad enough to encompass the desired range. Furthermore, the training set can always be broadened to better represent any class of test images that is found not to be segmented well (Gazit, Paragraph [0225]) and the method for segmenting images is very fast (Gazit, Paragraph [0226]).
Regarding claim 15, together Stone, Kepner, Zhan, and Kumar teaches all of the limitations of claim 12 as noted above.
Stone does not teach a system further comprising: setting the volume of the tumor detection area and a voxel size to discretize the volume of the tumor detection area at a predetermined level of resolution to a set of voxels Γ;
defining the tumor detection area of a biopsy core as a capsule surrounding the biopsy core with a cylindrical volume having hemispherical end caps of the diameter of the tumor to be detected;
iterating through all voxels of Γ and checking if a voxel center is within the tumor detection area of the biopsy cores of the plan;
adding the voxel center to a sampled voxel set Ω; and
calculating tumor prediction probability as the ratio of the number of elements of the detected voxel set Ω and tumor search area voxel set Γ.
Han, however, teaches a method of calculating tumor prediction with steps comprising: defining a tumor detection area (Pg. 3; Significant PCa Detection Rate Modeling; sampled volume is defined as the volume of the intersection between the respective capsule and the prostate) of a biopsy core (Pg. 3; Significant PCa Detection Rate Modeling; single core biopsy) as a capsule (Pg. 3; Significant PCa Detection Rate Modeling; the respective capsule) surrounding the biopsy core with a cylindrical volume having hemispherical end caps of the diameter (Pg. 3; Significant PCa Detection Rate Modeling; In the capsule modeling approach, we considered the tumor sampled and significant if its center was within a capsule with a radius equal to that of the tumor. See Fig. 2) of a tumor (Pg. 3; Significant PCa Detection Rate Modeling; a significant tumor) to be detected; and
calculating tumor probability as a ratio of nonoverlapping individual sampled volume to the total prostate volume (Pg. 3; Significant PCa Detection Rate Modeling)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have included defining a tumor detection area of a biopsy core as a capsule surrounding the biopsy core with a cylindrical volume having hemispherical end caps of the diameter of a tumor to be detected in the system of Stone as this approach is equivalent to intersecting a line segment (biopsy core) with a sphere (tumor) but it simplifies the geometric calculations (Han, Pg. 3; Significant PCa Detection Rate Modeling); and to have included the method of calculating tumor prediction probability of Han into the method of Stone in view of Kepner, Zhan, and Kumar as it is a simple substitution of the method to calculate tumor detection probability that would give obtain a predictable result of calculating the tumor detection probability.
The modified system of Stone in view of Han does not teach setting the volume of the tumor detection area and a voxel size to discretize the volume of the tumor detection area at a predetermined level of resolution to a set of voxels Γ;
iterating through all voxels of Γ and checking if a voxel center is within the tumor detection area of the biopsy cores of the plan;
adding the voxel center to a sampled voxel set Ω; and
calculating tumor prediction probability as the ratio of the number of elements of the detected voxel set Ω and tumor search area voxel set Γ.
Gazit, however, teaches a method of calculating tumor detection probability (Paragraphs [0096], [0125], [0147]-[0150], [0211]-[0218] and [0236]-[0239]; Examiner notes the method teaches determining a probability that a structure is part of an organ, for example a lesion, which is the probability of detecting the tumor in the organ. See Paragraphs [0096], [0125], and [0236]-[0239]) with steps comprising: setting the volume (Paragraphs [0027] and [0129]-[0131]; bounding region including all of the voxels of the target organ) of the tumor detection area (Paragraph [0129]; target organ) and a voxel size (Paragraph [0148]; voxels of uniform size) to discretize the volume (Paragraphs [0146]-[0148]; volume of the target organ, discrete set of locations) of the tumor detection area at a predetermined level of resolution (Paragraphs [0147] and [0148]; then the probabilistic atlas is optionally a cube of width 1, with each coordinate ranging from 0 to 1, and the width of the rectangular box in each principle direction, in each image, is optionally normalized to 1 when it is mapped to the probabilistic atlas. For example 100 voxels of uniform size across the width of the probabilistic atlas in each principle direction; Examiner notes that normalizing a volume to a set width and choosing a set number of voxels with uniform size would discretize the volume at a predetermined level of resolution) to a set of voxels Γ (Paragraph [0186]; all of the voxels of the target organ; Examiner notes these step are interpreted as steps to segment the tumor detection area into a discrete set of voxels. These steps together are equivalent to the steps of iterating through the voxels and obtaining the set of all voxels which include the target organ, as expressed in Gazit, Paragraphs [0185] and [0186]. Examiner notes that the total volume of the target organ would be proportional to the number of the elements of voxels including the target organ for some level of determined resolution);
iterating through all voxels of Γ and checking if a voxel center is within the tumor detection area of the biopsy cores of the plan; and adding the voxel center to the sampled voxel set Ω (Paragraph [0238]; the voxels that belong to any lesions; Examiner notes these steps are interpreted as steps to selecting voxels that contain the biopsy cores. These steps together are equivalent to the steps of selecting voxels that belong to any lesion. Examiner notes that the biopsy volume would be proportional to the number of the elements of voxels selected belonging to any lesion for some level of determined resolution).
Examiner notes that one of ordinary skill in the art before the effective filing date of the invention would have realized that dividing the number of voxels selected belonging to any lesion by the number of all voxels which include the target organ would have been equivalent to taking a ratio of nonoverlapping individual sampling volume to the total organ volume, thereby calculating the tumor detection probability as taught by Han.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have used the method of Gazit to set a volume for a tumor detection area, set a discrete voxel size, segment the target organ into a set of voxels containing the target organ, and select a set of voxels including the biopsy cores with the method of calculating tumor detection probability as taught by the modified method of Stone in view of Kepner, Zhan, Kumar, and Han as it would be generic for many types of organs, and a new, unseen organ class may be added without making any changes to the code, simply by adding training examples of this organ (Gazit, Paragraphs [0223] and [0224]), and the method works well for images with widely variant fields of view, resolutions, contrast agent usage, noise levels, anomalies and pathologies, and other characteristics, as long as the training set is broad enough to encompass the desired range. Furthermore, the training set can always be broadened to better represent any class of test images that is found not to be segmented well (Gazit, Paragraph [0225]) and the method for segmenting images is very fast (Gazit, Paragraph [0226]).
Claims 7, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Stone in view of Kepner, Zhan, and Kumar as applied to claims 1 and 12 above, respectively, and further in view of Han (non-patent literature, Han et al. 2012, Geometric Evaluation of Systemic Transrectal Ultrasound Guided Prostate Biopsy).
Regarding claim 7, together Stone, Kepner, Zhan, and Kumar teach all of the limitations of claim 1 as noted above.
Stone does not teach representing the biopsy cores as a capsule with a cylindrical volume having hemispherical end caps.
Han, however, teaches representing the biopsy cores as a capsule with a cylindrical volume having hemispherical end caps (Pg. 3; Significant PCa Detection Rate Modeling; In the capsule modeling approach, we considered the tumor sampled and significant if its center was within a capsule with a radius equal to that of the tumor. See Fig. 2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have included representing the biopsy cores as a capsule with a cylindrical volume having hemispherical end caps in the method of Stone in view of Kepner, Zhan, and Kumar as this approach is equivalent to intersecting a line segment (biopsy core) with a sphere (tumor) but it simplifies the geometric calculations (Han, Pg. 3; Significant PCa Detection Rate Modeling).
Regarding claim 11, together Stone, Kepner, Zhan, and Kumar teaches all of the limitations of claim 1 as noted above.
Stone does not teach defining a tumor detection area of a biopsy core as a capsule surrounding the biopsy core with a cylindrical volume having hemispherical end caps of the diameter of a tumor to be detected.
Han, however, teaches defining a tumor detection area (Pg. 3; Significant PCa Detection Rate Modeling; sampled volume is defined as the volume of the intersection between the respective capsule and the prostate) of a biopsy core (Pg. 3; Significant PCa Detection Rate Modeling; single core biopsy) as a capsule (Pg. 3; Significant PCa Detection Rate Modeling; the respective capsule) surrounding the biopsy core with a cylindrical volume having hemispherical end caps of the diameter (Pg. 3; Significant PCa Detection Rate Modeling; In the capsule modeling approach, we considered the tumor sampled and significant if its center was within a capsule with a radius equal to that of the tumor. See Fig. 2) of a tumor (Pg. 3; Significant PCa Detection Rate Modeling; a significant tumor) to be detected.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have included defining a tumor detection area of a biopsy core as a capsule surrounding the biopsy core with a cylindrical volume having hemispherical end caps of the diameter of a tumor to be detected in the method of Stone in view of Kepner, Zhan, and Kumar as this approach is equivalent to intersecting a line segment (biopsy core) with a sphere (tumor) but it simplifies the geometric calculations (Han, Pg. 3; Significant PCa Detection Rate Modeling).
Regarding claim 18, together Stone, Kepner, Zhan, and Kumar teach all of the limitations of claim 12 as noted above.
Stone does not teach representing the biopsy cores as a capsule with a cylindrical volume having hemispherical end caps.
Han, however, teaches representing the biopsy cores as a capsule with a cylindrical volume having hemispherical end caps (Pg. 3; Significant PCa Detection Rate Modeling; In the capsule modeling approach, we considered the tumor sampled and significant if its center was within a capsule with a radius equal to that of the tumor. See Fig. 2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have included representing the biopsy cores as a capsule with a cylindrical volume having hemispherical end caps in the system of Stone in view of Kepner, Zhan, and Kumar as this approach is equivalent to intersecting a line segment (biopsy core) with a sphere (tumor) but it simplifies the geometric calculations (Han, Pg. 3; Significant PCa Detection Rate Modeling).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 10751034 in view of Zhan (Non-Patent Literature: Zhan, Y. et al. 2007, Targeted Prostate Biopsy Using Statistical Image Analysis) and Kumar (US 20100172559 A1).
Although the claims at issue are not identical, they are not patentably distinct from each other because:
Claims 1 and 12 of the present application and Claims 1 and 12 of the issued patent are directed toward generating a three-dimensional biopsy plan and determining a number and length of cores.
Claims 1 and 12 of the present application further recite: “determining a volume… to be biopsied” and “determining a volume… necessary to detect tumors of a predetermined size”. This a is an obvious broadening of scope over the issued patent which explicitly requires “generating a three-dimensional biopsy plan that increases the probability of the significant tumor detection” which would require steps of determining volumes to biopsy and volumes necessary to detect tumors of a predetermined size.
Claims 1 and 12 of the present application further recite “determining a position of biopsy cores” and “determining needle paths for each of the biopsy cores”. This a is an obvious broadening of scope over the issued patent which explicitly requires “generating a three-dimensional biopsy plan” which includes “a and length of biopsy cores required to execute the revised three-dimensional plan” as generating the three-dimensional plan would include a step of determining positions and needle paths of biopsy cores in order to execute the biopsy plan and obtain the biopsy cores.
Claim 1 and 12 of the issued patent, however, fail to teach the limitations of “the needle path is determined in conjunction with an anatomical location for needle access” and
“the needle path is determined in order to avoid anatomical constraints”.
Zhan, however, teaches determining, using the processor and the image data, needle paths for each of the biopsy cores (Pg. 780, left col.; which proposed to optimize biopsy strategy based on a statistical atlas of spatial distribution of prostate cancer; Pg. 783, left col.; Given the prostate ultrasound image of a specific patient, the initial position of the deformable model is determined by transforming it to a pose which optimally matches with prostate boundary in the ultrasound image.), wherein the needle path is determined in conjunction with an anatomical location for needle access (Pg. 785; We also tested the optimized biopsy strategy for different physical constraints… for the optimal transperineal and trans-rectal biopsy cores; Fig. 9 show the optimized core distribution using the transperineal and transrectal biopsy operations. The placement of the cores based on the transperineal or transrectal constraints is considered to read on the claimed limitations of needle path is determined in conjunction with an anatomical location for needle access as understood in its broadest reasonable interpretation).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method and system of the issued patent such that the needle path is determined in conjunction with an anatomical location for needle access as taught by Zhan because it would have ensured the placement of the biopsy needles are compatible with known biopsy operations and protocols, and thereby allowed realistic sampling of biopsy cores (Pg. 785; Right col.).
The issued patent in view of Zhan further fails to teach the limitation of “the needle path is determined in order to avoid anatomical constraints”.
Kumar, however, teaches a method of biopsy planning (Paragraph [0005]; 3-D image guidance has been applied to prostate biopsy such that the prostate can be sampled in a desired fashion) comprising determining needle paths for each of the biopsy core (Paragraph [0054]; The presented invention does it automatically such that during planning, it computes which region the planned site lies in and the zone corresponding to the sampled core is also automatically computed) wherein the needle path is determined in order to avoid anatomical constraints (Paragraph [0064]; may automatically place planned sites on all grid elements lying inside the prostate that avoid certain regions; automatically load a system generated plan customized to the prostate. The custom plan may be computed such that the user-specified regions such as urethra and neighboring organs and nerve bundles may be avoided during plan generation).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have the method and system of the issued patent in view of Zhan such that the needle path is determined in order to avoid anatomical constraints as taught by Kumar because it would have avoided accidental placement of needle for either biopsy or placing a bead at these sensitive locations during a procedure (Paragraph [0064]) thereby reducing the possibility of damage to sensitive locations and improving the safety of the procedure.
Dependent claims 2-11 and 13-20 of the present application are nearly identical in wording to dependent claims 2-11 and 13-20 of the issued patent. When taken together with the respective independent claims, these claims constitute no more than an obvious broadening of scope over the issued patent.
Response to Arguments
Claim Rejections – 35 U.S.C. § 112
Examiner acknowledges the amendments to the claims and withdraws previous rejections under 35 USC 112(b) with respect to claim 9.
Claim Rejections – 35 U.S.C. § 102 and 103
Applicant’s arguments with respect to the previous 35 U.S.C. § 103 rejections have been considered but are moot in view of the updated grounds of rejection necessitated by amendments.
Double Patenting
Applicant’s arguments with respect to the previous nonstatutory double patenting rejections have been considered but are moot in view of the updated grounds of rejection necessitated by amendments.
Conclusion
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/DEAN N EDUN/Examiner, Art Unit 3797
/ANH TUAN T NGUYEN/Supervisory Patent Examiner, Art Unit 3795
04/24/26
Prosecution Insights