DETAILED ACTION
5otice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 8-12, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Bzdusek WO 2019/025460 in view of Vik et al. US 2018/0078786.
Regarding claim 1, Bzdusek teaches a method for optimizing a volumetric modulated arc therapy (VMAT) treatment plan (fig. 1, 2; abstract; pg. 7 ln. 26-28), comprising:
obtaining a VMAT treatment plan (52) from a treatment planning system (TPS), the VMAT treatment plan having a plurality of control points (weights 64), each control point (64) having a set of leaf positions corresponding to a set of leaves of a multileaf collimator (MLC) (16; pg. 8 In. 6-8) in a field of a linear accelerator (10; pg. 7 ln. 33) (linac) (figs 1, 2; pg. 8 In. 6-8 "…A multi-leaf collimator (MLC) 16 comprises adjustable leaves that can be set to shape the radiation beam during the radiation treatment, and these MLC settings can also be parameters of the parameterized radiation treatment plan..."; pg. 10 In. 13-18 "…the OFV goals 52 serve as inputs to a radiation treatment plan optimizer 58 that optimizes a dose distribution 60 with respect to a composite objective function 62 comprising a weighted sum of the objective functions 50 weighted by weights 64 for the objective functions 50 determined from OFV goals 52...");
calculating a radiation dose matrix (60) corresponding to each beamlet, wherein a beamlet is the change in field when an MLC leaf is moved a predetermined unit distance (figs. 1, 2; pg. 2 In. 13-14, "…The 'virtual' parameters, if employed, may for example include weights of 'beamlets" representing small-area segments of the radiation beam..."; pg. 10 In. 18-19, "...the dose distribution 60 in the patient (as represented by the planning image with ROIs 42) is initially computed..."; note: a dose distribution is deemed equivalent to a 2D matrix of intensity values corresponding to the planning image);
defining an enhanced objective function (EOF) (62) for achieving one or more clinical objectives, including achieving at least a minimum dose to a target volume and minimizing a dose to an organ at risk (figs. 1, 2; pg. 2 In. 33-34 "...the dosimetrist identifies objectives which are not met or which appear to be difficult to meet. and may manually adjust certain objectives (e.g. adjust the maximum or minimum dose specified for a Max Dose or Min Dose objective…"; pg. 10 In. 15-18 "...a composite objective function 62 comprising a weighted sum of the objective functions 50 weighted by weights 64 for the objective functions 50 determined from OFV goals 52 for the (corresponding) objective functions..."; pg. 13 In. 30-33 "...the ROIs defined in the planning image of the patient include at least one target ROI to be irradiated, such as a malignant tumor, and at least one organ-at-risk (OAR) ROI to be at least partly spared irradiation...");
minimizing the EOF iterating through the control points (figs. 1, 2; pg. 11 In. 17-18 "...if the current OFV is greater than the OFV goal, the parameters of the objective function are modified to decrease the OFV to approximately the OFV goal..."); and updating the set of leaf positions of the VMAT treatment plan according to the proposed leaf positions of the minimized EOF (figs. 1, 2; step 86; pg. 8 In. 6-8. "...A multi-leaf collimator (MLC) 16 comprises adjustable leaves that can be set to shape the radiation beam during the radiation treatment, and these MLC settings can also be parameters of the parameterized radiation treatment plan…"; pg. 13 In. 17-18, "...in an operation 86 if the OFVs are out of the desired balance then at least one OFV goal is updated by the OFV goals updater 70 to produce updated OFV goals…").
Bzdusek fails to teach minimizing the EOF for proposed leaf positions iterating through each leaf of at least a subset of the leaves of the VMAT treatment plan, wherein the proposed leaf positions move each leaf into the field or out of the field by the predetermined unit distance and corresponds to the addition or subtraction of the corresponding radiation dose matrix.
Vik teaches an optimizing radiation treatment (abstract) and optimization based on minimizing an objective function for proposed leaf positions iterating through each leaf of at least a subset of the leaves of the VMAT treatment plan, wherein the proposed leaf positions move each leaf into the field or out of the field by the predetermined unit distance and corresponds to the addition or subtraction of a corresponding radiation dose matrix (fig 2, para [0043], "..predictor-corrector optimizer unit 24...calculate the dose
d
c
u
r
r
in the formal parameter space 62. One method to map the approximated discrete fluence maps
x
c
u
r
r
to dose is with a fluence dose contribution matrix P. For each CP, a non-negative mapping exists from the set of MLC leaf positions 27 to the dose volume d...The set of leaf positions define ray blocking and passing regions represented with a function...optimizes the parameters x to minimize the objective function value O.").
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have minimizing the EOF for proposed leaf positions iterating through each leaf of at least a subset of the leaves of the VMAT treatment plan, wherein the proposed leaf positions move each leaf into the field or out of the field by the predetermined unit distance and corresponds to the addition or subtraction of the corresponding radiation dose matrix as taught by Vik in the method of Bzdusek for the purpose of optimizing the radiation treatment plan.
Regarding claim 2, Bzdusek teaches wherein the minimizing and updating steps are performed for each control point (each weight 64) of the VMAT treatment plan (fig. 2; pg. 10 In. 13-18 "...the OFV goals 52 serve as inputs to a radiation treatment plan optimizer 58 that optimizes a dose distribution 60 with respect to a composite objective function 62 comprising a weighted sum of the objective functions 50 weighted by weights 64 for the objective functions 50 determined from OFV goals 52…"; pg. 13 In. 17-18 "...in an operation 86 if the OFVs are out of the desired balance then at least one OFV goal is updated by the OFV goals updater 70 to produce updated OFV goals...").
Regarding claim 3, Bzdusek teaches wherein the one or more clinical objectives of the EOF (52) are different from clinical objectives used to generate the VMAT treatment plan (i.e., updated goals from step 86 of Fig 2 which, due to being updated, are implicitly different from previous non-updated goals; fig. 2; pg. 10 In. 13-18 "...the OFV goals 52 serve as inputs to a radiation treatment plan optimizer 58 that optimizes a dose distribution 60 with respect to a composite objective function 62 comprising a weighted sum of the objective functions 50 weighted by weights 64 for the objective functions 50 determined from OFV goals 52…"; pg. 13 In. 17-18 "...in an operation 86 if the OFVs are out of the desired balance then at least one OFV goal is updated by the OFV goals updater 70 to produce updated OFV goals…").
Regarding claim 8, Bzdusek teaches recalculating the updated VMAT treatment plan (pg. 13 In. 17-18 "...in an operation 86 if the OFVs are out of the desired balance then at least one OFV goal is updated by the OFV goals updater 70 to produce updated OFV goals…") with linac and/or leaf-motion constraints (pg. 1 In. 21-22 "...one or more hard constraints may be imposed on the optimization..."; pg. 8 In. 6-8 "...A multi-leaf collimator (MLC) 16 comprises adjustable leaves that can be set to shape the radiation beam during the radiation treatment, and these MLC settings can also be parameters of the parameterized radiation treatment plan...").
Regarding claim 9, Bzdusek teaches generating dose-volume histograms (figs. 5-6; pg. 5 ln. 33-34) and/or isodose curves of the updated VMAT treatment plan.
Regarding claim 10, Bzdusek teaches a VMAT treatment plan optimization system (fig. 1, 2; abstract; pg. 7 ln. 26-28), comprising: a processor (pg. 4 ln. 22); and a memory in electronic communication with the processor (pg. 4 ln. 21-24), the memory comprising instructions for the processor to:
obtaining a VMAT treatment plan (52) from a treatment planning system (TPS), the VMAT treatment plan having a plurality of control points (weights 64), each control point (64) having a set of leaf positions corresponding to a set of leaves of a multileaf collimator (MLC) (16; pg. 8 In. 6-8) in a field of a linear accelerator (10; pg. 7 ln. 33) (linac) (figs 1, 2; pg. 8 In. 6-8 "…A multi-leaf collimator (MLC) 16 comprises adjustable leaves that can be set to shape the radiation beam during the radiation treatment, and these MLC settings can also be parameters of the parameterized radiation treatment plan..."; pg. 10 In. 13-18 "…the OFV goals 52 serve as inputs to a radiation treatment plan optimizer 58 that optimizes a dose distribution 60 with respect to a composite objective function 62 comprising a weighted sum of the objective functions 50 weighted by weights 64 for the objective functions 50 determined from OFV goals 52...");
calculating a radiation dose matrix (60) corresponding to each beamlet, wherein a beamlet is the change in field when an MLC leaf is moved a predetermined unit distance (figs. 1, 2; pg. 2 In. 13-14, "…The 'virtual' parameters, if employed, may for example include weights of 'beamlets" representing small-area segments of the radiation beam..."; pg. 10 In. 18-19, "...the dose distribution 60 in the patient (as represented by the planning image with ROIs 42) is initially computed..."; note: a dose distribution is deemed equivalent to a 2D matrix of intensity values corresponding to the planning image);
defining an enhanced objective function (EOF) (62) for achieving one or more clinical objectives, including achieving at least a minimum dose to a target volume and minimizing a dose to an organ at risk (figs. 1, 2; pg. 2 In. 33-34 "...the dosimetrist identifies objectives which are not met or which appear to be difficult to meet. and may manually adjust certain objectives (e.g. adjust the maximum or minimum dose specified for a Max Dose or Min Dose objective…"; pg. 10 In. 15-18 "...a composite objective function 62 comprising a weighted sum of the objective functions 50 weighted by weights 64 for the objective functions 50 determined from OFV goals 52 for the (corresponding) objective functions..."; pg. 13 In. 30-33 "...the ROIs defined in the planning image of the patient include at least one target ROI to be irradiated, such as a malignant tumor, and at least one organ-at-risk (OAR) ROI to be at least partly spared irradiation...");
minimizing the EOF iterating through the control points (figs. 1, 2; pg. 11 In. 17-18 "...if the current OFV is greater than the OFV goal, the parameters of the objective function are modified to decrease the OFV to approximately the OFV goal..."); and updating the set of leaf positions of the VMAT treatment plan according to the proposed leaf positions of the minimized EOF (figs. 1, 2; step 86; pg. 8 In. 6-8. "...A multi-leaf collimator (MLC) 16 comprises adjustable leaves that can be set to shape the radiation beam during the radiation treatment, and these MLC settings can also be parameters of the parameterized radiation treatment plan…"; pg. 13 In. 17-18, "...in an operation 86 if the OFVs are out of the desired balance then at least one OFV goal is updated by the OFV goals updater 70 to produce updated OFV goals…").
Bzdusek fails to teach minimizing the EOF for proposed leaf positions iterating through each leaf of at least a subset of the leaves of the VMAT treatment plan, wherein the proposed leaf positions move each leaf into the field or out of the field by the predetermined unit distance and corresponds to the addition or subtraction of the corresponding radiation dose matrix.
Vik teaches an optimizing radiation treatment (abstract) and optimization based on minimizing an objective function for proposed leaf positions iterating through each leaf of at least a subset of the leaves of the VMAT treatment plan, wherein the proposed leaf positions move each leaf into the field or out of the field by the predetermined unit distance and corresponds to the addition or subtraction of a corresponding radiation dose matrix (fig 2, para [0043], "..predictor-corrector optimizer unit 24...calculate the dose
d
c
u
r
r
in the formal parameter space 62. One method to map the approximated discrete fluence maps
x
c
u
r
r
to dose is with a fluence dose contribution matrix P. For each CP, a non-negative mapping exists from the set of MLC leaf positions 27 to the dose volume d...The set of leaf positions define ray blocking and passing regions represented with a function...optimizes the parameters x to minimize the objective function value O.").
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have minimizing the EOF for proposed leaf positions iterating through each leaf of at least a subset of the leaves of the VMAT treatment plan, wherein the proposed leaf positions move each leaf into the field or out of the field by the predetermined unit distance and corresponds to the addition or subtraction of the corresponding radiation dose matrix as taught by Vik in the system of Bzdusek for the purpose of optimizing the radiation treatment plan.
Regarding claim 11, Bzdusek teaches wherein the minimizing and updating steps are performed for each control point (each weight 64) of the VMAT treatment plan (fig. 2; pg. 10 In. 13-18 "...the OFV goals 52 serve as inputs to a radiation treatment plan optimizer 58 that optimizes a dose distribution 60 with respect to a composite objective function 62 comprising a weighted sum of the objective functions 50 weighted by weights 64 for the objective functions 50 determined from OFV goals 52…"; pg. 13 In. 17-18 "...in an operation 86 if the OFVs are out of the desired balance then at least one OFV goal is updated by the OFV goals updater 70 to produce updated OFV goals...").
Regarding claim 12, Bzdusek teaches wherein the one or more clinical objectives of the EOF (52) are different from clinical objectives used to generate the VMAT treatment plan (i.e., updated goals from step 86 of Fig 2 which, due to being updated, are implicitly different from previous non-updated goals; fig. 2; pg. 10 In. 13-18 "...the OFV goals 52 serve as inputs to a radiation treatment plan optimizer 58 that optimizes a dose distribution 60 with respect to a composite objective function 62 comprising a weighted sum of the objective functions 50 weighted by weights 64 for the objective functions 50 determined from OFV goals 52…"; pg. 13 In. 17-18 "...in an operation 86 if the OFVs are out of the desired balance then at least one OFV goal is updated by the OFV goals updater 70 to produce updated OFV goals…").
Regarding claim 17, Bzdusek teaches recalculating the updated VMAT treatment plan (pg. 13 In. 17-18 "...in an operation 86 if the OFVs are out of the desired balance then at least one OFV goal is updated by the OFV goals updater 70 to produce updated OFV goals…") with linac and/or leaf-motion constraints (pg. 1 In. 21-22 "...one or more hard constraints may be imposed on the optimization..."; pg. 8 In. 6-8 "...A multi-leaf collimator (MLC) 16 comprises adjustable leaves that can be set to shape the radiation beam during the radiation treatment, and these MLC settings can also be parameters of the parameterized radiation treatment plan...").
Regarding claim 18, Bzdusek teaches generating dose-volume histograms (figs. 5-6; pg. 5 ln. 33-34) and/or isodose curves of the updated VMAT treatment plan.
Regarding claim 19, Bzdusek teaches a non-transitory computer-readable medium encoded with computer-executable instructions (pg. 4 ln. 21-24), which when executed by a processor, cause the processor to:
obtaining a VMAT treatment plan (52) from a treatment planning system (TPS), the VMAT treatment plan having a plurality of control points (weights 64), each control point (64) having a set of leaf positions corresponding to a set of leaves of a multileaf collimator (MLC) (16; pg. 8 In. 6-8) in a field of a linear accelerator (10; pg. 7 ln. 33) (linac) (figs 1, 2; pg. 8 In. 6-8 "…A multi-leaf collimator (MLC) 16 comprises adjustable leaves that can be set to shape the radiation beam during the radiation treatment, and these MLC settings can also be parameters of the parameterized radiation treatment plan..."; pg. 10 In. 13-18 "…the OFV goals 52 serve as inputs to a radiation treatment plan optimizer 58 that optimizes a dose distribution 60 with respect to a composite objective function 62 comprising a weighted sum of the objective functions 50 weighted by weights 64 for the objective functions 50 determined from OFV goals 52...");
calculating a radiation dose matrix (60) corresponding to each beamlet, wherein a beamlet is the change in field when an MLC leaf is moved a predetermined unit distance (figs. 1, 2; pg. 2 In. 13-14, "…The 'virtual' parameters, if employed, may for example include weights of 'beamlets" representing small-area segments of the radiation beam..."; pg. 10 In. 18-19, "...the dose distribution 60 in the patient (as represented by the planning image with ROIs 42) is initially computed..."; note: a dose distribution is deemed equivalent to a 2D matrix of intensity values corresponding to the planning image);
defining an enhanced objective function (EOF) (62) for achieving one or more clinical objectives, including achieving at least a minimum dose to a target volume and minimizing a dose to an organ at risk (figs. 1, 2; pg. 2 In. 33-34 "...the dosimetrist identifies objectives which are not met or which appear to be difficult to meet. and may manually adjust certain objectives (e.g. adjust the maximum or minimum dose specified for a Max Dose or Min Dose objective…"; pg. 10 In. 15-18 "...a composite objective function 62 comprising a weighted sum of the objective functions 50 weighted by weights 64 for the objective functions 50 determined from OFV goals 52 for the (corresponding) objective functions..."; pg. 13 In. 30-33 "...the ROIs defined in the planning image of the patient include at least one target ROI to be irradiated, such as a malignant tumor, and at least one organ-at-risk (OAR) ROI to be at least partly spared irradiation...");
minimizing the EOF iterating through the control points (figs. 1, 2; pg. 11 In. 17-18 "...if the current OFV is greater than the OFV goal, the parameters of the objective function are modified to decrease the OFV to approximately the OFV goal..."); and updating the set of leaf positions of the VMAT treatment plan according to the proposed leaf positions of the minimized EOF (figs. 1, 2; step 86; pg. 8 In. 6-8. "...A multi-leaf collimator (MLC) 16 comprises adjustable leaves that can be set to shape the radiation beam during the radiation treatment, and these MLC settings can also be parameters of the parameterized radiation treatment plan…"; pg. 13 In. 17-18, "...in an operation 86 if the OFVs are out of the desired balance then at least one OFV goal is updated by the OFV goals updater 70 to produce updated OFV goals…").
Bzdusek fails to teach minimizing the EOF for proposed leaf positions iterating through each leaf of at least a subset of the leaves of the VMAT treatment plan, wherein the proposed leaf positions move each leaf into the field or out of the field by the predetermined unit distance and corresponds to the addition or subtraction of the corresponding radiation dose matrix.
Vik teaches an optimizing radiation treatment (abstract) and optimization based on minimizing an objective function for proposed leaf positions iterating through each leaf of at least a subset of the leaves of the VMAT treatment plan, wherein the proposed leaf positions move each leaf into the field or out of the field by the predetermined unit distance and corresponds to the addition or subtraction of a corresponding radiation dose matrix (fig 2, para [0043], "..predictor-corrector optimizer unit 24...calculate the dose
d
c
u
r
r
in the formal parameter space 62. One method to map the approximated discrete fluence maps
x
c
u
r
r
to dose is with a fluence dose contribution matrix P. For each CP, a non-negative mapping exists from the set of MLC leaf positions 27 to the dose volume d...The set of leaf positions define ray blocking and passing regions represented with a function...optimizes the parameters x to minimize the objective function value O.").
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have minimizing the EOF for proposed leaf positions iterating through each leaf of at least a subset of the leaves of the VMAT treatment plan, wherein the proposed leaf positions move each leaf into the field or out of the field by the predetermined unit distance and corresponds to the addition or subtraction of the corresponding radiation dose matrix as taught by Vik in the device of Bzdusek for the purpose of optimizing the radiation treatment plan.
Claims 4 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Bzdusek WO 2019/025460 and Vik et al. US 2018/0078786 in further view of Owens et al. US 2018/0369611.
Regarding claim 4, Bzdusek and Vik fail to teach wherein the beamlet dose matrices are calculated using Monte Carlo routines.
Owens teaches wherein the dose matrices are calculated using Monte Carlo routines (para. 0103) for the purpose of improving accuracy.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have wherein the beamlet dose matrices are calculated using Monte Carlo routines as taught by Owens in the method of Bzdusek and Vik for the purpose of improving accuracy.
Regarding claim 13, Bzdusek and Vik fail to teach wherein the beamlet dose matrices are calculated using Monte Carlo routines.
Owens teaches wherein the dose matrices are calculated using Monte Carlo routines (para. 0103) for the purpose of improving accuracy.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have wherein the beamlet dose matrices are calculated using Monte Carlo routines as taught by Owens in the system of Bzdusek and Vik for the purpose of improving accuracy.
Allowable Subject Matter
Claims 5-7 and 14-16 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claims 5 and 14, the prior art of record does not disclose or suggest wherein the proposed leaf position of each leaf is represented by a vector (x) of ternary leaf variables, and the EOF (
f
E
) is a function of the vector (
f
E
(
x
)
), along with other claim limitations.
Bzdusek; Vik; and Owens, either singularly or in combination, does not disclose or suggest "wherein the proposed leaf position of each leaf is represented by a vector (x) of ternary leaf variables, and the EOF (
f
E
) is a function of the vector (
f
E
(
x
)
)", along with other claim limitations.
Additionally, Kuusela et al. US 2019/0240509 is also related to optimizing radiation treatment (abstract) and teaches adjusting leaf patterns in accordance with a dose leakage matrix (para. 0052) but fails to teach or suggest wherein the proposed leaf position of each leaf is represented by a vector (x) of ternary leaf variables, and the EOF (
f
E
) is a function of the vector (
f
E
(
x
)
).
Deasy et al. WO 2022/098875 teaches wherein the proposed leaf position of each leaf is represented by a vector (x) of ternary leaf variables (para. 0058) but fails to qualify as prior art under 35 U.S.C. 102(a)(1) or 35 U.S.C. 102(a)(2).
Claims 6-7 are dependent on claim 5 and claims 15-16 are dependent on claim 14 so they are allowable for the same reason.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Richard Toohey whose telephone number is (703)756-5818. The examiner can normally be reached Mon-Fri: 7:30am – 5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, the applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Uzma Alam can be reached on (571)272-2995. The fax number for the organization where this application or processing is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/RICHARD O TOOHEY/Examiner, Art Unit 2884
/EDWIN C GUNBERG/Primary Examiner, Art Unit 2884