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 .
This Office Action is in response to claims filed on 01/14/2026
Claims 1-30 are pending.
Claims 1, 18 and 26 were amended.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/14/2026 is being considered by the examiner.
Claim Rejections - 35 USC § 112
Applicant’s arguments and amendments, see remarks page 11, filed 01/14/2026, with respect to 35 USC 112 rejections have been fully considered and are persuasive. The 35 USC 112 rejections of claims 1-30 have been withdrawn.
Claim Rejections - 35 USC § 102
Applicant’s arguments and amendments, see remarks pages 11-13, filed 01/14/2026, with respect to the rejection(s) of claim(s) 1-30 under 35 USC 102 have been fully considered and are persuasive. Applicant argues “Sjolund does not disclose converting optimization problems into a first problem matrix, performing a first pass of dose optimization represented in the first problem matrix in parallel to solve the optimization problems on parallel processing hardware, and performing these operations a second time with a second pass and a second problem matrix”. Examiner notes, Sjolund discloses defining a radiotherapy treatment plan problem where the treatment plan is converted into an equation that comprises a dose influence matrix. Thus converts the treatment plan into a solving a influence matrix, Par 68-69. Examiner notes, Sjolund discloses solving the optimization problem by solvers that are distributed, asynchronous and running in parallel, Par 73. Therefore, optimization of the radiotherapy treatment plan defined using influence matrix is solved using distributed solvers running in parallel. Applicant argues, “Sjolund does not disclose the newly added recitation of using a linear programming (LP) solver operating based on an alternating direction method of multiplier technique”. Examiner notes, the claim recites “performing a first pass of the dose optimization by solving the first optimization problems represented in the first problem matrix in parallel on parallel processing hardware, with use of a linear programming solver operating based on an alternating direction method of multipliers technique, wherein the first pass produces a first set of multiple solutions, corresponding to a first plurality of multiple sets of weights, to the first optimization problems”, thus the use of the linear programming solver operating based on an alternating direction method of multipliers technique is directed to performing the dose optimization. Examiner notes claim 6 is directed to “converting the parameterized linear programming equations comprises applying an alternating direction method of multipliers technique”, thus it converts the parameterized linear programming equations before the optimization is performed. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection necessitated by claim amendment is made in view of Xinmin Liu, NPL, “Use of proximal operator graph solver for radiation therapy inverse treatment planning”, Published: April 2017.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-30 are rejected under 35 U.S.C. 103 as being unpatentable over Jens Olof Sjolund, US 2021/0158929 A1, Published: May 27, 2021, (hereafter Sjolund), in view of Xinmin Liu, NPL, “Use of proximal operator graph solver for radiation therapy inverse treatment planning”, Published: April 2017, (hereafter Liu).
Regarding claim 1. Sjolund teaches a computer-implemented method for radiotherapy treatment planning (Par 61, treatment planning)(Par 62, radiotherapy treatment plan optimization), comprising:
obtaining a set of first optimization problems for providing radiotherapy treatment to a human subject, the first optimization problems defined by a first plurality of parameters (Fig 7, receive optimization problem parameter);
performing dose optimization for delivery of the radiotherapy treatment to at least one treatment area of the human subject (Par 137, processes, optimization problem a first set of candidate parameters),
the dose optimization comprising:
converting the first optimization problems into a first problem matrix (Par 68, dose influence matrix);
performing a first pass of the dose optimization by solving the first optimization problems represented in the first problem matrix in parallel on parallel processing hardware, wherein the first pass produces a first set of multiple solutions, corresponding to a first plurality of multiple sets of weights, to the first optimization problems (Par 137, processes, optimization problem a first set of candidate parameters);
combining the first set of multiple solutions to the first optimization problems to produce a set of second optimization problems for providing the radiotherapy treatment, the second optimization problems defined by a second plurality of parameters (Par 138, converts first set of candidate parameters into an adapted representation, thus combines the first optimization to generate an adapted (second) optimization problem)(Par 139, defines an adapted radiotherapy optimization problem, given solution);
converting the second optimization problems into a second problem matrix (Par 138, converts, an adapted representation); and
performing a second pass of the dose optimization by solving the second optimization problems represented in the second problem matrix in parallel on the parallel processing hardware, wherein, the second pass produces a second set of multiple solutions, corresponding to a second plurality of multiple sets of weights, to the second optimization problems (Par 136, first set of candidate parameter of the first optimization)(Par 138, converts the first set of candidate parameters into adapted (second) optimization problem)(Par 140, processes the adapted radiotherapy optimization problem); and
generating treatment plan data based on at least one solution of the second set of multiple solutions to the second optimization problems, wherein the treatment plan data controls delivery of radiotherapy from a radiotherapy machine (Par 141, generate a deliverable radiotherapy treatment plan).
Sjolund does not teach performing the dose optimization by solving optimization problems represented in problem matrix in parallel on parallel processing hardware, with use of a linear programming solver operating based on an alternating direction method of multipliers technique.
Liu teaches performing the dose optimization by solving optimization problems represented in problem matrix in parallel on parallel processing hardware, with use of a linear programming solver operating based on an alternating direction method of multipliers technique (Page 1247, Col 1, par 2, ADMM, optimization algorithm, POGS, speed up optimization)(Page 1248, Sec 2.B.1, D is the dose matrix)(Page 1254, Col 1, Par 2, split and executed independently in parallel to further increase computation speed).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Sjolund to incorporate the teachings of Liu to optimize by solving a matrix in parallel based on ADMM because it improves in computation speed and memory usage (Liu, Page 1246, conclusions).
Regarding claim 2. Sjolund and Liu teach the method of claim 1, wherein the first and the second plurality of parameters define constraints for at least one target area and at least one low-dose area in the at least one treatment area (Sjolund, Par 107, target area (tumor area), dose applied to a voxel of a particular OAR)(Sjolund, Par 3, minimize damage to the surrounding healthy tissue (organs at risk OARs)) (Sjolund, Par 5, while as low a dose as possible to the surrounding healthy tissue)(Sjolund, Par 57, high dose to the target while minimizing the dose to healthy tissue (low dose area))(Sjolund, Par 107, distance between the voxel in the OAR and the closest boundary voxel in a target tumor, thus having a target tumor area and low dose area (OAR)).
Regarding claim 3. Sjolund and Liu teach the method of claim 1, wherein the first and second plurality of multiple sets of weights, corresponding to the first and second set of multiple solutions, relate to points defined for at least one low-dose volume (Sjolund, Par 125, maps organ specific parameters to voxel specific parameters, voxel wise dose)(Sjolund, Par 5, low dose for surrounding tissue, thus it relates to low dose volume).
Regarding claim 4. Sjolund and Liu teach the method of claim 1, wherein the first plurality of parameters and the second plurality of parameters relate to radiation delivery parameters of a radiotherapy treatment machine (Sjolund, Par 127, delivers a treatment plan)(Sjolund, Par 57, dose delivery, Linac based treatments).
Regarding claim 5. Sjolund and Liu teach the method of claim 1, wherein solving the first optimization problems or the second optimization problems in parallel on the parallel processing hardware comprises (Sjolund, Par 73, asynchronous running in parallel), for a respective set of problems:
identifying parameterized linear programming equations from the respective set of problems (Sjolund, Par 11, solved using a learned optimization process, linear least squares); and
converting the parameterized linear programming equations for execution by the parallel processing hardware (Sjolund, Par 100, Linear accelerator); and
wherein solving the respective set of problems in parallel comprises solving a plurality of the converted parameterized linear programming equations in parallel on the parallel processing hardware, to produce a plurality of solutions to the respective set of problems (Sjolund, Par 67, optimization problem, projection onto Q, )(Par 68, linear map, maps x to dose in voxel)(Sjolund, Par 120, queue in parallel to multiple solvers).
Regarding claim 6. Sjolund and Liu teach the method of claim 5, wherein converting the parameterized linear programming equations comprises applying an alternating direction method of multipliers technique (Sjolund, Par 67,projected gradient scheme, projected onto Q), and
wherein the alternating direction method of multipliers technique comprises transforming the converted parameterized linear programming equations to matrix and projection operations (Sjolund, Par 68, dose influence matrix, maps x to dose in voxel).
Regarding claim 7. Sjolund and Liu teach the method of claim 1, wherein the parallel processing hardware comprises a set of one or more graphics processing units (GPUs) (Sjolund, Par 75, GPU).
Regarding claim 8. Sjolund and Liu teach the method of claim 1, wherein the at least one treatment area includes a low-dose region and a target region (Sjolund, Par 107, target)(Sjolund, Par 5, while as low a dose as possible to the surrounding healthy tissue),
wherein a dose to be delivered in the low-dose region is a fraction of a dose to be delivered in the target region (Sjolund, Par 5, low dose as possible, thus less than the target), and
wherein combining the first set of multiple solutions to produce the second optimization problems comprises:
performing a union of the first set of multiple solutions to the first optimization problems for the low-dose region (Sjolund, Par 117, map adapted parameters or solutions back to the input domain to provide the adapted solution).
Regarding claim 9. Sjolund and Liu teach the method of claim 8, wherein each low-dose point selected from a common low-dose region of the first set of multiple solutions of the first optimization problems is represented in the second problem matrix (Sjolund, Par 68, collection of relevant structure sets)(Sjolund, Par 72, structure set S represented on a grid and concatenated with the dose maps), and
wherein performing the second pass of the dose optimization includes assigning a non-zero upper bound to a solution vector in a subset of points corresponding a respective low-dose region for each set of weights (Sjolund, Par 69, satisfy the constraints)(Sjolund, Par 108, dose constraint).
Regarding claim 10. Sjolund and Liu teach the method of claim 8, wherein each low-dose point selected from a common low-dose region of the first set of multiple solutions of the first optimization problem is represented in the second problem matrix (Sjolund, Par 68, dose influence matrix), and
wherein performing the second pass of the dose optimization includes applying a new low- dose weight to all low-dose points in the union of the first set of multiple solutions of the first optimization problems (Sjolund, Par 73, adapted radiotherapy optimization problem, maps parameters to solution).
Regarding claim 11. Sjolund and Liu teach the method of claims 8, wherein combining the first set of multiple solutions to produce the second optimization problems comprises:
performing a sampling of the union of the first set of multiple solutions to identify weights of the second plurality of parameters for the low-dose region (Sjolund, Par 125, sampling process is adapted based on user input, or radiotherapy optimization problem)(Sjolund, Par 127, adapted solution is tentatively acceptable or deliverable, thus identifying a solution to the optimization problem).
Regarding claim 12. Sjolund and Liu teach the method of claim 1, further comprising: selecting a solution to the second optimization problems based on an evaluation of the second set of multiple solutions (Sjolund, Par 127, adapted solution is tentatively acceptable or deliverable, thus identifying a solution to the optimization problem);
wherein the treatment plan data is generated based on the selected solution to the second optimization problems (Sjolund, Par 127, delivers a treatment plan).
Regarding claim 13. Sjolund and Liu teach the method of claim 12, wherein the selected solution to the second optimization problems provides an approximate solution (Sjolund, Par 62, approximate or exact, solution of the optimization problem),
with the method further comprising:
receiving an additional optimization to the selected solution (Sjolund, Par 85, generate intermediate data, to be used, thus input for the next step );
wherein the treatment plan data is generated based on the additional optimization to the selected solution (Sjolund, Par 148, performing a particular operation before or after another operation, thus the solution can be further optimize).
Regarding claim 14. Sjolund and Liu teach the method of claim 1, wherein the treatment plan data for the radiotherapy treatment comprises a set of treatment delivery parameters corresponding to capabilities of a radiotherapy treatment machine (Sjolund, Par 58, acceptable parameter values)(Sjolund, Par 62, solver can be implemented, thus the machine having the capabilities to solve the optimization problem faster).
Regarding claim 15. Sjolund and Liu teach the method of claim 14, wherein the radiotherapy treatment is to be provided with a Gamma knife, and wherein the set of treatment delivery parameters comprises a set of isocenters used for delivery of the radiotherapy treatment (Sjolund, Par 58, parameters describe the treatment, isocenter locations, Gamma Knife, ).
Regarding claim 16. Sjolund and Liu teach the method of claim 15, wherein the set of treatment delivery parameters further comprises timing for delivery of the radiotherapy treatment and a collimator sequence for the delivery of the radiotherapy treatment (Sjolund, Par 58, parameters describe the treatment, delivery time).
Regarding claim 17. Sjolund and Liu teach the method of claim 14, wherein the radiotherapy treatment is provided with a Volumetric-modulated arc therapy (VMAT) or Intensity modulated radiation therapy (IMRT) using a Linac radiotherapy machine (Sjolund, Par 57, Linac based treatments, VMAT, IMRT), and
wherein the set of treatment delivery parameters comprises: a set of arc control points for one or more arcs, fluence fields, gantry speed, and dose rate along the one or more arcs (Sjolund, Par 58, parameters describe the treatment, beam angle, delivery time).
Regarding claim 18. Sjolund teaches a non-transitory computer-readable storage medium comprising computer- readable instructions for radiotherapy treatment planning, wherein the instructions (Par computer readable storage medium), when executed, cause a computing machine to perform operations comprising:
obtaining a set of first optimization problems for providing radiotherapy treatment to a human subject, the first optimization problems defined by a first plurality of parameters (Fig 7, receive optimization problem parameter);
performing dose optimization for delivery of the radiotherapy treatment to at least one treatment area of the human subject (Par 137, processes, optimization problem a first set of candidate parameters), the dose optimization comprising:
converting the first optimization problems into a first problem matrix (Par 68, dose influence matrix);
performing a first pass of the dose optimization by solving the first optimization problems represented in the first problem matrix in parallel on parallel processing hardware, wherein the first pass produces a first set of multiple solutions, corresponding to a first plurality of multiple sets of weights, to the first optimization problems (Par 137, processes, optimization problem a first set of candidate parameters);
combining the first set of multiple solutions to the first optimization problems to produce a set of second optimization problems for providing the radiotherapy treatment, the second optimization problems defined by a second plurality of parameters (Par 138, converts firs set of candidate parameters into an adapted representation, thus combines the first optimization to generate an adapted (second) optimization problem)(Par 139, defines an adapted radiotherapy optimization problem, given solution);
converting the second optimization problems into a second problem matrix (Par 138, converts, an adapted representation); and
performing a second pass of the dose optimization by solving the second optimization problems represented in the second problem matrix in parallel on the parallel processing hardware, wherein, the second pass produces a second set of multiple solutions, corresponding to a second plurality of multiple sets of weights, to the second optimization problems (Par 136, first set of candidate parameter of the first optimization)(Par 138, converts the first set of candidate parameters into adapted (second) optimization problem) (Par 140, processes the adapted radiotherapy optimization problem); and
generating treatment plan data based on at least one solution of the second set of multiple solutions to the second optimization problems, wherein the treatment plan data controls delivery of radiotherapy from a radiotherapy machine (Par 141, generate a deliverable radiotherapy treatment plan).
Sjolund does not teach performing the dose optimization by solving optimization problems represented in problem matrix in parallel on parallel processing hardware, with use of a linear programming solver operating based on an alternating direction method of multipliers technique.
Liu teaches performing the dose optimization by solving optimization problems represented in problem matrix in parallel on parallel processing hardware, with use of a linear programming solver operating based on an alternating direction method of multipliers technique (Page 1247, Col 1, par 2, ADMM, optimization algorithm, POGS, speed up optimization)(Page 1248, Sec 2.B.1, D is the dose matrix)(Page 1254, Col 1, Par 2, split and executed independently in parallel to further increase computation speed).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Sjolund to incorporate the teachings of Liu to optimize by solving a matrix in parallel based on ADMM because it improves in computation speed and memory usage (Liu, Page 1246, conclusions).
Regarding claim 19. Sjolund and Liu teach the non-transitory computer-readable storage medium of claim 18, wherein the first and the second plurality of parameters define constraints for at least one target area and at least one low-dose area in the at least one treatment area (Sjolund, Par 107, target area, dose applied to a voxel of a particular OAR)(Sjolund, Par 3, minimize damage to the surrounding healthy tissue (organs at risk OARs)) (Sjolund, Par 5, while as low a dose as possible to the surrounding healthy tissue)(Sjolund, Par 57, high dose to the target while minimizing the dose to healthy tissue (low dose area))(Sjolund, Par 107, distance between the voxel in the OAR and the closest boundary voxel in a target tumor, thus having a target tumor area and low dose area (OAR)).
Regarding claim 20. Sjolund and Liu teach the non-transitory computer-readable storage medium of claim 18, wherein the first and second plurality of multiple sets of weights, corresponding to the first and second set of multiple solutions, relate to points defined for at least one low- dose volume (Sjolund, Par 125, maps organ specific parameters to voxel specific parameters, voxel wise dose)(Sjolund, Par 5, low dose for surrounding tissue, thus it relates to low dose volume).
Regarding claim 21. Sjolund and Liu teach the non-transitory computer-readable storage medium of claim 18, wherein the first plurality of parameters and the second plurality of parameters relate to radiation delivery parameters of a radiotherapy treatment machine (Sjolund, Par 127, delivers a treatment plan)(Sjolund, Par 57, dose delivery, Linac based treatments).
Regarding claim 22. Sjolund and Liu teach the non-transitory computer-readable storage medium of claim 18, wherein solving the first optimization problems or the second optimization problems in parallel on the parallel processing hardware comprises (Sjolund, Par 73, asynchronous running in parallel), for a respective set of problems:
identifying parameterized linear programming equations from the respective set of problems (Sjolund, Par 11, solved using a learned optimization process, linear least squares); and
converting the parameterized linear programming equations for execution by the parallel processing hardware (Sjolund, Par 100, Linear accelerator); and
wherein solving the respective set of problems in parallel comprises solving a plurality of the converted parameterized linear programming equations in parallel on the parallel processing hardware, to produce a plurality of solutions to the respective set of problems (Sjolund, Par 67, optimization problem, projection onto Q, )(Sjolund, Par 68, linear map, maps x to dose in voxel)(Sjolund, Par 120, queue in parallel to multiple solvers);
wherein converting the parameterized linear programming equations comprises applying an alternating direction method of multipliers technique (Sjolund, Par 67,projected gradient scheme, projected onto Q), and wherein the alternating direction method of multipliers technique comprises transforming the converted parameterized linear programming equations to matrix and projection operations (Sjolund, Par 68, dose influence matrix, maps x to dose in voxel).
Regarding claim 23. Sjolund and Liu teach the non-transitory computer-readable storage medium of claim 18, wherein the at least one treatment area includes a low-dose region and a target region (Sjolund, Par 107, target)(Sjolund, Par 5, while as low a dose as possible to the surrounding healthy tissue), wherein a dose to be delivered in the low-dose region is a fraction of a dose to be delivered in the target region (Sjolund, Par 5, while as low a dose as possible to the surrounding healthy tissue, thus being a small fraction of the dose delivered to the target region), and
wherein combining the first set of multiple solutions to produce the second optimization problems comprises: performing a union of the first set of multiple solutions to the first optimization problems for the low-dose region (Sjolund, Par 117, map adapted parameters or solutions back to the input domain to provide the adapted solution).
Regarding claim 24. Sjolund and Liu teach the non-transitory computer-readable storage medium of claim 23, wherein each low-dose point selected from a common low-dose region of the first set of multiple solutions of the first optimization problems is represented in the second problem matrix (Sjolund, Par 68, collection of relevant structure sets)(Sjolund, Par 72, structure set S represented on a grid and concatenated with the dose maps), and
wherein performing the second pass of the dose optimization includes assigning a non-zero upper bound to a solution vector in a subset of points corresponding a respective low-dose region for each set of weights (Sjolund, Par 69, satisfy the constraints)(Sjolund, Par 108, dose constraint).
Regarding claim 25. Sjolund and Liu teach the non-transitory computer-readable storage medium of claim 23, wherein each low-dose point selected from a common low-dose region of the first set of multiple solutions of the first optimization problem is represented in the second problem matrix (Sjolund, Par 68, dose influence matrix), and
wherein performing the second pass of the dose optimization includes applying a new low-dose weight to all low-dose points in the union of the first set of multiple solutions of the first optimization problems (Sjolund, Par 73, adapted radiotherapy optimization problem, maps parameters to solution).
Regarding claim 26. Sjolund teaches a computing system configured for radiotherapy treatment planning (Par 142, computer PC), the system comprising:
one or more parallel processing hardware devices (Par 73, asynchronous running in parallel);
one or more memory devices to store data of a set of first optimization problems for providing radiotherapy treatment to a human subject, the first optimization problems defined by a first plurality of parameters (Fig 7, receive optimization problem parameter); and
one or more processors configured to perform operations to: perform dose optimization for delivery of the radiotherapy treatment to at least one treatment area of the human subject (Par 137, processes, optimization problem a first set of candidate parameters), the dose optimization including:
conversion of the first optimization problems into a first problem matrix (Par 68, dose influence matrix);
performance of a first pass of the dose optimization by solving the first optimization problems represented in the first problem matrix in parallel on the parallel processing hardware devices, wherein the first pass produces a first set of multiple solutions, corresponding to a first plurality of multiple sets of weights, to the first optimization problems (Par 137, processes, optimization problem a first set of candidate parameters);
combination of the first set of multiple solutions to the first optimization problems to produce a set of second optimization problems for providing the radiotherapy treatment, the second optimization problems defined by a second plurality of parameters (Par 138, converts firs set of candidate parameters into an adapted representation, thus combines the first optimization to generate an adapted (second) optimization problem)(Par 139, defines an adapted radiotherapy optimization problem, given solution);
conversion of the second optimization problems into a second problem matrix (Par 138, converts, an adapted representation); and
performance of a second pass of the dose optimization by solving the second optimization problems represented in the second problem matrix in parallel on the parallel processing hardware devices, wherein, the second pass produces a second set of multiple solutions, corresponding to a second plurality of multiple sets of weights, to the second optimization problems (Par 136, first set of candidate parameter of the first optimization)(Par 138, converts the first set of candidate parameters into adapted (second) optimization problem)(Par 140, processes the adapted radiotherapy optimization problem); and
generate treatment plan data based on at least one solution of the second set of multiple solutions to the second optimization problems, wherein the treatment plan data controls delivery of radiotherapy from a radiotherapy machine (Par 141, generate a deliverable radiotherapy treatment plan).
Sjolund does not teach performing the dose optimization by solving optimization problems represented in problem matrix in parallel on parallel processing hardware, with use of a linear programming solver operating based on an alternating direction method of multipliers technique.
Liu teaches performing the dose optimization by solving optimization problems represented in problem matrix in parallel on parallel processing hardware, with use of a linear programming solver operating based on an alternating direction method of multipliers technique (Page 1247, Col 1, par 2, ADMM, optimization algorithm, POGS, speed up optimization)(Page 1248, Sec 2.B.1, D is the dose matrix)(Page 1254, Col 1, Par 2, split and executed independently in parallel to further increase computation speed).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Sjolund to incorporate the teachings of Liu to optimize by solving a matrix in parallel based on ADMM because it improves in computation speed and memory usage (Liu, Page 1246, conclusions).
Regarding claim 27. Sjolund and Liu teach the computing system of claim 26, wherein the at least one treatment area includes a low-dose region and a target region (Sjolund, Par 107, target)(Sjolund, Par 5, while as low a dose as possible to the surrounding healthy tissue),
wherein a dose to be delivered in the low-dose region is a fraction of a dose to be delivered in the target region (Sjolund, Par 5, low dose as possible, thus less than the target), and
wherein combining the first set of multiple solutions to produce the second optimization problems comprises:
performing a union of the first set of multiple solutions to the first optimization problems for the low-dose region (Sjolund, Par 117, map adapted parameters or solutions back to the input domain to provide the adapted solution).
Regarding claim 28. Sjolund and Liu teach the computing system of claim 27, wherein each low-dose point selected from a common low-dose region of the first set of multiple solutions of the first optimization problems is represented in the second problem matrix (Sjolund, Par 68, collection of relevant structure sets)(Sjolund, Par 72, structure set S represented on a grid and concatenated with the dose maps), and
wherein performance of the second pass of the dose optimization includes assignment of a non-zero upper bound to a solution vector in a subset of points corresponding a respective low-dose region for each set of weights (Sjolund, Par 69, satisfy the constraints)(Sjolund, Par 108, dose constraint).
Regarding claim 29. Sjolund and Liu teach the computing system of claim 28, wherein each low-dose point selected from a common low-dose region of the first set of multiple solutions of the first optimization problem is represented in the second problem matrix (Sjolund, Par 68, dose influence matrix), and
wherein performance of the second pass of the dose optimization includes application of a new low-dose weight to all low-dose points in the union of the first set of multiple solutions of the first optimization problems (Sjolund, Par 73, adapted radiotherapy optimization problem, maps parameters to solution).
Regarding claim 30. Sjolund and Liu teach the computing system of claim 26, wherein the parallel processing hardware devices comprise a set of one or more graphics processing units (GPUs) (Sjolund, Par 75, GPU).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/A.C./Examiner, Art Unit 2189
/REHANA PERVEEN/Supervisory Patent Examiner, Art Unit 2189
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