CTNF 18/233,039 CTNF 70625 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Regarding claim 1 , lines 3-4 set forth “dose distribution is calculated”, line 5 then sets forth what appears to be another “dose distribution for each reduced dose calculation” which appears to lack antecedent basis and it is unclear in lines 7-8 “the calculated dose distributions for each individual radiation filed” is unclear to which previous dose distribution it refers. Regarding claim 3, line 3, “the intersection of the field bounding box” lacks antecedent basis. Regarding claim 5, lines 1-2, “the dose distribution for a cropped treatment volume” lacks antecedent basis. Regarding claim 6, line 13, “the dose distribution for the treatment plan” lacks antecedent basis. Regarding claim 8, line 3, “the intersection of the field bounding box” lacks antecedent basis. Regarding claim 9, lines 1-2, “the dose distribution for a cropped treatment volume” lacks antecedent basis. Regarding claim 10, line 7, “the radiation beam delivery system” and the last line “the optimization algorithm” lack antecedent basis. Regarding claim 12, line 2, “the intersection of the field bounding box” lacks antecedent basis. Regarding claim 13, lines 1-2, “the calculating of the dose distribution for a cropped treatment volume” lacks antecedent basis. Regarding claim 14, line 2, “the dose distributions obtained for each of the cropped treatment volumes” lacks antecedent basis. Regarding claim 15, lines 9 and 13, “the optimization process”, line 11, “the desired dose distribution” lacks antecedent basis. Regarding claim 17, line 2, “the intersection of the field bounding box” lacks antecedent basis. Regarding claim 18 , the claim depends from itself. Regarding claim 19, lines 12-13, “the individual dose distributions” lacks antecedent basis. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claims are directed to an abstract idea without significantly more. With Respect to claims 1, 6, 10,15, and 19, the claims recite the following limitation(s): Claim 1: method for optimizing a treatment plan for delivering a radiation dose to a treatment volume within a patient from one or more radiation fields, comprising: for each radiation field, obtaining a reduced volume for which dose distribution is calculated; calculating a dose distribution for each reduced dose calculation volume; and calculating a dose distribution for the treatment plan by summing the calculated dose distributions for each individual radiation field. Claim 6: A method for optimizing a treatment plan for delivering a prescribed radiation dose to a predefined treatment volume within a patient using a radiation beam delivery system, the method comprising: receiving information related to the prescribed radiation dose, the predefined treatment volume, and a plurality of parameters associated with the radiation beam delivery system, the plurality of parameters including information regarding radiation beam fields; and applying a treatment plan optimization algorithm to calculate dose distribution within the treatment volume based on the received information by: reducing a dose calculation volume at each radiation field; calculating a dose distribution for each reduced dose calculation volume; and adding the dose distributions obtained for each of the reduced dose calculation volumes to obtain the dose distribution for the treatment plan. Claim 10: A system for developing a treatment plan for the delivery of a prescribed radiation dose to a treatment volume within a patient, comprising: a processor; and a memory coupled to the processor, the memory including instructions that when executed by the processor cause the processor to: receive information related to the prescribed radiation dose, the treatment volume, and a plurality of parameters associated with the radiation beam delivery system, the plurality of parameters including information regarding radiation beam fields; develop a treatment plan optimization model based on the received information, the treatment plan optimization model being configured to find an optimal dose distribution within the treatment volume for each radiation beam field; and generate an optimal treatment plan based on the treatment plan optimization model, wherein the developing of the treatment plan optimization model includes restricting the treatment volume for which the dose distribution is determined at each iteration of the optimization algorithm. Claim 15: A treatment planning system, comprising: a user interface; and a treatment planning module configured to generate a treatment plan for delivering a prescribed radiation dose to a treatment volume within a patient from one or more radiation fields based on prescribed clinical goals received via the user interface, wherein the treatment planning module optimizes the treatment plan by: calculating dose distribution within the treatment volume at each iteration of the optimization process; determining whether the calculated dose distribution is within an acceptable threshold of the desired dose distribution; modifying one or more treatment parameters and repeating the optimization process until a predetermined endpoint is reached, wherein the calculating of the dose distribution includes: determining dose distribution individually for each field; and summing the dose distributions obtained for each individual field, and wherein the determining dose distribution individually for each field includes: cropping the treatment volume to a minimum volume; and calculating the dose distribution for the corresponding minimum volume . Claim 19: A non-transitory computer-readable storage medium having treatment planning software stored thereon, the treatment planning software including software for optimizing a treatment plan for irradiating a treatment volume using a radiation therapy system, the treatment planning software comprising: instructions for causing the radiation system to define a field bounding box for each dose field of the treatment plan; instructions for causing the radiation system to reduce a dose calculation volume associated with each individual dose field using the correspondingly defined field bounding box; instructions for causing the radiation system to calculate dose distributions for each of the reduced calculation volumes; and instructions for causing the radiation system to sum the individual dose distributions to obtain the dose distribution for the treatment plan. Step 1 - Claims 1, 6, 10,15, and 19 are directed to a method, a system, and software for optimizing a treatment plan for delivering a radiation dose to a treatment volume within a patient. Step 2a Prong 1 – The claimed invention is directed to non-statutory subject matter. The above limitations, under their broadest reasonable interpretation, fall within the “Certain Mathematical concepts and mental processes grouping of abstract ideas, enumerated in MPEP 2106.04(a)(2)(I)(III), in that they recite a series of mathematical calculations and mental steps which produce an optimized radiation dose treatment plan. When given their BRI, the limitations are considered an abstract idea of being certain mathematical concepts and mental processes. With respect to claim 1 , the method merely sets forth a step of obtaining data and then a series of steps for performing calculations on the data: obtaining a reduced volume for which dose distribution is calculated - (represents insignificant data gathering) calculating a dose distribution for each reduced dose calculation volume; and calculating a dose distribution for the treatment plan by summing the calculated dose distributions. With respect to claim 6 , the method merely sets forth a step of receiving data and then a series of steps for applying an algorithm and performing calculations on the data: receiving information related to the prescribed radiation dose, the predefined treatment volume, and a plurality of parameters associated with the radiation beam delivery system - (represents insignificant data gathering) applying a treatment plan optimization algorithm to calculate dose distribution within the treatment volume based on the received information by: reducing a dose calculation volume at each radiation field; calculating a dose distribution for each reduced dose calculation volume; and adding the dose distributions obtained for each of the reduced dose calculation volumes to obtain the dose distribution for the treatment plan. With respect to claim 10 , the system claim merely adds a processor and memory for which the Mathematical concepts and mental processes of optimizing a treatment plan for delivering a radiation dose to a treatment volume within a patient are implemented. a processor; and ( generic computing elements to implement the abstract idea) a memory coupled to the processor , the memory including instructions that - ( generic computing elements to implement the abstract idea) when executed by the processor cause the processor to: receive information related to the prescribed radiation dose , the treatment volume, and a plurality of parameters associated; develop a treatment plan optimization model based on the received information; and generate an optimal treatment plan based on the treatment plan optimization model, wherein the developing of the treatment plan optimization model includes restricting the treatment volume for which the dose distribution is determined at each iteration of the optimization algorithm. With respect to claim 15 , the system claim merely adds a user interface and a treatment module/processor for which the Mathematical concepts and mental processes of optimizing a treatment plan for delivering a radiation dose to a treatment volume within a patient are implemented. a user interface ; and - ( generic computing elements to implement the abstract idea) a treatment planning module configured to generate a treatment - (generic computing elements to implement the abstract idea) wherein the treatment planning module optimizes the treatment plan by: calculating dose distribution within the treatment volume at each iteration of the optimization process; determining whether the calculated dose distribution is within an acceptable threshold of the desired dose distribution; modifying one or more treatment parameters and repeating the optimization process wherein the calculating of the dose distribution includes: determining dose distribution individually for each field ; and summing the dose distributions obtained for each individual field, and wherein the determining dose distribution individually for each field includes: cropping the treatment volume to a minimum volume; and calculating the dose distribution for the corresponding minimum volume. With respect to claim 19, merely sets forth software to implement the Mathematical concepts and mental processes of optimizing a treatment plan for delivering a radiation dose to a treatment volume within a patient are implemented including: instructions for causing the radiation system to define a field bounding box for each dose field of the treatment plan; instructions for causing the radiation system to reduce a dose calculation volume associated with each individual dose field using the correspondingly defined field bounding box; instructions for causing the radiation system to calculate dose distributions for each of the reduced calculation volumes; and instructions for causing the radiation system to sum the individual dose distributions to obtain the dose distribution for the treatment plan Step 2a Prong 2 - The recitation of the additional elements of a user device merely invokes such additional element(s) as a tool to perform the abstract idea. MPEP 2106.05(f). Further, the recitation of these additional element(s) in the claim generally links the use of the abstract idea to a particular technological environment or field of use, i.e., a computerized environment. MPEP 2106.05(h). As such, under Prong 2 of Step 2A, when considered both individually and as a whole, the limitations of claims 1, 6, 10,15, and 19 are not indicative of integration into a practical application (Prong 2, Step 2A: NO). MPEP 2106.04(d) With respect to claims 1, 6 and 19 , There do not appear to be any additional elements provided and the abstract idea is not integrated into a practical application of utilizing any system components or positively controlling the radiation therapy system to provide the radiation treatment to the patient based on the optimized treatment plan as set forth. With respect to claims 10 and 15 , the system merely sets forth additional elements including a processor and memory in claim 19 and a user interface and treatment planning module in claim 15 . These additional elements are all recited at an extremely high level of generality and may be interpreted as generic computing devices used to implement the abstract idea. Per MPEP 2106.05(f), implementing an abstract idea on a generic computing device does not integrate an abstract idea into a practical application in Step 2A Prong Two, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea on a generic computer. As such, these additional elements do not integrate the abstract idea into a practical application of utilizing any system components or positively controlling the radiation therapy system to provide the radiation treatment to the patient based on the optimized treatment plan as set forth. As such, these additional elements do not integrate the abstract idea into a practical application and therefore the claim is directed to the judicial exception. Step 2B - The recitation of the additional elements is acknowledged, as identified above with respect to Prong 2 of Step 2A. These additional elements do not add significantly more to the abstract idea for the same reasons as addressed above with respect to Prong 2 of Step 2A. Even when considered as an ordered combination, the additional elements of claims 1, 6, 10,15, and 19 do not add anything that is not already present when they are considered individually. Therefore, under Step 2B, there are no meaningful limitations in claims 1, 6, 10,15, and 19 that transform the judicial exception into a patent eligible application such that the claim amounts to significantly more than the judicial exception itself (Step 2B: NO). MPEP 2106.05. Accordingly, under the Subject Matter Eligibility test, claims 1, 6, 10,15, and 19 are ineligible. Furthermore, the dependent claims 2-5, 7-9,11-14,16-18, and 20 do not add significantly more to the abstract idea for the same reasons as addressed above with respect to Prong 2 of Step 2A. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 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. 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-15-aia AIA Claim(s) 1-3, 6, 7, 10, 11, 15-16, and 19-20 is/are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Eriksson(CN112203723) hereinafter Eriksson . Eriksson teaches a method of radiation therapy treatment planning involves dynamic target tracking and beam reorientation. According to the movement of the patient and the model of the delivery machine, calculating the cumulative dose of the sum of the dose distribution as all stages, deforming the dose distribution of each stage by the deformation mapping of the corresponding stage to match the reference image. Dynamic target tracking allows to reduce the treatment boundary ( interpreted as a reduction of the target volume) around the target or the geometric shift applied between the context of the robust plan, and still ensure sufficient dose of the target, at the same time, not damaging the healthy tissue. The delivery of the treatment beam is divided into one or more beamlets. These sub-beams may be, for example, control points or segments of a photon beam, or an energy layer or point of a charged particle beam. For different treatment techniques and different machines, the model of therapeutic delivery as a function of time will be different. The total dose is calculated as the weighted sum of the all the dose allocations of all stages . At least one total dose comprises one or more total stage dose, each total stage dose is associated with one of these stages, each total stage dose is calculated from the stage dose of stage, as the stage is the only stage occurring during the treatment period, that is, if there is no motion. In this case, the term treatment may refer to a treatment segment, subset segmented, or a total treatment comprising all segments. In this case, the optimization function can be composed of the optimization function, each of the optimization function is assigned to one stage and is arranged to optimize the total dose of the stage. The method according to the invention is suitable for the treatment plan of multiple different delivery technologies, including but not limited to segmented multi-leaf collimator (SMLC), dynamic multi-leaf collimator (DMLC). Regarding claim 1 , Eriksson teaches for each radiation field, obtaining a reduced volume for which dose distribution is calculated; calculating a dose distribution for each reduced dose calculation volume; and calculating a dose distribution for the treatment plan by summing the calculated dose distributions for each individual radiation field. It is the examiner’s interpretation that claim 1 does not set forth any specifics of the radiation field or the reduced volume and can be interpreted as multiple radiation fields delivered at once, or a single radiation field delivered over several distinct times or days or treatments. Having a “reduced volume” could refer to a general reference volume and an associated specific or sub-volume within that reference volume that is more focused on the target of interest. Calculating a dose distribution for each reduced volume broadly could refer to calculating the sub-volume of a reference volume over multiple distinct treatment time frames/days and summing a total dose distribution over those multiple time frames to ensure a max dose is not exceeded. Furthermore, “the reduced volume” could be the same volume for each radiation field and could simply refer to a single sub-volume of a larger reference volume. Eriksson teaches as noted above, multiple fields as it relates to multiple stages of dose delivery, calculating dosages at multiple stages and volumes where each stage would represent an applied field towards a target where dynamic target tracking allows the system to reduce the treatment boundary ( interpreted as a reduction of the target volume) around the target which would be included as part of each stage of delivery “at least one dose-based optimization function is arranged to optimize the total dose to the cumulative dose calculated from the stage dose to each stage of at least two stages” and obtaining a total dose by summing all the dose allocations of all stages which would result in a treatment plan dosage. Regarding claim 2, Eriksson teaches wherein obtaining a reduced volume for a radiation field includes applying a corresponding field bounding box to crop the treatment volume. Eriksson teaches as noted above and further teaches “boundaries can be applied around the target and/or at risk of organ surrounding, to process uncertainty of its position. The method according to the invention can more accurately process the position of all key organs, comprising a target (or a plurality of targets) and any organ in risk, to reduce the boundary . Applying boundaries around targets is interpreted as applying a “boundary box” to “crop” or reduce the boundary . Regarding claim 3 , Eriksson teaches wherein the treatment volume is a body structure containing at least a target volume. Eriksson teaches “generally defining a certain boundary around the target to define planned target volume, namely PTV, it is considered as a plan target. This ensures the minimum dose of the whole target, but also results in undesirable radiation of the surrounding tissue. Similarly, the boundary can be added around the organ with risk to ensure that the organ in risk does not receive too high dose”. Regarding claim 6, Eriksson teaches receiving information related to the prescribed radiation dose, the predefined treatment volume, and a plurality of parameters associated with the radiation beam delivery system, the plurality of parameters including information regarding radiation beam fields; and applying a treatment plan optimization algorithm to calculate dose distribution within the treatment volume based on the received information by: reducing a dose calculation volume at each radiation field; calculating a dose distribution for each reduced dose calculation volume; and adding the dose distributions obtained for each of the reduced dose calculation volumes to obtain the dose distribution for the treatment plan. Eriksson teaches as noted above and further teaches “The invention also relates to a computer program product for controlling the radiotherapy planning device, the computer program product is preferably stored on a carrier such as a non-temporary storage device, the computer program product comprises a computer readable code device, When the computer readable code device runs in the processor of the radiotherapy planning device, the device executes the method”, “When the computer readable code device is run in the processor of the radiation therapy delivery device, the radiation therapy delivery device receives information about at least one region of the patient in vivo tracking with time, and according to the received information, controlling the delivery of the plan.” , “ obtaining ( a plurality of parameters) the patient of the 4 D image, the 4 D image comprises a group of 3 D image, each of the 3 D image corresponding to the target has a specified position of the stage, the stage forms a group of stages, b. obtaining a model of the treatment machine , including machine restriction of the treatment machine, c. obtaining an optimization problem, comprising a stage dose based on each stage , at least one dose-based optimization function defined at one or more of the total dose, d. using direct machine parameter optimization by considering machine limit during optimization , using optimization function based on dosage to optimize the beam set for the dose delivery of each stage, wherein the stage dose of each stage is calculated based on the 3 D image representing the stage and the beam setting representing the stage, and e . obtaining one or more total doses from the dose calculated from each stage .” The total dose is preferably a cumulative dose, which is weighted and calculated as a dose calculated for each stage. Generally, the treatment plan is delivered to the patient in a plurality of segments, for example, one segment of each day plan for a few consecutive days. The total dose may refer to the total dose of the entire treatment plan of the patient, or the total dose of one segment of the treatment, or the total dose of the segment subset In the simplest case, the step of obtaining one or more total doses comprises retrieving the stage dose from the dose engine when the optimization function value is calculated. Regarding claim 7 , Eriksson teaches wherein the treatment volume is a body structure containing at least a target volume. Eriksson teaches “generally defining a certain boundary around the target to define planned target volume, namely PTV, it is considered as a plan target. This ensures the minimum dose of the whole target, but also results in undesirable radiation of the surrounding tissue. Similarly, the boundary can be added around the organ with risk to ensure that the organ in risk does not receive too high dose”. Regarding claim 10 , Eriksson teaches a processor; and a memory coupled to the processor, the memory including instructions that when executed by the processor cause the processor to: receive information related to the prescribed radiation dose, the treatment volume, and a plurality of parameters associated with the radiation beam delivery system, the plurality of parameters including information regarding radiation beam fields; develop a treatment plan optimization model based on the received information, the treatment plan optimization model being configured to find an optimal dose distribution within the treatment volume for each radiation beam field; and generate an optimal treatment plan based on the treatment plan optimization model, wherein the developing of the treatment plan optimization model includes restricting the treatment volume for which the dose distribution is determined “ at each iteration” ( interpreted as “each stage of radiation delivery” in Eriksson) of the optimization algorithm. Eriksson teaches the claimed invention as set forth above including to a radiotherapy planning device, comprising a processor and a program memory storing the computer program product according to the above, wherein the computer program product is arranged to run in the processor to control the radiation treatment planning device. Regarding claim 11 , Eriksson teaches wherein the restricting of the treatment volume includes cropping, for each radiation field, the treatment volume for which radiation dose is calculated. Eriksson teaches the claimed invention as set forth above including Dynamic target tracking allows to reduce the treatment boundary ( interpreted as a reduction of the target volume) around the target or the geometric shift applied between the context of the robust plan. Reduction of the target boundary is interpreted as a “cropping” of the target volume. Regarding claim 15 , Eriksson teaches a user interface; and a treatment planning module configured to generate a treatment plan for delivering a prescribed radiation dose to a treatment volume within a patient from one or more radiation fields based on prescribed clinical goals received via the user interface, wherein the treatment planning module optimizes the treatment plan by: calculating dose distribution within the treatment volume at each iteration of the optimization process; determining whether the calculated dose distribution is within an acceptable threshold of the desired dose distribution; modifying one or more treatment parameters and repeating the optimization process until a predetermined endpoint is reached, wherein the calculating of the dose distribution includes: determining dose distribution individually for each field; and summing the dose distributions obtained for each individual field, and wherein the determining dose distribution individually for each field includes: cropping the treatment volume to a minimum volume; and calculating the dose distribution for the corresponding minimum volume. Eriksson teaches the claimed invention as set forth above including “The computer 41 includes a processor 43, a data memory 44, and a program memory 45. Preferably, there is also one or more user input devices 47, 48, in the form of a keyboard, a mouse, a joystick, a voice recognition device or any other available user input device. The user input device may also be arranged to receive data from an external storage unit.” ,” during the optimization period, according to the moving condition of the target in all stages, calculating the dose of each stage in other stages by adjusting the machine parameter. according to the movement model as a function of time, if applicable, limiting the property of the delivery machine as a target function or as a constraint included in the optimization function.” ,” As is common in the field, the optimization problem comprises a plurality of optimization function, the optimization function can be a target function or constraint. The target function is formulated as an effort to achieve the purpose (e.g., minimizing the dose of the risk organ ), and the constraint is a specified limit (e.g., setting a value for the maximum dose of the risk organ ).” Where maximum dose risk is interpreted as an acceptable threshold of desired dose. Regarding claim 16, Eriksson teaches wherein obtaining a reduced volume for a radiation field includes applying a corresponding field bounding box to crop the treatment volume. Eriksson teaches as noted above and further teaches “boundaries can be applied around the target and/or at risk of organ surrounding, to process uncertainty of its position. The method according to the invention can more accurately process the position of all key organs, comprising a target (or a plurality of targets) and any organ in risk, to reduce the boundary . Applying boundaries around targets is interpreted as applying a “boundary box” to “crop” or reduce the boundary . Regarding claim 19, Eriksson teaches non-transitory computer-readable storage medium having treatment planning software stored thereon, the treatment planning software including software for optimizing a treatment plan for irradiating a treatment volume using a radiation therapy system, the treatment planning software comprising: instructions for causing the radiation system to define a field bounding box for each dose field of the treatment plan; instructions for causing the radiation system to reduce a dose calculation volume associated with each individual dose field using the correspondingly defined field bounding box; instructions for causing the radiation system to calculate dose distributions for each of the reduced calculation volumes; and instructions for causing the radiation system to sum the individual dose distributions to obtain the dose distribution for the treatment plan. Eriksson teaches the claimed invention as set forth above including “boundaries can be applied around the target and/or at risk of organ surrounding, to process uncertainty of its position. The method according to the invention can more accurately process the position of all key organs, comprising a target (or a plurality of targets) and any organ in risk, to reduce the boundary . Applying boundaries around targets is interpreted as applying a “boundary box” to “crop” or reduce the boundary . Regarding claim 20, Eriksson teaches wherein the optimizing is iteratively performed until a predetermined endpoint is reached. Eriksson teaches the claimed invention as set forth above including “The total dose is preferably a cumulative dose, which is weighted and calculated as a dose calculated for each stage. Generally, the treatment plan is delivered to the patient in a plurality of segments, for example, one segment of each day plan for a few consecutive days. The total dose may refer to the total dose of the entire treatment plan of the patient, or the total dose of one segment of the treatment, or the total dose of the segment subset In the simplest case, the step of obtaining one or more total doses comprises retrieving the stage dose from the dose engine when the optimization function value is calculated.” ,” he use of dose delivered in the whole treatment process is the precondition of using biological index as optimization function. This is advantageous because the biological index is directly related to the therapeutic purpose, so the increase of the dose generated from the plan reaches the opportunity to achieve these objectives.” , “ at least one dose-based optimization function is arranged to optimize the total dose to the cumulative dose calculated from the stage dose to each stage of at least two stages”, and “ As is common in the field, the optimization problem comprises a plurality of optimization function, the optimization function can be a target function or constraint. The target function is formulated as an effort to achieve the purpose (e.g., minimizing the dose of the risk organ), and the constraint is a specified limit (e.g., setting a value for the maximum dose of the risk organ).” . Conclusion Allowable Subject Matter 07-43-02 Claims 4-5,8-9,12-14, 17-18 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), 2nd paragraph and 35 U.S.C. 101, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims. 13-03-01 AIA The following is a statement of reasons for the indication of allowable subject matter : The prior art of record teaches optimizing a treatment plan for delivering a radiation dose to a treatment volume within a patient from one or more radiation fields, including for each field defining a reference target volume or “boundary box” and defining a sub-volume or organ or target of interest within the reference volume where the sub-volume may be smaller or a reduced (“cropped”) volume as compared to the reference target volume and calculating a dose distribution for the sub-volume and as the target is tracked optimizing the radiation treatment by reducing the treatment boundary around the target while still ensuring the correct dose is applied to the target and radiation dose to healthy tissue is minimized. The prior art of record also teaches calculating a total dose by summing the intermediate treatment dosages. The prior art of record does not reasonably teach the subject matter of the independent claims in combination with claim 4 including defining a corresponding field bounding box for each radiation field to enclose the target volume and cropping the treatment volume at the intersection of each field boundary box and the body structure. The prior art of record does not reasonably teach the subject matter of the independent claims in combination including defining a corresponding field bounding box for each radiation field to enclose the target volume and cropping the treatment volume at the intersection of each field boundary box and the body structure and further including wherein the calculating of the dose distribution for a cropped treatment volume includes calculating absorbed dose of radiation at each voxel included in the cropped treatment volume . 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. YOSHINAGA et al.( JP 2014023741) teaches intensity modulation radiotherapy planning capable of reducing the computing time in spite of the optimization computing having convergence to the minimum solution while keeping the positivity and upper bound of the solution, and obtaining the solution as a set value. An ideal dose distribution can be obtained by reproducing the beam pattern generated by the inverse plan and irradiating from multiple directions. In this intensity-modulated radiotherapy, although the irradiation dose from each radiation beam is different at each point in the irradiation field, the target dose distribution is finally realized by an integrated value obtained by summing the distributions of all the radiation beams . HARDY et al(WO 9118552) teaches optimized dose delivery system using computer graphics simulation techniques and computerized numerical optimization. A volume, such as a tumor volume (10), is graphically simulated and meshed with node points. The dose delivery is calculated (55, 85) depending upon input variables, deriving an objective function (56, 86) related to dose efficacy. A numerical optimization algorithm (62) optimizes the input variables based upon such objective function. Each major beam path within each isocenter's application pattern is separated 84 into a predetermined number of individual beams. This is done so that the actual continuous beam paths can be approximated and evaluated by the dose function. Given the orientation angles (direction cosines) of each discrete beam, the radiation dose to each individual node is calculated 85 within the meshed volume by summing each beam's dose contribution to each node. Siljamaki et al(US 20110091014) teaches a treatment plan for irradiating a treatment volume within a patient where [0044] the effective fluence is determined by projecting the fluences from each constituent control point 346(a-d, n-p) into an isocenter plane of the field of the effective control point, and summing the fluences. In this embodiment, the projection direction lies between the directions of the fields produced by the constituent control points 346(a-d, n-p) and the new effective control point 366(a). Schell et al(WO2019166105) teaches computer-implemented medical method of irradiation treatment planning. Taking into account the determined amount of violation, a reduction coverage volume is calculated for the planning target volume and a virtual planning object is generated based on changing, modifying, varying and/or adjusting a volume of the organ at risk. Therein, the volume of the virtual planning object, which corresponds to the changed volume of the organ at risk, is determined, such that an overlap region of the virtual planning object and the planning target volume substantially matches and/or substantially equals the reduction coverage volume, as determined based on the amount of violation. Generally, the virtual planning object may be generated based on increasing (e.g. expanding) or decreasing (e.g. shrinking and/or reducing) the volume of the organ at risk. The initial coverage volume of the planning target volume is then reduced based on removing at least a part of the overlap region of the virtual planning object and the planning target volume. Thereby, an optimized planning target volume, which may refer to an optimized coverage volume of the planning target volume, is generated. Ranganathan et al(WO2014181204) teaches method for dose-gradient based optimization of an intensity modulated radiation therapy plan, In accordance with one preferred embodiment of the present application, a treatment plan optimization system is provided, comprising a user interface to receive an input from a user; a non-transitory memory module for storing a treatment plan data set comprising data from multiple sources; and an optimizer. The optimizer is programmed to optimize the plan according to initial plan specified settings to create optimized dose distributions in beam's eye view; generate dose gradient maps from the optimized does distributions in beam's eye view; and specify new dose gradients for user specified regions in beam's eye view. In accordance with another method of the present application, a dose-gradient based optimization method is provided, comprising optimizing the plan according to initial plan specified settings to create optimized dose distributions for a beam's eye view; dividing the dose distributions into a plurality of beamlets; calculating a first dose gradient value for each beamlet of the plurality of beamlets; compiling dose gradient maps from the dose gradient values of each beamlet; determining insufficient dose gradients within the dose gradient maps of each beam's eye view; delineating sub-regions within the dose gradient maps with respect to the insufficient dose gradients; specifying user dose gradients for the delineated sub- regions; and performing a second optimization using the user dose gradients. In accordance with one preferred embodiment of the present application, a treatment plan optimization system is provided, comprising a user interface to receive an input from a user; a non-transitory memory module for storing a treatment plan data set comprising data from multiple sources; and an optimizer. The optimizer is programmed to optimize the plan according to initial plan specified settings to create optimized dose distributions in beam's eye view; generate dose gradient maps from the optimized does distributions in beam's eye view; and specify new dose gradients for user specified regions in beam's eye view. In accordance with another method of the present application, a dose-gradient based optimization method is provided, comprising optimizing the plan according to initial plan specified settings to create optimized dose distributions for a beam's eye view; dividing the dose distributions into a plurality of beamlets; calculating a first dose gradient value for each beamlet of the plurality of beamlets; compiling dose gradient maps from the dose gradient values of each beamlet; determining insufficient dose gradients within the dose gradient maps of each beam's eye view; delineating sub-regions within the dose gradient maps with respect to the insufficient dose gradients; specifying user dose gradients for the delineated sub- regions; and performing a second optimization using the user dose gradients. Kapatoes et al.( US 7609809) teaches system and method of defining a new region of interest for an existing region of interest using a dose volume histogram . The method includes the acts of generating a dose volume histogram of radiation dose for a pre-existing region of interest, selecting a subset of the dose volume histogram, and defining a new region of interest that corresponds to the selected subset of the dose volume histogram. Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN L CASLER whose telephone number is (571)272-4956. The examiner can normally be reached M-Th 6:30 to 4:30. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, 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, Charles Marmor can be reached at (571)272-4730. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /BRIAN L CASLER/Primary Examiner, Art Unit 3791 Application/Control Number: 18/233,039 Page 2 Art Unit: 3791 Application/Control Number: 18/233,039 Page 3 Art Unit: 3791 Application/Control Number: 18/233,039 Page 4 Art Unit: 3791 Application/Control Number: 18/233,039 Page 5 Art Unit: 3791 Application/Control Number: 18/233,039 Page 6 Art Unit: 3791 Application/Control Number: 18/233,039 Page 7 Art Unit: 3791 Application/Control Number: 18/233,039 Page 8 Art Unit: 3791 Application/Control Number: 18/233,039 Page 9 Art Unit: 3791 Application/Control Number: 18/233,039 Page 10 Art Unit: 3791 Application/Control Number: 18/233,039 Page 11 Art Unit: 3791 Application/Control Number: 18/233,039 Page 12 Art Unit: 3791 Application/Control Number: 18/233,039 Page 13 Art Unit: 3791 Application/Control Number: 18/233,039 Page 14 Art Unit: 3791 Application/Control Number: 18/233,039 Page 15 Art Unit: 3791 Application/Control Number: 18/233,039 Page 16 Art Unit: 3791 Application/Control Number: 18/233,039 Page 17 Art Unit: 3791 Application/Control Number: 18/233,039 Page 18 Art Unit: 3791 Application/Control Number: 18/233,039 Page 19 Art Unit: 3791 Application/Control Number: 18/233,039 Page 20 Art Unit: 3791 Application/Control Number: 18/233,039 Page 21 Art Unit: 3791 Application/Control Number: 18/233,039 Page 22 Art Unit: 3791 Application/Control Number: 18/233,039 Page 23 Art Unit: 3791 Application/Control Number: 18/233,039 Page 24 Art Unit: 3791
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