DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This final office action is in response to the response filed 11 March 2026.
Claims 1-6, 8-9, 11-16, and 18-19 are pending. Claims 1 and 11 are independent claims.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3-6, 8-9, 11, 13-16, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Hibbard (US 2022/0305291, provisional filed 20 June 2019) and further in view of Kuusela et al. (US 2021/0304866, filed 27 March 2020, hereafter Kuusela) and further in view of Carlson et al. (A machine learning approach to the accurate prediction of multi-leaf collimator positional errors, 2016, hereafter Carlson), and further in view of Cooley et al. (US 11291861, filed 6 March 2020, hereafter Cooley).
Claim 1:
Hibbard teaches: “An apparatus to facilitate administering therapeutic radiation to a patient” ([0002], Hibbard: “Embodiments of the present disclosure pertain generally to determining machine parameters that direct the radiation therapy performed by a radiation therapy treatment system”; Examiner’s Note (EN): Under the BRI, in view of applicant’s specification, directing and performing radiation therapy treatments is reasonably understood to be encompassed by facilitating administration of therapeutic radiation and a radiation therapy treatment system is reasonably understood to be encompassed by an apparatus. Hibbard further states that “it is to be understood that the embodiments may be combined” ([0049], Hibbard)).
Hibbard further teaches “the apparatus comprising: a memory” (page 2, figure 1, element 116, Hibbard:
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Hibbard further teaches “a memory having stored therein: - a fluence map corresponding to the patient” ([0123], HIBBARD: “The control point parameters 910
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[sic] represent the gantry angles, the MLC apertures at each gantry angle, and the radiation intensity at that angle... The projection images 850 and the graphical aperture images 920 are scaled and aligned to ensure that each anatomy pixel in the projection images 850 is aligned with the corresponding aperture pixel irradiating that anatomy element”; and [0058], HIBBARD: “The memory device 116 may store data, including medical images 146, patient data 145, and other data required to create and implement a radiation therapy treatment plan 142”; (EN): [0030] of the instant specification states “Those skilled in the art will understand that fluence represents radiative flux integrated over time and comprises a fundamental metric in dosimetry (i.e., the measurement and calculation of an absorbed dose of ionizing radiation in matter and tissue). This fluence map, in turn, comprises a map of fluence values for various portions of the patient's body”. Those of reasonable skill in the art will understand that fluence maps represent beam intensity at each point on the MLC aperture for all gantry angles. Under the BRI, in view of applicant’s specification by expressing beamlet intensities at a specified gantry angle with a given aperture which are aligned with the projection images of a patient (“Patient data 145 may include information such as… radiation dosage data”, [0064], Hibbard; and “patient’s projection images 850”, [0124], Hibbard) and may be retrieved from memory, Hibbard teaches memory storing a fluence map corresponding to the patient).
Hibbard further teaches “a memory having stored therein: - a deep learning model trained to deduce a leaf sequence for a multi-leaf collimator from a fluence map” ([0022], Hibbard: “FIG. 7 illustrates an embodiment of a method for training a Deep Convolutional Neural Network (pCNN), such as the DCNN for determining a set of machine parameters or graphical aperture image(s) for that particular gantry angle based on at least one medical image projection depicting a view of a patient anatomy from a gantry angle… The machine parameters can include at least one gantry angle, at least one multi-leaf collimator leaf position, and at least one aperture weight or intensity”; and [0058], Hibbard: “The memory device 116 may store… software programs 144 (e.g., artificial intelligence, deep learning neural networks, radiotherapy treatment plan software)”; (EN): Those reasonably skilled in the art will appreciate that leaf-sequencing is the process of translating or converting a fluence map into the opening and closing of leaves. Thus, under the BRI, in view of applicant’s specification, by teaching a deep neural network stored in a memory device which is utilized to “estimate a graphical aperture image representation of multi-leaf collimator (MLC) leaf positions” ([0022], Hibbard) by determining a set of machine parameters, which may include at least one leaf position, gantry angle, and aperture intensity, based on the aforementioned patient data, Hibbard teaches a deep learning model which is stored in memory and trained to deduce a leaf sequence for a multi-leaf collimator from a fluence map).
Hibbard further teaches “a control circuit operably coupled to the memory” ([0066], Hibbard: “The processor 114 may also be implemented by one or more special-purpose processing devices such as an application specific integrated circuit (ASIC)”; (EN): With reference to figure 1, as discussed above, Hibbard teaches a processor 114 which is operably coupled with memory 116. Under the BRI, in view of applicant’s specification an application specific integrated circuit is reasonably understood to be encompassed by a circuit).
Hibbard further teaches “and configured to iteratively optimize a radiation treatment plan to administer the therapeutic radiation to the patient” ([0006]-[0007], Hibbard: “A radiation therapy treatment plan (“treatment plan”) can then be created using an optimization technique based on the clinical and dosimetric objectives and constraints… The treatment plan can then be later executed by positioning the patient in the treatment machine and delivering the prescribed radiation therapy directed by the optimized plan parameters”; (EN): As discussed above, Hibbard teaches a machine learning approach to the optimization of the treatment plan. [0031] of the instant specification states “Those skilled in the art understand that machine learning comprises a branch of artificial intelligence. Machine learning typically employs learning algorithms such as Bayesian networks, decision trees, nearest-neighbor approaches, and so forth”, where the learning algorithms outlined are reasonably understood to be iterative under the BRI, in view of applicant’s specification).
Hibbard further teaches “by, at least in part, generating a leaf sequence as a function of the deep learning model and the fluence map that corresponds to the patient” ([0002], Hibbard: “In particular, the present disclosure pertains to using deep learning technologies to determine machine parameters that define a treatment plan in a radiation therapy system”; (EN): As discussed above, Hibbard teaches machine or equipment parameters that include the positioning and angle of leaves, which is reasonably understood to be encompassed by a leaf sequence, using a deep machine learning system in tandem with the beam intensity, gantry angle, and apertures, or fluence map, as discussed above).
Hibbard further teaches “a radiation treatment platform that includes the multi-leaf collimator” ([0004], Hibbard: “A specified or selectable beam energy can be used, such as for delivering diagnostic energy level range or a therapeutic level range. Modulation of a radiation beam can be provided by one or more attenuators of collimators (e.g., a multi-leaf collimator (MLC))”; and [0100], Hibbard: “In yet another embodiment, the therapy output can be fixed, such as located in a region laterally separated from the patient, and a platform supporting the patient can be used to align a radiation therapy isocenter with a specified target locus within the patient”).
Hibbard further teaches “and that is configured to provide the therapeutic radiation to the patient as a function of the radiation treatment plan” ([0101], Hibbard: “The leaves 532A through 532J permit modulation of the radiation therapy beam. The leaves 532A through 532J can be made of a material specified to attenuate or block the radiation beam in regions other than the aperture, in accordance with the radiation treatment plan”; (EN): Under the BRI, in light of the instant specification radiation therapy beams are reasonably understood to be encompassed by therapeutic radiation).
Hibbard does not appear to explicitly disclose “wherein the deep learning model comprises a neural network model that was trained, at least in part, via a reinforcement learning method”.
In the same field, analogous art Kuusela provides this additional functionality by teaching “wherein the deep learning model comprises a neural network model that was trained, at least in part, via a reinforcement learning method” ([0063], Kuusela: “An artificial intelligence (AI) agent trained using reinforcement learning (and/or some other suitable form of machine learning) is used to control the radiation delivery parameters in effort to achieve desired delivery of radiation therapy”).
Hibbard and Kuusela are analogous art because they are from the same field of endeavor as the claimed invention, namely artificial intelligence approaches to therapeutic radiation treatment control design. Hibbard teaches a method for utilizing machine learning to generate leaf sequences utilizing deep learning and fluence data, but does not appear to explicitly teach a neural network model which was trained, at least in part, via a reinforcement learning method. Kuusela provides the additional functionality by disclosing a system for leaf sequence generation via deep learning which utilizes reinforcement learning. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have improved upon the machine learning system of Hibbard with Kuusela reinforcement learning method because “Basing the reward function on such treatment objectives 111A means that the reward function may reflect how clinicians evaluate the conformity to , and the success of a radiation treatment plan… During the block 112 training, it is possible for an oncologist or clinician to interfere with the training process to provide more accurate guidance, and in doing so, alter the reward function. For example, an oncologist may provide further instructions on what kinds of dose distributions are beneficial and what kinds are detrimental”, as suggested by Kuusela ([0098], Kuusela).
The combination of Hibbard and Kuusela does not appear to explicitly teach “by employing a plurality of agents to each separately use the deep learning model to each generate a leaf sequence for only a single leaf pair of the multi-leaf collimator”.
Carlson provides this additional functionality by teaching “by employing a plurality of agents to each separately use the deep learning model to each generate a leaf sequence for only a single leaf pair of the multi-leaf collimator” (section 2.6, paragraph 1, Carlson: “The inputs to the models were the leaf motion parameters described above, and the target response for each model was the difference between the planned and delivered MLC leaf positions”; and section 2.6.3, paragraph 1, Carlson: “the cubist algorithm builds a linear regression model at each terminal node”; (EN): [0036] of the instant specification states “Typically the leaves of a multi-leaf collimator are organized in pairs that are aligned collinearly with respect to one another and which can selectively move towards and away from one another. A typical multi-leaf collimator has many such pairs of leaves, often upwards of twenty, fifty, or even one hundred such pairs”, but does not appear to explicitly define a single leaf pair. As such, the BRI, as written and in light of the specification, includes any two leaves which differ in position, speed, dimension, or other feature. Further, [0045] of the instant specification states “Since a given leaf-pair typically affects neighboring fluence rows (through a tongue-and-groove effect, [sic] these teachings will accommodate having each single leaf agent interact with some or all neighboring leaves” which demonstrates each agent may possess information about and interact with the leaves surrounding the “given”, or corresponding, leaf pair. As previously discussed, a leaf sequence is reasonably understood to include any sequence of positions of a leaf or set of leaves within a MLC. Thus, by teaching a Cubist rule-based model which utilizes a tree of agents (regression models) to determine a measure of error between the planned and delivered MLC leaf position for each leaf (section 2.6, paragraphs 1-2, Carlson) for the purpose of “predicting MLC positional errors before delivery, and incorporated those errors into the dose distribution calculation”, Carlson teaches by employing a plurality of agents to each separately use the deep learning model to each generate a leaf sequence for only a single leaf pair of the multi-leaf collimator).
Carlson further teaches “wherein the multi-leaf collimator is comprised of a first kind of leaf and a second kind of leaf” (section 2.6, paragraph 2, Carlson: “four qualitative parameters: movement towards or away from the center, whether the leaf was at rest/starting/stopping/moving for a single CP, the CP number, and the leaf bank were utilized in the final models”; (EN): [0037] of the instant specification states “the first and second kinds of leaves are different from one another (with respect, for example, to width, thickness, material composition, and so forth)”, but does not explicitly define a first and second kind of leaf. Leaves which are at rest or in motion are reasonably understood to be encompassed by a first and second kind of leaf under the BRI, in light of the instant specification).
Carlson further teaches “wherein the first and second kind of leaves are different from one another” (section 2.6, paragraph 2, Carlson: “four qualitative parameters: movement towards or away from the center, whether the leaf was at rest/starting/stopping/moving for a single CP, the CP number, and the leaf bank were utilized in the final models”; (EN): The examiner further notes Carlson teaches a method for sequence generation with respect to the error margin for a pair of planned and delivered leaves for all leaves in a MLC, which includes leaves of two widths (“The Millennium 120 MLC consists of two banks of 60 MLC leaves, with the outer 20 and inner 40 on each side having widths of 1 cm and 0.5 cm, respectively”, section 2.1, paragraph 3, Carlson)).
Carlson further teaches “and wherein the plurality of agents include a first agent that generates leaf sequences for leaf pairs comprised of the first kind of leaf and a second agent that generates leaf sequences for leaf pairs comprised of the second kind of leaf, wherein the first and second agents are different from one another” (section 2.6, paragraph 2, Carlson: “four qualitative parameters: movement towards or away from the center, whether the leaf was at rest/starting/stopping/moving for a single CP, the CP number, and the leaf bank were utilized in the final models”; and section 2.6.3, paragraph 1, Carlson: “the cubist algorithm builds a linear regression model at each terminal node”; (EN): A person of reasonable skill in the art will appreciate that a Cubist model in machine learning utilizes the Cubist algorithm which first partitions the provided data into subsets based on shared characteristics of the data, such as the parameters associated with each leaf, and then fits a linear model to each subset. By teaching a Cubist model which parameterizes the data provided by leaf characteristics and movement characteristics associated with the leaf in a setting with multiple leaf widths, as discussed above, and trains a linear or regression model for each subset, Carlson teaches a plurality of first and second agents which are different and generate leaf sequences for first and second leaf pairs, respectively).
Hibbard and Carlson are analogous art because they are from the same field of endeavor as the claimed invention, namely artificial intelligence approaches to therapeutic radiation treatment control design. The combination of Hibbard and Kuusela teaches a method for utilizing reinforcement machine learning to generate leaf sequences utilizing deep learning and fluence data, but does not appear to explicitly teach employing a plurality of agents to each separately use the deep learning model to each generate a leaf sequence for only a single leaf pair of the multi-leaf collimator. Carlson provides the additional functionality by disclosing a cubist model which trains a regression model for each subset of data to generate a predicted measure of error for each leaf in a radiotherapy treatment plan generation setting. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have improved upon the machine learning system of the combination of Hibbard and Kuusela with Carlson’s cubist model because “By incorporating and correcting for the predicted errors in MLC positions, optimization routines for encoding MLC leaf positions may be improved, and would allow for more realistic calculation of the dose distributions as truly delivered to the patient” (section 5, paragraph 2, Carlson) and “dose volumetric histograms (DVH) recalculated with predicted positions incorporated into the plan provide the treatment planner with a better representation of the deliverable dose distributions for the PTV and OARs” (section 4, paragraph 1, Carlson).
While Carlson discloses wherein the leaves are different from one another with respect to moving leaves and non-moving leaves (section 2.6, paragraph 2), Carlson fails to specifically disclose wherein the first and second kind of leaves are different from one another with respect to at least one of width, thickness, or material composition. However, Cooley, which is analogous to the claimed invention because it is directed toward multi-leaf collimators having multiple leaves, wherein the first and second kind of leaves are different from one another with respect to at least one of width, thickness, or material composition (Figures 16-19; column 20, lines 23-43; column 20, line 65- column 21, line 17: Here, each of the multiple leaves may be composed of different materials. This assists in trimming the particle beam to focus on spots of different thickness). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Cooley with Hibbard-Kuusela-Carlson, with a reasonable expectation of success, as it would have allowed for using different collimators, having different thicknesses and materials, into the path of the particle beam in order to more accurately focus the beam (Cooley: column 20, lines 23-43).
Claim 3:
The combination of Hibbard, Kuusela, Carlson, and Cooley teaches “The apparatus of claim 1”, as discussed above.
Hibbard further teaches “wherein the neural network model was trained using a training corpus that includes fluence maps for each of a plurality of corresponding exposure fields and/or control points” ([0052], Hibbard: “training a machine learning model on training data consisting of pairs of control points and the corresponding images of a patient. These data pairs are constructed and aligned so that each element of the image data corresponds to an element of the control point data”; (EN): As discussed above, a plurality of corresponding field/control points is reasonably understood to include a plurality of field points, control points, or field points and control points).
Claim 4:
The combination of Hibbard, Kuusela, Carlson, and Cooley teaches “The apparatus of claim 1”, as discussed above.
Hibbard further teaches “wherein the neural network model comprises a convolutional neural network model” ([0103], Hibbard: “FIG. 7 illustrates an exemplary flow diagram for deep learning, where a deep learning model (or a machine learning model), such as a deep convolutional neural network (DCNN), can be trained and used to determine machine parameters for a treatment plan”; (EN): A deep convolutional neural network is reasonably understood to be encompassed by a convolutional neural network).
Claim 5:
The combination of Hibbard, Kuusela, Carlson, and Cooley teaches “The apparatus of claim 1”, as discussed above.
Kuusela further teaches “wherein the reinforcement learning method comprises a deep learning method” ([00940, Kuusela: “Non-limiting examples of suitable reinforcement learning algorithms include: Monte Carlo techniques, Q-learning techniques, SARSA (state-action-reward-state-action) techniques, deep Q-network techniques and/or the like”; (EN): Q-network techniques is reasonably understood to be encompassed by “Q reinforcement learning” ([0041], instant specification)).
Claim 6:
The combination of Hibbard, Kuusela, Carlson, and Cooley teaches “The apparatus of claim 1”, as discussed above.
Carlson further teaches “wherein the plurality of agents are each identical to one another” (section 2.5, paragraph 1, CARLSON: “For the second model, identical model parameters as used to train the initial models were used”).
Claim 8:
The combination of Hibbard, Kuusela, Carlson, and Cooley teaches “The apparatus of claim 1”, as discussed above.
Kuusela further teaches “wherein the reinforcement learning method provides for rewarding an agent during training” ([0093], Kuusela: “Block 110 may optionally also involve obtaining one or more treatment constraints 111B… Such block 110 treatment 111B constraints may comprise… soft constraints (e.g. which may manifest themselves as terms in a reward function)”).
Claim 9:
The combination of Hibbard, Kuusela, Carlson, and Cooley teaches “The apparatus of claim 1”, as discussed above.
Kuusela further teaches “wherein the reinforcement learning method provides for calculating a reward” ([0093], Kuusela: “Block 110 may optionally also involve obtaining one or more treatment constraints 111B… Such block 110 treatment 111B constraints may comprise… soft constraints (e.g. which may manifest themselves as terms in a reward function)”).
Kuusela further teaches “based, at least in part, on how well a created leaf sequence reproduces a target fluence” ([0097], Kuusela: “The reward function may comprise a function that represents a reward metric obtained by the performance of an action a based on a current treatment states (including, for example, observations of the current environment) and expected changes in the treatment state (including, for example, expected environment changes) stemming from that action”; (EN): Where “Determining the current treatment state of the patient may comprise determining an estimated cumulative dose absorbed by target tissue during the radiation delivery fraction ([0028], Kuusela), which is reasonably understood to be encompassed by a fluence map).
Claim 11:
Claim 11 recites a method corresponding to the limitations of claim 1 and thus is rejected under the same rational as claim 1, mutatis mutandis.
Claim 13:
Claim 13, which depends on claim 11, recites a method corresponding to the limitations of claim 2 and thus is rejected under the same rational as claims 3 and 11, mutatis mutandis.
Claim 14:
Claim 14, which depends on claim 11, recites a method corresponding to the limitations of claim 4 and thus is rejected under the same rational as claims 4 and 11, mutatis mutandis.
Claim 15:
Claim 15, which depends on claim 11, recites a method corresponding to the limitations of claim 5 and thus is rejected under the same rational as claims 5 and 11, mutatis mutandis.
Claim 16:
Claim 16, which depends on claim 11, recites a method corresponding to the limitations of claim 6 and thus is rejected under the same rational as claims 6 and 11, mutatis mutandis.
Claim 18:
Claim 18, which depends on claim 11, recites a method corresponding to the limitations of claim 8 and thus is rejected under the same rational as claims 8 and 11, mutatis mutandis.
Claim 19:
Claim 19, which depends on claim 11, recites a method corresponding to the limitations of claim 9 and thus is rejected under the same rational as claims 9 and 11, mutatis mutandis.
Claim 2 and claim 12 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Hibbard, Kuusela, Carlson, and Cooley and further in view of Osman et al. (Prediction of the individual multileaf collimator positional deviations during dynamic IMRT delivery priori with artificial neural network, 2020, hereafter Osman).
Claim 2:
The combination of Hibbard, Kuusela, Carlson, and Cooley teaches “The apparatus of claim 1”, as discussed above.
The combination of Hibbard, Kuusela, Carlson, and Cooley does not appear to explicitly teach “wherein the neural network model was trained, at least in part, via a supervised learning method”.
However, Osman provides this additional functionality by teaching “wherein the neural network model was trained, at least in part, via a supervised learning method” (section 2.2, paragraph 1, Osman: “Delivered leaf positional data were used as a target response for training the supervised ANN model”).
Osman is analogous art because it is from the same field of endeavor as the claimed invention, namely artificial intelligence approaches to therapeutic radiation treatment control design. The combination of Hibbard, Kuusela, Carlson, and Cooley teaches a method for utilizing reinforcement machine learning to generate leaf sequences utilizing deep learning, fluence data, and a plurality of agents which each generate a leaf sequence for only a single leaf pair of the multi-leaf collimator, but does not appear to explicitly teach training the model, at least in part, via supervised learning. Osman provides the additional functionality by disclosing a method for generating a therapeutic radiation treatment plan via supervised learning. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have improved upon the machine learning system of the combination of Hibbard, Kuusela, Carlson, and Cooley with Osman’s method of supervised learning because “ANNs have the advantage of working even if one or a few units fail to respond to the network” given “While ANNs commonly feature one or two hidden layers and are considered as supervised ML, deep learning algorithms have a higher number of hidden layers. In an ANN architecture, two or more of the neurons can be combined in a single layer and a particular network could contain one or more such layers” (section 2.3, paragraph 1, Osman).
Claim 12:
Claim 12, which depends on claim 11, recites a method corresponding to the limitations of claim 2 and thus is rejected under the same rational as claims 2 and 11, mutatis mutandis.
Response to Arguments
Applicant's arguments filed 11 March 2026 have been fully considered but they are not persuasive.
The applicant appears to argue that Hibbard teaches away from using a fluence map (pages 6-7). Specifically, the applicant indicates that Hibbard discloses that “the planning process can be shortened including eliminating the Fluence Map phase (paragraph 0116).” The examiner respectfully disagrees.
First, the applicant appears to admit that use of a Fluence Map is prior art. Specifically, the applicant states “Hibbard makes the following observations regarding the prior art (with emphasis added):… The first phase [of the prior art] (e.g., Fluence Map Optimization or “FMO”) produces an idealized does distribution that satisfies the planner’s requirements (Hibbard: paragraph 0115; Remarks: page 7).”
Additionally, the MPEP states: “"the nature of the teaching is highly relevant and must be weighed in substance. A known or obvious composition does not become patentable simply because it has been described as somewhat inferior to some other product for the same use." In re Gurley, 27 F.3d 551, 553, 31 USPQ2d 1130, 1132 (Fed. Cir. 1994) (Claims were directed to an epoxy resin based printed circuit material. A prior art reference disclosed a polyester-imide resin based printed circuit material, and taught that although epoxy resin based materials have acceptable stability and some degree of flexibility, they are inferior to polyester-imide resin based materials. The court held the claims would have been obvious over the prior art because the reference taught epoxy resin based material was useful for the inventor’s purpose, applicant did not distinguish the claimed epoxy from the prior art epoxy, and applicant asserted no discovery beyond what was known to the art.) (MPEP 2145(X)(D)(1)).”
While the use of a Fluence Map may be “described as somewhat inferior to some other product for the same use,” the obvious composition does not become patentable for this reason. For this reason, this argument is not persuasive.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Wu et al. (US 2022/0241614): Discloses a fluence map predication and treatment plan generation for automatic radiation treatment (paragraphs 0008-0009)
Huang et al. (US 2022/0008748): Discloses fluence map optimization for all feasible bean angles and adding the best performing beam angle to the ensample (paragraph 0125)
Harju et al. (US 2021/0299469): Discloses fluence maps representing the intensity of the radiation beam at each point on the fluence plane at a particular beam angle and providing treatment based on the fluence map (paragraph 0084)
Zhou et al. (US 2021/0077827): Discloses a fluence map optimization technique that determines an optimized fluence map for each beam and decomposes the optimized fluence maps into deliverable apertures based on a leaf sequencing algorithm (paragraph 0098)
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE R STORK whose telephone number is (571)272-4130. The examiner can normally be reached 8am - 2pm; 4pm - 6pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez Rivas can be reached at 571/272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KYLE R STORK/Primary Examiner, Art Unit 2128
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