DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on July 12, 2023 and August 07, 2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings filed on May 11, 2023 are accepted.
Specification
The abstract of the disclosure is objected to. At the end of the abstract, the term “[Fig. 2]” should be deleted. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Rejections - 35 USC § 101
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 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 of the subject matter eligibility test (see MPEP 2106.03).
Claims 1-14 are directed to a “method” which describes one of the four statutory categories of patentable subject matter, i.e., a process.
Claim 15 is drawn to a “non-transitory computer-readable medium” which describes one of the four statutory categories, i.e., a manufacture.
Claims 16-17 are drawn to a “system” which describes one of the four statutory categories, i.e., a machine.
Step 2A of the subject matter eligibility test (see MPEP 2106.04).
Prong One:
Claims 1, 12 and 15-17 recite (“sets forth” or “describes”) the abstract idea of “a mental process” (MPEP 2106.04(a)(2).III.), and “mathematical concepts” (MPEP 2106.04(a)(2).I.), substantially as follows: “optimizing a set of parameters, according to the received first reference objective and the received second reference objective, the set of parameters relating to characteristics of radiation to be delivered by the radiotherapy system, wherein optimizing the set of parameters comprises: optimizing the set of parameters based on the first reference objective to obtain a dose-volume metric for the anatomical structure; and further optimizing the set of parameters based on the second reference objective using the dose-volume metric as a constraint; and generating a radiation treatment plan using the further optimized set of parameters” (claims 1 and 15-16); and “optimizing a set of parameters, according to the received reference objective, the set of parameters relating to characteristics of radiation to be delivered by the radiotherapy system, wherein optimizing the set of parameters comprises: optimizing the set of parameters based on the reference objective to obtain a dose- volume metric; and further optimizing the set of parameters, based on the received reference objective and a further objective that is based on the dose-volume metric; and generating a radiation treatment plan using the further optimized set of parameters” (claims 12 and 17).
In claims 1, 12 and 15-17, the above recited steps can be practically performed in the human mind, with the aid of a pen and paper or with a generic computer, in a computer environment, or merely using the generic computer as a tool to perform the steps. Once the person receives the first reference objective and the second reference objective, with each objective representing a goal to be achieved by a radiotherapy system, he or she may optimize the parameters based on the objectives. To optimize parameters means to obtain the parameters that fulfill the intended purpose the best or as close to the best situation as possible. The intended purpose maybe, for example, the most effective treatment, the most homogeneous coverage, or the least load to the patient…etc. For any of these non-limited situations, he or she may determine a set of parameters based on their knowledge, their experiences, some reference values, or some lookup table/chart/graph. He or she may graph a dose-volume plot based on existing data. He or she may then compare the determined set of parameters with the plot or perform data fitting to determine if the determined set of parameters is good enough in regard to its intended purpose. He or she may repeat these procedures to make the set of parameters better, i.e., more suitable for the intended purpose. To perform the optimization, and to determine if the optimization approach the objective close enough, a mathematical operation may be performed. At the end, a radiation treatment plan may be generated based on the finally determined and optimized set of parameters. There is nothing recited in the claim to suggest an undue level of complexity in how the set of parameters are optimized and how the radiation treatment plan is generated. Therefore, a person would be able to perform the optimization and the treatment plan generation mentally, with a pen and paper, or with a generic computer.
Prong Two: Claims 1, 12 and 15-17 do not include additional elements that integrate the mental process into a practical application.
This judicial exception is not integrated into a practical application. In particular, the claims recites an additional step of “receiving a first reference objective and a second reference objective, each reference objective representing a goal to be achieved by the radiotherapy system, the first reference objective comprising a reference dose to be delivered to a reference volume of an anatomical structure” (claims 1 and 15-16); and an additional step of “receiving a reference objective, the reference objective representing a goal to be achieved by the radiotherapy system, the reference objective comprising a reference dose to be delivered to a reference volume of an anatomical structure” (claims 12 and 17).
These steps represent merely data gathering or pre-solution activities that are necessary for use of the recited judicial exception and are recited at a high level of generality with conventionally used tools, i.e., a processor (see below Step IIB for further details).
As a whole, the additional elements merely serve to gather and feed information to the abstract idea and to output a notification based on the abstract idea, while generically implementing it on conventionally used tools. There is no practical application because the abstract idea is not applied, relied on, or used in a meaningful way. No improvement to the technology is evident, and the generated radiation treatment plan is not outputted in any way such that a practical benefit is realized. Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application.
Step 2B of the subject matter eligibility test (see MPEP 2106.05).
Claims 1, 12 and 15-17 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the claims recite additional steps of “receiving a first reference objective and a second reference objective, each reference objective representing a goal to be achieved by the radiotherapy system, the first reference objective comprising a reference dose to be delivered to a reference volume of an anatomical structure” (claims 1 and 15-16); and “receiving a reference objective, the reference objective representing a goal to be achieved by the radiotherapy system, the reference objective comprising a reference dose to be delivered to a reference volume of an anatomical structure” (claims 12 and 17). These steps represents mere data gathering, data outputting or pre/post/extra-solution activities that are necessary for use of the recited judicial exception and are recited at a high level of generality.
To perform any type of optimization it requires a goal or an objective. In these claims, the objective comprises a reference dose to be delivered to a reference volume of an anatomical structure. To perform a radiation treatment, a radiation dosage needs to be determined based on the volume of the treatment region. Hence, these additional limitations merely represent insignificant, conventional pre-solution activities well-understood in the industry of radiation treatment planning, as evidenced by Kilby et al., US 2010/0104068 A1, hereinafter Kilby.
In Kilby, it teaches that dosage and volume of the targeted tissue are used as the objective when optimizing a radiation treatment plan and when evaluating the quality of a radiation treatment plan. See FIG.6: the Objective column; [0059]: Using a sequential approach, the TPS can generate a script that sets the absolute constraints, such as those illustrated in FIGS. 5A and 5B, as well as an ordered sequence of optimization steps corresponding to a treatment-planning objective (e.g., OMI, OCO, OHI, OMA, OME, OCI, or OMU), as illustrated in FIG. 6; and [0044]: FIG.4C illustrates another embodiment of how the TPS optimizes a treatment plan. The TPS beings with receiving multiple radiation treatment-planning objectives of a treatment plan (block 452) from memory or from the user (for example, values for the desired minimum dose to the target volume…; and FIGS.8-9.
Similar teaching in regard to consider the dose and the target volume as the objectives of a radiation treatment planning optimization is also found in Hindi et al., “A tutorial on optimization methods for cancer radiation treatment planning”. American Control Conference (ACC). 2013. See p.6804, Col. Right, Section II. Problem description.
Accordingly, these additional steps amount to no more than insignificant conventional extra-solution activity. Mere insignificant conventional extra-solution activity cannot provide an inventive concept. The claims hence are not patent eligible.
Dependent Claims
The dependent claims incorporate all the limitations of their respective independent claims. The following analysis focus on the limitations recited in the dependent claims to determine whether they merely recite further abstract idea, or whether or not they recite additional elements that may either amount to significantly more than the abstract idea in their respective independent claims, or may integrate the abstract idea in their respective independent claims to a practical application.
The following dependent claims merely further define the abstract idea and are, therefore, directed to an abstract idea for similar reasons as stated in the analysis for their respective independent claims, hence are patent ineligible:
Describe the dose-volume metric that may be obtained by plotting the dose and volume data and obtain the value corresponding to the percentage recited in the claim (claims 2);
Further describe the optimization (claims 4-8 and 10: describe the constraint used for the optimization and the subsequent consideration to tune the optimization. Similar to the analysis to claim 1, these are considered being able to be performed as mental steps);
Describing further abstract idea of a mental step and a mathematical operation (claims 11 and 14: a weighted sum of the objectives, without reciting any further details, is considered a mathematical operation that may be performed mentally, with a pen and paper or with a generic computer).
The following dependent claims merely further describe the extra-solution activities and therefore, do not amount to significantly more than the judicial exception or integrate the abstract idea into a practical application for similar reasons as stated in the analysis for their respective independent claims, hence are patent ineligible:
Describing the objective (claims 2-3 and 9, 13).
Taken alone and in combination, the additional elements do not integrate the judicial exception into a practical application at least because the abstract idea is not applied, relied on, or used in a meaningful way. They also do not add anything significantly more than the abstract idea. Their collective functions merely provide computer/electronic implementation and processing, and no additional elements beyond those of the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. There is no indication that the combination of elements improves the functioning of a computer, output device, improves technology other than the technical field of the claimed invention, etc. Therefore, the claims are rejected as being directed to non-statutory subject matter.
Based on the above consideration and analysis, claims xx-xx are patent ineligible, i.e., rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
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 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 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 –
(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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-7, 12-13 and 15-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kilby et al., US 2010/0104068 A1, hereinafter Kilby.
Claims 1, 15 and 16. Kilby teaches a method of radiation treatment planning for a radiotherapy system, a non-transitory computer-readable medium comprising computer-executable instructions, and a radiation treatment planning system comprising a processor configured to (Abstract: a method and apparatus for radiation treatment planning; and [0122]: Processing device may be configured to execute instructions for performing the TPS operations), the method comprising and the processor is configured for:
receiving a first reference objective and a second reference objective, each reference objective representing a goal to be achieved by the radiotherapy system (FIG.6: the Objective column; [0059]: Using a sequential approach, the TPS can generate a script that sets the absolute constraints, such as those illustrated in FIGS. 5A and 5B, as well as an ordered sequence of optimization steps corresponding to a treatment-planning objective (e.g., OMI, OCO, OHI, OMA, OME, OCI, or OMU), as illustrated in FIG. 6; and [0044]: FIG.4C illustrates another embodiment of how the TPS optimizes a treatment plan. The TPS beings with receiving multiple radiation treatment-planning objectives of a treatment plan (block 452) from memory or from the user (for example, values for the desired minimum dose to the target volume…; and FIGS.8-9),
the first reference objective comprising a reference dose to be delivered to a reference volume of an anatomical structure ([0044]: FIG.4C illustrates another embodiment of how the TPS optimizes a treatment plan. The TPS beings with receiving multiple radiation treatment-planning objectives of a treatment plan (block 452) from memory or from the user (for example, values for the desired minimum dose to the target volume…);
optimizing a set of parameters, according to the received first reference objective and the received second reference objective, the set of parameters relating to characteristics of radiation to be delivered by the radiotherapy system (claim 1: receiving at a treatment planning system a plurality of radiation treatment-planning parameters, wherein the plurality of radiation-treatment planning parameters represent characteristics of radiation applied to an anatomical region via radiation beams; and sequentially optimizing the plurality of radiation treatment-planning parameter using the treatment planning system to obtain an optimized treatment plan of a plurality of radiation beams to be directed at the anatomical region), wherein
optimizing the set of parameters comprises: optimizing the set of parameters based on the first reference objective to obtain a dose-volume metric for the anatomical structure (FIGS.2 and 3); and further optimizing the set of parameters based on the second reference objective using the dose-volume metric as a constraint; and generating a radiation treatment plan using the further optimized set of parameters (claim 1: sequentially optimizing…to obtain an optimized treatment plan; and claim 2: performing a first optimization step to optimize a first of the plurality of treatment-planning parameters; applying a result of the first optimization step as an additional constraint to a second optimization step; and performing the second optimization step to optimize a second of the plurality of treatment-planning parameters).
Claim 2. Kilby further teaches that
the dose-volume metric comprises an achieved dose at a volume that is between 70% to 100% of the reference volume, and/or an achieved volume at a dose that is between 80% to 120% of the reference dose ([0005]: Based on specified minimum and maximum doses to the target region and the maximum dose to the critical region, the treatment planning system generates a dose is contour for the target region (e.g., lines joining points of equal dose, expressed…as a percentage of a maximal or user defined dose, for example, 60%, 70%, 80%, etc)).
Claim 3. Kilby further teaches that
the dose-volume metric comprises an achieved dose at the reference volume, and/or an achieved volume at the reference dose (FIGS.2, 3, 8 and 9: a dose volume histogram (DVH)) – a DVH is a graphical representation used in radiation treatment planning to correlate radiation dose to tissue volume.
Claim 4. Kilby further teaches
responsive to the dose-volume metric not meeting the first reference objective, further optimizing the set of parameters based on the second reference objective using the dose-volume metric as a constraint ([0085]: the result of each optimization step is retained as an additional for all subsequent optimization steps; [0086]: FIG.9: line 906 represents the goal value. Panel 905 represents the optimization constrained after a user-defined relaxation 905; and [0087]: for the optimization steps that apply to target volumes (e.g., OMI, OCO, and OHI), the new constraints is set to the minimum of the goal value or the minimum dose minus the relaxation value. Constraining the dose above the goal value may retain an undesirably high does within the target volumes and may over constrain the problem for the subsequent optimization steps. The new constraint is also set to be at least that minimal dose constraints established by a previous optimization step to guarantee that constraint is retained) – to use the relaxation value in the optimization is considered performing the optimization using the dose-volume metric as a constraint.
Claim 5. Kilby further teaches
responsive to the achieved dose at the reference volume and/or the achieved volume at the reference dose not meeting the first reference objective, modifying the achieved dose at the reference volume to obtain a relaxed dose; and further optimizing the set of parameters based on the second reference objective using the relaxed dose at the reference volume as a constraint ([0046]: the TPS may complete the optimization step on reaching the goal value, or on reaching the optimum value, i.e., closest possible to the goal value, if the goal value cannot be achieved. The relaxation value is the amount by which the TPS may degrade a previously optimized treatment-planning parameter in subsequent optimization steps; [0085]: the result of each optimization step is retained as an additional for all subsequent optimization steps; [0086]: FIG.9: line 906 represents the goal value. Panel 905 represents the optimization constrained after a user-defined relaxation 905; and [0087]: for the optimization steps that apply to target volumes (e.g., OMI, OCO, and OHI), the new constraints is set to the minimum of the goal value or the minimum dose minus the relaxation value. Constraining the dose above the goal value may retain an undesirably high does within the target volumes and may over constrain the problem for the subsequent optimization steps. The new constraint is also set to be at least that minimal dose constraints established by a previous optimization step to guarantee that constraint is retained).
Claim 6. Kilby further teaches
obtaining a dose distribution in the anatomical structure using the optimized set of parameters; and obtaining the dose-volume metric from the dose distribution (FIG.8: Objective: optimize coverage (OCO): set the goal to the dose level for the desired volume to be maximized (e.g. a prescription dose), and optimize homogeneity (OHI): the goal is automatically set to the maximal dose constraint, and therefore the action is to maximize dose homogeneity).
Claim 7. Kilby further teaches that
the dose distribution is represented by a dose-volume histogram, DVH (FIG.8: 804: dose volume histogram (DVH) for pathological anatomy).
Claims 12 and 17. A method of radiation treatment planning for a radiotherapy system, and a radiation treatment planning system comprising a processor (Abstract: a method and apparatus for radiation treatment planning; and [0122]: Processing device may be configured to execute instructions for performing the TPS operations), the method comprising and the processor configured for:
receiving a reference objective, the reference objective representing a goal to be achieved by the radiotherapy system (FIG.6: the Objective column; [0059]: Using a sequential approach, the TPS can generate a script that sets the absolute constraints, such as those illustrated in FIGS. 5A and 5B, as well as an ordered sequence of optimization steps corresponding to a treatment-planning objective (e.g., OMI, OCO, OHI, OMA, OME, OCI, or OMU), as illustrated in FIG. 6; and [0044]: FIG.4C illustrates another embodiment of how the TPS optimizes a treatment plan. The TPS beings with receiving multiple radiation treatment-planning objectives of a treatment plan (block 452) from memory or from the user (for example, values for the desired minimum dose to the target volume…; and FIGS.8-9),
the reference objective comprising a reference dose to be delivered to a reference volume of an anatomical structure ([0044]: FIG.4C illustrates another embodiment of how the TPS optimizes a treatment plan. The TPS beings with receiving multiple radiation treatment-planning objectives of a treatment plan (block 452) from memory or from the user (for example, values for the desired minimum dose to the target volume…);
optimizing a set of parameters, according to the received reference objective, the set of parameters relating to characteristics of radiation to be delivered by the radiotherapy system (claim 1: receiving at a treatment planning system a plurality of radiation treatment-planning parameters, wherein the plurality of radiation-treatment planning parameters represent characteristics of radiation applied to an anatomical region via radiation beams; and sequentially optimizing the plurality of radiation treatment-planning parameter using the treatment planning system to obtain an optimized treatment plan of a plurality of radiation beams to be directed at the anatomical region), wherein
optimizing the set of parameters comprises: optimizing the set of parameters based on the reference objective to obtain a dose-volume metric (FIGS.2 and 3); and further optimizing the set of parameters, based on the received reference objective and a further objective that is based on the dose-volume metric; and generating a radiation treatment plan using the further optimized set of parameters (claim 1: sequentially optimizing…to obtain an optimized treatment plan; and claim 2: performing a first optimization step to optimize a first of the plurality of treatment-planning parameters; applying a result of the first optimization step as an additional constraint to a second optimization step; and performing the second optimization step to optimize a second of the plurality of treatment-planning parameters) – the second optimization optimizes a second parameter, which is considered the “further objective” as claimed.
Claim 13. Kilby further teaches that
the dose-volume metric comprises an achieved dose at the reference volume, and/or an achieved volume at the reference dose (FIGS.2, 3, 8 and 9: a dose volume histogram (DVH)) – a DVH is a graphical representation used in radiation treatment planning to correlate radiation dose to tissue volume.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Kilby et al., US 2010/0104068 A1, hereinafter Kilby, in view of Hindi et al., “A tutorial on optimization methods for cancer radiation treatment planning”. American Control Conference (ACC). 2013, hereinafter Hindi.
Claim 8. Kilby teaches all the limitations of claim 1.
Kilby further teaches that the first reference objective comprises a cost function ([0046]: the optimization constraints are the constraints of a multi-constraint cost function), yet it does not teach that the cost function is any one of the following cost functions: a target penalty cost function; an underdose dose-volume histogram (DVH) cost function, an overdose DVH cost function, and a parallel cost function.
However, in an analogous radiation therapy planning field of endeavor, Hindi teaches that the cost function is any one of the following cost functions: a target penalty cost function; an underdose dose-volume histogram (DVH) cost function, an overdose DVH cost function, and a parallel cost function (p.6808: A Costs: overdose or excess metrics, underdose or shortfall metrics; FIG.4; and FIG.5: typical penalty functions used in treatment planning optimization).
Therefore, it would have been obvious to one of the ordinary skilled in the art before the effective filing date of the claimed invention to have the cost function of Kilby employ such a feature of being any one of the following cost functions: a target penalty cost function; an underdose dose-volume histogram (DVH) cost function, an overdose DVH cost function, and a parallel cost function as taught in Hindi for the advantage of providing further details of the computation of optimization to incorporate the consideration of critical objectives, as suggested in Hindi, p.6804, Introduction and Problem Description.
Claim 9. Kilby further teaches that
the constraint used in the further optimization comprise the same cost function as the first reference objective (Table 2-2: step 2: same constraints in step 1, step 3: same constrains in step 2, and step 4: same constraints in step 3).
Claims 10-11 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Kilby et al., US 2010/0104068 A1, hereinafter Kilby, in view of McNutt et al., US 2003/0219098 A1, hereinafter McNutt.
Claim 10. Kilby teaches all the limitations of claim 1, including the features of “optimizing the set of parameters based on the first reference objective to obtain the dose-volume metric” (Kilby, FIGS.8 and 9).
Kilby further teaches
further optimizing the set of parameters, using the dose-volume metric as an objective, and using the dose-volume metric as a constraint, to obtain the further optimized set of parameters ([0085]: the result of each optimization step is retained as an additional for all subsequent optimization steps; [0086]: FIG.9: line 906 represents the goal value. Panel 905 represents the optimization constrained after a user-defined relaxation 905; and [0087]: for the optimization steps that apply to target volumes (e.g., OMI, OCO, and OHI), the new constraints is set to the minimum of the goal value or the minimum dose minus the relaxation value. Constraining the dose above the goal value may retain an undesirably high does within the target volumes and may over constrain the problem for the subsequent optimization steps. The new constraint is also set to be at least that minimal dose constraints established by a previous optimization step to guarantee that constraint is retained) – to use the relaxation value in the optimization is considered performing the optimization using the dose-volume metric as a constraint.
Kilby does not teach that the further optimization is to obtain an the claimed feature associated with the intermediate set of parameters; and further optimizing the intermediate set of parameters, based on the second reference objective, to obtain the further optimized set of parameters
However, in an analogous radiation treatment planning optimization field of endeavor, MuNutt teaches
optimizing the set of parameters based on the first reference objective to obtain the dose-volume metric; further optimizing the set of parameters, using the dose-volume metric as an objective, to obtain an intermediate set of parameters; and 46further optimizing the intermediate set of parameters, based on the second reference objective, to obtain the further optimized set of parameters ([0008]: A first group is selected. A first intermediate radiation dosage distribution objective is computed based on the treatment radiation dosage distribution objective and the first group weighting. The first group of beamlet parameters is optimized respective to the first intermediate radiation dosage distribution objective. A next group is selected. A second intermediate radiation dosage distribution objective is determined based on the treatment radiation dosage distribution objective and the next group weighting. The next group of beamlet parameters is optimized respective of the second intermediate radiation dosage distribution objective. The next group select, second intermediate objective determination, and next group optimization steps are repeated to optimize all the beamlet intensity parameters).
Claim 11. Kilby teaches all the limitations of claim 10.
Kilby further teaches that the dose-volume metric comprises an achieved volume at the reference dose and/or an achieved dose at the reference volume (FIGS.2, 3, 8 and 9: a dose volume histogram (DVH)) – a DVH is a graphical representation used in radiation treatment planning to correlate radiation dose to tissue volume, and
a cost function ([0046]: the optimization constraints are the constraints of a multi-constraint cost function).
Kilby does not teach that the optimization is performed using a cost function based on a weighted sum of a first cost function based on the achieved volume at the reference dose and a second cost function based on the achieved dose at the reference volume.
However, in an analogous radiation treatment planning optimization field of endeavor, McNutt teaches
using a cost function based on a weighted sum of the reference objective and the further objective ([0008]: a method is provided for delivering to a subject a selected radiation treatment described by a treatment radiation dosage distribution objective. The delivering includes application of at least one intensity-modulated beam whose radiation output is described by a plurality of beamlet parameters. The beamlet parameters are divided into a plurality of groups, each group including one or more beamlet parameters. A group weighting is assigned to each group based at least on a fraction of the beamlet parameters included in the group) – each group of parameters is considered an objective, including “the achieved volume at the reference dose and the achieved dose at the reference volume” as claimed.
Therefore, it would have been obvious to one of the ordinary skilled in the art before the effective filing date of the claimed invention to have the cost function of Kilby employ such a feature of being used based on a weighted sum of a first cost function based on the achieved volume at the reference dose and a second cost function based on the achieved dose at the reference volume as taught in McNutt for the advantage of “determination of appropriate radiotherapy parameters for delivering a selected radiation dosage profile”, as suggested in McNutt, [0006].
Claim 14. Kilby teaches all the limitations of claim 12.
Kilby does not teach using a cost function based on a weighted sum of the reference objective and the further objective.
However, in an analogous radiation treatment planning optimization field of endeavor, McNutt teaches
using a cost function based on a weighted sum of the reference objective and the further objective ([0008]: a method is provided for delivering to a subject a selected radiation treatment described by a treatment radiation dosage distribution objective. The delivering includes application of at least one intensity-modulated beam whose radiation output is described by a plurality of beamlet parameters. The beamlet parameters are divided into a plurality of groups, each group including one or more beamlet parameters. A group weighting is assigned to each group based at least on a fraction of the beamlet parameters included in the group) – each group of parameters is considered an objective, including “the reference objective and the further objective” as claimed.
Therefore, it would have been obvious to one of the ordinary skilled in the art before the effective filing date of the claimed invention to have the cost function of Kilby employ such a feature of being used based on a weighted sum of the reference objective and the further objective as taught in McNutt for the advantage of “determination of appropriate radiotherapy parameters for delivering a selected radiation dosage profile”, as suggested in McNutt, [0006].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zhang et al., “Direct optimization of dose-volume histogram metrics in radiation therapy treatment planning”. 2020. RaySearch Laboratories, Sweden. This reference discloses a method of directly optimizing on deviations in clinical goal values in radiation therapy treatment planning.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YI-SHAN YANG whose telephone number is (408) 918-7628. The examiner can normally be reached Monday-Friday 8am-4pm PST.
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/YI-SHAN YANG/Primary Examiner, Art Unit 3798