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 .
Claim Objections
Claim 4 is objected to because of the following informalities: line 2, “performing” should be –performed--. Appropriate correction is required.
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, 3-19 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, 9, and 17 the claims recite the following limitation(s):
Claim 1: A method for needle position optimization for prostate brachytherapy for use with a radiation delivery device configured to use a plurality of needles inserted into a prostate of a patient, the method comprising:
obtaining imagery of the prostate of the patient;
generating a needle pool for prostate brachytherapy treatment of the patient based on the imagery of the prostate of the patient; and
determining at a computing device an optimum prostate brachytherapy treatment plan for the patient by iteratively removing needles from the needle pool by forming and computationally solving a convex optimization problem wherein the convex optimization problem uses a quadratic dosimetric penalty function, dwell time regularization by total variation, and a block sparsity regularization term.
Claim 9: A radiation treatment planning system for prostate brachytherapy for use with a radiation delivery device configured to use a plurality of needles inserted into a prostate of a patient, the radiation treatment planning system comprising: a processor;
a memory operatively connected to the processor having instructions stored thereon for execution by the processor to:
obtain imagery of the prostate of the patient;
generate a needle pool for prostate brachytherapy treatment of the patient based on the imagery of the prostate of the patient; and
determine an optimum prostate brachytherapy treatment plan for the patient by iteratively removing needles from the needle pool by forming and computationally solving a convex optimization problem
wherein the convex optimization problem uses a quadratic dosimetric penalty function, dwell time regularization by total variation, and a block sparsity regularization term.
Claim 17: A system for prostate brachytherapy comprising:
a plurality of needles;
a radiation delivery device configured to use the plurality of needles when inserted into a prostate of a patient to delivery radiation thereto;
a processor;
a memory operatively connected to the processor having instructions stored thereon for execution by the processor to:
obtain imagery of the prostate of the patient;
generate a needle pool for prostate brachytherapy treatment of the patient based on the imagery of the prostate of the patient; and
determine an optimum prostate brachytherapy treatment plan for the patient by iteratively removing needles from the needle pool by forming and computationally solving a convex optimization problem wherein the convex optimization problem uses a quadratic dosimetric penalty function, dwell time regularization by total variation, and a block sparsity regularization term.
Step 1- Claims 1, 9, and 17 are directed to a method and a system for brachytherapy respectively.
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)(II), in that they recite a series of mathematical calculations and mental steps which
produce a Brachytherapy 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 sets forth obtaining image data, generating a needle pool which is merely a collection of potential needle positions generated by the NEEPO algorithm and is based on the image data, and determining an optimum treatment plan using the algorithm to deselect needles within the pool.
With respect to claim 9, The system claim merely adds a processor and a memory for which the Mathematical concepts and mental processes of claim 1 are implemented.
With respect to claim 17, the system claim adds needles, a radiation device, a processor and memory for which the Mathematical concepts and mental processes of claim 1 are implemented.
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, 9 , and 17 are not indicative of integration into a practical application (Prong 2, Step 2A: NO). MPEP 2106.04(d)
With respect to claim 1, 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 and actually performing the prostate Brachytherapy in accordance with the optimum treatment plan using the algorithm set forth.
With respect to claim 9, the system merely sets forth additional elements including a processor and memory. 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.
With respect to claim 17, the system merely sets forth additional elements including needles, a radiation delivery device, a processor and memory. Regarding the processor and memory, 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 MPEP2106.05(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. With respect to the needles and the radiation delivery device, they appear to be generic and could cover any possible set of needles or radiation device and are not positively connected to or integrated with the processor, memory and the recited judicial exception.
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 1,9 and 17 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, 9, and 17 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, 9, and 17 are ineligible.
Furthermore, the dependent claims, 3-8,10-16, and 18-19 do not add significantly more to the
abstract idea for the same reasons as addressed above with respect to Prong 2 of Step 2A.
Allowable Subject Matter
Claim 2 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: The prior art of record does not reasonably teach alone or in combination the subject matter set forth in the independent claims 1, 9 ,or 17. Utilizing image data including MRI, CT or Ultrasound to optimize needle placement for Brachytherapy is well known, selecting a subset of radiation configurations from a set of planned configurations, Iterative convex optimization algorithms, POGS( proximal operator graph solver), and penalty functions and adjusting for dwell times are also well known.
The prior art of record does not specifically teach alone or in combination the subject matter of the independent claims including generating a needle pool for prostate brachytherapy treatment of the patient based on the imagery of the prostate of the patient; and determining at a computing device an optimum prostate brachytherapy treatment plan for the patient by iteratively removing needles from the needle pool by forming and computationally solving a convex optimization problem wherein the convex optimization problem uses a quadratic dosimetric penalty function, dwell time regularization by total variation, and a block sparsity regularization term.
Note, Borot de Battistiet al.(10933254) sets forth in paragraph 4: Examples of brachytherapy are low dose brachytherapy (LDR brachytherapy) and high dose brachytherapy (HDR brachytherapy). To determine LDR seed positions, or HDR dwell positions and dwell times, so-called inverse planning methods are used. These algorithms optimize the radiation dose, considering objectives for the target volume, as well as constraints with respect organs at risk, which are translated into a set of linear constraints for a linear system that is to be solved.
Note Luan(US20170036037) teaches in paragraphs 8-10, [0008] In order to provide dose conformity and efficient treatment time, radiotherapy relies on specialized optimization algorithms, for instance simulated annealing, deterministic optimization models such as linear programming, non-negative least squares, linear programming, among others; genetic algorithms, neural networks, mixed integer linear programming, and graph algorithms, etc. Usually these algorithms try to model all competing treatment goals and radiation source configurations as a unique optimization problem.
[0009] Modern radiation therapy treatment planning typically involves the following set of steps: patient imaging, target definition (i.e., structure contouring), dose prescription, beam configuration optimization, plan generation, and quality assurance. [0010] Imaging is performed by taking computer tomography scans (CT scans), magnetic resonance imaging (MRI), positron emission tomography (PET), ultrasound or combinations of these depending on the type of cancer. CT scan is the most widely used imaging modality and can provide anatomical information of the patient. Once these images are obtained, physicians contour the tumor and organs at risk (OARs) as well as prescribe the desired dose to treat the tumor.
Note Strehl et al.(US20120089008) sets forth in paragraph 77, [0077] According to an embodiment of the invention, a parametric rigid cylinder is used to model the needle artifact and a cost function is derived by weighting the image intensities depending on their position relative to the needle artifact. Based on an initialization, the needle position is estimated by finding the best fit of the model to the available images. This results in a non-linear optimization task, and according to embodiments of the invention, either a best neighbor or a gradient descent algorithm can be applied to iteratively update the needle position until the cost function yields a local minimum. Tracking is accomplished by continuously repeating this model-fitting algorithm for newly acquired MR images.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Flynn et al.( US 20150367144) sets forth Systems and methods for rotating shield brachytherapy. In an aspect, some of the systems and methods can be used to facilitate shield selection for use in rotating shield brachytherapy. In an aspect, the invention is a shielded needle or catheter system with a rotational controller for delivering radioisotope-based interstitial rotating shield brachytherapy (I-RSBT), In an aspect, I-RSBT needles can deliver dose distributions that can be non-radially symmetric about each needle, enabling reduced doses to sensitive normal tissues. Further provided are methods and systems for selecting an emission angle for use in S-RSBT and for sequencing the rotating shields. Further provided are methods and system for the multiple application of M-RSBT in a single setting.
Binnekamp et al.( WO 2017037060) teaches The device performs updating or revising the treatment plan during execution of inverse optimization algorithms by assessing impact of planned needles on resulting dose, directly, and/or by assessing the impact of planned needles on the resulting dose in a simulation metric in relation to a location of a brachytherapy instrument. The device maximizes distance between the needles for fixating the tissue, thus preventing deformation while minimizing the distance between needles and increasing freedom in re-optimization of a dose plan for needles in another part of a target volume. The device provides the updating repeatedly after each step or portion of the procedure is carried out allows for adjustments to be provided, thus maximizing a chance of optimum results.
Long et al.(US20200075151) teaches a method and system for generating a voxel-based quadratic penalty model for automatic intensity modulated radiation therapy (IMRT) treatment planning are disclosed herein. A computing system generates an initial assignment of threshold values to a penalty function for IMRT treatment planning. The computing system receives an update to a dose value associated with the IMRT treatment planning The computing system dynamically updates the threshold values based on the updated dose value. The computing system continues to iterate the threshold values based on further updated dose values.
Nordström et al.( US 20190255355) teaches In the field of radiotherapy, methods for dose or treatment planning for a radiotherapy system are disclosed, wherein a spatial dose delivered can be adjusted and delivered radiation is determined using an optimization problem that steers the delivered radiation according to a frame description reflecting criteria for regions of interest that include at least one of targets to be treated during treatment of the patient, organs at risk and/or healthy tissue. The method includes estimating a voxel set receiving a higher dose than a predetermined threshold dose level, which voxel set includes voxels from at least one target volume. Further, a low dose voxel set is determined and a frame description for the voxels in the low dose voxel set is provided where voxels receiving a dose exceeding a predetermined threshold value is penalized such that the dose delivered to the low dose voxel set is suppressed. The frame description is then used in the optimization problem that steers the delivered radiation. [0008] In improved inverse treatment planning methods provided by the applicant, a number of objectives reflecting clinical criteria for regions of interest, including targets to be treated during treatment of the patient, organs at risk and/or healthy tissue are set and radiation dose profiles to be delivered to the target are generated. A convex optimization problem that steers the delivered radiation according to the objectives is provided and dose profiles for specific treatment configurations including beam shape settings for the radiation dose profiles are calculated using the convex optimization problem. Thereafter, a treatment plan, including determining the radiation dose profiles to be delivered during treatment based on the treatment configurations are created, wherein each radiation dose profile is modelled by a spatial dose volume distribution of radiation, the shape of the spatial distribution depending on the beam shape settings and an optimal treatment plan that satisfies the clinical criteria is selected.
Diaz et al.(US20110091014) teaches a method that involves comparing a desired dose i.e. dose distribution, with a calculated dose to determine an effect of each of multiple initial control points on the comparison. A subset of initial control points is selected based on the comparison. The subset of initial control points is consolidated into a lesser number of effective control points, where a calculated dose related to the subset of initial control points and a calculated dose related to the lesser number of effective control points are equal.
Yu(US6200255) teaches an implant planning engine plans implants for radiotherapy, e.g., prostrate brachytherapy. The system optimizes intraoperative treatment planning on a real-time basis using a synergistic formulation of a genetic algorithm, multi-objective decision theory and a statistical sensitive analysis. A total solution for prostate seed implant brachytherapy is achieved.
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.
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/BRIAN L CASLER/Primary Examiner, Art Unit 3791