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 .
Allowable Subject Matter
Claim 7, 8, 11, 12, 15-17 and 20 are 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.
35 USC § 112 6th
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
Use of the word “means” (or “step for”) in a claim with functional language creates a rebuttable presumption that the claim element is to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is invoked is rebutted when the function is recited with sufficient structure, material, or acts within the claim itself to entirely perform the recited function.
Absence of the word “means” (or “step for”) in a claim creates a rebuttable presumption that the claim element is not to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is not invoked is rebutted when the claim element recites function but fails to recite sufficiently definite structure, material or acts to perform that function.
Claim elements in this application that use the word “means” (or “step for”) are presumed to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Similarly, claim elements that do not use the word “means” (or “step for”) are presumed not to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
CLAIM INTERPRETATION
The claim limitations for claim 6-15, are considered to be specialized, and are considered to be specialized under the rationale that they would not be considered a standard function on any computing system, but are rather specific to the instant application.
The examiner notes the claim limitations meets each of the prongs of the 3-prong analysis detailed in MPEP 2181 (I).
Prong 1: The claimed “means” , “step”, or substitute for “means” that is a generic placeholder for performing claimed function. The instances of such means type languages are detailed below in a claim number, line and quoted word format to aid in understanding what is considered to be means type language:
Claim 1, line 2 “an image processing module”, line 6, “a data processing module”, line 9, “an overlap detecting module”, line 11 “a treatment plan generating module”.
Claim 13, line 2 “a model acquiring step”, line 5, "a positional parameter acquiring step", line 8 "an overlap determining step".
Claim 18, line 2, “a model data acquiring step”, line 6, “a positional parameter determining step”, line 9, “an overlap determining step”, line 12, “a treatment plan generating step”.
Prong 2: Functional language such as, “for”, “configured to”, or “so that” is present in the claim language.
Prong 3: No “means” or “step” type language is present modified by sufficient structure, materials or acts for performing the claimed function.
The examiner notes that claims 2-12, 14-17, 19 and 20 inherit the 112 sixth interpretations from the claims from which they depend from.
The examiner further notes the specification possess an adequate algorithm.
The examiner further notes the structure, ¶88, “The Monte Carlo method can accurately simulate collision trajectories and energy distributions of nuclear particles in a 3D space in the radiation object. In the BNCT, in order to simulate an absorbed dose of a human body under certain radiation conditions to facilitate formulation of the treatment plan, a medical image is processed repeatedly with a computer technology, so as to establish an accurate lattice model required by the Monte Carlo software, and perform simulation and calculation with the Monte Carlo software. The medical image data may be magnetic resonance imaging (MRI) data, computed tomography (CT) data, a positron emission tomography (PET) data, PET-CT data or X-ray imaging data. In the embodiment, description is made on the basis of the CT data. The CT data is often in a digital imaging and communications in medicine (DICOM) format. As is known to those skilled in the art, other medical image data may also be used in the modular treatment planning system and the system construction method in the present disclosure, provided that the medical image data can be converted into a 3D voxel prosthesis tissue model.” that is a specialized computer for the specialized functions.
The examiner notes that whether 35 U.S.C § 112(f) or 35 U.S.C. § 112 6th is invoked or not is controlled by the claim language, not by applicant’s intent or mere statements to the contrary.
An appropriate response to avoid interpretation under 35 U.S.C § 112(f) or 35 U.S.C. § 112 6th would be to amend the claim limitation to clearly recite a definite structure, material or act that entirely performs the recited function; or to present a sufficient showing that the claim limitation recites a structure, material or act that entirely performs the recited function.
An appropriate response to obtain interpretation under 35 U.S.C § 112(f) or 35 U.S.C. § 112 would be to amend the claim limitation to remove the structure, material or act that performs the recited function; or to present a sufficient showing that the claim limitation does not recite any structure, material or act that entirely performs the recited function.
Claim Rejections - 35 USC § 102
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.
Claim(s) 13 and 14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yang et al. (WO 2021198080 A1), as cited in an IDS.
Regarding claim 13, Yang teaches an automatic overlap checking method (See title, abstract, ¶113) comprising:
a model acquiring step: acquiring a three-dimensional (3D) voxel model of an irradiated body and a beam source model, the 3D voxel model of the irradiated body comprising a plurality of voxel grids (¶9, "automatically planning radiation-based treatment of a treatment volume of a particular patient", ¶25, "mapping voxels of
the atlas image to voxels of the patient image. Model-based structure
segmentation delineates structure by detecting edges and points directly
on patient images. Multiple image processing techniques are often
employed in these regards". See ¶19, "utilizing a three-dimensional virtual volumetric structure that is derived using only information provided by a CT apparatus".);
a positional parameter acquiring step: acquiring a positional parameter of the beam source model and a positional parameter of the 3D voxel model of the irradiated body (¶89, " This process can automatically optimize radiation source trajectories by defining one or more arcs and collimator positions as a function of ordering consideration of one or more organs at risk in the breast template and the patient treatment volume (the latter considered with or without nodes as desired and/or specified). By one approach collision detection can run in the background to check that trajectories are collusion free. By one approach the available clinical goals are translated into optimization objectives, which objectives are then utilized to guide the iterative optimization process. By one approach the accelerator photon energy is defined. ", ¶101 "virtual dry run of the treatment delivery of the defined treatment fields via a 3-D animation. This virtual dry run can comprise a representation of the treatment delivery using the
actual treatment machine, couch, fixation device, and patient dimensions".
); and
an overlap determining step: determining a positional relationship between the 3D voxel model of the irradiated body and the beam source model on the basis of a positional relationship between the voxel grid and the beam source model (¶113 "the control circuit 101 uses the aforementioned virtual volumetric structure to predict potential collisions when assessing a radiation treatment plan for the patient that utilizes this specific radiation
treatment platform".).
Regarding claim 14, Yang teaches the automatic overlap checking method according to claim 13, before the overlap determining step, further comprising a reference object selecting step: selecting a reference object from the plurality of voxel grids, wherein in the overlap determining step, the positional relationship between the 3D voxel model of the irradiated body and the beam source model is determined on the basis of a positional relationship between the reference object and the beam source model (¶113 "the control circuit 101 uses the aforementioned virtual volumetric structure to predict potential collisions when assessing a radiation treatment plan for the patient that utilizes this specific radiation
treatment platform".).
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.
Claim(s) 1-6, 9-10, 18, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Yang et al. (WO 2021198080 A1), as cited in an IDS.
Regarding claim 1 Yang teaches a treatment planning system (See title, “METHOD AND APPARATUS TO DERIVE AND UTILIZE VIRTUAL VOLUMETRIC STRUCTURES FOR PREDICTING POTENTIAL COLLISIONS WHEN ADMINISTERING THERAPEUTIC RADIATION “ ¶24, “"automatically planning radiation-based treatment of a treatment volume of a particular patient”), comprising:
an image processing module configured to acquire medical image data of an irradiated body, and establish a three-dimensional (3D) voxel model of the irradiated body on the basis of the medical image data, the 3D voxel model of the irradiated body comprising a plurality of voxel grids (¶9, "automatically planning radiation-based treatment of a treatment volume of a particular patient", ¶25, "mapping voxels of
the atlas image to voxels of the patient image. Model-based structure
segmentation delineates structure by detecting edges and points directly
on patient images. Multiple image processing techniques are often
employed in these regards". See ¶19, "utilizing a three-dimensional virtual volumetric structure that is derived using only information provided by a CT apparatus".);
a data processing module configured to acquire a beam source model, and determine a positional parameter of the beam source model and a positional parameter of the 3D voxel model of the irradiated body (¶89, " This process can automatically optimize radiation source trajectories by defining one or more arcs and collimator positions as a function of ordering consideration of one or more organs at risk in the breast template and the patient treatment volume (the latter considered with or without nodes as desired and/or specified). By one approach collision detection can run in the background to check that trajectories are collusion free. By one approach the available clinical goals are translated into optimization objectives, which objectives are then utilized to guide the iterative optimization process. By one approach the accelerator photon energy is defined. ", ¶101 "virtual dry run of the treatment delivery of the defined treatment fields via a 3-D animation. This virtual dry run can comprise a representation of the treatment delivery using the
actual treatment machine, couch, fixation device, and patient dimensions".
);
an overlap detecting module configured to determine a positional relationship between the voxel grid and the beam source model (¶113 "the control circuit 101 uses the aforementioned virtual volumetric structure to predict potential collisions when assessing a radiation treatment plan for the patient that utilizes this specific radiation
treatment platform".); but the main embodiment doesn’t explicitly disclose
a treatment plan generating module configured to generate a treatment plan.
Yang, in an alternate embodiment, teaches a treatment plan generating module configured to generate a treatment plan (¶14 "utilizing the resultant optimized radiation treatment plan 113 that has been vetted for collision avoidance to administer radiation to the patient using this particular radiation treatment platform").
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the main embodiment of Yang with an alternate embodiment because utilizing the resultant optimized radiation treatment plan 113 that has been vetted for collision avoidance to administer radiation to the patient using this particular radiation treatment platform (¶14)
Regarding claim 2, Yang teaches the treatment planning system according to claim 1, wherein the overlap detecting module is configured to determine the positional relationship between the voxel grid and the beam source model on the basis of a positional relationship between a reference object and the beam source model; and the reference object is selected from the plurality of voxel grids (¶113 "the control circuit 101 uses the aforementioned virtual volumetric structure to predict potential collisions when assessing a radiation treatment plan for the patient that utilizes this specific radiation
treatment platform".).
Regarding claim 3, Yang teaches the treatment planning system according to claim 1, wherein the overlap detecting module is configured to determine a positional relationship between a reference object and the beam source model, as well as a positional relationship between the reference object and an internal irradiation space of the beam source model (¶113 "the control circuit 101 uses the aforementioned virtual volumetric structure to predict potential collisions when assessing a radiation treatment plan for the patient that utilizes this specific radiation treatment platform". See ¶25, “By one approach, these teachings provide for accessing imaging information for a treatment zone that includes the treatment volume of the particular patient. The control circuit can then employ that imaging information along with deep learning to automatically segment at least some breast tissue of the particular patient (and perhaps the heart) and non deep learning to automatically segment at least portions of some organs-at-risk (such as, but not limited to, a lung, a portion of a spinal column, and a portion of a chest wall) to provide automatically segmented patient content. Atlas and model-based approaches two examples of non-deep learning approaches. Atlas-based segmentation assumes that given patient images can be segmented by propagating structures from manually-segmented atlases. The atlas image is deformed to match the patient image using one or more deformable image registration algorithms and structures are propagated using deformation vector fields mapping voxels of the atlas image to voxels of the patient image. Model-based structure segmentation delineates structure by detecting edges and points directly on patient images. Multiple image processing techniques are often employed in these regards. In many cases it will be appropriate for the user to manually define the volumes. Generally speaking these approaches combine deep learning with density and heuristic searching algorithms. The latter are existing algorithms that are sometimes employed in existing treatment planning systems. The combination of such existing density and heuristics-based tools with deep learning, however, was previously unknown to the applicant.”).
Regarding claim 4, Yang teaches the treatment planning system according to claim 2, wherein the overlap detecting module is configured to determine a type of the voxel grid (See ¶25, “By one approach, these teachings provide for accessing imaging information for a treatment zone that includes the treatment volume of the particular patient. The control circuit can then employ that imaging information along with deep learning to automatically segment at least some breast tissue of the particular patient (and perhaps the heart) and non deep learning to automatically segment at least portions of some organs-at-risk (such as, but not limited to, a lung, a portion of a spinal column, and a portion of a chest wall) to provide automatically segmented patient content. Atlas and model-based approaches two examples of non-deep learning approaches. Atlas-based segmentation assumes that given patient images can be segmented by propagating structures from manually-segmented atlases. The atlas image is deformed to match the patient image using one or more deformable image registration algorithms and structures are propagated using deformation vector fields mapping voxels of the atlas image to voxels of the patient image. Model-based structure segmentation delineates structure by detecting edges and points directly on patient images. Multiple image processing techniques are often employed in these regards. In many cases it will be appropriate for the user to manually define the volumes. Generally speaking these approaches combine deep learning with density and heuristic searching algorithms. The latter are existing algorithms that are sometimes employed in existing treatment planning systems. The combination of such existing density and heuristics-based tools with deep learning, however, was previously unknown to the applicant.”).
Regarding claim 5, Yang teaches the treatment planning system according to claim 4, wherein the type of the voxel grid comprises a first-type grid and a second-type grid (¶26-27); the first-type grid is composed of a tissue of the irradiated body (¶26-27); the second-type grid is composed of air; and the reference object is selected from the first-type grid (¶26-27, ¶57).
Regarding claim 6, Yang teaches the treatment planning system according to claim 2, wherein the overlap detecting module is configured to determine a tissue type of an overlapping reference object (¶25).
Regarding claim 9, Yang teaches the treatment planning system according to claim 2, wherein the reference object comprises one, more or all of a vertex, a centroid, a random point, a profile or an outer surface of the voxel grid (See ¶21, volumetric models include random point, or vertex. ¶26-27: skin has an outer surface. MPEP 2173.05(h)).
Regarding claim 10, Yang teaches the treatment planning system according to claim 1, wherein the positional parameter comprises a relative distance between the beam source model and the 3D voxel model of the irradiated body (¶110-111), a relative angle (¶81), and a beam irradiating direction (¶51).
Claim 18 recites similar limitations to that of claim 1 but doesn’t explicitly disclose a method for formulating a treatment plan, comprising.
Yang teaches a method for formulating a treatment plan, comprising. (See title, “METHOD AND APPARATUS TO DERIVE AND UTILIZE VIRTUAL VOLUMETRIC STRUCTURES FOR PREDICTING POTENTIAL COLLISIONS WHEN ADMINISTERING THERAPEUTIC RADIATION “ ¶24, “"automatically planning radiation-based treatment of a treatment volume of a particular patient”).
Therefore claim 18 is rejected under similar rationale as detailed in claim 1.
Regarding claim 19, Yang teaches the method for formulating a treatment plan according to claim 18, wherein before the overlap determining step, the method for formulating a treatment plan further includes a reference object selecting step: selecting a reference object in the voxel grid; and in the overlap determining step, the positional relationship between the 3D voxel model of the irradiated body and the beam source model is determined on the basis of a positional relationship between the reference object and the beam source model (¶113 "the control circuit 101 uses the aforementioned virtual volumetric structure to predict potential collisions when assessing a radiation treatment plan for the patient that utilizes this specific radiation
treatment platform".).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT J CRADDOCK whose telephone number is (571)270-7502. The examiner can normally be reached Monday - Friday 10:00 AM - 6 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Devona E Faulk can be reached at 571-272-7515. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ROBERT J CRADDOCK/Primary Examiner, Art Unit 2618
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