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 .
Claims 1-21 are pending regarding this application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/25/2024 and 09/16/2025 are
considered and attached.
Examiner’s Comment
Examiner considered U.S. Publication No. 20250272852 A1 regarding a potential double patenting rejection regarding claims 1-7 and corresponding claims 15-21 (see double patenting rejection of claims 8, 9, 11, and 13 below). However, multiple differences between the App. No. 18/586,532 and U.S. Publication No. 20250272852 A1 were persuasive enough to establish a clear distinction between claims 1-7 and corresponding claims 15-21 in the current application and claims 1-7 and corresponding claims 15-21 in the aforementioned patent application publication.
U.S. Publication No. 20250272852 A1 recites “obtaining treatment image data associated with a target structure of a patient requiring radiation therapy, wherein the treatment image data is acquired using an imaging system during a treatment phase of the radiation therapy” in claim 1, while App. No. 18/586,532 recites “obtaining (a) planning image data that is associated with a target structure of a patient requiring radiation therapy and acquired prior to a treatment phase of the radiation therapy, or (b) transformed image data that is generated based on the planning image data” in claim 1. U.S. Publication No. 20250272852 A1 further recites “wherein the material property data is matchable against a template that also represents the particular material property for tracking the target structure based on the particular material property during the treatment phase” in claim 1, while App. No. 18/586,532 recites “based on the first material property data, generating a template that represents the particular material property” in claim 1. App. No. 18/586,532 further recites “wherein the template is matchable against second material property data that also represents the particular material property for tracking the target structure during the treatment phase” (emphasis added) which is not in any of the claims in U.S. Publication No. 20250272852 A1. These corresponding sections differ wherein the current application further specifies the template is matchable against second material property data. These differences present a clear distinction between the two claim sets.
Double Patenting - Nonstatutory
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 8, 9, and 11 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 8-10 of U.S. Patent Application No. 18/586,533, herein after referred to as the ‘533 application. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims 8, 9, and 11 of the current application are anticipated by claims 8-10 in application ‘533, as indicated below.
Regarding claim 8, claim 8 compares to claim 8 of the ‘533 application as indicated below:
Current Application
‘533 Application
Notes
A method for a computer system to perform target structure tracking for radiation therapy, wherein the method comprises:
Claim 8: A method for a computer system to perform target structure tracking for radiation therapy, wherein the method comprises:
Verbatim the same
obtaining material property data that represents a particular material property associated with a target structure of a patient requiring radiation therapy, wherein the material property data is generated based on treatment image data acquired during a treatment phase of the radiation therapy; and
Claim 8: obtaining material property data that represents a particular material property associated with a target structure of a patient requiring radiation therapy, wherein the material property data is generated using an artificial intelligence (AI) engine based on treatment image data acquired during a treatment phase of the radiation therapy;
The underlined portion of the ‘852 application is additional to the subject matter of the current application, however this does not change that the section of claim 8 in ‘852 teaches the entirety of the corresponding section in the current application.
obtaining a template that also represents the particular material property associated with the target structure, wherein the template is generated based on (a) planning image data that is acquired prior to the treatment phase or (b) transformed image data that is generated based on the planning image data; and
Claim 8: obtaining a template that also represents the particular material property associated with the target structure, wherein the template is generated based on (a) planning image data that is acquired prior to the treatment phase or (b) transformed image data that is generated based on the planning image data; and
Verbatim the same
based on the material property data and the template, performing template matching during the treatment phase to track the target structure based on the particular material property.
Claim 8: based on the material property data and the template, performing template matching during the treatment phase to track the target structure based on the particular material property.
Verbatim the same
Thus, as can be seen above, claim 8 of the current application is anticipated by claim 8 of the ‘533 application. Therefore, any patent granted on the current application would result in the unjustifiable timewise extension of the monopoly granted on claim 8 of U.S. Application No. 18/586,533.
As for claim 9, the limitations of claim 9 can be found in claim 9 in the ‘852 publication.
With regard to claim 11, claim 11 is substantively equivalent to claim 10 of the ‘852 publication.
Double Patenting - Statutory
A rejection based on double patenting of the “same invention” type finds its support in the language of 35 U.S.C. 101 which states that “whoever invents or discovers any new and useful process... may obtain a patent therefor...” (Emphasis added). Thus, the term “same invention,” in this context, means an invention drawn to identical subject matter. See Miller v. Eagle Mfg. Co., 151 U.S. 186 (1894); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Ockert, 245 F.2d 467, 114 USPQ 330 (CCPA 1957).
A statutory type (35 U.S.C. 101) double patenting rejection can be overcome by canceling or amending the claims that are directed to the same invention so they are no longer coextensive in scope. The filing of a terminal disclaimer cannot overcome a double patenting rejection based upon 35 U.S.C. 101.
Claim 13 is provisionally rejected under 35 U.S.C. 101 as claiming the same invention as that of claim 8 of copending Application No. 18/586,533 (reference application). This is a provisional statutory double patenting rejection since the claims directed to the same invention have not in fact been patented.
Regarding claim 13, claim 13 (and claim 8, since claim 13 depends upon claim 8 and therefore includes the entirety of the limitations claimed in claim 8) compares to claim 8 of the ‘533 application as indicated below:
Current Application
‘533 Application
Notes
Claim 8: A method for a computer system to perform target structure tracking for radiation therapy, wherein the method comprises:
Claim 8: A method for a computer system to perform target structure tracking for radiation therapy, wherein the method comprises:
Verbatim the same
Claim 8: obtaining material property data that represents a particular material property associated with a target structure of a patient requiring radiation therapy, wherein the material property data is generated based on treatment image data acquired during a treatment phase of the radiation therapy; and
Claim 13: The method of claim 8, wherein obtaining the material property data comprises: generating the material property data by processing the treatment image data using an artificial intelligence (AI) engine.
Claim 8: obtaining material property data that represents a particular material property associated with a target structure of a patient requiring radiation therapy, wherein the material property data is generated using an artificial intelligence (AI) engine based on treatment image data acquired during a treatment phase of the radiation therapy;
The combination of claim 8 of the current application and claim 13, which depends upon claim 8, teaches the entirety of claim 8 in the ‘533 application.
Claim 8: obtaining a template that also represents the particular material property associated with the target structure, wherein the template is generated based on (a) planning image data that is acquired prior to the treatment phase or (b) transformed image data that is generated based on the planning image data; and
Claim 8: obtaining a template that also represents the particular material property associated with the target structure, wherein the template is generated based on (a) planning image data that is acquired prior to the treatment phase or (b) transformed image data that is generated based on the planning image data; and
Verbatim the same
Claim 8: based on the material property data and the template, performing template matching during the treatment phase to track the target structure based on the particular material property.
Claim 8: based on the material property data and the template, performing template matching during the treatment phase to track the target structure based on the particular material property.
Verbatim the same
Thus, as can be seen above, claim 13 of the current application is identical to claim 8 of the ‘533 application. Therefore, any patent granted on the current application would result in the unjustifiable timewise extension of the monopoly granted on claim 8 of U.S. Application No. 18/586,533.
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 (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 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.
Claims 1, 8, and 13-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Fu et al. (“Enhancing the target visibility with synthetic target specific digitally reconstructed radiograph for intrafraction motion monitoring: A proof-of-concept study”), hereinafter Fu.
Regarding claim 1, Fu teaches a method for a computer system to perform template generation for target structure tracking (Fu teaches “the tumor in the sTS-DRR generated from the real-time kV projection images was registered to the tumor template in the TS-DRR for motion tracking” in Section 2.1; see also Figure 2), wherein the method comprises:
obtaining
(a) planning image data that is associated with a target structure of a patient requiring radiation therapy and acquired prior to a treatment phase of the radiation therapy (Fu teaches a planning CT (see Section 1, Figure 2, and Section 2.1) which is acquired prior to the treatment phase and represents the target structure of the patient), or
(b) transformed image data that is generated based on the planning image data (Fu teaches “build[ing] a patient-specific model to generate the synthetic Target-Specific DRR (sTS-DRR) from intrafraction kV images for intrafraction tumor monitoring” wherein this process occurs in order to “improve the target positioning accuracy before beam delivery” as shown in Section 1. Here the digitally reconstructed radiograph is interpreted as the transformed image data that is based on the planning data; see DRR in Figure 1; the planning data is interpreted as equivalent to the planning target volume (PTV) which is also taught in Section 1 and Figure 1);
based on the planning image data or the transformed image data, or both, generating first material property data that represents a particular material property associated with the target structure (Fu teaches, for example, “considering one thoracic vertebra as the planning target volume (PTV), the CT volume is truncated to include only the section that overlaps with the PTV for TS-DRR generation” in section 1. Here, the TS-DRR generation is interpreted as representing the particular material property (volume) which is based on the planning target volume (PTV)); and
based on the first material property data, generating a template that represents the particular material property (Fu teaches generating a template of the TS-DRR (which is based on the planning CT (i.e. volume)) in Section 2.1), wherein the template is matchable against second material property data that also represents the particular material property for tracking the target structure during the treatment phase (Fu, see Figure 2 which teaches the template within the treatment phase wherein “the synthetic TS-DRR was registered to the tumor template TS-DRR generated from the planning CT for intrafraction tumor motion monitoring” in Section 2.1. Here, the template is matchable against both the TS-DRR and the sTS-DRR. “the tumor in the sTS-DRR generated from the real-time kV projection images was registered to the tumor template in the TS-DRR for motion tracking” in Section 2.1. The registration process here is interpreted as equivalent to making it possible for the template to be matchable against second material property data wherein the second material property data is the synthetic TS-DRR).
Regarding claim 8, Fu teaches a method for a computer system to perform target structure tracking for radiation therapy (Fu teaches “the tumor in the sTS-DRR generated from the real-time kV projection images was registered to the tumor template in the TS-DRR for motion tracking” in Section 2.1; see also Figure 2), wherein the method comprises:
obtaining material property data that represents a particular material property associated with a target structure of a patient requiring radiation therapy, wherein the material property data is generated based on treatment image data acquired during a treatment phase of the radiation therapy (Fu teaches, for example, “considering one thoracic vertebra as the planning target volume (PTV), the CT volume is truncated to include only the section that overlaps with the PTV for TS-DRR generation” in section 1. Here, the TS-DRR/sTR-DRR generation is interpreted as representing the particular material property (volume) which is based on the planning target volume (PTV) which is associated with the target structure of a patient. See also Figure 2 in which this process takes place in the patient treatment phase); and
obtaining a template that also represents the particular material property associated with the target structure, wherein the template is generated based on
(a) planning image data that is acquired prior to the treatment phase (Fu teaches generating a template of the TS-DRR (which is based on the planning CT (i.e. volume/planning image data)) in Section 2.1 and Figure 2. See Section 1 regarding the capturing of the CT before the radiation is administered) or
(b) transformed image data that is generated based on the planning image data; and
based on the material property data and the template, performing template matching during the treatment phase to track the target structure based on the particular material property (Fu teaches using a template of the TS-DRR and registering (matching) it with a sTS-DRR in order to track motion of the tumor (target structure) in Figure 2, and Sections 1 and 2.1).
Regarding claim 13, Fu teaches the method of claim 8, wherein obtaining the material property data comprises:
generating the material property data (Fu teaches a TS-DRR and sTS-DRR which represent the volume (material property) in Figures 1 and 2, and Sections 1 and 2.1) by processing the treatment image data using an artificial intelligence (AI) engine (Fu teaches building a patient-specific model (artificial intelligence) to generate the sTS-DRR in Figure 2, and Sections 1 and 2.1, wherein the sTS-DRR is based on the planning CT (treatment image data)).
Regarding claim 14, Fu teaches the method of claim 8, wherein performing the template matching comprises:
performing the template matching to match the material property data against the template to determine two-dimensional (2D) position data associated with the target structure based on the particular material property (Fu teaches matching the TS-DRR template with the sTR-DRR in order to track the motion of the tumor in section 2.1 and Figure 2, see Figures 6 and 8 wherein the template matching/motion monitoring is analyzed to determine 2D position data).
Regarding claim 15, Fu teaches a computer system, comprising:
a processor; and a non-transitory computer-readable medium having stored thereon instructions that, when executed by the processor (Fu teaches that “the code [instructions] was implemented using Pytorch version 1.12.1 and modified based on the GitHub repository” in Section 2.3. Here, because the code is stored and inherently implemented using a processor, Fu’s teaching of the implementation of the code is interpreted as equivalent to the above limitations), cause the processor to perform the following:
obtain
(a) planning image data that is associated with a target structure of a patient requiring radiation therapy and acquired prior to a treatment phase of the radiation therapy (Fu teaches a planning CT (see Section 1, Figure 2, and Section 2.1) which is acquired prior to the treatment phase and represents the target structure of the patient), or
(b) transformed image data that is generated based on the planning image data (Fu teaches “build[ing] a patient-specific model to generate the synthetic Target-Specific DRR (sTS-DRR) from intrafraction kV images for intrafraction tumor monitoring” wherein this process occurs in order to “improve the target positioning accuracy before beam delivery” as shown in Section 1. Here the digitally reconstructed radiograph is interpreted as the transformed image data that is based on the planning data; see DRR in Figure 1; the planning data is interpreted as equivalent to the planning target volume (PTV) which is also taught in Section 1 and Figure 1);
based on the planning image data or the transformed image data, or both, generate first material property data that represents a particular material property associated with the target structure (Fu teaches, for example, “considering one thoracic vertebra as the planning target volume (PTV), the CT volume is truncated to include only the section that overlaps with the PTV for TS-DRR generation” in section 1. Here, the TS-DRR generation is interpreted as representing the particular material property (volume) which is based on the planning target volume (PTV)); and
based on the first material property data, generate a template that represents the particular material property (Fu teaches generating a template of the TS-DRR (which is based on the planning CT (i.e. volume)) in Section 2.1), wherein the template is matchable against second material property data that also represents the particular material property for tracking the target structure during the treatment phase (Fu, see Figure 2 which teaches the template within the treatment phase wherein “the synthetic TS-DRR was registered to the tumor template TS-DRR generated from the planning CT for intrafraction tumor motion monitoring” in Section 2.1. Here, the template is matchable against both the TS-DRR and the sTS-DRR. “the tumor in the sTS-DRR generated from the real-time kV projection images was registered to the tumor template in the TS-DRR for motion tracking” in Section 2.1. The registration process here is interpreted as equivalent to making it possible for the template to be matchable against second material property data wherein the second material property data is the synthetic TS-DRR).
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.
Claims 2-4, 9-10, and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Fu et al. (“Enhancing the target visibility with synthetic target specific digitally reconstructed radiograph for intrafraction motion monitoring: A proof-of-concept study”), hereinafter Fu in view of Ding et al. (U.S. Publication No. 2012/0041685 A1), hereinafter Ding.
Regarding claim 2, Fu teaches the method of claim 1.
Fu fails to teach wherein generating the first material property data comprises: generating the first material property data that represents the particular material property in the form of material density or material thickness associated with the target structure.
However, Ding teaches wherein generating the first material property data comprises:
generating the first material property data that represents the particular material property in the form of material density (Ding teaches “convert[ing] each voxel in the patient DICOM image from CT number to a specific material and density” in para. [0077]) or material thickness associated with the target structure (Ding teaches calculating a material thickness associated with a specific CT scan (target structure) in para. [0080] and representing it in a bone depth matrix as shown in para. [0083]).
Fu and Ding are both considered to be analogous to the claimed invention because they are in the same field of radiation therapy planning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Fu to incorporate the teachings of Ding and include “wherein generating the first material property data comprises: generating the first material property data that represents the particular material property in the form of material density or material thickness associated with the target structure”. The motivation for doing so would have been to “allow[] calculation of effective bone depth matrix in order to obtain correction factors”, as suggested by Ding in para. [0077]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Fu with Ding to obtain the invention specified in claim 2.
Regarding claim 3, Fu and Ding teach the method of claim 2, wherein generating the first material property data comprises:
generating the first material property data that includes material density volume data based on the transformed image data (While Fu teaches the transformed image data (see claim 1), Ding teaches “convert[ing] each voxel in the patient DICOM image from CT number to a specific material and density” in para. [0077]); and
generating the first material property data that includes projected material thickness data associated with the target structure by performing a forward projection based on the material density volume data (Ding teaches calculating a material thickness associated with a specific CT scan (target structure) in para. [0080] and representing it in a bone depth matrix based on the patient material and density data derived from CT images as shown in para. [0083]; see also para. [0083] for the specific x ray projection process that is being interpreted as equivalent to the claimed forward projection). Similar motivations as applied to claim 2 can be applied here.
Regarding claim 4, Fu and Ding teach the method of claim 3, wherein generating the template comprises:
generating the template representing material thickness associated with the target structure based on the projected material thickness data (Ding teaches “The effective bone depth matrix is calculated by computing an average of the bone thicknesses calculated for each ray, weighted by a function that specifies the contribution of each of the individual rays to the effective depth at each voxel” in para. [0083], wherein the bone depth matrix is used to determine the gold standard correction factors which are used as the template based on the projected material thickness and the target structure as shown in para. [0087]). Similar motivations as applied to claim 2 can be applied here.
Regarding claim 9, Fu teaches method of claim 8, wherein performing the template matching comprises:
performing the template matching based on the material property data and the template that both represent the particular material property (Fu teaches using a template of the TS-DRR (which represents the volume (material property)) and registering (matching) it with a sTS-DRR (which also represents the volume) in order to track motion of the tumor (target structure) in Figure 2, and Sections 1 and 2.1). in the form of material thickness associated with the target structure.
Fu fails to teach performing the template matching based on the material property data and the template that both represent the particular material property in the form of material thickness associated with the target structure (emphasis added).
However, Ding teaches performing the template matching based on the material property data and the template that both represent the particular material property in the form of material thickness associated with the target structure data (Ding teaches “the effective bone depth matrix is calculated by computing an average of the bone thicknesses calculated for each ray, weighted by a function that specifies the contribution of each of the individual rays to the effective depth at each voxel” in para. [0083], wherein the bone depth matrix is the template based on the projected material thickness and the target structure. Here, the matrix represents material thickness of the CT data (target structure data) which is used to determine the gold standard dose correction factors and matched with the gold standard dose correction factors (template) in order to determine a correlation between the two quantities for a specific CT scan protocol as shown in para. [0087] and [0093]. See also Fig. 8E).
Fu and Ding are both considered to be analogous to the claimed invention because they are in the same field of radiation therapy planning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Fu to incorporate the teachings of Ding and include “performing the template matching based on the material property data and the template that both represent the particular material property in the form of material thickness associated with the target structure data”. The motivation for doing so would have been to “allow[] calculation of effective bone depth matrix in order to obtain correction factors”, as suggested by Ding in para. [0077]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Fu with Ding to obtain the invention specified in claim 9.
Regarding claim 10, Fu and Ding teach the method of claim 9, wherein obtaining the template comprises:
obtaining the template representing material thickness based on projected material thickness data associated with the target structure (Ding teaches “the effective bone depth matrix is calculated by computing an average of the bone thicknesses calculated for each ray, weighted by a function that specifies the contribution of each of the individual rays to the effective depth at each voxel” in para. [0083], wherein the bone depth matrix is the template based on the projected material thickness and the target structure. Here, the matrix represents material thickness of the CT data (target structure data) which is used to determine the gold standard dose correction factors and matched with the gold standard dose correction factors (template) in order to determine a correlation between the two quantities for a specific CT scan protocol as shown in para. [0087] and [0093]. See also Fig. 8E), wherein
(a) the projected material thickness data is generated based on material density volume data (Ding teaches calculating a material thickness associated with a specific CT scan (target structure) in para. [0080] and representing it in a bone depth matrix based on the patient material and density data derived from CT images as shown in para. [0083]; see also para. [0083] for the specific x ray projection process that is being interpreted as equivalent to the claimed forward projection) and
(b) the material density volume data is generated based on the transformed image data (While Fu teaches the transformed image data (see claim 8), Ding teaches “convert[ing] each voxel in the patient DICOM image from CT number to a specific material and density” in para. [0077]). Similar motivations as applied to claim 9 can be applied here.
Regarding claim 16, Fu teaches the computer system of claim 15.
Fu fails to teach wherein the instructions for generating the first material property data cause the processor to: generate the first material property data that represents the particular material property in the form of material density or material thickness associated with the target structure.
However, Ding teaches wherein the instructions for generating the first material property data cause the processor to:
generate the first material property data that represents the particular material property in the form of material density (Ding teaches “convert[ing] each voxel in the patient DICOM image from CT number to a specific material and density” in para. [0077]) or material thickness associated with the target structure (Ding teaches calculating a material thickness associated with a specific CT scan (target structure) in para. [0080] and representing it in a bone depth matrix as shown in para. [0083]).
Fu and Ding are both considered to be analogous to the claimed invention because they are in the same field of radiation therapy planning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Fu to incorporate the teachings of Ding and include “wherein the instructions for generating the first material property data cause the processor to: generate the first material property data that represents the particular material property in the form of material density or material thickness associated with the target structure”. The motivation for doing so would have been to “allow[] calculation of effective bone depth matrix in order to obtain correction factors”, as suggested by Ding in para. [0077]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Fu with Ding to obtain the invention specified in claim 16.
Regarding claim 17, Fu and Ding teach the computer system of claim 16, wherein the instructions for generating the first material property data cause the processor to:
generate the first material property data that includes material density volume data based on the transformed image data (While Fu teaches the transformed image data (see claim 1), Ding teaches “convert[ing] each voxel in the patient DICOM image from CT number to a specific material and density” in para. [0077]); and
generate the first material property data that includes projected material thickness data associated with the target structure by performing a forward projection based on the material density volume data (Ding teaches calculating a material thickness associated with a specific CT scan (target structure) in para. [0080] and representing it in a bone depth matrix as shown in para. [0083]; see also para. [0083] for the specific x ray projection process that is being interpreted as equivalent to the claimed forward projection). Similar motivations as applied to claim 16 can be applied here.
Regarding claim 18, Fu and Ding teach the computer system of claim 17, wherein the instructions for generating the template cause the processor to:
generate the template representing material thickness associated with the target structure based on the projected material thickness data (Ding teaches “The effective bone depth matrix is calculated by computing an average of the bone thicknesses calculated for each ray, weighted by a function that specifies the contribution of each of the individual rays to the effective depth at each voxel” in para. [0083], wherein the bone depth matrix is the template based on the projected material thickness and the target structure). Similar motivations as applied to claim 16 can be applied here.
Claims 5 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Fu et al. (“Enhancing the target visibility with synthetic target specific digitally reconstructed radiograph for intrafraction motion monitoring: A proof-of-concept study”), hereinafter Fu in view of Torii et al. (U.S. Publication No. 2022/0076397 A1), hereinafter Torii.
Regarding claim 5, Fu teaches the method of claim 1.
Fu further teaches first material property data that represents the particular material property (see claim 1).
Fu fails to teach first material property data that represents the particular material property in the form of effective atomic number associated with the target structure (emphasis added).
However, Torii teaches wherein generating the first material property data comprises:
generating the first material property data that represents the particular material property in the form of effective atomic number associated with the target structure (Torii teaches the process of generating data that represents material property in the form of an effective atomic number in para. [0058]. Here, the atomic number is found for an image (which can be broadly interpreted as a target structure as shown in FIG. 2). However, Torii’s teaching of the atomic number generation can be combined with Fu’s teaching of determining material properties for a target structure in claim 1 to specifically teaching determining the effective atomic number associated with the target structure. See also para. [0065] regarding comparing the effective atomic number to identify specific materials inside a human body).
Fu and Torii are both considered to be analogous to the claimed invention because they are in the same field of radiation dosage planning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Fu to incorporate the teachings of Torii and include “generating the first material property data that represents the particular material property in the form of effective atomic number associated with the target structure”. The motivation for doing so would have been to “allow a material characteristic image with reduced noise and good quantitative properties to be obtained”, as suggested by Torii in para. [0007]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Fu with Torii to obtain the invention specified in claim 5.
Regarding claim 19, Fu teaches the computer system of claim 15.
Fu further teaches first material property data that represents the particular material property (see claim 1).
Fu fails to teach first material property data that represents the particular material property in the form of effective atomic number associated with the target structure (emphasis added).
However, Torii teaches wherein the instructions for generating the first material property data cause the processor to:
generate the first material property data that represents the particular material property in the form of effective atomic number associated with the target structure (Torii teaches the process of generating data that represents material property in the form of an effective atomic number in para. [0058]. Here, the atomic number is found for an image (which can be broadly interpreted as a target structure as shown in FIG. 2). However, Torii’s teaching of the atomic number generation can be combined with Fu’s teaching of determining material properties for a target structure in claim 1 to specifically teaching determining the effective atomic number associated with the target structure. See also para. [0065] regarding comparing the effective atomic number to identify specific materials inside a human body).
Fu and Torii are both considered to be analogous to the claimed invention because they are in the same field of radiation dosage planning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Fu to incorporate the teachings of Torii and include “wherein the instructions for generating the first material property data cause the processor to: generate the first material property data that represents the particular material property in the form of effective atomic number associated with the target structure”. The motivation for doing so would have been to “allow a material characteristic image with reduced noise and good quantitative properties to be obtained”, as suggested by Torii in para. [0007]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Fu with Torii to obtain the invention specified in claim 19.
Claims 6-7, 11-12, and 20-21 are rejected under 35 U.S.C. 103 as being unpatentable over Fu et al. (“Enhancing the target visibility with synthetic target specific digitally reconstructed radiograph for intrafraction motion monitoring: A proof-of-concept study”), hereinafter Fu in view of Torii et al. (U.S. Publication No. 20220076397 A1), hereinafter Torii and Raupach (U.S. Publication No. 2017/0352166 A1).
Regarding claim 6, Fu and Torii teach the method of claim 5, wherein generating the first material property data comprises:
generating the first material property data that includes effective atomic number volume data based on the planning image data (While Fu teaches the planning image data (see claim 1), Torii teaches the process of generating data that represents material property in the form of an atomic number in para. [0058]). Similar motivations as applied to claim 5 can be applied here in regards to the combination of Fu and Torii.
Fu and Torii fail to teach generating the first material property data that includes projected effective atomic number data associated with the target structure by performing a forward projection based on the effective atomic number volume data.
However, Raupach teaches generating the first material property data that includes projected effective atomic number data associated with the target structure by performing a forward projection based on the effective atomic number volume data (Raupach teaches “with the aid of the spectral forward projection, line integrals are calculated from a material property distribution, for example an electron density and/or an atomic charge distribution, for the X-ray spectrum used by taking into account the physical absorption processes, for example the photo effect and the Compton effect, which integrals represent the absorption of the X-ray radiation along the projection line or wayline of the respective line integral” in para. [0060]. Here, line integrals are generated using a forward projection based on a material property distribution which can be based on the atomic number data. The process of generating line integrals through forward projection based on atomic charge (which is further based on atomic numbers) as taught by Raupach combined with the atomic number as taught by Torri is interpreted as equivalent to the above claimed limitation. See para. [0093] of applicant’s specification regarding the forward projection process wherein the projected effective atomic number data takes the form of a line integral).
Fu, Torii, and Raupach are all considered to be analogous to the claimed invention because they are in the same field of radiation dosage planning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Fu (as modified by Torii) to incorporate the teachings of Raupach and include “generating the first material property data that includes projected effective atomic number data associated with the target structure by performing a forward projection based on the effective atomic number volume data”. The motivation for doing so would have been that “the effect of an inaccurately known distribution of different materials on the end result is therefore advantageously reduced or cancelled”, as suggested by Raupach in para. [0076]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Fu and Torii with Raupach to obtain the invention specified in claim 6.
Regarding claim 7, Fu, Torii, and Raupach teach the method of claim 6, wherein generating the template comprises:
generating the template representing effective atomic number based on the projected effective atomic number data (Raupach teaches that “since the set of projection scan data and the data set obtained with the aid of the spectral forward projection are each vectorial quantities, forming a standard across the two quantities allows a scalar comparative quantity to be formed whose extremal value, preferably a minimum, is associated with the sought material property distribution” in para. [0082]. Here, the result of the forward projection of the material property (the projected effective atomic number data) is used to form “a scalar comparative quantity [] whose extremal value, preferably a minimum, is associated with the sought material property distribution” in para. [0082]. This quantity is interpreted as the generated templated. See Applicant’s Specification regarding the definition of the template in para. [0098]. This process can be combined with Torii’s teaching of the effective atomic number (as opposed to Raupach’s teaching of the atomic distribution which is inherently based on the effective atomic number) to teach the above limitation in its entirety). Similar motivations as applied to claim 6 can be applied here.
Regarding claim 11, Fu teaches method of claim 8,
wherein performing the template matching comprises performing the template matching based on the material property data and the template that both represent the particular material property (Fu teaches using a template of the TS-DRR (which represents the volume (material property)) and registering (matching) it with a sTS-DRR (which also represents the volume) in order to track motion of the tumor (target structure) in Figure 2, and Sections 1 and 2.1).
Fu fails to teach performing the template matching based on the material property data and the template that both represent the particular material property in the form of effective atomic number associated with the target structure.
However, Torii teaches wherein the particular material property is in the form of effective atomic number associated with the target structure (Torii teaches the process of generating data that represents material property in the form of an effective atomic number in para. [0058]. Here, the atomic number is found for an image (which can be broadly interpreted as a target structure as shown in FIG. 2). However, Torii’s teaching of the atomic number generation can be combined with Fu’s teaching of determining material properties for a target structure in claim 1 to specifically teaching determining the effective atomic number associated with the target structure. See also para. [0065] regarding comparing the effective atomic number to identify specific materials inside a human body).
Fu and Torii are both considered to be analogous to the claimed invention because they are in the same field of radiation dosage planning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Fu to incorporate the teachings of Torii and include “wherein the particular material property is in the form of effective atomic number associated with the target structure”. The motivation for doing so would have been to “allow a material characteristic image with reduced noise and good quantitative properties to be obtained”, as suggested by Torii in para. [0007]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Fu with Torii to obtain the invention specified in the above limitation.
Fu and Torii fail to specifically teach performing the template matching based on the material property data and the template that both represent the particular material property in the form of effective atomic number associated with the target structure.
However, Raupach teaches performing the template matching based on the material property data and the template that both represent the particular material property in the form of effective atomic number associated with the target structure (Raupach teaches that “since the set of projection scan data and the data set obtained with the aid of the spectral forward projection are each vectorial quantities, forming a standard across the two quantities allows a scalar comparative quantity to be formed whose extremal value, preferably a minimum, is associated with the sought material property distribution” in para. [0082]. Here, the result of the forward projection of the material property (the projected effective atomic number data) is used to form “a scalar comparative quantity [] whose extremal value, preferably a minimum, is associated with the sought material property distribution” in para. [0082]. See Applicant’s Specification regarding the definition of the template in para. [0098]. This process can be combined with Torii’s teaching of the effective atomic number (as opposed to Raupach’s teaching of the atomic distribution which is inherently based on the effective atomic number) to teach the above limitation in its entirety).
Fu, Torii, and Raupach are all considered to be analogous to the claimed invention because they are in the same field of radiation dosage planning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Fu (as modified by Torii) to incorporate the teachings of Raupach and include “performing the template matching based on the material property data and the template that both represent the particular material property in the form of effective atomic number associated with the target structure”. The motivation for doing so would have been that “the effect of an inaccurately known distribution of different materials on the end result is therefore advantageously reduced or cancelled”, as suggested by Raupach in para. [0076]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Fu and Torii with Raupach to obtain the invention specified in claim 11.
Regarding claim 12, Fu, Torii, and Raupach teach the method of claim 11, wherein obtaining the template comprises:
obtaining the template representing effective atomic number based on projected effective atomic number data associated with the target structure (Raupach teaches that “since the set of projection scan data and the data set obtained with the aid of the spectral forward projection are each vectorial quantities, forming a standard across the two quantities allows a scalar comparative quantity to be formed whose extremal value, preferably a minimum, is associated with the sought material property distribution” in para. [0082]. Here, the result of the forward projection of the material property (the projected effective atomic number data) is used to form “a scalar comparative quantity [] whose extremal value, preferably a minimum, is associated with the sought material property distribution” in para. [0082]. See Applicant’s Specification regarding the definition of the template in para. [0098]. This process can be combined with Torii’s teaching of the effective atomic number (as opposed to Raupach’s teaching of the atomic distribution which is inherently based on the effective atomic number) to teach the above limitation in its entirety), wherein
(a) the projected effective atomic number data is generated based on effective atomic number volume data and the transformed image data (While Torii teaches effective atomic number data, Raupach teaches projected effective atomic can be determined on the basis of image data, which was reconstructed on the basis of the acquired projection scan data” as shown in para. [0085]. Here, the projected effective atomic , and
(b) the effective atomic number volume data is generated based on the planning image data (While Fu teaches the planning image data (see claim 8), Torii teaches generating effective atomic number volume data based on images taken of the human body in FIG. 2 and para. [0065]-[0066], wherein the images are interpreted as equivalent to the planning image data). Similar motivations as applied to claim 11 can be applied here.
Regarding claim 20, Fu and Raupach teach the computer system of claim 19, wherein the instructions for generating the first material property data cause the processor to:
generate the first material property data that includes effective atomic number volume data based on the planning image data (While Fu teaches the planning image data (see claim 1), Torii teaches the process of generating data that represents material property in the form of an atomic number in para. [0058]). Similar motivations as applied to claim 5 can be applied here in regards to the combination of Fu and Torii.
Fu and Torii fail to teach generating the first material property data that includes projected effective atomic number data associated with the target structure by performing a forward projection based on the effective atomic number volume data.
However, Raupach teaches generating the first material property data that includes projected effective atomic number data associated with the target structure by performing a forward projection based on the effective atomic number volume data (Raupach teaches “with the aid of the spectral forward projection, line integrals are calculated from a material property distribution, for example an electron density and/or an atomic charge distribution, for the X-ray spectrum used by taking into account the physical absorption processes, for example the photo effect and the Compton effect, which integrals represent the absorption of the X-ray radiation along the projection line or wayline of the respective line integral” in para. [0060]. Here, line integrals are generated using a forward projection based on a material property distribution which can be based on the atomic number data. The process of generating line integrals through forward projection based on atomic charge (which is further based on atomic numbers) as taught by Raupach combined with the atomic number as taught by Torri is interpreted as equivalent to the above claimed limitation. See para. [0093] of applicant’s specification regarding the forward projection process wherein the projected effective atomic number data takes the form of a line integral).
Fu, Torii, and Raupach are all considered to be analogous to the claimed invention because they are in the same field of radiation dosage planning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Fu (as modified by Torii) to incorporate the teachings of Raupach and include “generating the first material property data that includes projected effective atomic number data associated with the target structure by performing a forward projection based on the effective atomic number volume data”. The motivation for doing so would have been that “the effect of an inaccurately known distribution of different materials on the end result is therefore advantageously reduced or cancelled”, as suggested by Raupach in para. [0076]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Fu and Torii with Raupach to obtain the invention specified in claim 20.
Regarding claim 21, Fu, Torii, and Raupach teach the computer system of claim 20, wherein the instructions for generating the template cause the processor to:
generate the template representing effective atomic number based on the projected effective atomic number data (Raupach teaches that “since the set of projection scan data and the data set obtained with the aid of the spectral forward projection are each vectorial quantities, forming a standard across the two quantities allows a scalar comparative quantity to be formed whose extremal value, preferably a minimum, is associated with the sought material property distribution” in para. [0082]. Here, the result of the forward projection of the material property (the projected effective atomic number data) is used to form “a scalar comparative quantity [] whose extremal value, preferably a minimum, is associated with the sought material property distribution” in para. [0082]. This quantity is interpreted as the generated templated. See Applicant’s Specification regarding the definition of the template in para. [0098]. This process can be combined with Torii’s teaching of the effective atomic number (as opposed to Raupach’s teaching of the atomic distribution) to teach the above limitation in its entirety). Similar motivations as applied to claim 20 can be applied here.
Conclusion
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/Kyla Guan-Ping Tiao Allen/
Examiner, Art Unit 2661
/JOHN VILLECCO/Supervisory Patent Examiner, Art Unit 2661