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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 15, 16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 15, 16 recite the limitation "a first value" in lines 2-3. There is insufficient antecedent basis for this limitation in the claim. The claim states the calculation unit calculates a “first value corresponding to position information based on a model and a second value.” The claim immediately defines the model as being “configured to calculate the first value.” The claim is unclear whether the calculation unit calculates the “first value” independently by using the model or if the model itself outputs the “first value” which the unit then recalculates. The relationship between the unit’s calculation and the model’s output is ambiguous.
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.
Claim(s) 1-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ohyu et al (US 11,942,212 B2) in view of Kim et al (“Multi-Task Learning for Integrated Automated Contouring and Voxel-Based Dose Prediction in Radiotherapy”).
Regarding claim 1, Ohyu et al discloses an information processing apparatus comprising: a calculation unit (21) configured to calculate a first value corresponding to position information based on a model (trained model) and a second value, the model being generated using training data regarding a plurality of subjects and configured to calculate the first value regarding a side effect of radiation therapy (effect of treatment) (col. 32, lines 62-col. 33, line 18). Ohyu et al is silent with regards to calculating radiation therapy side effects based on organ mathematical information as claimed. Kim et al discloses calculation units that accept structural CT/MRI matrices (i.e. mathematical information regarding an organ), cross-reference them with voxel-level radiation grids and generate localized dose metrics or outcome values (See Abstract). Thus, it would have been obvious to modify Ohyu et al with the teaching of Kim et al so as to enable an adjustable radiation plan to reduce dose in high-risk areas (side effects of concern).
Regarding claim 2, Ohyu et al in view of Kim et al discloses wherein the second value is an exposure dose based on the mathematical information and exposure information regarding the radiation therapy (accept structural CT/MRI matrices (i.e. mathematical information regarding an organ)) (See Abstract).
Regarding claim 3, Ohyu et al in view of Kim et al discloses wherein the exposure dose is a plurality of exposure doses divided based on the mathematical information (page 5, paragraph 2).
Regarding claim 4, Ohyu et al in view of Kim et al discloses the training data is a set of data including the first value and the plurality of exposure doses (page 2).
Regarding claim 5, Ohyu et al discloses wherein the model is a model represented by a weighted sum of the plurality of exposure doses (col. 11, lines 35-39).
Regarding claim 6, Ohyu et al discloses further comprising: an image generation unit configured to generate an image regarding the side effect based on the position information and the first value calculated by the calculation unit (col. 5, lines 41-45 and col. 32, lines 62-col. 33, line 18).
Regarding claim 7, Ohyu et al discloses a display unit configured to display an image regarding the side effect acquired from the information processing apparatus (col. 27, lines 55-col. 28, line 8).
Regarding claim 8, Ohyu et al discloses a display unit (14) configured to display an image regarding the side effect and an image of the organ of the first subject with which an irradiation range of radiation in the radiation therapy is associated (col. 6, lines 61-col. 7, line 14 and col. 32, lines 62-col. 33, line 18).
Regarding claim 9, Ohyu et al discloses a processing unit (21) configured to perform processing related to planning of the radiation therapy based on the first value calculated by the calculation unit, which are acquired from the information processing apparatus (col. 32, lines 62-col. 33, line 18). Kim et al further discloses perform processing related to planning of the radiation therapy based on the position information corresponding to the mathematical information (See Abstract). Thus, it would have been obvious to modify Ohyu et al with the teaching of Kim et al so as to enable an adjustable radiation plan to reduce dose in high-risk areas (side effects of concern).
Regarding claim 10, Ohyu et al discloses wherein the first value is a value of which a larger magnitude indicates higher susceptibility to the side effect, and the processing unit sets an irradiation range of radiation such that a dose at a position where the first value calculated by the calculation unit exceeds a threshold or is equal to or greater than the threshold is relatively lower than a dose at another position (col. 11, lines 21-53, col. 32, lines 62-col. 33, line 18).
Regarding claim 11, Ohyu et al discloses wherein the first value is a value of which a larger magnitude indicates higher susceptibility to the side effect, and the processing unit changes the first value calculated by the calculation unit SO that the first value does not exceed a threshold or is not equal to or greater than the threshold, except for a specific site (col. 11, lines 21-53, col. 32, lines 62-col. 33, line 18).
Regarding claim 12, Ohyu et al in view of Kim et al discloses wherein the specific site includes at least a site within a predetermined range in vicinity of a target site of the radiation therapy (page 2).
Regarding claim 13, Ohyu et al discloses wherein the specific site includes a site of a specific organ different from the organ (target tumor and surrounding organs at risk (OARs) (page 2).
Regarding claim 14, Ohyu et al discloses wherein the first value is a value of which a larger magnitude indicates higher susceptibility to the side effect, and the processing unit changes the first value calculated by the calculation unit so that the first value does not exceed a threshold or is not equal to or greater than the threshold in the organ, except for a site within a predetermined range in vicinity of a target site of the radiation therapy (col. 11, lines 21-53, col. 32, lines 62-col. 33, line 18).
Regarding claim 15, Ohyu et al discloses an information processing method comprising: calculating (21) a first value corresponding to position information based on a model (trained model) and a second value, the model being generated using training data regarding a plurality of subjects and configured to calculate the first value regarding a side effect of radiation therapy (effect of treatment) (col. 32, lines 62-col. 33, line 18). Ohyu et al is silent with regards to calculating radiation therapy side effects based on organ mathematical information as claimed. Kim et al discloses calculation units that accept structural CT/MRI matrices (i.e. mathematical information regarding an organ), cross-reference them with voxel-level radiation grids and generate localized dose metrics or outcome values (See Abstract). Thus, it would have been obvious to modify Ohyu et al with the teaching of Kim et al so as to enable an adjustable radiation plan to reduce dose in high-risk areas (side effects of concern).
Regarding claim 16, Ohyu et al discloses a program for causing a computer to execute: calculating a first value corresponding to position information based on a model and a second value, the model being generated using training data regarding a plurality of subjects and configured to calculate the first value regarding a side effect of radiation therapy (col. 32, lines 62-col. 33, line 18). Ohyu et al is silent with regards to calculating radiation therapy side effects based on organ mathematical information as claimed. Kim et al discloses calculation units that accept structural CT/MRI matrices (i.e. mathematical information regarding an organ), cross-reference them with voxel-level radiation grids and generate localized dose metrics or outcome values (See Abstract). Thus, it would have been obvious to modify Ohyu et al with the teaching of Kim et al so as to enable an adjustable radiation plan to reduce dose in high-risk areas (side effects of concern).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gronberg et al (“Deep learning–based dose prediction to improve the plan quality of volumetric modulated arc therapy for gynecologic cancers”) teaches automated 3D U-Net Dose-Volume Information Processing; systems outlined in deep learning-based dose prediction.
Daykin et al (US 20210097381 A1) discloses an apparatus comprises processing circuitry configured to: obtain first trained parameters for a model, wherein the first trained parameters have been generated by training the model using data from a first data cohort; obtain second trained parameters for the model, wherein the second trained parameters have been generated by training the model using data from a second, different data cohort; determine a first evaluation value by inputting data from the first data cohort into a model having the first trained parameters; and determine a second evaluation value by inputting data from the first data cohort into a model having the second trained parameters.
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/F.P.B./Examiner, Art Unit 2884 /UZMA ALAM/Supervisory Patent Examiner, Art Unit 2884
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