DETAILED ACTION
Contents
Notice of Pre-AIA or AIA Status 2
Claim Rejections - 35 USC § 101 2
Claim Rejections - 35 USC § 103 3
Conclusion 14
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 .
This action is responsive to applicant’s claim set received on 4/18/24. Claims 1-2, 5-14 are currently pending.
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 1-2, 5-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter as follows. Claim 1 is considered a machine that recites an abstract idea but does not integrate the judicial exception into a practical application. The claim recites generic computing technology and no inventive concept is present. Claims 2 and 5-12 continue to recite mathematical similarity computation and mental processes. The claims do not integrate the abstract idea into a practical technological application. Claims 13 and 14 again do not integrate the abstract idea into a practical application. The claims are well-understood, routine and conventional elements. Thus, all claims are considered non-statutory subject matter.
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 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 claimedinvention is not identically disclosed as set forth in section 102 of this title, 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 1, 8, 9 are rejected under 35 U.S.C. 103 as being unpatentable over Kazemifar et al (RO: “MRI-only brain radiotherapy: Assessing the dosimetric accuracy of synthetic CT images generated using a deep learning approach”) in view of Allaire (US 2022/0323789 A1).
Regarding claim 1, Kazemifar teaches a method configured to estimate an outer shape of a target using an identifier that performed learning (see abstract; We designed a generative adversarial network (GAN) to generate synthetic CT images from MRI images.) using a loss function configured to identify a graphic similarity of the outer shape (see abstract; We used Mutual Information (MI) as the loss function in the generator to overcome the misalignment between MRI and CT images (unregistered data)) of the target from a first image and a third image, based on the first image capturing in vivo information of a subject (see pg. 61; MI quantifies the ‘‘amount of information” of one variable when another variable is known), a second image showing the target in vivo (see abstract; We conducted a study in 77 patients with brain tumors who had undergone both MRI and computed tomography (CT) imaging), and the third image capturing the same subject using a different device from that used to capture the first image (see pg. 57, abstract; Because this is a retrospective study and the CT and MRI scans were per formed in different departments, images were acquired using different vendors…. The model was trained using all MRI slices with corresponding CT slices from each training subject’s MRI/CT pair. ). Kazemifar does not teach a target outer shape estimation device.
Allaire, in the same field of endeavor, teaches a target outer shape estimation device (see 0075; The radiation therapy planning apparatus 200 may be embodied as, or in, a device or apparatus, such as a server, workstation, imaging system or mobile device. The radiation therapy planning apparatus 200 may comprise one or more microprocessors or computer processors which execute appropriate software. The processor for the apparatus may be embodied by one or more of these processors. The software may have been downloaded and/or stored in a corresponding memory, e.g., a volatile memory such as RAM or a non-volatile memory such as Flash. The software may comprise instructions configuring the one or more processors to perform the functions described with reference to the processor of the apparatus. Alternatively, the functional units of the apparatus, e.g., the input unit, and the processing unit, may be implemented in the device or apparatus in the form of programmable logic, e.g., as a Field-Programmable Gate Array (FPGA). In general, each functional unit of the apparatus may be implemented in the form of a circuit. It is noted that the apparatus 200 may also be implemented in a distributed manner, e.g., involving different devices or apparatuses).
It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Kazemifar to utilize the cited limitations as suggested by Allaire. The suggestion/motivation for doing so would have been to improve the DIR accuracy in radiation therapy planning (see 0005). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Kazemifar, while the teaching of Allaire continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Regarding claims 8, 9, Kazemifar teaches generator configured to generate information of the outer shape of the target from the first image or the third image, anda discriminator configured to perform discrimination based on the outer shape of the target generated by the generator based on the first image, and to perform discrimination based on the outer shape of the target identified in advance and the outer shape of the target generated by the generator based on the third image, and the identifier performs learning based on a discrimination result of the discriminator (see abstract);
first image or the third image is directly input to the identifier (see abstract).
Claims 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Kazemifar et al (RO: “MRI-only brain radiotherapy: Assessing the dosimetric accuracy of synthetic CT images generated using a deep learning approach”) in view of Clough et al (IEEE: “ATopological Loss Function for Deep-Learning Based Image Segmentation Using Persistent Homology”).
Regarding claim 5, Kazemifar teaches all elements as mentioned above in claim 1. Kazemifar does not teach loss function is derived from a number of regions of the target and a number of internal holes of the target with respect to a shape of the target derived using the third image and the identifier; loss function is derived from a number of regions of the target and a number of internal holes of the target with respect to a shape of the target derived using the first image and the identifier.
Clough, in the same field of endeavor, teaches loss function is derived from a number of regions of the target and a number of internal holes of the target with respect to a shape of the target derived using the third image and the identifier (see 8768, 8767); loss function is derived from a number of regions of the target and a number of internal holes of the target with respect to a shape of the target derived using the first image and the identifier (see 3.1, 3.2).
It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Kazemifar to utilize the cited limitations as suggested by Clough. The suggestion/motivation for doing so would have been to improve accuracy of the segmentation (see conclusion). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Kazemifar, while the teaching of Clough continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Kazemifar et al (RO: “MRI-only brain radiotherapy: Assessing the dosimetric accuracy of synthetic CT images generated using a deep learning approach”) in view of Koch et al (ICML: “Siamese Neural Networks for One-shot Image Recognition”).
Regarding claim 7, Kazemifar teaches all elements as mentioned above in claim 1. Kazemifar does not teach a similar image similar to the third image is obtained from a database storing the first image, and the loss function is based on a degree of similarity between the outer shape of the target derived using the identifier and the similar image, and a similar target image identifying the outer shape of the target in the similar image.
Koch, in the same field of endeavor, teaches a similar image similar to the third image is obtained from a database storing the first image, and the loss function is based on a degree of similarity between the outer shape of the target derived using the identifier and the similar image, and a similar target image identifying the outer shape of the target in the similar image (see 3, 4.3, 3.2, 1).
It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Kazemifar to utilize the cited limitations as suggested by Koch. The suggestion/motivation for doing so would have been to produce strong results (see abstract). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Kazemifar, while the teaching of Koch continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Kazemifar et al (RO: “MRI-only brain radiotherapy: Assessing the dosimetric accuracy of synthetic CT images generated using a deep learning approach”) in view of Kervadec et al (CV: “Constrained-CNN Losses for Weakly Supervised Segmentation”).
Regarding claim 10, Kazemifar teaches all elements as mentioned above in claim 1. Kazemifar does not teach loss function is provided in which a loss increases as a difference between an area of the regions of the target and a predetermined value becomes larger.
Kervadec, in the same field of endeavor, teaches loss function is provided in which a loss increases as a difference between an area of the regions of the target and a predetermined value becomes larger (see pgs. 1-5).
It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Kazemifar to utilize the cited limitations as suggested by Kervadec. The suggestion/motivation for doing so would have been to produce substantially better results (see abstract). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Kazemifar, while the teaching of Kervadec continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Kazemifar et al (RO: “MRI-only brain radiotherapy: Assessing the dosimetric accuracy of synthetic CT images generated using a deep learning approach”) in view of Zheng et al (IEEE: “Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation”).
Regarding claim 11, Kazemifar teaches all elements as mentioned above in claim 8. Kazemifar does not teach loss function is provided in which a loss increases as a difference between an aspect ratio of the regions of the target and a predetermined value becomes larger.
Zheng, in the same field of endeavor, teaches loss function is provided in which a loss increases as a difference between an aspect ratio of the regions of the target and a predetermined value becomes larger (see 8574-8575, 8578).
It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Kazemifar to utilize the cited limitations as suggested by Zheng. The suggestion/motivation for doing so would have been to produce substantially better results (see conclusion). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Kazemifar, while the teaching of Zheng continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Kazemifar et al (RO: “MRI-only brain radiotherapy: Assessing the dosimetric accuracy of synthetic CT images generated using a deep learning approach”) in view of Kervadec et al (CV: “Constrained-CNN Losses for Weakly Supervised Segmentation”).
Regarding claim 13, Kazemifar teaches all elements as mentioned above in claim 1. Kazemifar does not teach each of the images is one of an X-ray image, a magnetic resonance imaging image, an ultrasonic inspection image, a positron emission tomography image, a body surface shape image, and a photoacoustic imaging image, or a combination thereof.
Kervadec, in the same field of endeavor, teaches each of the images is one of an X-ray image, a magnetic resonance imaging image, an ultrasonic inspection image, a positron emission tomography image, a body surface shape image, and a photoacoustic imaging image, or a combination thereof (see introduction, 4.1.1, 4.1.2).
It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Kazemifar to utilize the cited limitations as suggested by Kervadec. The suggestion/motivation for doing so would have been to yield better results, while reducing computational demand (see abstract). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Kazemifar, while the teaching of Kervadec continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Kazemifar et al (RO: “MRI-only brain radiotherapy: Assessing the dosimetric accuracy of synthetic CT images generated using a deep learning approach”) in view of Saunders et al (US 8,295,906).
Regarding claim 14, Kazemifar teaches all elements as mentioned above in claim 1. Kazemifar further teaches the target outer shape estimation device according to claim 1 (see rejection of claim 1). Kazemifar does not teach a therapeutic device, comprising: an irradiation unit configured to irradiate radiation for treatment based on the outer shape of the target estimated by the target outer shape estimation device.
Saunders, in the same field of endeavor, teaches a therapeutic device, comprising: an irradiation unit configured to irradiate radiation for treatment based on the outer shape of the target estimated by the target outer shape estimation device (see col. 5, col. 7).
It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Kazemifar to utilize the cited limitations as suggested by Saunders. The suggestion/motivation for doing so would have been to guide the radiation therapy (see abstract). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Kazemifar, while the teaching of Saunders continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Kazemifar et al (RO: “MRI-only brain radiotherapy: Assessing the dosimetric accuracy of synthetic CT images generated using a deep learning approach”) in view of Karimi et al (IEEE: “Reducing the Hausdorff Distance in Medical Image Segmentation With Convolutional Neural Networks”).
Regarding claim 12, Kazemifar teaches all elements as mentioned above in claim 8. Kazemifar does not teach loss function is provided in which a loss increases as a difference between a diagonal length of the regions of the target and a predetermined value becomes larger.
Karimi, in the same field of endeavor, teaches loss function is provided in which a loss increases as a difference between a diagonal length of the regions of the target and a predetermined value becomes larger (see abstract, section 1, 2-b, 2-c).
It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Kazemifar to utilize the cited limitations as suggested by Karimi. The suggestion/motivation for doing so would have been to reduce the large segmentation errors (see abstract). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Kazemifar, while the teaching of Karimi continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Kazemifar et al (RO: “MRI-only brain radiotherapy: Assessing the dosimetric accuracy of synthetic CT images generated using a deep learning approach”) in view of Schulz et al (US 9,262,590 B2).
Regarding claim 2, Kazemifar teaches all elements as mentioned above in claim 1. Kazemifar does not teach device uses the third image that is captured before a treatment day or immediately before treatment.
Schulz, in the same field of endeavor, teaches device uses the third image that is captured before a treatment day or immediately before treatment (see col. 1-2, 6, 10-11).
It would have been obvious (before the effective filing date of the claimed invention) or (at the time the invention was made) to one of ordinary skill in the art to modify Kazemifar to utilize the cited limitations as suggested by Schulz. The suggestion/motivation for doing so would have been to provide more accurate radiation therapy plans (see col. 3, lines 30-65). Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in the manner explained above using known engineering design, interface and/or programming techniques, without changing a “fundamental” operating principle of Kazemifar, while the teaching of Schulz continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Conclusion
Claims 1-2, 5-14 are rejected.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EDWARD PARK. The examiner’s contact information is as follows:
Telephone: (571)270-1576 | Fax: 571.270.2576 | Edward.Park@uspto.gov
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The examiner can normally be reached on M-F 9-6 CST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Moyer, can be reached on (571) 272-9523. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/EDWARD PARK/
Primary Examiner, Art Unit 2666