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 § 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 recites:
executing, by the processor, an artificial intelligence model to predict an attribute of a Multi-Leaf Collimator (MLC) opening and a corresponding movement attribute of an accelerator of the radiation therapy machine, wherein the artificial intelligence model is trained via a training dataset that comprises training treatment objectives and attributes associated with previously performed radiation therapy treatments comprising at least actual or projected dose-volume for the treated patients and corresponding MLC opening positions; and
presenting, by the processor, a predicted MLC opening position and a corresponding predicted movement attribute of the accelerator of the radiation therapy machine. This judicial exception is not integrated into a practical application because the recitation merely represents an abstract idea with additional generic processor. A generic processor does not add meaningful limitation to the abstract idea. Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the recited steps such as “receiving, by a processor”, “executing, by the processor”, and “presenting, by the processor” merely constitute an abstract idea.
Claim 2 characterizes how “the artificial intelligence” is trained. This characterization represents an abstract idea that does not integrate the method as claimed into a practical application.
Claim 3 recites “the predicted MLC opening position is a binary mask indicating opening of the MLC”. This recitation is considered as an abstract idea without integrating the method into a practical application.
Claims 4 and 5 respectively recite “the predicted movement attribute is a time associated with the accelerator’s movement” and “the predicted movement attribute is an angel associated with the accelerator’s movement” are considered as an abstract idea without integrating the method as claimed into a practical application.
Claims 6 and 7 further limit the artificial intelligence model. These limitations are abstract ideas as they fail to integrate the method as claimed into a practical application.
Claims 8 and 9 recite “generating, by the processor, machine-readable instructions”. They represent an abstract idea without integrating the claimed method into a practical application.
Claims 10-20 recite a computer system that performs a set of instructions not surmounting to a method similarly to the method of claim 1. These claims merely convey an abstract idea because they fail to provide implementation specific that integrates the method into a practical and transforming purpose.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 2, 4, 5, 9, 10, 11, 12, 13, 19 and 20 are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Hakala et al. (US 2023/0087944 A1).
A method comprising:
receiving, by a processor, treatment objectives for a patient including at least a dose-volume for at least one structure of the patient to be treated via a radiation therapy machine (paragraph 0044 says analytic server 110a trains an executes computer model 111, which is configured to retrieve patient data and treatment data, such as patient’s physical attributes, treatment characteristics);
executing, by the processor, an artificial intelligence model to predict an attribute of a Multi-Leaf Collimator (MLC) opening (paragraph 0048 says analytic server 110a may execute the computer model 112, aka. AI model (noted in paragraph 0045 to generate a treatment plan which includes information such as a dose distribution, radiation parameters such as a beam angles, etc. Paragraph 0067 says the treatment plan may be optimized by changing the speed and shape of the beam with a multi-leaf collimator (MLC))) and a corresponding movement attribute of an accelerator of the radiation therapy machine (which would corresponds to changing the speed as mentioned), wherein the artificial intelligence model is trained via a training dataset that comprises training treatment objectives and attributes associated with previously performed radiation therapy treatments comprising at least actual or projected dose-volume for the treated patients and corresponding MLC opening positions; and
presenting, by the processor, a predicted MLC opening position and a corresponding predicted movement attribute of the accelerator of the radiation therapy machine (this is an inherent step of changing the shape of beam as mentioned.).
2. The method of claim 1, wherein the artificial intelligence model is trained via a deep learning protocol to correlate the actual or projected dose-volume for the treated patients and corresponding MLC opening position (this is an inherency in dose-volume constraints. Note paragraph 0061).
4. The method of claim 1, wherein the predicted movement attribute is a time associated with the accelerator's movement (paragraph 0067 mentions changing speed of the beam. The speed corelates to time associated with accelerator movement).
5. The method of claim 1, wherein the predicted movement attribute is an angle associated with the accelerator's movement (note paragraph 0003 mentions beam angles).
9. The method of claim 8, further comprising: transmitting, by the processor, the machine-readable instructions to the radiation therapy machine (step 206 Figure 2).
10. A computer system comprising: a server comprising a processor and a non-transitory computer-readable medium containing instructions that when executed by the processor causes the processor to perform operations comprising: receiving treatment objectives for a patient including at least a dose-volume for at least one structure of the patient to be treated via a radiation therapy machine; executing an artificial intelligence model to predict an attribute of a Multi-Leaf Collimator (MLC) opening and a corresponding movement attribute of an accelerator of the radiation therapy machine, wherein the artificial intelligence model is trained via a training dataset that comprises training treatment objectives and attributes associated with previously performed radiation therapy treatments comprising at least actual or projected dose-volume for the treated patients and corresponding MLC opening positions; and presenting a predicted MLC opening position and a corresponding predicted movement attribute of the accelerator of the radiation therapy machine. (this claim in essence substantially repeats the limitations of claim 1 and is held anticipated by Hakala for the reasons as that noted in rejecting claim 1.)
11. The computer system of claim 10, wherein the artificial intelligence model is trained via a deep learning protocol to correlate the actual or projected dose-volume for the treated patients and corresponding MLC opening position (note paragraph 0067).
13. The computer system of claim 10, wherein the predicted movement attribute is a time associated with the accelerator's movement. (paragraph 0067 mentions changing speed of the beam. The speed corelates to time associated with accelerator movement).
14. The computer system of claim 10, wherein the predicted movement attribute is an angle associated with the accelerator's movement. (note paragraph 0003 mentions beam angles).
19. A system comprising a server having one or more processors configured to: receive treatment objectives for a patient including at least a dose-volume for at least one structure of the patient to be treated via a radiation therapy machine; execute an artificial intelligence model to predict an attribute of a Multi-Leaf Collimator (MLC) opening and a corresponding movement attribute of an accelerator of the radiation therapy machine, wherein the artificial intelligence model is trained via a training dataset that comprises training treatment objectives and attributes associated with previously performed radiation therapy treatments comprising at least actual or projected dose-volume for the treated patients and corresponding MLC opening positions; and present a predicted MLC opening position and a corresponding predicted movement attribute of the accelerator of the radiation therapy machine (note the explanation for holding claim 1 anticipated).
20. The computer system of claim 19, wherein the artificial intelligence model is trained via a deep learning protocol to correlate the actual or projected dose-volume for the treated patients and corresponding MLC opening position. (this is an inherency in dose-volume constraints. Note paragraph 0061)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DON KITSUN WONG whose telephone number is (571)272-1834. The examiner can normally be reached on Monday – Friday 9:00am – 6:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Uzma Alam can be reached on 571 272 3995. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DON K WONG/Primary Examiner, Art Unit 2884