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 Objections
Claims 13 are objected to because of the following informalities: there are two claim 13s; wherein from now on the Examiner will take the claim 13 that states "13. The system according to claim 12, wherein the artificial neural network is a conditional Generative Adversarial Network (cGAN).” as 13(1) and the claim 13 that states “13. The system according to claim 12, wherein the treatment is guided radiation therapy.” as 13(2); and wherein 13(2) is what claim 14 is dependent on, with claim 15 dependent on claim 14. One way to correct this would be to cancel one of the claim 13s and then add it as a new dependent claim 22. Appropriate correction is required.
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.
Claim 16 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claim states “16. A computer software product comprising a sequence of instructions…”, wherein this points to software which is non-statutory subject matter. One correction could be to amend the claim to state “16. One or more non-transitory computer-readable storage media, with a computer software product comprising a sequence of instructions stored thereon, …”.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-12, 13(1), 13(2), 14-18, 20, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Nguyen et al., US 2021/0038921 A1 (Nguyen), and further in view of Hibbard, US 2021/0308487 A1 (Hibbard).
Regarding claim 1, Nguyen teaches an image guidance method for treatment by a medical device (a method and system for guiding a radiation therapy system) (Abstract) (wherein radiation therapy is a treatment modality) ([0002]), comprising:
imaging a target area (capturing an image of a target area) (Abstract) to which the treatment is to be delivered (to which the radiation is to be delivered; the radiation therapy being a treatment) (Abstract and [0002]);
during the interventional procedure (guided radiation therapy) (Abstract and [0038]), analysing an image (analysing the image) (Abstract) from the imaging with a patient-specific (each treatment is tailored to the individual patient) ([0002] and [0022]), individually trained neural network (with a trained convolutional neural network) (Abstract and [0022]) to determine the position of at least one or more anatomical objects of interest (objects of interest, such as tumours and intrinsic anatomical features) ([0016]) present in the target area (to determine the position of one or more objects of interest present in the target area) (Abstract); and
outputting the determined position(s) (outputting the determined position(s) to the radiation therapy system) (Abstract).
Although Nguyen teaches using a trained convolutional neural network (Abstract), Nguyen does not explicitly teach a trained “artificial” neural network.
Hibbard teaches systems and methods for generating radiotherapy treatment machine parameters based on projection images of a target anatomy (Abstract); and wherein analysing an image from the imaging with a patient-specific, individually trained artificial (artificial intelligence) ([0052] and [0065]) neural network (training a machine learning model, such as a generative adversarial network (GAN), on training data consisting of pairs of control points (represented by graphical aperture images) and the corresponding images of a patient (represented by projection images)) ([0046]) (wherein the GAN can include a cGAN) ([0009] and [0119-0122]) to determine the position of at least one or more anatomical objects of interest present in the target area (to determine a location of a target organ or a target tumor in the patient) ([0077-0078]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nguyen to include using trained “artificial” neural network since it can improve and enhance radiotherapy treatment, including improved accuracy of radiotherapy treatment (Hibbard; [0045]).
Regarding claim 2, Hibbard teaches wherein the artificial neural network is a conditional Generative Adversarial Network (cGAN) (wherein the GAN can include a cGAN) ([0009] and [0119-0122]).
Regarding claim 3, Nguyen teaches wherein the treatment is an interventional procedure being any one from the group consisting of: guided radiation therapy, needle biopsy and minimally invasive surgery (guided radiation therapy) ([0038]). Hibbard teaches wherein the procedure can be non-invasive radiotherapy ([0081]) and other surgeries ([0064] and [0102]).
Regarding claim 4, Nguyen teaches wherein the anatomical object of interest is any one from the group of: soft tissue and hard tissue (wherein the target is soft tissue, such as a tumour and intrinsic anatomical features) ([0016] and [0064]). Hibbard teaches that the object of interest can be the male pelvic anatomy (which includes bones) (Fig. 8B; [0055]).
Regarding claim 5, Nguyen teaches wherein the soft tissue is an organ or tumour (wherein the target is soft tissue, such as a tumour) ([0004], [0016], and [0064]).
Regarding claim 6, Nguyen teaches wherein the image is an X-ray image (the spatial intensity of the received radiation is converted to an x-ray image that is a projection of said at least one imaging beam in a plane normal to the direction its emission) ([0061] and claim 10).
Regarding claim 7, Nguyen teaches wherein the determined position(s) is output to a radiation therapy system (outputting the determined position(s) to the radiation therapy system) (Abstract) for the guided radiation therapy (for guiding a radiation therapy system) (Abstract).
Regarding claim 8, Nguyen teaches further comprising: identifying the target area to which radiation is to be delivered on a basis of the outputted positions (identifying the target area to which radiation is to be delivered on the basis of the output object position(s)) ([0031]).
Regarding claim 9, Nguyen teaches further comprising: directing a treatment beam from the radiation therapy system based on a position of the identified target area (directing a treatment beam from the radiation therapy system based on a position of the identified target area) ([0032]).
Regarding claim 10, Nguyen teaches further comprising: tracking the target area by reference to successive output of positions over time (the target area may be tracked by reference to successive output of object positions over time) ([0033]); and directing the treatment beam at the target based on said tracking (directing the beam at the target based on said tracking) ([0033]).
Regarding claim 11, Nguyen teaches wherein directing the beam based on the position of the identified target area (directing a treatment beam from the radiation therapy system based on a position of the identified target area) ([0032]) includes adjusting or setting one or more of the following parameters of the radiation therapy system (adjusting or setting one or more of the following parameters of the radiation therapy system) ([0033]): at least one geometrical property of said at least one emitted beam (at least one geometrical property of said at least one emitted beam) ([0034]); a position of the target relative to the beam (a position of the target relative to the beam) ([0035]); a time of emission of the beam (a time of emission of the beam) ([0036]); and an angle of emission of the beam relative to the target area about a system rotational axis (an angle of emission of the beam relative to the target area about a system rotational angle) ([0037]).
Regarding claim 12, Nguyen teaches an image guidance system for treatment provided by a medical device (a method and system for guiding a radiation therapy system) (Abstract and [0038]) (wherein radiation therapy is a treatment modality) ([0002]) comprising:
an imaging system arranged to generate a succession of images of a target area for directing the treatment (an imaging system arranged to generate a succession of images of a target area to which the treatment beam is to be directed) ([0040]) provided by the medical device (system/device for image guided radiation therapy) (Fig. 1; Abstract and [0061]);
a control system (a control system) ([0041]) configured to:
receive images from the imaging system (receive images from the imaging system) ([0042]);
analyse the images (analyse the images) ([0043]) with a patient-specific (each treatment is tailored to the individual patient) ([0002]), individually trained neural network (trained convolutional neural network) ([0043]) during the treatment (during treatment) ([0043-0044]) to:
determine the position of the target area (determine the position of one or more objects of interest present in the target area) ([0043]); and
adjust the medical device using the determined positions to direct the treatment to the target area (adjust the guided radiation therapy system to direct the treatment beam at the target area) ([0044]).
Although Nguyen teaches using a trained convolutional neural network (Abstract), Nguyen does not explicitly teach a trained “artificial” neural network.
Hibbard teaches systems and methods for generating radiotherapy treatment machine parameters based on projection images of a target anatomy (Abstract); and wherein analyse the images a patient-specific, individually trained artificial (artificial intelligence) ([0052] and [0065]) neural network (training a machine learning model, such as a generative adversarial network (GAN), on training data consisting of pairs of control points (represented by graphical aperture images) and the corresponding images of a patient (represented by projection images)) ([0046]) (wherein the GAN can include a cGAN) ([0009] and [0119-0122]) during treatment (treatment plan) ([0064-0065] and [0077-0078]) to determine the position of at least one or more anatomical objects of interest present in the target area (to determine a location of a target organ or a target tumor in the patient) ([0077-0078]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nguyen to include using trained “artificial” neural network since it can improve and enhance radiotherapy treatment, including improved accuracy of radiotherapy treatment (Hibbard; [0045]).
Regarding claim 13(1), Hibbard teaches wherein the artificial neural network is a conditional Generative Adversarial Network (cGAN) (wherein the GAN can include a cGAN) ([0009] and [0119-0122]).
Regarding claim 13(2), Nguyen teaches wherein the treatment is guided radiation therapy (a system for guided radiation therapy) ([0038]).
Regarding claim 14, Nguyen teaches wherein the medical device is a radiation therapy treatment system (a system for guided radiation therapy) ([0038]) comprising a radiation source for emitting at least one treatment beam of radiation (a radiation source for emitting at least one treatment beam of radiation) ([0039]).
Regarding claim 15, Nguyen teaches wherein the treatment beam is directed to the target area (direct the treatment beam at the target area) ([0044]).
Regarding claim 16, Nguyen teaches a computer software product comprising a sequence of instructions storable on one or more computer-readable storage media (a computer software product comprising a sequence of instructions storable on one or more computer-readable storage media) ([0045]), said instructions when executed by one or more processors, cause the processor to (said instructions when executed by one or more processors, cause the processor to) ([0045]):
receive an image (receive an image) ([0046]), from an imaging system (from a radiation therapy system) ([0046]), of a target area for directing treatment by a medical device (from a radiation therapy system of a target area to which radiation is to be delivered) ([0046]);
analyse the image (analyse the image) ([0047]) with a patient-specific (each treatment is tailored to the individual patient) ([0002]), individually trained neural network (a trained convolutional neural network) ([0047]) to determine the position of one or more anatomical objects of interest present in the target area (to determine the position of one or more objects of interest present in the target area) ([0047]); and
output the position of the one or more anatomical objects of interest (objects of interest, such as tumours and intrinsic anatomical features) ([0016]) to the medical device (output the fiducial marker position(s) to the radiation therapy system) ([0048]).
Although Nguyen teaches using a trained convolutional neural network (Abstract), Nguyen does not explicitly teach a trained “artificial” neural network.
Hibbard teaches systems and methods for generating radiotherapy treatment machine parameters based on projection images of a target anatomy (Abstract); and wherein analyse the image a patient-specific, individually trained artificial (artificial intelligence) ([0052] and [0065]) neural network (training a machine learning model, such as a generative adversarial network (GAN), on training data consisting of pairs of control points (represented by graphical aperture images) and the corresponding images of a patient (represented by projection images)) ([0046]) (wherein the GAN can include a cGAN) ([0009] and [0119-0122]) during treatment (treatment plan) ([0064-0065] and [0077-0078]) to determine the position of at least one or more anatomical objects of interest present in the target area (to determine a location of a target organ or a target tumor in the patient) ([0077-0078]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nguyen to include using trained “artificial” neural network since it can improve and enhance radiotherapy treatment, including improved accuracy of radiotherapy treatment (Hibbard; [0045]).
Regarding claim 17, Nguyen teaches a method of monitoring movement of an organ or portion of an organ or surrogates of the organ (track objects of interest such as tumours (part of an organ) and intrinsic anatomical features) ([0003-0004], [0016], and [0018]) during treatment (during treatment) ([0066-0068]), comprising:
directing treatment to at least a portion of an organ in a body part or human or animal subject (adjust the guided radiation therapy system to direct the treatment beam at the target area) ([0044]) (objects of interest such as tumours and intrinsic anatomical features) ([0016]) (tumours in organs) ([0003-0004]);
imaging multiple two-dimensional images of the organ or surrogates of the organ from varying positions and angles relative to the body part (the radiation source and imaging system rotates around the patient during treatment; wherein the imaging system acquires 2D projections of the target separated by an appropriate time interval) ([0068]);
digitally processing at least a plurality of the multiple two-dimensional images using a one or more computers with a software application (the control system 30 uses the periodically received 2D projections (e.g. kV X-ray images) to estimate the tumour's position) ([0068]) (wherein the control system 30 controls the parameters of operation of the radiation therapy system; wherein the control system 30 is a computer system comprising one or more processors with associated working memory, data storage and other necessary hardware, that operates under control of software instructions to receive input data from one or more of a user, other components of the system (e.g. the imaging system 16), and outputs control signals to control the operation of the radiation therapy system) (Fig. 1; [0065]) executing patient-specific (each treatment is tailored to the individual patient) ([0002]), individually trained neural network (trained convolutional neural network) ([0043] and [0071]); and
estimated three-dimensional (estimation of the target’s location and orientation in 3-dimensions) ([0068] and [0086]) motion of the organ or portion of the organ in the body part based on output from the digital processing (the control system receives images from the imaging system, analyses those images to determine the position of fiducial markers present in the target (thereby estimating the motion of the target), and then issues a control signal to adjust the system 10 to better direct the treatment beam 14 at the target) ([0065]).
Although Nguyen teaches using a trained convolutional neural network (Abstract), Nguyen does not explicitly teach a trained “artificial” neural network or “displaying”.
Hibbard teaches systems and methods for generating radiotherapy treatment machine parameters based on projection images of a target anatomy (Abstract); wherein analysing the images from the imaging with a patient-specific, individually trained artificial (artificial intelligence) ([0052] and [0065]) neural network (training a machine learning model, such as a generative adversarial network (GAN), on training data consisting of pairs of control points (represented by graphical aperture images) and the corresponding images of a patient (represented by projection images)) ([0046]) (wherein the GAN can include a cGAN) ([0009] and [0119-0122]) to determine the position of at least one or more anatomical objects of interest present in the target area (to determine a location of a target organ or a target tumor in the patient) ([0077-0078]); wherein the radiotherapy system can include a display that displays medical imaging, localizing a target and/or tracking a target ([0083]); and wherein the imaging can be a three-dimensional image of the patient to identify a target region ([0005] and [0057]) including movement of the target ([0081]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nguyen to include using trained “artificial” neural network since it can improve and enhance radiotherapy treatment, including improved accuracy of radiotherapy treatment (Hibbard; [0045]).
Regarding claim 18, Nguyen teaches wherein the multiple two-dimensional images are obtained using a linear accelerator gantry mounted (wherein the imaging system can be rotatably mounted, such as on a structure commonly called a gantry) (Fig. 1; [0066]) kilovoltage X-ray imager system (wherein the imaging system will be a kilovolt imaging system built into the linear accelerator) ([0062-0063]) (x-ray image) ([0062-0063]).
Regarding claim 20, Nguyen teaches an image guidance method for treatment of a predetermined type of organ (wherein the predetermined organ can be a prostate, lungs, etc.) ([0080-0082]) by a medical device (a method and system for guiding a radiation therapy system) (Abstract) (wherein radiation therapy is a treatment modality) ([0002]), comprising:
imaging a target area to which the treatment is to be delivered (an imaging system arranged to generate a succession of images of a target area to which the treatment beam is to be directed) ([0040]);
during the interventional procedure (guided radiation therapy) (Abstract and [0038]), analysing an image from the imaging (analyse the images from the imaging system) ([0042-0043]) with a population-based trained (trained based on the type of organ for the patient) ([0080-0082]) to determine the position of the predetermined type of organ (wherein the predetermined organ can be a prostate, lungs, etc.) ([0080-0082]) present in the target area (analyse the images with a trained convolutional neural network to determine the position of one or more objects of interest present in the target area) ([0043]); and
outputting the determined position(s) (and outputting the determined position(s) to the radiation therapy system) (Abstract and [0013]).
Although Nguyen teaches using a trained convolutional neural network (Abstract), Nguyen does not explicitly teach a “conditional Generative Adversarial Network (cGAN)”.
Hibbard teaches systems and methods for generating radiotherapy treatment machine parameters based on projection images of a target anatomy (Abstract); and wherein analysing an image from the imaging with a population-based trained conditional Generative Adversarial Network (cGAN) (training a machine learning model, such as a generative adversarial network (GAN), on training data consisting of pairs of control points (represented by graphical aperture images) and the corresponding images of a patient (represented by projection images)) ([0046]) (wherein the GAN can include a cGAN) ([0009] and [0119-0122]) to determine the position of at least one or more anatomical objects of interest present in the target area (to determine a location of a target organ or a target tumor in the patient) ([0077-0078]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nguyen to include using a conditional Generative Adversarial Network (cGAN) since it can improve and enhance radiotherapy treatment, including improved accuracy of radiotherapy treatment (Hibbard; [0045]).
Regarding claim 21, Nguyen teaches wherein the predetermined type of organ is any one from the group consisting of: bones, spinal cord, prostate (wherein the organ is the prostate) ([0080-0081]), heart, uterus, kidneys (kidneys) ([0118]), thyroid and pancreas (pancreas) ([0118]). Hibbard teaches wherein it can include a spinal cord ([0082]) and/or prostate ([0057]).
Allowable Subject Matter
Claim 19 is allowed. None of the prior art, either alone or in combination, teaches each and every limitation within the language of claim 19, and thus claim 19 is allowable.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL J VANCHY JR whose telephone number is (571)270-1193. The examiner can normally be reached Monday - Friday 9am - 5pm.
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/MICHAEL J VANCHY JR/Primary Examiner, Art Unit 2666 Michael.Vanchy@uspto.gov
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