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 .
Priority
Receipt is acknowledged that application claims priority to foreign application with application number EP22170018.0 dated 4/26/2022. Copies of certified papers required by 37 CFR 1.55 have been received. Priority is acknowledged under 35 USC 119(e) and 37 CFR 1.78.
Information Disclosure Statement
The IDS dated 10/16/2024 has been considered and placed in the application file.
Claim Objections
Claims 2-19 all recite “The method of claim …”. “The method” should read as “The computer-implemented method of claim …”. Appropriate correction is required.
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.
Claim(s) 9 and 15 are rejected under 35 U.S.C. 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim(s) 9 recites “wherein determining the arrangement of the detected landmarks comprises determining a distance between the detected landmarks wherein based on the arrangement of the detected landmarks, the presence, the type, and/or the degree of the misalignments is determined”. It is unclear if the distance between the detected landmarks is between the landmarks of one X-ray image, or a comparison between two X-ray images, and each respective landmark. The Applicant introduced “an X-ray image” in claim 7 that contains pre-determined landmarks, in which the examiner is assuming is a different X-ray image from the one introduced in claim 1. To the examiner’s understanding, an arrangement of a landmark is determined by having a “reference image” that acts as the template for where landmarks should be. Thus, for examination purposes, the examiner will interpret this limitation as a difference between the detected landmarks from one X-ray reference image to the current X-ray image (to determine the imaging error).
Claim 15 recites “wherein the method comprises determining the location of pre-determined landmarks in the 3D medical image data and in one or more of the artificially created X-Ray images…”. “The 3d medical image data” lacks insufficient antecedent basis. For examination purposes, the examiner will interpret this as “a 3d medical image data”.
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 1-19, 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites:
“acquiring X-Ray image data of a subject using a mobile X-Ray imaging system, the X-Ray image data comprising at least one X-Ray image” is a generically recited insignificant extra-solution activity of data gathering.
“based on the X-Ray image data, performing an automatic image-based detection of imaging errors indicative of a misalignment of the X-Ray imaging system relative to the subject” which can be reasonably interpreted as a human observer mentally determining the misalignments in an X-ray image.
“And in response to detecting an imaging error indicative of the misalignment, prompting a display device to display a user interface comprising an indicator indicating 1) a presence of the misalignment, 2) a type of the misalignment and/or 3) a degree of the misalignment” is a generically recited insignificant extra-solution activity of data outputting.
Claim 2 recites “wherein performing the automatic image-based detection of imaging errors comprises making use of a Deep Learning method”. This is a well-understood, routine, and conventional activity of deep learning.
Claim 3 recites “wherein performing the automatic image-based detection of imaging errors comprises making use of a first Deep Learning method for image-classification-based detection of the misalignments and/or a second Deep Learning method for landmark-detection-based detection of the misalignments”. A person can mentally classify certain misalignments (such as the severity or degree) and using a deep learning method is a well-understood, routine, and conventional activity.
Claim 4 recites “wherein performing the automatic image-based detection of imaging errors comprises image-classification-based detection of misalignments making use of a first neural network trained with real and/or artificially created X-Ray images labeled with regard to the presence the type, and/or the degree of the misalignments, wherein the first neural network is a Convolutional Neural Network”. This is a well-understood, routine, and conventional structure of supervised learning.
Claim 5 recites “wherein the image-classification-based detection comprises the first neural network performing image classification and/or regression on the X- Ray image data of the subject to detect the imaging error”. This is a well-understood, routine, and conventional structure of using a neural network to perform classification.
Claim 6 recites “wherein the image-classification-based detection comprises the first neural network performing image classification and/or regression on the X-Ray image data of the subject to detect the imaging error to identify the presence, the type, and/or the degree of the misalignments”. This is a well-understood, routine, and conventional structure of using a neural network to perform classification to identify misalignments.
Claim 7 recites “wherein performing the automatic image-based detection of imaging errors comprises landmark-detection-based detection of imaging errors making use of a second neural network configured to detect pre- determined landmarks in an X-Ray image, the landmarks being suitable for deriving an imaging error from their arrangement in the X-Ray image, wherein the second neural network is a Convolutional Neural Network for image segmentation”. A person can mentally determine landmarks on a patient’s body and imaging errors (such as if the image is too blurry). Using a convolutional neural network is simply a well-understood, routine, and conventional activity.
Claim 8 recites “wherein the landmark-detection-based detection of imaging errors comprises detecting the pre-determined landmarks in the X-Ray image by the second neural network, determining an arrangement of the detected landmarks, and, based thereon, detecting the imaging error”. A person can mentally detect pre-determined landmarks, the arrangement, and an imaging error in an X-ray image. Using a neural network is simply a well-understood, routine, and conventional activity.
Claim 9 recites “wherein determining the arrangement of the detected landmarks comprises determining a distance between the detected landmarks, wherein based on the arrangement of the detected landmarks, the presence, the type, and/or the degree of the misalignments is determined”. A person can determine the distance between landmarks in an image, and can determine an imaging error if the distances are different from standard distances.
Claim 10 recites “wherein detecting an imaging error indicative of a misalignment is performed using artificially created X-Ray images”. This is simply a well-understood, routine, and conventional activity of using X-ray images as input.
Claim 11 recites “wherein the artificially created X-Ray images are obtained by applying a plurality of different parametrizations to Digitally Reconstructed Radiographs reconstructed from 3D medical image data, so as to obtain a plurality of artificial X-Ray images with imaging errors representative of different misalignments”. This is simply a well-understood, routine, and conventional activity of using DRR as input.
Claim 12 recites “wherein a first neural network and/or a second neural network is trained based on the artificially created X-Ray images”. This is simply a well-understood, routine, and conventional activity of using X-ray images as input for a neural network.
Claim 13 recites “wherein the first neural network is trained at least with the artificially created X-Ray images labeled with regard to the presence, the type and/or the degree of the misalignments”. This is simply a well-understood, routine, and conventional structure of supervised learning.
Claim 14 recites “wherein detecting an imaging error indicative of a misalignment using artificially created X-Ray images comprises using the artificially created X-Ray images for automatically determining at least one of: corrected landmarks, a misalignment-induced error or uncertainty margin for measurements made based on the X-Ray imaged, a corrected measurement by correcting misalignment- induced errors of a measurement made based on the X-Ray image”. This is simply a well-understood, routine, and conventional activity of using X-ray images as input for detection.
Claim 15 recites “wherein the method comprises determining the location of pre-determined landmarks in the 3D medical image data and in one or more of the artificially created X-Ray images and, based thereon, detecting an imaging error and/or determining the corrected landmarks and/or the misalignment- induced error or uncertainty margin and/or corrected measurement”. This is simply a well-understood, routine, and conventional activity of using medical images as input for imaging errors.
Claim 16 recites “comprising training a first neural network with real and/or artificially created X-Ray images labeled with regard to the presence, the typer and/or the degree of the misalignments”. This is simply a well-understood, routine, and conventional activity of supervised learning for a neural network.
Claim 17 recites “comprising training a second neural network to detect pre-determined landmarks in an X-Ray image by image segmentation”. This is simply a well-understood, routine, and conventional activity of training neural networks for detection by segmentation.
Claim 18 recites “wherein the misalignment comprises an angulation and/or a distance deviation from a target alignment of the X-Ray system relative to the subject”. A person can mentally determine an angulation/distance deviation of a patient in an X-ray image.
Claim 19 recites “wherein the imaging error comprises a projection error and/or a distortion”. A person can mentally determine a projection or distortion error in an image.
Claim 21 corresponds to claim 1, additionally reciting a system, memory, and processor. These parts are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea.
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 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 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-6, 10, 12-16, 18-19, 21 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Shimizu (US 20220328168 A1).
Regarding claim 1, Shimizu discloses a computer-implemented method for X-Ray quality assurance (Shimizu, paragraph [0002], "The present disclosure relates to a failed-image decision support apparatus, a failed-image decision support system, a failed-image decision support method, and a computer readable storage medium."), the method comprising:
acquiring X-Ray image data of a subject using a mobile X-Ray imaging system, the X-Ray image data comprising at least one X-Ray image (Shimizu, paragraph [0116], "When an imaging selection button 241a is pressed with the operation unit 25 to select imaging (imaging part, imaging direction) to be performed (Step S2), the controller 21 sets imaging conditions (image reading conditions and radiation emission conditions) in the imaging apparatus 1 and the emission apparatus 3 (Step S3)."),
based on the X-Ray image data, performing an automatic image-based detection of imaging errors indicative of a misalignment of the X-Ray imaging system relative to the subject (Shimizu, paragraph [0126], "More specifically, in Step S7, the controller 21 first inputs the image data of the received radiograph to, among the learned models M stored in the storage 22, a learned model M for the determined failed-image determination process to cause the learned model M to infer and output an analysis result suitable for the imaging part."),
And in response to detecting an imaging error indicative of the misalignment, prompting a display device to display a user interface comprising an indicator indicating 1) a presence of the misalignment, 2) a type of the misalignment and/or 3) a degree of the misalignment (Shimizu, paragraph [0136], "When finishing the (at least one) failed-image determination process, the controller 21 causes the display 24 to display the determination result(s) of the failed-image determination process(es) and the determination basis/bases (Step S8).", there is an indicator as shown in Fig. 4-5 241j).
Regarding claim 2, Shimizu discloses the method of claim 1, wherein performing the automatic image-based detection of imaging errors comprises making use of a Deep Learning method (Shimizu, paragraph [0095], "The learned models M have been generated by machine learning (e.g., deep learning) using image data of radiographs and correct determination results (correct labels) as to whether the image data are failed images.").
Regarding claim 3, Shimizu discloses the method of claim 1, wherein performing the automatic image-based detection of imaging errors comprises making use of a first Deep Learning method for image-classification-based detection of the misalignments and/or a second Deep Learning method for landmark-detection-based detection of the misalignments (Shimizu, paragraph [0095], "The learned models M have been generated by machine learning (e.g., deep learning) using image data of radiographs and correct determination results (correct labels) as to whether the image data are failed images.").
Regarding claim 4, Shimizu discloses the method of claim 1, wherein performing the automatic image-based detection of imaging errors comprises image-classification-based detection of misalignments making use of a first neural network trained with real and/or artificially created X-Ray images labeled with regard to the presence the type, and/or the degree of the misalignments, wherein the first neural network is a Convolutional Neural Network (Shimizu, paragraph [0095], "The learned models M have been generated by machine learning (e.g., deep learning) using image data of radiographs and correct determination results (correct labels) as to whether the image data are failed images.").
Regarding claim 5, Shimizu discloses the method of claim 4, wherein the image-classification-based detection comprises the first neural network performing image classification and/or regression on the X- Ray image data of the subject to detect the imaging error (Shimizu, paragraph [0139], "The “Positioning: B” denoted by 241g is the determination result of this performed failed-image determination process").
Regarding claim 6, Shimizu discloses the method of claim 4, wherein the image-classification-based detection comprises the first neural network performing image classification and/or regression on the X-Ray image data of the subject to detect the imaging error to identify the presence the typer and/or the degree of the misalignments (Shimizu, paragraph [0139], "The “Positioning: B” denoted by 241g is the determination result of this performed failed-image determination process").
Regarding claim 10, Shimizu discloses the method of claim 1, wherein detecting an imaging error indicative of a misalignment is performed using artificially created X-Ray images (Shimizu, paragraph [0065], "The imaging apparatus 1 generates digital data of radiographs where the imaging part of a subject S is captured.").
Regarding claim 12, Shimizu discloses the method of claim 10, wherein a first neural network and/or a second neural network is trained based on the artificially created X-Ray images (Shimizu, paragraph [0095], "The learned models M have been generated by machine learning (e.g., deep learning) using image data of radiographs and correct determination results (correct labels) as to whether the image data are failed images.").
Regarding claim 13, Shimizu discloses the method of claim 12, wherein the first neural network is trained at least with the artificially created X-Ray images labeled with regard to the presence, the type and/or the degree of the misalignments (Shimizu, paragraph [0167], " For example, by using machine learning, such as GAN, a learned model has been generated by being trained with data patterns of images obtained with error in positioning (misalignment) and images having a misalignment amount of 0 (zero) obtained with the error in positioning corrected, and the controller 21 generates the properly-taken target image 241n by inputting the radiograph decided as a failed image to the learned model, and causes the target image 241n to be displayed alongside the radiograph.").
Regarding claim 14, Shimizu discloses the method of claim 10, wherein detecting an imaging error indicative of a misalignment using artificially created X-Ray images comprises using the artificially created X-Ray images for automatically determining at least one of: corrected landmarks, a misalignment-induced error or uncertainty margin for measurements made based on the X-Ray imaged (Shimizu, paragraph [0129], "For example, when generating the determination result of the failed-image determination process about the wrong-side part, the controller 21 compares the probability of the subject S imaged from the right with the probability of the subject S imaged from the left, and determines that the subject S is imaged from a direction having a higher probability.", as cited below, if there is an error in imaging, then the model will re-image to achieve the corrected landmarks), a corrected measurement by correcting misalignment- induced errors of a measurement made based on the X-Ray image (Shimizu, paragraph [0165], "After pressing the reject button 241d, the user resets imaging conditions and/or redoes positioning, and then performs re-imaging.").
Regarding claim 15, Shimizu discloses the method of claim 10, wherein the method comprises determining the location of pre-determined landmarks in the 3D medical image data and in one or more of the artificially created X-Ray images and, based thereon, detecting an imaging error and/or determining the corrected landmarks and/or the misalignment- induced error or uncertainty margin and/or corrected measurement (Shimizu, paragraph [0127], "The learned models M of this embodiment output numerical values as analysis results, such as “probability of the subject S imaged (radiographed) from the right (or left): X %” and “misalignment between the lateral condyle and the medial condyle: Y mm”, the model detects the imaging part in question and detects a misalignment).
Regarding claim 16, Shimizu discloses the method of claim 1, comprising training a first neural network with real and/or artificially created X-Ray images labeled with regard to the presence, the typer and/or the degree of the misalignments (Shimizu, paragraph [0167], " For example, by using machine learning, such as GAN, a learned model has been generated by being trained with data patterns of images obtained with error in positioning (misalignment) and images having a misalignment amount of 0 (zero) obtained with the error in positioning corrected, and the controller 21 generates the properly-taken target image 241n by inputting the radiograph decided as a failed image to the learned model, and causes the target image 241n to be displayed alongside the radiograph.").
Regarding claim 18, Shimizu discloses the method of claim 1, wherein the misalignment comprises an angulation and/or a distance deviation from a target alignment of the X-Ray system relative to the subject (Shimizu, paragraph [0129], "For example, when generating the determination result of the failed-image determination process about the wrong-side part, the controller 21 compares the probability of the subject S imaged from the right with the probability of the subject S imaged from the left, and determines that the subject S is imaged from a direction having a higher probability.", the angle of the subject is compared to a correct angle of the subject).
Regarding claim 19, Shimizu discloses the method of claim 1, wherein the imaging error comprises a projection error and/or a distortion (Shimizu, paragraph [0129], "For example, when generating the determination result of the failed-image determination process about the wrong-side part, the controller 21 compares the probability of the subject S imaged from the right with the probability of the subject S imaged from the left, and determines that the subject S is imaged from a direction having a higher probability.", what is being displayed on the screen is interpreted as a projection).
Claim 21 corresponds to claim 1, additionally reciting a system (Shimizu, paragraph [0012], “The first and second image data sets may be recorded by a medical imaging device. The medical imaging device may be configured as an X-ray device and/or a C-arm X-ray device and/or magnetic resonance system (MRT) and/or computed tomography system (CT) and/or sonography system and/or positron emission tomography system (PET).”), comprising
A memory that stores a plurality of instructions (Shimizu, paragraph [0009], “Receiving of the first and/or the second image data set may include, for example, acquisition and/or reading out from a computer-readable data memory and/or receiving from a data memory unit, for example a database”),
a processor coupled to the memory and configured to execute the plurality of
instructions (Shimizu, paragraph [0098], “An interface IF and/or a training interface TIF may be a hardware or software interface (for example PCI bus, USB or Firewire). A computing unit CU and/or a training computing unit TCU may include hardware elements or software elements, for example a microprocessor or what is known as an FPGA (acronym for “Field Programmable Gate Array”)”). Thus, it is rejected for the same reasons of anticipation as claim 1.
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) 7, 8, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Shimizu (US 20220328168 A1) in view of Siemionow (US 20190105009 A1).
Regarding claim 7, Shimizu discloses the method of claim 1.
While Shimizu teaches wherein performing the automatic image-based detection of imaging errors comprises landmark-detection-based detection of imaging errors and detecting pre-determined landmarks in an X-Ray image, the landmarks being suitable for deriving an imaging error from their arrangement in the X-Ray image (Shimizu, paragraph [0099], “For example, when the imaging part is a joint (knee joint, elbow joint, ankle joint, etc.), at least one of failed-image determination processes about positioning (misalignment between the lateral condyle and the medial condyle), wrong-side part and wrong part is performed”), they do not do so using a second convolutional neural network for image segmentation.
However, Siemionow teaches wherein performing the automatic image-based detection of imaging errors comprises landmark-detection-based detection of imaging errors making use of a second neural network configured to detect pre- determined landmarks in an X-Ray image, the landmarks being suitable for deriving an imaging error from their arrangement in the X-Ray image wherein the second neural network is a Convolutional Neural Network for image segmentation (Siemionow, paragraph [0016], "training, by at least one processor, a segmentation CNN, that is a fully convolutional neural network model with layer skip connections, to segment semantically at least one part of the bony structure utilizing the received learning data").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to use a neural network to segment landmarks on Shimizu’s image, as taught by Siemionow.
The suggestion/motivation for doing so would have been to specialize the neural networks and speed up each respective task.
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Shimizu in view of Siemionow to obtain the invention as specified in claim 7.
Regarding claim 8, Shimizu in view of Siemionow discloses the method of claim 7, wherein the landmark-detection-based detection of imaging errors comprises detecting the pre-determined landmarks in the X-Ray image by the second neural network, determining an arrangement of the detected landmarks (Siemionow, paragraph [0016], "training, by at least one processor, a segmentation CNN, that is a fully convolutional neural network model with layer skip connections, to segment semantically at least one part of the bony structure utilizing the received learning data"),
and, based thereon, detecting the imaging error (Shimizu, paragraph [0127], " The learned models M of this embodiment output numerical values as analysis results, such as “probability of the subject S imaged (radiographed) from the right (or left): X %” and “misalignment between the lateral condyle and the medial condyle: Y mm”.", using the neural network of Siemionow, the imaging error in Shimizu’s images can be detected).
Regarding claim 17, Shimizu discloses the method of claim 1.
Shimizu does not teach “comprising training a second neural network to detect pre-determined landmarks in an X-Ray image by image segmentation”.
However, Siemionow teaches comprising training a second neural network to detect pre-determined landmarks in an X-Ray image by image segmentation (Siemionow, paragraph [0016], "training, by at least one processor, a segmentation CNN, that is a fully convolutional neural network model with layer skip connections, to segment semantically at least one part of the bony structure utilizing the received learning data").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to use a neural network to determine landmarks on Shimizu’s image, as taught by Siemionow.
The suggestion/motivation for doing so would have been to specialize the neural networks and speed up each respective task.
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Shimizu in view of Siemionow to obtain the invention as specified in claim 17.
Claim(s) 9 is rejected under 35 U.S.C. 103 as being unpatentable over Shimizu (US 20220328168 A1) in view of Siemionow (US 20190105009 A1) and in further view of Kaethner (US 20210150739 A1).
Regarding claim 9, Shimizu in view of Siemionow discloses the method of claim 8.
Shimizu in view of Siemionow does not teach “wherein determining the arrangement of the detected landmarks comprises determining a distance between the detected landmarks wherein based on the arrangement of the detected landmarks, the presence, the type, and/or the degree of the misalignments is determined”.
However, Kaethner teaches wherein determining the arrangement of the detected landmarks comprises determining a distance between the detected landmarks wherein based on the arrangement of the detected landmarks, the presence, the type, and/or the degree of the misalignments is determined (Kaethner, paragraph [0021], "For example, the identification of misaligned image features in the distance data set in may be based on a comparison of the distance data set with the first image data set, the second image data set and/or the registered image data set. For example, anatomical and/or geometric image features, that are present in the first image data set, the second image data set and/or the registered image data set, are compared with the misaligned image features identified in the distance data set.", by comparing the features (landmarks) of Shimizu’s image with another image, the distance of the misalignments can be known).
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to use Shimizu’s (in view of Siemionow) model to determine misaligned data based on the difference between a standard X-ray image and the current X-ray image, as taught by Kaethner.
The suggestion/motivation for doing so would have been to gain a more thorough understanding of the change in a patient’s body part.
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Shimizu in view of Siemionow and in further view of Kaethner to obtain the invention as specified in claim 9.
Claim(s) 11 are rejected under 35 U.S.C. 103 as being unpatentable over Shimizu (US 20220328168 A1) in view of Nichols (US 20220249105 A1).
Regarding claim 11, Shimizu discloses the method of claim 10.
While Shimizu teaches wherein the artificially created X-Ray images are obtained by applying a plurality of different parametrizations (Shimizu, paragraph [0075], “The imaging conditions include conditions about the subject S (part/site of the body to be imaged (imaging part), imaging direction, body build, etc.), conditions about emission of radiation R (tube voltage, tube current, emission time, current-time product (mAs value), etc.), and conditions about image reading by the imaging apparatus 1”), they do not teach doing so with “Digitally Reconstructed Radiographs reconstructed from 3D medical image data, so as to obtain a plurality of artificial X-Ray images with imaging errors representative of different misalignments”.
However, Nichols teaches wherein the artificially created X-Ray images are obtained by applying a plurality of different parametrizations to Digitally Reconstructed Radiographs reconstructed from 3D medical image data, so as to obtain a plurality of artificial X-Ray images with imaging errors representative of different misalignments (Nichols, paragraph [0005], "Causing the apparatus to obtain the at least one two-dimensional image of the first bone and the second bone includes causing the apparatus to obtain two or more two-dimensional images captured from at least two different angles of the first bone and the second bone, where the apparatus is further caused to generate a three-dimensional reconstruction and/or a digitally reconstructed radiograph of the first bone and the second bone from the two or more two-dimensional images.").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to replace Shimizu’s images with DRR images, as taught by Nichols.
The suggestion/motivation for doing so would have been to prevent exposing patients to radiation.
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Shimizu in view of Nichols to obtain the invention as specified in claim 11.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WAYNE ZHANG whose telephone number is (571) 272-0245. The examiner can normally be reached Monday-Friday 10:00-6:00 EST.
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/WAYNE ZHANG/Examiner, Art Unit 2672
/SUMATI LEFKOWITZ/Supervisory Patent Examiner, Art Unit 2672
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