DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 2 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 2 recites the limitation “a non-transitory computer-readable storage medium including instructions that, when executed by a processor, configure the processor to perform the method of claim 1” which is considered indefinite. As currently written claim 2 does not require the processor to perform the method of claim 1, the claim only requires the processor to be configured to perform. Therefore, it is not clear whether claim 2 includes all the limitations of claim 1. Examiner recommends amending the claim to recite the instructions cause the processor to perform the method of claim 1.
Claim Rejections - 35 USC § 103
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) 1-3, 5-6, 9-10, and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gromley et al. (US 20220409281, hereinafter Gromley) in view of Dippel et al. (US 20250049517, hereinafter Dippel).
Regarding claim 1, Gromley teaches a method for performing an imaging registration and a cranial surgical procedure navigation ([0172] process 550 in fig. 28), the method comprising:
obtaining modality scans of a cranium of a patient ([0131] discloses obtaining CT imaging of a subject’s head);
creating, using the modality scans, a first 3D model of the cranium of the patient ([0132] discloses generating a 3D model of the subject’s head using the CT imaging data);
obtaining, using a movable elliptical mask attached to a robotic arm, surface imaging data of a face or a portion of the cranium of the patient while the patient is lying in a supine position atop an operating table ([0172] discloses at step 552 acquiring images of the head of the subject using a camera attached to an electrically actuated mounting structure (robotic arm). Fig. 30 shows the subject is lying in a supine position atop an operating table), wherein the robotic arm gradually moves the elliptical mask around the face or the portion of the cranium of the patient ([0172] discloses the mounting structure is used to capture images at different positions around the subject’s head)
creating, using the surface imaging data, a second 3D model of the face or the portion of the cranium of the patient ([0174] discloses generating a 3D model of the subject’s head at step 554 using the imaging data acquired at step 552);
registering the second 3D model to the first 3D model ([0175] discloses registering the 3D models at step 558); and
guiding a physician during a cranial surgical procedure by displaying the first 3D model, the second 3D model, or a combination thereof on a display screen ([0183] discloses displaying the 3D image to the user to assist with guiding the user).
Gromley does not specifically teach the robotic arm moves approximately 180 degrees around the face or the portion of the cranium of the patient in a circular motion from:
i) a first position to a second position, wherein the first position comprises the elliptical mask approximately facing a nose of the patient, and the second position comprises the elliptical mask approximately facing an ear of the patient; ii) the second position to a third position, wherein the third position comprises the elliptical mask approximately facing another ear of the patient; and iii) the third position to the first position.
However,
Dippel in a similar field of endeavor teaches obtaining surface imaging data of a face using a robotic arm that moves approximately 180 degrees around the face or the portion of the cranium of the patient in a circular motion from ([0025] discloses scanning a face of a patient using a robot camera from multiple sides):
i) a first position to a second position, wherein the first position comprises the elliptical mask approximately facing a nose of the patient, and the second position comprises the elliptical mask approximately facing an ear of the patient; ii) the second position to a third position, wherein the third position comprises the elliptical mask approximately facing another ear of the patient; and iii) the third position to the first position ([0049]-[0050] and figs. 1-2 disclose using the robot camera to scan the patient from all relevant sides which includes the front of face (nose) and each side of the face (ears)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method disclosed by Gromley to have the robotic arm move approximately 180 degrees around the face or the portion of the cranium of the patient in a circular motion from: i) a first position to a second position, wherein the first position comprises the elliptical mask approximately facing a nose of the patient, and the second position comprises the elliptical mask approximately facing an ear of the patient; ii) the second position to a third position, wherein the third position comprises the elliptical mask approximately facing another ear of the patient; and iii) the third position to the first position in order to ensure an overall image of the face is obtained, as recognized by Dippel ([0025], [0050]).
Regarding claim 2, Gromley in view of Dippel teach a non-transitory computer-readable storage medium including instructions that, when executed by a processor, configure the processor to perform the method of claim 1 ([0256]-[0257] disclose a non-transitory computer-readable medium for implementing the disclosed method).
Regarding claim 3, Gromley in view of Dippel teaches the method of claim 1, as set forth above. Gromley further teaches the modality scans comprise computerized tomography (CT) scans, magnetic resonance imaging (MRI) scans, positron emission tomography (PET) scans, single photon emission computed tomography (SPECT) scans, or a combination thereof ([0131]-[0132] disclose the modality scan is a CT scan or MRI scan).
Regarding claim 5, Gromley in view of Dippel teaches the method of claim 1, as set forth above. Dippel further teaches the first position comprises a neutral position of the elliptical mask, and wherein the neutral position comprises a zero-degree movement of the elliptical mask (fig. 2 shows the first position is a neutral position of the robotic camera with a zero-degree movement); the second position comprises a first lateral position of the elliptical mask, and wherein the first lateral position comprises a positive 90 degrees movement of the elliptical mask (figs. 1-2 show the second position is a first lateral position of the robotic camera with a positive 90 degree movement); and the third position comprises a second lateral position of the elliptical mask, and wherein the second lateral position comprises a negative 90 degrees movement of the elliptical mask (fig. 2 further shows the robotic camera is able to move to a third position opposite the position shown in fig. 1 that is a second lateral position comprising a negative 90 degree movement).
Regarding claim 6, Gromley in view of Dippel teaches the method of claim 1, as set forth above. Gromley further teaches the elliptical mask utilizes a machine vision system to obtain the surface imaging data ([0166] discloses the camera is structured as known in the art including an image sensor).
Regarding claim 9, Gromley in view of Dippel teaches the method of claim 1, as set forth above. Gromley further teaches detecting, using the elliptical mask, a movement of the face, the cranium, the operating table, a fixture holding the cranium, or a combination thereof during the cranial surgical procedure; quantifying the movement; and performing an autocorrection of the imaging registration ([0175]-[0176] disclose a process of tracking movement of the patient head during the procedure and adjusting the registration based on the amount of movement).
Regarding claim 10, Gromley in view of Dippel teaches the method of claim 9, as set forth above. Gromley further teaches the autocorrection of the imaging registration obviates a need to initiate another registration of the second 3D model to the first 3D model ([0175]-[0176] disclose adjusting the registration includes performing the registration process again).
Regarding claim 14, Gromley in view of Dippel teaches the method of claim 1, as set forth above. Gromley further teaches said creating of the first 3D model and said creating of the second 3D model are performed using the same volume rendering technique ([0132] discloses generating the 3D model of the subjects head from the CT image data and the 3D surface model are generates using typical processes in the art (e.g., the marching cube algorithm)).
Claim(s) 4 and 11-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gromley in view of Dippel as applied to claim 1 above, and further in view of Chen et al. (US 20220175454, hereinafter Chen).
Regarding claim 4, Gromley in view of Dippel teaches the method of claim 1, as set forth above. Gromley in view of Dippel does not specifically teach the modality scans comprise a first plurality of 2D images obtained preoperatively; and the surface imaging data comprise a second plurality of 2D images obtained during the imaging registration, the cranial surgical procedure navigation, or a combination thereof.
However,
Chen in a similar field of endeavor teaches the modality scans comprise a first plurality of 2D images obtained preoperatively ([0019] discloses obtaining a CT image preoperatively which consists of obtaining a plurality of 2D slice images before generating a 3D image); and the surface imaging data comprise a second plurality of 2D images obtained during the imaging registration, the cranial surgical procedure navigation, or a combination thereof ([0022] discloses obtaining 2D images of the face).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method disclosed by Gromley in view of Dippel to have the modality scans comprise a first plurality of 2D images obtained preoperatively; and the surface imaging data comprise a second plurality of 2D images obtained during the imaging registration, the cranial surgical procedure navigation, or a combination thereof in order to increase the number of systems that can perform the method, thereby increasing the methods versatility.
Regarding claim 11, Gromley in view of Dippel teaches the method of claim 1, as set forth above. Gromley in view of Dippel does not specifically teach said registering of the second 3D model to the first 3D model meets or exceeds a predetermined accuracy threshold.
However,
Chen in a similar field of endeavor teaches registering of the second 3D model to the first 3D model meets or exceeds a predetermined accuracy threshold ([0039]-[0045] discloses a process of registering the models where a step of the process is whether a specific threshold has been reached).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method disclosed by Gromley in view of Dippel to have the registering of the second 3D model to the first 3D model meets or exceeds a predetermined accuracy threshold in order to increase the accuracy of the method, as recognized by Chen ([0043]).
Regarding claim 12, Gromley in view of Dippel and Chen teaches the method of claim 11, as set forth above. Gromley further teaches said registering of the second 3D model to the first 3D model further comprises: detecting features of the face or the portion of the cranium in the first 3D model and the second 3D model; and mapping the features of the second 3D model to the first 3D model ([0129] and [0174]-[0175] disclose the registration process includes detecting anatomical features in the 3D models and matching the anatomical features between the 3D models).
Regarding claim 13, Gromley in view of Dippel and Chen teaches the method of claim 12, as set forth above. Chen further teaches the detection of the features is performed using a convolution neural network (CNN) algorithm, a region-based convolutional network (RCNN) algorithm, a region-based fully convolutional network (R-FCN) algorithm, a feedforward neural network (FNN) algorithm, a Harris corner detection algorithm, a Shi-Tomasi corner detector algorithm, a scale-invariant feature transform (SIFT) algorithm, a speeded-up robust features (SURF) algorithm, a binary large object (BLOB) detection algorithm, one or more feature descriptor algorithms, a histogram of oriented gradients (HOG) algorithm, a binary robust independent elementary features (BRIEF) algorithm, or a combination thereof ([0027]-[0029] discloses the use of an artificial intelligence network (convolutional neural network) to identify the feature points (anatomical features)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to substitute the detecting of the features of Gromley in view of Dippel and Chen for the identifying of anatomical features using artificial intelligence of Chen because it amounts to simple substitution of one known element for another to obtain the predictable results of identifying the features
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gromley in view of Dippel as applied to claim 1 above, and further in view of Clug et al. (US 20220409314, hereinafter Clug).
Regarding claim 7, Gromley in view of Dippel teaches the method of claim 6, as set forth above. Gromley further teaches the machine vision system utilizes an imaging sensor ([0166] discloses the camera is structured as known in the art including an image sensor). Gromley in view of Dippel does not specifically teach the machine vision system captures red-green-blue (RGB) and infrared (IR) depth (RGBIRD) information of the surface imaging data ([0165] disclose using the LiDAR camera transmits in the infrared spectrum to determine distance (depth) information. [0178] discloses the LiDAR sensor produces RGB data).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method disclosed by Gromley in view of Dippel to have the machine vision system captures red-green-blue (RGB) and infrared (IR) depth (RGBIRD) information of the surface imaging data in order to more easily monitor and update the position of the head of the patient, as recognized by Clug ([0042]).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gromley in view of Dippel as applied to claim 1 above, and further in view of Piron et al. (US 20160067007, hereinafter Piron).
Regarding claim 8, Gromley in view of Dippel teaches the method of claim 1, as set forth above. Gromley in view of Dippel does not specifically teach tracking positions of instruments during the cranial surgical procedure and presenting positional information of the instruments relative to the first 3D model, the second 3D model, or a combination thereof.
However,
Piron in a similar field of endeavor teaches tracking positions of instruments during the cranial surgical procedure ([0161] and [0202] discloses each medical instrument includes a reflective sphere for tracking the instrument during the procedure) and presenting positional information of the instruments relative to the first 3D model, the second 3D model, or a combination thereof ([0172] discloses displaying an overlay of the instrument position on a previously acquired or current scan (3D model)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method disclosed by Gromley in view of Dippel to track positions of instruments during the cranial surgical procedure and presenting positional information of the instruments relative to the first 3D model, the second 3D model, or a combination thereof in order to know the position and orientation of the instruments during the procedure, thereby knowing if the instruments are in the correct location, as recognized by Piron ([0202]).
Claim(s) 15-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gromley et al. (US 20220409281, hereinafter Gromley) in view of Robar (US 20210077829).
Regarding claim 15, Gromley teaches a system for image registration for cranial surgical procedures (the system shown in figs. 1A-1B), the system comprising:
a movable machine vision system mountable to a robotic arm and configured to obtain surface imaging data of a face or a portion of a cranium of a patient ([0172] discloses at step 552 acquiring images of the head of the subject using a camera (machine vision system) attached to an electrically actuated mounting structure (robotic arm));
a console (the electronic circuitry of the system shown in figs. 1A-1B) configured to:
create a first 3D model of the face or the portion of the cranium using the surface imaging data ([0174] discloses generating a 3D model of the subjects head at step 554 using the imaging data acquired at step 552); and
register the first 3D model to a second 3D model ([0175] discloses registering the 3D models at step 558), wherein the second 3D model is created using preoperative medical imaging data ([0131]-[0132] disclose generating a 3D model of the subjects head using preoperative CT imaging data); and
a display configured to present to a physician the first 3D model, the second 3D model, or a combination thereof during the cranial surgical procedure ([0183] discloses displaying the 3D image to the user to assist with guiding the user).
Gromley does not specifically teach a fixture holding the cranium, wherein the fixture comprises a first, a second, and a third arm to collectively define a size and a position of the cranium by utilizing respective reflective spheres visible or trackable by the movable machine vision system.
However,
Robar in a similar field of endeavor teaches a fixture holding the cranium, wherein the fixture comprises a first, a second, and a third arm to collectively define a size and a position of the cranium by utilizing respective reflective spheres visible or trackable by the movable machine vision system ([0122] and fig. 13 disclose a support structure holding the cranium that includes a first, second, and third arm that define a size and position of the cranium utilizing reflective spheres 610 for tracking that are visible by a machine vision system).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the known technique of having a fixture holding the cranium, wherein the fixture comprises a first, a second, and a third arm to collectively define a size and a position of the cranium by utilizing respective reflective spheres visible or trackable by the movable machine vision system to the system of Gromley to allow for the predictable results of more easily tracking the movement of the system.
Regarding claim 16, Gromley in view of Robar teaches the system of claim 15, as set forth above. Gromley further teaches the preoperative medical imaging data are obtained using CT scans, MRI scans, PET scans, SPECT scans, or a combination thereof ([0131]-[0132] disclose the modality scan is a CT scan or MRI scan).
Regarding claim 17, Gromley in view of Robar teaches the system of claim 15, as set forth above. Gromley further teaches the movable machine vision system is embedded in or on an elliptical mask, and wherein the elliptical mask comprises at least: one camera, one infrared sensor, one light source, or a combination thereof ([0171] disclose the machine vision system is embedded on a guidance system 536 (mask) that comprises at least one camera 538/540).
Claim(s) 18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gromley in view of Robar as applied to claim 15 above, and further in view of Piron et al. (US 20160067007, hereinafter Piron).
Regarding claim 18, Gromley in view of Robar teaches the system of claim 15, as set forth above. Robar further teaches each reflective sphere is mounted at a visible or a trackable end of each arm of the fixture (fig. 13 shows the reflective spheres 610 are located at the end of each arm of the support structure). Gromley in view of Robar does not specifically teach each reflective sphere comprises a passive infrared (PIR) sensor used to determine a position of each arm of the fixture from the cranium of the patient.
However,
Piron in a similar field of endeavor teaches each reflective sphere comprises a passive infrared (PIR) sensor used to determine a position of each of the support structures holding the reflective spheres ([0136] and [0202] disclose using passive infrared reflective spheres detectable via an optical camera in order to track the one or more objects attached to the spheres).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to substitute the reflective spheres of Gromley in view of Robar for the reflective spheres comprising a PIR sensor of Piron because it amounts to simple substitution of one known element for another to obtain the predictable results of tracking the fixture.
Regarding claim 20, Gromley in view of Robar teaches the system of claim 15, as set forth above. Gromley in view of Robar does not specifically teach the movable machine vision system tracks positions of instruments during the cranial surgical procedure, and wherein each instrument comprises one or more reflective spheres.
However,
Piron in a similar field of endeavor teaches the movable machine vision system tracks positions of instruments during the cranial surgical procedure, and wherein each instrument comprises one or more reflective spheres ([0161] and [0202] discloses each medical instrument includes a reflective sphere for tracking the instrument during the procedure).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system disclosed by Gromley in view of Robar to have the movable machine vision system track positions of instruments during the cranial surgical procedure, and wherein each instrument comprises one or more reflective spheres in order to know the position and orientation of the instruments during the procedure, thereby knowing if the instruments are in the correct location, as recognized by Piron ([0202]).
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gromley in view of Robar and Piron as applied to claim 18 above, and further in view of Tyc et al. (US 20140128881, hereinafter Tyc).
Regarding claim 19, Gromley in view of Robar and Piron teaches the system of claim 18, as set forth above. Piron as set forth above teaches the reflective spheres are PIR sensors ([0136], [0202]). Gromley in view of Robar and Piron does not specifically teach each arm of the fixture is adjusted mechanically or electromechanically to fit a size of the cranium using position information determined using each sensor.
However,
Tyc in a similar field of endeavor teaches each arm of the fixture is adjusted mechanically or electromechanically to fit a size of the cranium using position information determined using each sensor ([0219] discloses adjusting the tilt, rotation and leg lengths of the frame using the position information received from the fiducial markers).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the known technique of having each arm of the fixture be adjusted mechanically or electromechanically to fit a size of the cranium using position information determined using each sensor of Tyc to the system of Gromley in view of Robar and Piron to allow for the predictable results of ensuring the fixture is an appropriate size for the cranium of the patient.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW BEGEMAN whose telephone number is (571)272-4744. The examiner can normally be reached Monday-Thursday 8:30-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Keith Raymond can be reached at 5712701790. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREW W BEGEMAN/Primary Examiner, Art Unit 3798
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