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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/20/2026 has been entered.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-7, 9-13, 16-19, 21-22, & 25-26 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claims 1-7, 9-13, 16-19, 21-22, & 25-26 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 1 recites the limitation “a first output image including a first output location of each keypoint of the plurality of keypoints annotated on a first output image of the surgical instrument” which renders the claim unclear. Since 2 first output images are disclosed above its unclear whether they are the same or different.
Claim 1 also recites the limitation “a second output image including a second output location of each keypoint of the plurality of keypoints annotated on a second output image of the surgical instrument” which renders the claim unclear. Since 2 second output images are disclosed above its unclear whether they are the same or different.
Claim 13 recites the limitation “a first output image including a first output location of each keypoint of the plurality of keypoints annotated on a first output image of the surgical instrument” which renders the claim unclear. Since 2 first output images are disclosed above its unclear whether they are the same or different.
Claim 13 also recites the limitation “a second output image including a second output location of each keypoint of the plurality of keypoints annotated on a second output image of the surgical instrument” which renders the claim unclear. Since 2 second output images are disclosed above its unclear whether they are the same or different.
Claim 21 recites the limitation “a first output image including a first output location of each keypoint of the plurality of keypoints annotated on a first output image of the surgical instrument” which renders the claim unclear. Since 2 first output images are disclosed above its unclear whether they are the same or different.
Claim 21 also recites the limitation “a second output image including a second output location of each keypoint of the plurality of keypoints annotated on a second output image of the surgical instrument” which renders the claim unclear. Since 2 second output images are disclosed above its unclear whether they are the same or different.
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.
Claims 1-5, 7, 9, 12-13, 16-17, 19, 21-22, & 25 are rejected under 35 U.S.C. 103 as being unpatentable over Muntion et al (US20240206989A1; hereinafter referred to as Muntion) in view of Zhao et al (US20090088634A1; hereinafter referred to as Zhao).
Regarding Claim 1, Muntion discloses a method for detecting a location of a plurality of keypoints of a surgical instrument (“The instrument network 401 can be trained to identify groups of keypoints associated with surgical instruments.” [0078]), the method comprising:
receiving, at a first neural network model, a video input including a plurality of video frame images of a surgical procedure (“At block 202, the system 100 can access input data including, for example, video data, spatial data, and/or sensor data temporally associated with a video stream of a surgical procedure. At block 204, the one or more machine-learning models 702 can predict a state of the surgical procedure based on the input data.” [0068]);
generating, using the first neural network model, a first output image including a first output location of each keypoint of the plurality of keypoints annotated on a first output image of the surgical instrument (“At block 206, the one or more machine-learning models 702 can detect one or more surgical instruments at least partially depicted in the video stream based on the input data, when such features are present. Detection of surgical instruments can include determining a presence or localization of one or more surgical instruments. The localization can include, for example, a bounding box, a medial axis, and/or any other marker or keypoint identifying the location of one or more surgical instruments… At block 208, a state indicator and/or one or more surgical instrument indicators temporally correlated with the video stream can be output.” [0068]);
receiving, at a second neural network model, the first output image generated by the first neural network mode (“At block 208, a state indicator and/or one or more surgical instrument indicators temporally correlated with the video stream can be output.“, “At block 210, based on the state as predicted and tracked instrument information of the one or more surgical instruments, the system 100 can output a notification, a request, a visualization, and/or a scheduling update depending on how the state and tracked instrument information are used…The visualization can include displaying one or more motion profiles of surgical instruments that can include tracking observed motion and/or a predicted path of motion. ” [0069], “FIG. 12 depicts an example of a process 1200 of using machine learning to generate motion profiles 1102 of surgical instruments 1008 according to one or more examples.” [0128], Block 208 outputs the state indicator from the first machine learning model [0068] which is used in Block 210 by another machine learning model to create a motion profile and visualize it to the user [0069]);
receiving, at the second neural network model, historic keypoint trajectory data including a historic trajectory for each keypoint of the plurality of keypoints (“ The visualization can include displaying one or more motion profiles of surgical instruments that can include tracking observed motion and/or a predicted path of motion.” [0069], “For the frames, e.g., including images 302, between two successive input windows 320, the locations of the anatomical structures and surgical instruments can be predicted based on the locations predicted in the most recent input window 320. For example, a movement vector of the surgical instrument can be computed based on the changes in the location of the surgical instrument in the frames in the prior input window 320. The movement vector can be computed using a machine learning model, such as a deep neural network. The movement vector is used to predict the location of the surgical instrument in the subsequent frames after the input window 320, until a next input window 320 is analyzed.” [0097]);
determining, using the second neural network model, a trajectory for each keypoint of the plurality of keypoints (“The visualization can include displaying one or more motion profiles of surgical instruments that can include tracking observed motion and/or a predicted path of motion. ” [0069],” “Keypoints” of surgical instruments can be defined within a bounded region between a starting keypoint and an end, such as a tip of surgical instrument. Keypoints may also represent pivot points, such as joints, shaft starting and end points, and other such features depending upon the physical structure and function of each surgical instrument.” [0086] “For the frames, e.g., including images 302, between two successive input windows 320, the locations of the anatomical structures and surgical instruments can be predicted based on the locations predicted in the most recent input window 320. For example, a movement vector of the surgical instrument can be computed based on the changes in the location of the surgical instrument in the frames in the prior input window 320. The movement vector can be computed using a machine learning model, such as a deep neural network. The movement vector is used to predict the location of the surgical instrument in the subsequent frames after the input window 320, until a next input window 320 is analyzed.” [0097]);
wherein the trajectory for each keypoint is based on the first output image generated by the first neural network model and the historic keypoint trajectory data for each keypoint (“Temporal information that is provided by state information can be used to refine confidence of the instrument prediction or anatomy prediction in one or more aspects. In one or more aspects, the temporal information can be fused with a feature space, and the resulting fused information can be used by a decoder to output instrument localization and/or anatomical localization, for example. Other visual or temporal cues may also be fused“ [0081], “For the frames, e.g., including images 302, between two successive input windows 320, the locations of the anatomical structures and surgical instruments can be predicted based on the locations predicted in the most recent input window 320. For example, a movement vector of the surgical instrument can be computed based on the changes in the location of the surgical instrument in the frames in the prior input window 320. The movement vector can be computed using a machine learning model, such as a deep neural network. The movement vector is used to predict the location of the surgical instrument in the subsequent frames after the input window 320, until a next input window 320 is analyzed.” [0097]);
generating, using the second neural network model, a second output image including a second output location of each keypoint of the plurality of keypoints annotated on a second output image of the surgical instrument (“The visualization can include displaying one or more motion profiles of surgical instruments that can include tracking observed motion and/or a predicted path of motion. ” [0069] , “The location of structure(s) predicted by the one or more machine-learning models 702 can also be predicted in the frames between two successive input windows 320 in the same manner. Graphical overlays that are used to overlay the images 302 to represent predicted features (e.g., surgical instruments, anatomical structures, etc.) are accordingly adjusted, if required, based on the predicted locations.” [0098]).
Muntion does not specifically disclose that based on the second output image and in response to one or more received user inputs, actuating one or more actuators to move the surgical instrument.
However, in a similar field of endeavor, Zhao teaches acquisition and tracking of the position and orientation of robotic tools or instruments [0001].
Zhao also teaches that based on the second output image and in response to one or more received user inputs, actuating one or more actuators to move the surgical instrument (“Tool tracking has a number of applications in robotic surgical systems. One illustrative application of tool tracking is to automatically control the motion of the endoscopic camera so that a surgeon automatically views regions of interest in a surgical site, thus freeing the surgeon from the camera control task. For example, robotic instruments are tracked so that the endoscopic camera is centered in the field of view of the surgical site. Another illustrative application for accurate tool tracking may be used to move a robotic instrument to reach a surgical target (e.g., a tumor) either automatically or by a surgeon.” [0054]).
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Muntion as outlined above with based on the second output image and in response to one or more received user inputs, actuating one or more actuators to move the surgical instrument as taught by Zhao, because it may be used to move a robotic instrument to reach a surgical target either automatically or by a surgeon [0054].
Regarding Claim 2, Muntion discloses the second output location of each keypoint of the plurality of keypoints identifies a location of a corresponding landmark of the surgical instrument ((“The visualization can include displaying one or more motion profiles of surgical instruments that can include tracking observed motion and/or a predicted path of motion. ” [0069] “’Keypoints’ of surgical instruments can be defined within a bounded region between a starting keypoint and an end, such as a tip of surgical instrument. Keypoints may also represent pivot points, such as joints, shaft starting and end points, and other such features depending upon the physical structure and function of each surgical instrument.” [0086], “The location of structure(s) predicted by the one or more machine-learning models 702 can also be predicted in the frames between two successive input windows 320 in the same manner. Graphical overlays that are used to overlay the images 302 to represent predicted features (e.g., surgical instruments, anatomical structures, etc.) are accordingly adjusted, if required, based on the predicted locations.” [0098]).
Regarding Claim 3, Muntion discloses generating the second output image includes generating, using the second neural network model, a second output image corresponding to each video frame image of the plurality of video frame images received by the first neural network model (“In yet more examples, the motion profiles 1102 can be used to provide real time guidance during surgery.” [0133]).
Regarding Claim 4, Muntion discloses further comprising evaluating a performance of the surgical procedure based on the second output images corresponding to each video frame image of the plurality of video frame images (“In yet more examples, the motion profiles 1102 can be used to provide real time guidance during surgery. For example, if the motion of the surgical instrument 1008 is beyond one or more thresholds, a warning can be provided to the operator. For example, a predetermined path of the surgical instrument 1008 can be configured for a surgical procedure. Alternatively, or in addition, the path for a surgical instrument 1008 can be configured per phase. If, during the surgical procedure, the surgical instrument 1008 is detected to veer off of the predetermined path by more than a predetermined threshold, a user feedback can be provided.“ [0133]).
Regarding Claim 5, Muntion discloses further comprising: determining whether the surgical instrument will exceed a range of motion of the surgical instrument based on the determined trajectory of each keypoint of the plurality of keypoints; and if a determination is made that the surgical instrument will exceed the range of motion, generating a warning indicating that the surgical instrument will exceed the range of motion(“In yet more examples, the motion profiles 1102 can be used to provide real time guidance during surgery. For example, if the motion of the surgical instrument 1008 is beyond one or more thresholds, a warning can be provided to the operator. For example, a predetermined path of the surgical instrument 1008 can be configured for a surgical procedure. Alternatively, or in addition, the path for a surgical instrument 1008 can be configured per phase. If, during the surgical procedure, the surgical instrument 1008 is detected to veer off of the predetermined path by more than a predetermined threshold, a user feedback can be provided.“ [0133]).
Regarding Claim 7, Muntion discloses determining the trajectory for each keypoint of the plurality of keypoints includes matching, using the second neural network model, the first output location for each keypoint of the plurality of keypoints with a corresponding historic trajectory of the historic keypoint trajectory data (“ The visualization can include displaying one or more motion profiles of surgical instruments that can include tracking observed motion and/or a predicted path of motion. When observed in real-time, motion profiles can guide usage of surgical instruments during a surgical procedure. When used during post-operative analysis, motion profiles track whether a surgical instrument followed or deviated from a predetermined path.” [0069], “The one or more machine-learning models 702 of FIG. 7 can operate on the surgical data per frame, but can use information from a previous frame, or a window of previous frames.” [0070]).
Regarding Claim 9, Muntion discloses generating the first output image includes: generating, using the first neural network model, a heatmap indicating an estimated location of the plurality of keypoints in each video frame image of the plurality of video frame images; and generating the first output image based on the estimated location of the plurality of keypoints (“The one or more machine-learning models can then be used in real-time to process one or more data streams (e.g., video streams, audio streams, RFID data, etc.). The processing can include predicting and characterizing one or more surgical states, instruments, and/or other structures within various instantaneous or block time periods…The localization can be represented as coordinates in images that map to pixels depicting the surgical instrument(s) in the images. Localization of other structures, such as anatomical structures, can be used to provide locations, e.g., coordinates, heatmaps, bounding boxes, boundaries, masks, etc., of one or more anatomical structures identified and distinguish between other structures, such as surgical instruments.” [0047]).
Regarding Claim 12, Muntion discloses further comprising displaying the second output image on a display system (“the motion profile can be displayed as an overlay on the video stream.” [0012]).
Regarding Claim 13, Muntion discloses a system for detecting a location of a plurality of keypoints of a surgical instrument (“The instrument network 401 can be trained to identify groups of keypoints associated with surgical instruments.” [0078]), the system comprising:
a memory configured to store a first neural network model and a second neural network model; and a processor coupled to the memory (“a computer program product includes a memory device having computer executable instructions stored thereon, which when executed by one or more processors cause the one or more processors to perform a method.” [0018]), the processor configured to:
receive, at a first neural network model, a video input including a plurality of video frame images of a surgical procedure (“At block 202, the system 100 can access input data including, for example, video data, spatial data, and/or sensor data temporally associated with a video stream of a surgical procedure. At block 204, the one or more machine-learning models 702 can predict a state of the surgical procedure based on the input data.” [0068]);
generate, using the first neural network model, a first output image including a first output location of each keypoint of the plurality of keypoints annotated on a first output image of the surgical instrument (“At block 206, the one or more machine-learning models 702 can detect one or more surgical instruments at least partially depicted in the video stream based on the input data, when such features are present. Detection of surgical instruments can include determining a presence or localization of one or more surgical instruments. The localization can include, for example, a bounding box, a medial axis, and/or any other marker or keypoint identifying the location of one or more surgical instruments… At block 208, a state indicator and/or one or more surgical instrument indicators temporally correlated with the video stream can be output.” [0068]);
receive, at a second neural network model, the first output image generated by the first neural network mode (“At block 210, based on the state as predicted and tracked instrument information of the one or more surgical instruments, the system 100 can output a notification, a request, a visualization, and/or a scheduling update depending on how the state and tracked instrument information are used…The visualization can include displaying one or more motion profiles of surgical instruments that can include tracking observed motion and/or a predicted path of motion. ” [0069], “FIG. 12 depicts an example of a process 1200 of using machine learning to generate motion profiles 1102 of surgical instruments 1008 according to one or more examples.” [0128]);
receive, at the second neural network model, historic keypoint trajectory data including a historic trajectory for each keypoint of the plurality of keypoints (“ The visualization can include displaying one or more motion profiles of surgical instruments that can include tracking observed motion and/or a predicted path of motion.” [0069], “For the frames, e.g., including images 302, between two successive input windows 320, the locations of the anatomical structures and surgical instruments can be predicted based on the locations predicted in the most recent input window 320. For example, a movement vector of the surgical instrument can be computed based on the changes in the location of the surgical instrument in the frames in the prior input window 320. The movement vector can be computed using a machine learning model, such as a deep neural network. The movement vector is used to predict the location of the surgical instrument in the subsequent frames after the input window 320, until a next input window 320 is analyzed.” [0097]);
determine, using the second neural network model, a trajectory for each keypoint of the plurality of keypoints (“The visualization can include displaying one or more motion profiles of surgical instruments that can include tracking observed motion and/or a predicted path of motion. ” [0069],” “Keypoints” of surgical instruments can be defined within a bounded region between a starting keypoint and an end, such as a tip of surgical instrument. Keypoints may also represent pivot points, such as joints, shaft starting and end points, and other such features depending upon the physical structure and function of each surgical instrument.” [0086] “For the frames, e.g., including images 302, between two successive input windows 320, the locations of the anatomical structures and surgical instruments can be predicted based on the locations predicted in the most recent input window 320. For example, a movement vector of the surgical instrument can be computed based on the changes in the location of the surgical instrument in the frames in the prior input window 320. The movement vector can be computed using a machine learning model, such as a deep neural network. The movement vector is used to predict the location of the surgical instrument in the subsequent frames after the input window 320, until a next input window 320 is analyzed.” [0097]);
wherein the trajectory for each keypoint is based on the first output image generated by the first neural network model and the historic keypoint trajectory data for each keypoint (“Temporal information that is provided by state information can be used to refine confidence of the instrument prediction or anatomy prediction in one or more aspects. In one or more aspects, the temporal information can be fused with a feature space, and the resulting fused information can be used by a decoder to output instrument localization and/or anatomical localization, for example. Other visual or temporal cues may also be fused“ [0081], “For the frames, e.g., including images 302, between two successive input windows 320, the locations of the anatomical structures and surgical instruments can be predicted based on the locations predicted in the most recent input window 320. For example, a movement vector of the surgical instrument can be computed based on the changes in the location of the surgical instrument in the frames in the prior input window 320. The movement vector can be computed using a machine learning model, such as a deep neural network. The movement vector is used to predict the location of the surgical instrument in the subsequent frames after the input window 320, until a next input window 320 is analyzed.” [0097]);
generate, using the second neural network model, a second output image including a second output location of each keypoint of the plurality of keypoints annotated on a second output image of the surgical instrument (“The visualization can include displaying one or more motion profiles of surgical instruments that can include tracking observed motion and/or a predicted path of motion. ” [0069] , “The location of structure(s) predicted by the one or more machine-learning models 702 can also be predicted in the frames between two successive input windows 320 in the same manner. Graphical overlays that are used to overlay the images 302 to represent predicted features (e.g., surgical instruments, anatomical structures, etc.) are accordingly adjusted, if required, based on the predicted locations.” [0098]);
Muntion does not specifically disclose that based on the second output image and in response to one or more received user inputs, actuating one or more actuators to move the surgical instrument.
However, in a similar field of endeavor, Zhao teaches acquisition and tracking of the position and orientation of robotic tools or instruments [0001].
Zhao also teaches that based on the second output image and in response to one or more received user inputs, actuating one or more actuators to move the surgical instrument (“Tool tracking has a number of applications in robotic surgical systems. One illustrative application of tool tracking is to automatically control the motion of the endoscopic camera so that a surgeon automatically views regions of interest in a surgical site, thus freeing the surgeon from the camera control task. For example, robotic instruments are tracked so that the endoscopic camera is centered in the field of view of the surgical site. Another illustrative application for accurate tool tracking may be used to move a robotic instrument to reach a surgical target (e.g., a tumor) either automatically or by a surgeon.” [0054]).
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Muntion as outlined above with based on the second output image and in response to one or more received user inputs, actuating one or more actuators to move the surgical instrument as taught by Zhao, because it may be used to move a robotic instrument to reach a surgical target either automatically or by a surgeon [0054].
Regarding Claim 16, Muntion discloses that the processor is further configured to: evaluating a performance of the surgical procedure based on the second output image corresponding to each video frame image of the plurality of video frame images (“In yet more examples, the motion profiles 1102 can be used to provide real time guidance during surgery. For example, if the motion of the surgical instrument 1008 is beyond one or more thresholds, a warning can be provided to the operator. For example, a predetermined path of the surgical instrument 1008 can be configured for a surgical procedure. Alternatively, or in addition, the path for a surgical instrument 1008 can be configured per phase. If, during the surgical procedure, the surgical instrument 1008 is detected to veer off of the predetermined path by more than a predetermined threshold, a user feedback can be provided.“ [0133]).
Regarding Claim 17, Muntion discloses that the processor is further configured to: determining whether the surgical instrument will exceed a range of motion of the surgical instrument based on the determined trajectory of each keypoint of the plurality of keypoints; and if a determination is made that the surgical instrument will exceed the range of motion, generating a warning indicating that the surgical instrument will exceed the range of motion(“In yet more examples, the motion profiles 1102 can be used to provide real time guidance during surgery. For example, if the motion of the surgical instrument 1008 is beyond one or more thresholds, a warning can be provided to the operator. For example, a predetermined path of the surgical instrument 1008 can be configured for a surgical procedure. Alternatively, or in addition, the path for a surgical instrument 1008 can be configured per phase. If, during the surgical procedure, the surgical instrument 1008 is detected to veer off of the predetermined path by more than a predetermined threshold, a user feedback can be provided.“ [0133]).
Regarding Claim 19, Muntion discloses determining the trajectory for each keypoint of the plurality of keypoints includes matching, using the second neural network model, the first output location for each keypoint of the plurality of keypoints with a corresponding historic trajectory of the historic keypoint trajectory data (“ The visualization can include displaying one or more motion profiles of surgical instruments that can include tracking observed motion and/or a predicted path of motion. When observed in real-time, motion profiles can guide usage of surgical instruments during a surgical procedure. When used during post-operative analysis, motion profiles track whether a surgical instrument followed or deviated from a predetermined path.” [0069], “The one or more machine-learning models 702 of FIG. 7 can operate on the surgical data per frame, but can use information from a previous frame, or a window of previous frames.” [0070]).
Regarding Claim 21, Muntion discloses a non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause a machine to perform operations that detect a location of a plurality of keypoints of a surgical instrument (“The instrument network 401 can be trained to identify groups of keypoints associated with surgical instruments.” [0078], “a computer program product includes a memory device having computer executable instructions stored thereon, which when executed by one or more processors cause the one or more processors to perform a method.” [0018]), the operations comprising:
receiving, at a first neural network model, a video input including a plurality of video frame images of a surgical procedure (“At block 202, the system 100 can access input data including, for example, video data, spatial data, and/or sensor data temporally associated with a video stream of a surgical procedure. At block 204, the one or more machine-learning models 702 can predict a state of the surgical procedure based on the input data.” [0068]);
generating, using the first neural network model, a first output image including a first output location of each keypoint of the plurality of keypoints annotated on a first output image of the surgical instrument (“At block 206, the one or more machine-learning models 702 can detect one or more surgical instruments at least partially depicted in the video stream based on the input data, when such features are present. Detection of surgical instruments can include determining a presence or localization of one or more surgical instruments. The localization can include, for example, a bounding box, a medial axis, and/or any other marker or keypoint identifying the location of one or more surgical instruments… At block 208, a state indicator and/or one or more surgical instrument indicators temporally correlated with the video stream can be output.” [0068]);
receiving, at a second neural network model, the first output image generated by the first neural network mode (“At block 210, based on the state as predicted and tracked instrument information of the one or more surgical instruments, the system 100 can output a notification, a request, a visualization, and/or a scheduling update depending on how the state and tracked instrument information are used…The visualization can include displaying one or more motion profiles of surgical instruments that can include tracking observed motion and/or a predicted path of motion. ” [0069], “FIG. 12 depicts an example of a process 1200 of using machine learning to generate motion profiles 1102 of surgical instruments 1008 according to one or more examples.” [0128]);
receiving, at the second neural network model, historic keypoint trajectory data including a historic trajectory for each keypoint of the plurality of keypoints (“ The visualization can include displaying one or more motion profiles of surgical instruments that can include tracking observed motion and/or a predicted path of motion.” [0069], “For the frames, e.g., including images 302, between two successive input windows 320, the locations of the anatomical structures and surgical instruments can be predicted based on the locations predicted in the most recent input window 320. For example, a movement vector of the surgical instrument can be computed based on the changes in the location of the surgical instrument in the frames in the prior input window 320. The movement vector can be computed using a machine learning model, such as a deep neural network. The movement vector is used to predict the location of the surgical instrument in the subsequent frames after the input window 320, until a next input window 320 is analyzed.” [0097]);
determining, using the second neural network model, a trajectory for each keypoint of the plurality of keypoints (“The visualization can include displaying one or more motion profiles of surgical instruments that can include tracking observed motion and/or a predicted path of motion. ” [0069],” “Keypoints” of surgical instruments can be defined within a bounded region between a starting keypoint and an end, such as a tip of surgical instrument. Keypoints may also represent pivot points, such as joints, shaft starting and end points, and other such features depending upon the physical structure and function of each surgical instrument.” [0086] “For the frames, e.g., including images 302, between two successive input windows 320, the locations of the anatomical structures and surgical instruments can be predicted based on the locations predicted in the most recent input window 320. For example, a movement vector of the surgical instrument can be computed based on the changes in the location of the surgical instrument in the frames in the prior input window 320. The movement vector can be computed using a machine learning model, such as a deep neural network. The movement vector is used to predict the location of the surgical instrument in the subsequent frames after the input window 320, until a next input window 320 is analyzed.” [0097]);
wherein the trajectory for each keypoint is based on the first output image generated by the first neural network model and the historic keypoint trajectory data for each keypoint (“Temporal information that is provided by state information can be used to refine confidence of the instrument prediction or anatomy prediction in one or more aspects. In one or more aspects, the temporal information can be fused with a feature space, and the resulting fused information can be used by a decoder to output instrument localization and/or anatomical localization, for example. Other visual or temporal cues may also be fused“ [0081], “For the frames, e.g., including images 302, between two successive input windows 320, the locations of the anatomical structures and surgical instruments can be predicted based on the locations predicted in the most recent input window 320. For example, a movement vector of the surgical instrument can be computed based on the changes in the location of the surgical instrument in the frames in the prior input window 320. The movement vector can be computed using a machine learning model, such as a deep neural network. The movement vector is used to predict the location of the surgical instrument in the subsequent frames after the input window 320, until a next input window 320 is analyzed.” [0097]);
generating, using the second neural network model, a second output image including a second output location of each keypoint of the plurality of keypoints annotated on a second output image of the surgical instrument (“The visualization can include displaying one or more motion profiles of surgical instruments that can include tracking observed motion and/or a predicted path of motion. ” [0069] , “The location of structure(s) predicted by the one or more machine-learning models 702 can also be predicted in the frames between two successive input windows 320 in the same manner. Graphical overlays that are used to overlay the images 302 to represent predicted features (e.g., surgical instruments, anatomical structures, etc.) are accordingly adjusted, if required, based on the predicted locations.” [0098]).
Muntion does not specifically disclose that based on the second output image and in response to one or more received user inputs, actuating one or more actuators to move the surgical instrument.
However, in a similar field of endeavor, Zhao teaches acquisition and tracking of the position and orientation of robotic tools or instruments [0001].
Zhao also teaches that based on the second output image and in response to one or more received user inputs, actuating one or more actuators to move the surgical instrument (“Tool tracking has a number of applications in robotic surgical systems. One illustrative application of tool tracking is to automatically control the motion of the endoscopic camera so that a surgeon automatically views regions of interest in a surgical site, thus freeing the surgeon from the camera control task. For example, robotic instruments are tracked so that the endoscopic camera is centered in the field of view of the surgical site. Another illustrative application for accurate tool tracking may be used to move a robotic instrument to reach a surgical target (e.g., a tumor) either automatically or by a surgeon.” [0054]).
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Muntion as outlined above with based on the second output image and in response to one or more received user inputs, actuating one or more actuators to move the surgical instrument as taught by Zhao, because it may be used to move a robotic instrument to reach a surgical target either automatically or by a surgeon [0054].
Regarding Claim 22, Muntion discloses the second output location of each keypoint of the plurality of keypoints identifies a location of a corresponding landmark of the surgical instrument ((“The visualization can include displaying one or more motion profiles of surgical instruments that can include tracking observed motion and/or a predicted path of motion. ” [0069] “’Keypoints’ of surgical instruments can be defined within a bounded region between a starting keypoint and an end, such as a tip of surgical instrument. Keypoints may also represent pivot points, such as joints, shaft starting and end points, and other such features depending upon the physical structure and function of each surgical instrument.” [0086], “The location of structure(s) predicted by the one or more machine-learning models 702 can also be predicted in the frames between two successive input windows 320 in the same manner. Graphical overlays that are used to overlay the images 302 to represent predicted features (e.g., surgical instruments, anatomical structures, etc.) are accordingly adjusted, if required, based on the predicted locations.” [0098]).
Regarding Claim 25, Muntion discloses that the processor is further configured to: determining whether the surgical instrument will exceed a range of motion of the surgical instrument based on the determined trajectory of each keypoint of the plurality of keypoints; and if a determination is made that the surgical instrument will exceed the range of motion, generating a warning indicating that the surgical instrument will exceed the range of motion(“In yet more examples, the motion profiles 1102 can be used to provide real time guidance during surgery. For example, if the motion of the surgical instrument 1008 is beyond one or more thresholds, a warning can be provided to the operator. For example, a predetermined path of the surgical instrument 1008 can be configured for a surgical procedure. Alternatively, or in addition, the path for a surgical instrument 1008 can be configured per phase. If, during the surgical procedure, the surgical instrument 1008 is detected to veer off of the predetermined path by more than a predetermined threshold, a user feedback can be provided.“ [0133]).
Claims 6, 18, & 26 are rejected under 35 U.S.C. 103 as being unpatentable over Muntion in view of Zhao as applied to Claim 1 above, and further in view of Gomez et al (US20190298463A1; hereinafter referred to as Gomez).
Regarding Claim 6, Muntion in view of Zhao discloses all limitations noted above except that further comprising: determining whether the surgical instrument will collide with a second surgical instrument based on the determined trajectory of each keypoint of the plurality of keypoints; andif a determination is made that the surgical instrument will collide with the second surgical instrument, generating a warning indicating that the surgical instrument will collide with the second surgical instrument.
However, in a similar field of endeavor, Gomez teaches further comprising: determining whether the surgical instrument will collide with a second surgical instrument based on the determined trajectory of each keypoint of the plurality of keypoints; andif a determination is made that the surgical instrument will collide with the second surgical instrument, generating a warning indicating that the surgical instrument will collide with the second surgical instrument (“In this arrangement of views, indications of joggle joint boundary limits may be provided in the lower central window as described in reference to FIGS. 13-29 and indications of extension limits for the articulatable instruments 241, 231 may be provided in the lower side views as previously explained. Visual cues or warnings may also be provided in the auxiliary views as described herein when the articulatable instruments extending out of the distal end of the entry guide 200 are approaching their respective range of motion limitations and/or threatening to collide with one another.” [0109]).
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Muntion in view of Zhao as outlined above with further comprising: determining whether the surgical instrument will collide with a second surgical instrument based on the determined trajectory of each keypoint of the plurality of keypoints; andif a determination is made that the surgical instrument will collide with the second surgical instrument, generating a warning indicating that the surgical instrument will collide with the second surgical instrument as taught by Gomez, because it reduces harm to the patient and prevents otherwise adversely impact to the performance of the medical procedure [0007].
Regarding Claim 18, Muntion in view of Zhao discloses all limitations noted above except that the operations further comprise: determining whether the surgical instrument will exceed a range of motion of the surgical instrument based on the determined trajectory of each keypoint of the plurality of keypoints; and if a determination is made that the surgical instrument will exceed the range of motion, generating a warning indicating that the surgical instrument will exceed the range of motion.
However, in a similar field of endeavor, Gomez teaches the operations further comprise: determining whether the surgical instrument will exceed a range of motion of the surgical instrument based on the determined trajectory of each keypoint of the plurality of keypoints; and if a determination is made that the surgical instrument will exceed the range of motion, generating a warning indicating that the surgical instrument will exceed the range of motion (“In this arrangement of views, indications of joggle joint boundary limits may be provided in the lower central window as described in reference to FIGS. 13-29 and indications of extension limits for the articulatable instruments 241, 231 may be provided in the lower side views as previously explained. Visual cues or warnings may also be provided in the auxiliary views as described herein when the articulatable instruments extending out of the distal end of the entry guide 200 are approaching their respective range of motion limitations and/or threatening to collide with one another.” [0109]).
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Muntion in view of Zhao as outlined above with the operations further comprise: determining whether the surgical instrument will exceed a range of motion of the surgical instrument based on the determined trajectory of each keypoint of the plurality of keypoints; and if a determination is made that the surgical instrument will exceed the range of motion, generating a warning indicating that the surgical instrument will exceed the range of motion as taught by Gomez, because it reduces harm to the patient and prevents otherwise adversely impact to the performance of the medical procedure [0007].
Regarding Claim 26, Muntion in view of Zhao discloses all limitations noted above except that the processor is further configured to: determine whether the surgical instrument will collide with a second surgical instrument based on the determined trajectory of each keypoint of the plurality of keypoints; and if a determination is made that the surgical instrument will collide with the second surgical instrument, generate a warning indicating that the surgical instrument will collide with the second surgical instrument.
However, in a similar field of endeavor, Gomez teaches the processor is further configured to: determine whether the surgical instrument will collide with a second surgical instrument based on the determined trajectory of each keypoint of the plurality of keypoints; and if a determination is made that the surgical instrument will collide with the second surgical instrument, generate a warning indicating that the surgical instrument will collide with the second surgical instrument (“In this arrangement of views, indications of joggle joint boundary limits may be provided in the lower central window as described in reference to FIGS. 13-29 and indications of extension limits for the articulatable instruments 241, 231 may be provided in the lower side views as previously explained. Visual cues or warnings may also be provided in the auxiliary views as described herein when the articulatable instruments extending out of the distal end of the entry guide 200 are approaching their respective range of motion limitations and/or threatening to collide with one another.” [0109]).
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Muntion in view of Zhao as outlined above with the operations further comprise: determining whether the surgical instrument will collide with a second surgical instrument based on the determined trajectory of each keypoint of the plurality of keypoints; and if a determination is made that the surgical instrument will collide with the second surgical instrument, generating a warning indicating that the surgical instrument will collide with the second surgical instrument as taught by Gomez, because it reduces harm to the patient and prevents otherwise adversely impact to the performance of the medical procedure [0007].
Claims 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Muntion in view of Zhao as applied to Claim 1 above, and further in view of Nwoye et al (C. I. Nwoye, D. Mutter, J. Marescaux, and N. Padoy, “Weakly supervised convolutional LSTM approach for Tool Tracking in laparoscopic videos,” International Journal of Computer Assisted Radiology and Surgery, vol. 14, no. 6, pp. 1059–1067, Apr. 2019; hereinafter referred to as Nwoye).
Regarding Claim 10, Muntion in view of Zhao discloses all limitations noted above except that further comprising applying a smoothness filter to the second output image to generate a refined second output location of each keypoint of the plurality of keypoints annotated on the second output image of the surgical instrument.
However, in a similar field of endeavor, Nwoye teaches a Convolutional LSTM (ConvLSTM) to learn the spatio-temporal coherence across the surgical video frames [Pg. 2].
Nwoye also teaches further comprising applying a smoothness filter to the second output image to generate a refined second output location of each keypoint of the plurality of keypoints annotated on the second output image of the surgical instrument (“R+C+CL Configuration: In this configuration, illustrated in Figure 2, the ConvLSTM receives spatial input features from the C layer, refines them with temporal information and outputs spatio-temporal Lh-maps. The motivation for adding the ConvLSTM unit immediately after the baseline FCN (R+C) is to refine the spatial Lh-maps with spatio-temporal information. This helps to smooth the class peak activations as well as the shape and size of the tools segmentation masks. It is important to note that the localization process is performed on the spatio-temporal Lh-maps.” [Pg. 7]).
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Muntion in view of Zhao as outlined above with further comprising applying a smoothness filter to the second output image to generate a refined second output location of each keypoint of the plurality of keypoints annotated on the second output image of the surgical instrument as taught by Nwoye, because it improves tool presence detection, spatial localization, and motion tracking [Abstract].
Regarding Claim 11, Muntion in view of Zhao discloses all limitations noted above except that further comprising generating, using the second neural network model, a refined output image based on the refined second output location of each keypoint of the plurality of keypoints.
However, in a similar field of endeavor, Nwoye teaches further comprising generating, using the second neural network model, a refined output image based on the refined second output location of each keypoint of the plurality of keypoints (“Qualitative results showing the localization and tracking performance of the baseline and ConvLSTM models for the 7 tools. For each tool, we present a comparison of the detected bounding box (cyan in colour) with the ground truth (dotted yellow box), the Lh-map, and the overlay of the segmented mask with the original image (best seen in colour).” [Pg. 11]), Fig. 3 is provided below).
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It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Muntion in view of Zhao as outlined above with further comprising applying a smoothness filter to the second output image to generate a refined second output location of each keypoint of the plurality of keypoints annotated on the second output image of the surgical instrument as taught by Nwoye, because it improves tool presence detection, spatial localization, and motion tracking [Abstract].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure (X. Du et al., “Articulated multi-instrument 2-D pose estimation using fully convolutional networks,” IEEE Transactions on Medical Imaging, vol. 37, no. 5, pp. 1276–1287, May 2018; N. Louis et al., “Temporally guided articulated hand pose tracking in surgical videos,” SSRN Electronic Journal, 2022; US20190090969A1).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEVEN MALDONADO whose telephone number is 703-756-1421. The examiner can normally be reached 8:00 am-4:00 pm PST M-Th Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at
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/Steven Maldonado/
Patent Examiner, Art Unit 3797
/CHRISTOPHER KOHARSKI/Supervisory Patent Examiner, Art Unit 3797
