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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 5/11/2026 has been entered.
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.
Claim(s) 1, 11, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Roh (US 20190262084 A1), and further in view of Kimura (US 20180036882 A1).
Regarding claim 1, Roh teaches a computer-implemented method for clinical workplace simulation, the method comprising:
capturing a surgical parameter from one or more prior robotic surgical operations, based on a sensor (par. 0029: “In certain embodiments, systems and methods can utilize artificial intelligence to operate one or more surgical robot systems, including a surgical robot apparatus, an artificial intelligence guidance system, an image recognition system, an image recognition database, and/or a database of past procedures with sensor data, electronic medical records, and/or imaging data.”); and
determining an optimized surgical parameter using a machine learning network trained on the captured surgical parameter (par. 0029, as above),
wherein the surgical parameter includes at least one of a patient habitus of a previous patient (par. 0040: “The procedure database can include medical records data, images (e.g., pre-and post-surgical images), physician input, sensor data, or the like. The images can include MRI or CAT scans, fluoroscopic images, or other types of images. The sensor data can be collected during procedures, etc. related to all procedures of this type. This database is queried by the surgical control for all medical imaging from the current patient and by the progression module for data for all similar patients who had the same procedure.”), a surgical port location in the previous patient (par. 0064: “the system is applicable to one or more steps in the spinal surgery process, from initial incision, port placement, retractor docking, lamina removal, disc removal and/or hardware insertion.”), or a robotic arm placement relative to the previous patient (par. 0112: “A method of using a hand-held ball-tip probe with sensors located in the robotic arm/surgical tool to determine the position of the ball tip probes location for creating a 3D map of a patient's spine to assist the surgeon during the operation.”), and
wherein the optimized surgical parameter includes at least one of an optimized surgical port placement location in a simulated patient (par. 0064, as above) or an optimized robotic arm placement location relative to the simulated patient (par. 0112, as above).
Roh fails to teach receiving at least one of dimensions or a geometric configuration of a simulated clinical workspace, including importing a computer-aided design (CAD) file representing an operating room.
Kimura teaches receiving at least one of dimensions or a geometric configuration of a simulated clinical workspace, including importing a computer-aided design (CAD) file representing an operating room (par. 0006: “With use of a simulation technique, this kind of simulator device is used for programming (teaching) an operation (motion) of the robot arm by using 3D (three-dimensional) CAD (computer-aided design) data of the robot arm, a work piece and the like. To optimize the layout using this kind of simulator device, 3D CAD data of a peripheral device, 3D CAD data of an obstacle in the room, and the like are added in addition to the 3D CAD data of the robot arm and the work piece, and the robot arm is operated in such a virtual environment.”).
It would have been obvious to one familiar in the art prior to the effective filing date of the claimed invention to incorporate the CAD file of Kimura into the guidance system of Roh, as both are in the same field of endeavor of remote manipulation of robotic arms in a virtual environment. Doing so would prove obviously beneficial to the guidance system of Roh, as it would provide a more accurate representation of the room in which the operations take place.
Regarding claim 11, Roh teaches a system for clinical workplace simulation, the system comprising:
a sensor configured to sense a surgical parameter (par. 0118: “As a drill or knife is robotically controlled, the drill or knife would have highly sensitive sensors for (1) RPMs, (2) armature current, (3) angle and direction, (4) sound of motor, etc. These parameters provide real-time robot set of data.”);
a processor (par. 0157: “The computer-readable instructions can be executed by a processor of a mobile unit, a network element, and/or any other computing device.”); and
a memory, including instructions stored thereon, which, when executed by the processor, cause the system to perform the elements of the method of claim 1 taught by Roh (par. 0159: “Those having skill in the art will recognize that a typical data processing system generally includes one or more of a system unit housing, a video display device, a memory such as volatile and non-volatile memory, processors such as microprocessors and digital signal processors, computational entities such as operating systems, drivers, graphical user interfaces, and applications programs, one or more interaction devices, such as a touch pad or screen, and/or control systems including feedback loops and control motors (e.g., feedback for sensing position and/or velocity; control motors for moving and/or adjusting components and/or quantities).”).
Kimura teaches receiving at least one of dimensions or a geometric configuration of a simulated clinical workspace, including importing a computer-aided design (CAD) file representing an operating room (as above in claim 1 rejection).
It would have been obvious to one familiar in the art prior to the effective filing date of the claimed invention to incorporate the CAD file of Kimura into the guidance system of Roh, as both are in the same field of endeavor of remote manipulation of robotic arms in a virtual environment. Doing so would prove obviously beneficial to the guidance system of Roh, as it would provide a more accurate representation of the room in which the operations take place.
Claim(s) 2-9 and 12-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Roh (US 20190262084 A1) and Kimura (US 20180036882 A1) as applied to claim 1 above, and further in view of Fuerst (US 20210121232 A1).
Regarding claim 2, Roh and Kimura teach the computer-implemented method of claim 1, but fail to teach generating a simulated patient including a simulated patient habitus based on at least one of a first user input or a patient medical record.
Fuerst teaches generating a simulated patient including a simulated patient habitus based on at least one of a first user input or a patient medical record (par. 0037: “In one embodiment, an optimization procedure 110 is performed to arrange the virtual objects (e.g., the patient, the robotic arm and tool, the platform, the user console, the control tower, and other surgical equipment) based on one or more inputs and changes to inputs. For example, inputs can [include] workspace, patient geometry, operating room, procedure type, platform height, etc.”).
It would have been obvious to one familiar in the art prior to the effective filing date of the claimed invention to combine Roh’s AI guidance system for robotic surgery with Fuerst’s virtual reality system for simulating surgical workflows with patient models, as both inventions are in the same field of endeavor of robotic surgery. Doing so would allow for an accurate surgical simulation.
Regarding claim 3, Roh, Kimura, and Fuerst teach the computer-implemented method of claim 2. Fuerst further teaches receiving a second user input for modifying the simulated patient habitus (par. 0037: “Changes to inputs can include a modification to a virtual patient, a change to a procedure, a change to the operating room, a change to platform height, and/or any change that would modify a location or size of a workspace in a virtual patient.”); and
modifying the simulated patient habitus based on the user input (as above).
Regarding Claim 4, Roh, Kimura, and Fuerst teach the computer-implemented method of claim 2. Fuerst further teaches generating a setup guide based on the generated simulated patient habitus and the determined optimized surgical parameter (par. 0047: “the user can tailor the workflow and layout optimization to suit a particular patient and procedure type. This can streamline setup of robotic arms for different patients, different procedures, and help selection of operating rooms to facilitate such procedures.”); and
displaying the setup guide on a display (par. 0047: “In one embodiment, the VR simulation 100 can be communicated or output through a display, for example, on a user console 120.”),
wherein the setup guide includes at least one of the optimized surgical port placement location or the optimized robotic arm placement location (as above).
Regarding Claim 5, Roh, Kimura, and Fuerst teach the computer-implemented method of claim 4. Fuerst further teaches wherein generating the setup guide includes:
generating a revised surgical port placement location in a trunk segment of the simulated patient (par. 0027: “A port 312 can be determined based on where the trocar passes through the virtual patient's abdomen 308.”).
Regarding Claim 6, Roh, Kimura, and Fuerst teach the computer-implemented method of claim 5. Fuerst further teaches wherein the revised surgical port placement location is further based on the simulated patient habitus (par. 0027: “A position of a virtual surgical robotic arm 302 can be determined based on the port location, the trocar position, and/or the workspace.”).
Regarding Claim 7, Roh, Kimura, and Fuerst teach the computer-implemented method of claim 4. Fuerst further teaches wherein generating the setup guide includes:
generating a revised optimized robotic arm placement location relative to the simulated patient (as above, in claim 6 rejection).
Regarding Claim 8, Roh, Kimura, and Fuerst teach the computer-implemented method of claim 7. Fuerst further teaches wherein the optimized robotic arm placement location is further based on the simulated patient habitus (as above, in claim 6 rejection).
Regarding Claim 9, Roh, Kimura, and Fuerst teach the computer-implemented method of claim 7. Fuerst further teaches receiving an indicated anatomy from a user (par. 0047: “The view of the simulation can then be re-generated for the user, thus providing adjustments and optimization of the workflow based on user generated inputs. In this manner, the user can tailor the workflow and layout optimization to suit a particular patient and procedure type. This can streamline setup of robotic arms for different patients, different procedures, and help selection of operating rooms to facilitate such procedures.”),
wherein the optimized robotic arm placement location is further based on targeting the indicated anatomy (as above).
It would have been obvious to one familiar in the art prior to the effective filing date of the claimed invention to combine Roh’s AI guidance system for robotic surgery with Fuerst’s virtual reality system for simulating surgical workflows with patient models, as both inventions are in the same field of endeavor of robotic surgery. Doing so would allow for an accurate surgical simulation.
Claim 12 is functionally identical to Claim 2, save that it relies on Claim 11 as opposed to Claim 1. As such, it is rejected on the same basis as Claim 2.
Claim 13 is functionally identical to Claim 3, save that it relies on Claim 12 as opposed to Claim 2. As such, it is rejected on the same basis as Claim 3.
Claim 14 is functionally identical to Claim 4, save that it relies on Claim 12 as opposed to Claim 2. As such, it is rejected on the same basis as Claim 4.
Claim 15 is functionally identical to Claim 5, save that it relies on Claim 14 as opposed to Claim 4. As such, it is rejected on the same basis as Claim 5.
Claim 16 is functionally identical to Claim 6, save that it relies on Claim 15 as opposed to Claim 5. As such, it is rejected on the same basis as Claim 6.
Claim 17 is functionally identical to Claim 7, save that it relies on Claim 14 as opposed to Claim 4. As such, it is rejected on the same basis as Claim 7.
Claim 18 is functionally identical to Claim 8, save that it relies on Claim 17 as opposed to Claim 7. As such, it is rejected on the same basis as Claim 8.
Claim 19 is functionally identical to Claims 9 and 10, save that it is a single claim which relies on Claim 17 as opposed to two claims which each rely on Claim 7. As such, it is rejected on the same basis as Claims 9 and 10.
Regarding claim 20, Roh teaches a non-transitory computer-readable medium storing instructions which, when executed by a processor, cause the processor to perform a method comprising:
accessing a surgical parameter from one or more prior robotic surgical operations, based on a sensor (par. 0029, as above in claim 1 rejection), wherein the surgical parameter includes at least one of a patient habitus of a previous patient (par. 0040, as above in claim 1 rejection), a surgical port location in the previous patient (par. 0064, as above in claim 1 rejection), or a robotic arm placement relative to the previous patient (par. 0112, as above in claim 1 rejection); and
determining an optimized surgical parameter using a machine learning network trained on the captured surgical parameter (claim 1), wherein the optimized surgical parameter includes an optimized surgical port placement location in at least one of a simulated patient, or an optimized robotic arm placement location relative to the simulated patient (par. 0029, as above in claim 1 rejection).
Roh fails to teach receiving at least one of dimensions or a geometric configuration of a simulated clinical workspace, including importing a computer-aided design (CAD) file representing an operating room;
generating the simulated patient including a simulated patient habitus based on at least one of a first user input or a patient medical record;
generating a setup guide based on the generated simulated patient habitus and the determined optimized surgical parameter, wherein the setup guide includes at least one of the optimized surgical port placement location or the optimized robotic arm placement location; and
displaying the setup guide on a display.
Kimura teaches receiving at least one of dimensions or a geometric configuration of a simulated clinical workspace, including importing a computer-aided design (CAD) file representing an operating room (as above in claim 1 rejection).
Fuerst teaches generating the simulated patient including a simulated patient habitus based on at least one of a first user input or a patient medical record (par. 0037, as above in claim 2 rejection);
generating a setup guide based on the generated simulated patient habitus and the determined optimized surgical parameter, wherein the setup guide includes at least one of the optimized surgical port placement location or the optimized robotic arm placement location (par. 0047, as above in claim 4 rejection); and
displaying the setup guide on a display (par. 0047, as above in claim 4 rejection).
It would have been obvious to one familiar in the art prior to the effective filing date of the claimed invention to combine Roh’s AI guidance system for robotic surgery with Fuerst’s virtual reality system for simulating surgical workflows with patient models, as both inventions are in the same field of endeavor of robotic surgery. Doing so would allow for an accurate surgical simulation.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Roh (US 20190262084 A1), Kimura (US 20180036882 A1), and Feurst (US 20210121232 A1) as applied to claim 7 above, and further in view of Meglan (US 11548140 B2).
Regarding Claim 10, F Roh, Kimura, and Fuerst teach the computer-implemented method of claim 7. Fuerst further teaches wherein the optimized robotic arm placement location includes a location for a first robotic arm and a second location for a second robotic arm (par. 0029: “In some cases, workflow optimization can determine positions of the trocars, ports and surgical robotic arms so that reach of the two or more robots and corresponding tools overlap in the workspace.”),
wherein the method further comprises:
determining a risk of collision between the first robotic arm and a second robotic arm, based on at least one of the first location, the second location, the optimized surgical port placement location, or the simulated patient habitus (par. 0029: “Optimization, as used herein, can refer to software and hardware supported algorithms that arrange virtual surgical equipment (e.g., workflow and/or layout) based on one or more criteria and inputs described herein such as, but not limited to, location of a workspace, reach and movements in a workspace, convenience, and risk of collisions.”).
Fuerst fails to teach displaying a warning of the risk of collision on the display.
Meglan teaches displaying a warning of the risk of collision on the display (col. 2, lines 43-48: “In yet another aspect of the present disclosure, the system may further include a display. The instructions, when executed, may further cause the system to predict a possible collision with a second robotic arm based on the cross-reference and display an alert, on the display, indicating the possibility of a collision.”).
It would have been obvious to one familiar in the art prior to the effective filing date of the claimed invention to include Meglan’s warning system in Fuerst’s display, as both are in the same field of endeavor of robotic arms to be used in surgery. Such a warning system would alert the user to a potential collision while in the midst of a delicate operation, which would be obviously helpful in avoiding such collisions. Avoiding collisions is stated in Fuerst to be one goal of optimization.
Response to Amendment
The amendment filed 12/18/2025 is sufficient to overcome the rejection of claims 1, 11, and 20 based upon Roh (US 20190262084 A1). Applicant has amended each independent claim such that Roh is rendered insufficient grounds for rejection.
Response to Arguments
Applicant’s arguments, see applicant’s response, filed 12/18/2025, with respect to the rejection(s) of claim(s) 1-9 and 11-20 under 35 U.S.C. § 102 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Roh (US 20190262084 A1).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RYAN A BARHAM whose telephone number is (571)272-4338. The examiner can normally be reached Mon-Fri, 8:30am-5pm EST.
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/RYAN ALLEN BARHAM/Examiner, Art Unit 2613
/XIAO M WU/Supervisory Patent Examiner, Art Unit 2613
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