DETAILED ACTION
This action is in response to the application filed 26 March 2024.
Claims 1–25 are pending. Claims 1, 24, and 25 are independent.
Claims 1–25 are rejected.
Notice of Pre-AIA or AIA Status
The present application, filed on or after 16 March 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. §§ 102 and 103 (or as subject to pre-AIA 35 U.S.C. §§ 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claim Rejections—35 U.S.C. § 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 C.F.R. § 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention.
Claims 1–25 are rejected under 35 U.S.C. § 103 as being unpatentable over Wolf et al. (US 2020/0273552 A1) [hereinafter Wolf] in view of Roh et al. (US 2024/0245458 A1) [hereinafter Roh].
Regarding independent claim 1, Wolf discloses [a] system for recommending a tool for a computer-assisted robotic system, comprising: A surgical procedure, including robotic surgery (Wolf, ¶ 151). A surgeon may include a surgical robot (Wolf, ¶ 535). a non-transitory memory and one or more processors to: Instructions are loaded from memory and executed by at least one processor (Wolf, ¶ 77). receive skill data and patient data, the skill data indicative of a skill level associated with a surgeon, and the patient data corresponding to a patient; Estimated outcomes are associated with, e.g., patient characteristics and/or surgeon skill level (Wolf, ¶ 126). A decision making junction of a surgical procedure may be associated with a surgeon skill level (Wolf, ¶ 128). A recommended action to take during the surgery may be generated based on avoiding an undesired outcome, and/or based on the skill of the surgeon (Wolf, ¶¶ 562–563). A recommended sequence of events may be based on patient characteristics, or a vital sign of a patient, or condition of a tissue or organ of the patient (Wolf, ¶¶ 486, 567, 568). determine, using a model trained with machine learning and receiving as input the skill data and the patient data, tool data indicative of a recommended tool; and The surgeon is provided with real-time decision support, including real-time recommendations (Wolf, ¶¶ 21, 536). The recommendations may be to undertake a specific action, or avoid a specific action (Wolf, ¶ 21). A specific action may include actions performed by a human or robotic surgeon, or a human or robotic surgical assistant (Wolf, ¶ 550). A specific action may include engaging a medical instrument [tool] with a biological structure, or any other action that may occur during a surgical procedure (Wolf, ¶ 550). The recommendation may include suggestion of an instrument (Wolf, ¶ 571). A specific action may include activating and/or providing instructions to the robot (Wolf, ¶ 552). The recommendation may be based on the skill level of the surgeon (Wolf, ¶ 563). The recommendation may be based on information about the patient, including vital signs or tissue/organ conditions [patient data] (Wolf, ¶¶ 567–568). The recommendation may be determined using a trained machine learning model, based on patient and/or surgeon characteristics (Wolf, ¶ 573). The surgical robot may manipulate tools or instruments using a robotic structure, e.g., arm, fingers, or graspers (Wolf, ¶ 582).
Wolf teaches displaying a recommendation or transmitting a recommendation to a robot (Wolf, ¶¶ 553–554), but does not expressly teach that the recommendation can be for a user to install a tool onto a robot. However, Roh teaches: cause, based on the tool data, an indication to be presented to a user to install the recommended tool onto the computer-assisted robotic system. A robotic surgical system controls a robotic surgical apparatus according to a surgical plan generated by a real-time planning machine learning model; if the surgical robotic apparatus is not configured for performing one of the surgical actions in the plan, a user is notified that the configuration of the robot should be modified by, e.g., installing a new instrument (Roh, ¶ 312).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the teachings of Wolf with those of Roh. Doing so would have been a matter of applying a known technique [notifying a user to install an instrument to a robot] to a known device ready for improvement [the robotic surgical system of Wolf] to yield predictable results [the system of Wolf, wherein the various recommended actions during surgery can include a recommendation to install a tool to a robot].
Regarding dependent claim 2, the rejection of claim 1 is incorporated and Wolf/Roh further teaches: wherein the skill data comprises at least one of: an overall skill level of the surgeon; The skill level may be a global skill level assigned to each surgeon (Wolf, ¶ 184). a procedure-based skill level of the surgeon associated with a type of a medical procedure to be performed on the patient; The skill level may be a skill level for performing a surgical procedure (Wolf, ¶ 184). a task-based skill level of the surgeon associated with a type of task to be performed in the medical procedure; or The skill level may be a skill level for an intraoperative surgical event (Wolf, ¶ 184). an instrument-based skill level of the surgeon associated with the recommended tool.
Regarding dependent claim 3, the rejection of claim 1 is incorporated and Wolf/Roh further teaches: comprising the one or more processors to: identify a plurality of peer groups comprising a first peer group and a second peer group, wherein a first set of tools are made available for recommendation to the first peer group, and a second set of tools are made available for recommendation to the second peer group; Each surgeon may be assigned a skill level, e.g., “highly skilled”, “moderately skilled”, “unskilled”, etc. [skill level peer groups] (Wolf, ¶ 184). map the surgeon to the first peer group of the plurality of peer groups; Each surgeon is assigned [mapped] to a skill level (Wolf, ¶ 184). identify, responsive to the mapping, that the first set of tools are available for recommendation to the surgeon; and The recommendations may be based on a surgeon’s skill level, such that recommended actions are selected from a plurality of alternatives based on the skill level (Wolf, ¶ 563). select the recommended tool from the first set of tools that are available for recommendation to the first peer group. The recommendation is output, e.g., as a direction to perform a step [i.e., a step of selecting a tool] (Wolf, ¶ 565).
Regarding dependent claim 4, the rejection of claim 3 is incorporated and Wolf/Roh further teaches: comprising the one or more processors to: map the surgeon to the first peer group based at least in part on the skill level of the surgeon; and A surgeon is assigned a skill level, e.g. a high or a lower skill level (Wolf, ¶ 563). input the first set of tools or an indication of the mapped first peer group into the model to determine the recommended tool. The recommendation, including a specific action, may be based on the skill level of the surgeon (Wolf, ¶ 563).
Regarding dependent claim 5, the rejection of claim 1 is incorporated and Wolf/Roh further teaches: comprising the one or more processors to: capture intra-operative data during a medical procedure; and The system may analyze video during a surgery to detect and remediate, e.g., abnormal fluid leakage (Wolf, ¶ 24). A machine learning model may analyze the video data in real time (Wolf, ¶ 545). The machine learning model receives data regarding surgical events, e.g., an incision about to be performed (Wolf, ¶ 520). determine, using the model and based on the intra-operative data, the tool data during the medical procedure. The analysis and recommendation generation may be performed in real time during the surgical procedure (Wolf, ¶ 574).
Regarding dependent claim 6, the rejection of claim 5 is incorporated and Wolf/Roh further teaches: comprising the one or more processors to: detect, based on the intra-operative data, an environmental event during the medical procedure, wherein the environmental event corresponds to at least one of smoke during the medical procedure or bleeding during the medical procedure. An adverse event, such as bleeding, may be detected during the procedure, and a decision making junction is identified by the machine learning model (Wolf, ¶ 122).
Regarding dependent claim 7, the rejection of claim 5 is incorporated and Wolf/Roh further teaches: wherein the intra-operative data comprises at least one of one or more intra-operative images of an anatomy of the patient, intra-operative audio information, intra-operative information indicating occurrence of a predetermined event during the medical procedure, or intra-operative information indicating force imparted to a tool by an anatomical feature of the patient. The operating room may include audio sensors, video sensors, chemical sensors, etc. (Wolf, ¶ 85). A machine learning model may analyze video footage of the surgical procedure in real time, e.g., to determine surgical events (Wolf, ¶ 577). A machine learning model may estimate contact force from images or videos (Wolf, ¶ 597). The actual contact force may be determined based on sensor data that measures force (Wolf, ¶ 598).
Regarding dependent claim 8, the rejection of claim 1 is incorporated and Wolf/Roh further teaches: comprising the one or more processors to: determine the tool data during a medical procedure performed on the patient, using the model and based upon intra-operative kinematic state information corresponding to the computer-assisted robotic system. The real-time surgical planning is based on the current status of the patient and the functionality of the robotic apparatus [intra-operative kinematic state] (Roh, ¶ 312).
Regarding dependent claim 9, the rejection of claim 1 is incorporated and Wolf/Roh further teaches: comprising the one or more processors to: provide a notification via an interface to install the recommended tool onto the computer-assisted robotic system. The notifications to the user are displayed on a console [interface] of the robotic surgical system (Roh, ¶ 126).
Regarding dependent claim 10, the rejection of claim 9 is incorporated and Wolf/Roh further teaches: comprising the one or more processors to: identify, using a second model trained using machine learning to detect image features, a portion of the patient under manipulation by a portion of the computer-assisted robotic system; and The system collects real-time data including patient data, to determine surgical steps (Roh, ¶ 312). reconfigure, based on a property of the portion of the patient, the portion of the computer-assisted robotic system to receive the recommended tool. The system notifies the user to change the configuration of the robot by installing a new instrument [i.e., the system is in a state ready to receive the installation of the new tool] (Roh, ¶ 312).
Regarding dependent claim 11, the rejection of claim 9 is incorporated and Wolf/Roh further teaches: comprising the one or more processors to: provide, based on the notification, an instruction to the computer-assisted robotic system to cause the computer-assisted robotic system to reconfigure at least a portion of the computer-assisted robotic system to receive the recommended tool. The system uses the real-time data and analysis to control the robotic surgical apparatus (Roh, ¶ 312).
Regarding dependent claim 12, the rejection of claim 9 is incorporated and Wolf/Roh further teaches: comprising the one or more processors to: determine, based on the skill level of the surgeon, a constraint on a force to be imparted by the recommended tool; and A contact force threshold may be based on a skill level of a surgeon (Wolf, ¶ 595). configure the computer-assisted robotic system to prevent the surgeon from imparting force, with the recommended tool, greater than or equal to the constraint. The surgeon may be notified or warned based on the force compared to the selected force threshold (Wolf, ¶¶ 601–603).
Regarding dependent claim 13, the rejection of claim 1 is incorporated and Wolf/Roh further teaches: comprising the one or more processors to: receive medical procedure information that indicates at least one of a stage or an event of the medical procedure, wherein the tool data includes a recommendation for the recommended tool during at least one of the stage or the event. The recommendation may be based on a phase of the surgery (Wolf, ¶ 571).
Regarding dependent claim 14, the rejection of claim 1 is incorporated and Wolf/Roh further teaches: comprising the one or more processors to: determine, using the model and based on at least a portion of the patient data obtained before a medical procedure is performed on the patient, the tool data before the medical procedure is performed on the patient. A patient characteristic may be based on pre-operative characteristics, e.g., blood test values or vital signs (Wolf, ¶ 719). The recommended sequence of events [i.e., usage of tools] may be based on, e.g., patient characteristics (Wolf, ¶ 486). A trained machine learning may be used to select the recommended sequence of events (Wolf, ¶ 487).
Regarding dependent claim 15, the rejection of claim 1 is incorporated and Wolf/Roh further teaches: comprising the one or more processors to: determine, using the model and based on at least a portion of the patient data obtained during a medical procedure performed on the patient, the tool data during the medical procedure. The robotic surgical system may be controlled by real-time analysis of medical imaging (Roh, ¶ 133).
Regarding dependent claim 16, the rejection of claim 1 is incorporated and Wolf/Roh further teaches: comprising the one or more processors to: receive pre-operative data captured during a medical procedure performed on the patient; and A patient characteristic may be based on pre-operative characteristics, e.g., blood test values or vital signs (Wolf, ¶ 719). determine, using the model based on the pre-operative data, the tool data after the medical procedure. The recommended sequence of events [i.e., usage of tools] may be based on, e.g., patient characteristics (Wolf, ¶ 486).
Regarding dependent claim 17, the rejection of claim 16 is incorporated and Wolf/Roh further teaches: wherein the pre-operative data comprises at least one of one or more intra-operative images of an anatomy of the patient, pre-operative audio information, pre-operative information indicating occurrence of a predetermined event during the medical procedure, or pre-operative information indicating force imparted to a tool by an anatomical feature of the patient. A patient characteristic may be based on pre-operative characteristics, e.g., blood test values or vital signs (Wolf, ¶ 719).
Regarding dependent claim 18, the rejection of claim 1 is incorporated and Wolf/Roh further teaches: comprising the one or more processors to: receive, via a user interface, one or more preferences corresponding to the surgeon and indicative of one or more preferred tools associated with a medical procedure for the patient; The user may modify or change surgical instruments to be used by the robot (Roh, ¶ 38). receive, via the user interface, a selection of at least one of the one or more preferred tools; and A user can use a CAD GUI to select tools for the surgery (Roh, ¶¶ 199, 200, 209). configure a robotic system according to the selection. The selected tools and actions are stored in a surgery database (Roh, ¶ 200). The surgical robot retrieves data from the surgery database (Roh, ¶ 184).
Regarding dependent claim 19, the rejection of claim 18 is incorporated and Wolf/Roh further teaches: comprising the one or more processors to: automatically configure at least a portion of the robotic system in response to the selection. The configuration of the robot may be based on the selected simulation [from the surgery database] (Roh, ¶ 309).
Regarding dependent claim 20, the rejection of claim 1 is incorporated and Wolf/Roh further teaches: comprising the one or more processors to: modify, based on preference data indicative of a preference of the surgeon, the tool data. The instruments in the set are modified based on the user selections (Roh, ¶ 38).
Regarding dependent claim 21, the rejection of claim 1 is incorporated and Wolf/Roh further teaches: comprising the one or more processors to: train the model using machine learning based on one or more data sets indicative of a plurality of medical procedures, the one or more data sets including corresponding patient data at least partially indicative of patient health, corresponding skill data, and corresponding tool data associated with the plurality of medical procedures. A machine learning model may be trained using patient characteristics and medical history (Wolf, ¶ 233). A machine learning model may be trained to determine the skill level displayed in a video, and to predict an outcome based on skill levels of surgeons (Wolf, ¶¶ 189, 282, 624). A model may be trained to identify medical instruments or identify interactions between anatomy and instruments (Wolf, ¶¶ 189, 205)
Regarding dependent claim 22, the rejection of claim 1 is incorporated and Wolf/Roh further teaches: comprising the one or more processors to: train the model using machine learning, using input including historical data corresponding to one or more instances of a medical procedure. A machine learning model may be trained on historical surgical footage (Wolf, ¶ 197).
Regarding dependent claim 23, the rejection of claim 22 is incorporated and Wolf/Roh further teaches: wherein the historical data includes at least one first feature indicative of respective outcomes of the one or more instances of the medical procedure, and the historical data includes at least one second feature identifying surgical waste generated during the one or more instances of the medical procedure. The system identifies surgical outcomes based on historical data including historical footage (Wolf, ¶¶ 10–11). The analysis of the historical surgical footage includes identifying surgical tools, including single-use items such as needles, tapes, PPE, etc. [surgical waste] (Wolf, ¶ 199).
Regarding independent claim 24, this claim recites limitations similar to those of claim 1, and therefore is rejected for the same reasons.
Regarding independent claim 25, this claim recites limitations similar to those of claim 1, and therefore is rejected for the same reasons. The references further teach [a] non-transitory computer-readable medium (Wolf, ¶ 77; Roh, ¶ 113).
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
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/Tyler Schallhorn/Examiner, Art Unit 2144
/TAMARA T KYLE/Supervisory Patent Examiner, Art Unit 2144
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