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 .
Formal Matters
Applicant’s Response and Amendments filed 16 July 2026 are acknowledged. Claims 6, 17-20, 22, 25, and 26 are cancelled. Claims 1 and 21 are currently amended. New claim 28 is added. Claims 1-5, 7-16, 21, 23, 24, 27, and 28 are pending and under examination.
Rejections/Objections Withdrawn
The rejection of claims 1-5, 7-16, 21, 23, 24, 26, and 27 under 35 U.S.C. 103 as being unpatentable over Shelton et al., US 20190125458 (2 May 2019) (previously cited of record) in view of Harris et al., US 20190200981 (4 July 2019), are withdrawn in light of Applicant’s amendments. However, modified rejections, necessitated by Amendment, are set forth below.
Response to Arguments
Applicant argues that Harris teaches the sizes and/or types of staples and not the claimed mechanical parameter of the end effector. Applicant has also amended the claims. Applicant’s arguments have been fully considered and are persuasive in light of Applicant’s amendments. However, further search and consideration in light of Applicant’s amendments to the independent claims and the addition of new claim 28 require modified rejections as set forth below.
Modified Objections/Rejections – Necessitated by Amendment
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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, 7-16, 21, 23, 24, 27, and new claim 28 are rejected under 35 U.S.C. 103 as being unpatentable over Shelton et al., US 20190125458 (2 May 2019) (hereinafter Shelton ‘458) (previously cited of record) in view of Harris et al., US 20190200981 (4 July 2019) (previously cited of record), and further in view of Shelton et al., US 20200405311 (21 December 2020, benefit to 28 Jun 2019) (hereinafter Shelton ‘311).
Regarding currently amended independent claim 1, Shelton ‘458 teaches a robotic surgical system (FIGs 255 and 256, system 13800; ¶1954), comprising:
a robot arm (13830/13840/13850; FIGs 255, 256);
an end effector (tool, ¶1954) adapted to be coupled to the robot arm, the end effector retaining a tool (¶1954), and
a processor (13822) configured to send the mechanical parameter (¶1956, “processor 13822 configured to run an algorithm”; ¶1972, the algorithm configured “to send information”, “for example, identification of the tool attachment, force data, and position”) and
a system controller (FIG 261, controller 13862, ¶1968) operably connected to the robot arm (FIG 261, 13861; ¶1968) for positioning the tool with respect to a patient (FIGs 258),
the system controller (FIG 261, 13862) being configured to (FIG 258, algorithm 13500):
determine whether the end effector is coupled to the robot arm (¶1969, tool attachment);
receive data sent by the end effector (¶1972)
identify the end effector using the unique identifier of the end effector (¶1972); and
adjust operation of the surgical system based on the data received from the end effector (¶1973).
Shelton ‘458 does not expressly teach the end effector comprising a memory storing a mechanical parameter of the end effector comprising at least one of a size dimension, a weight, or a center of mass of the end effector.
However, Shelton ‘458 expressly teach that “each modular component (e.g., handle 8204, modular adapter 8206, end effector 8208, staple cartridge 8210, etc.) may comprise a processor and a memory unit (not shown) that stores its respective serial number”) (¶1681).
Harris teaches end effector assemblies comprising end effector memory (23136) internal databases where the data may comprise sizes (¶734).
Harris also teaches that the computed response of the physical system takes into account properties like mass, inertial, viscous friction, inductance resistance, etc., to predict what the states and outputs of the physical system will be by knowing the input (¶452).
Shelton ‘311 teaches an end effector (FIG 19, instrument 2200, ¶303) comprising a memory (FIG 19, memory 1212 comprising a local memory of instrument 2200, ¶303) storing a mechanical parameter of the end effector (FIG 22, “end-effector information 2271”, ¶303) comprising at least one of a size dimension (FIG 18, end-effector size of 25mm or 31mm; ¶303; see also FIG 19, “identifies an end effector 2210”; ¶303), a weight (FIG 22, load in lbs; ¶303). Shelton ‘311 teaches default and adjustment values of end effectors based on size differences and potential adjustment values (¶304). “Adjustments to the default minimum closer stroke or distance 2282 are based on end effector information 2271” (¶305).
Embodiment FIGs 19-22 of Shelton ‘311 do not expressly teach the end-effector information 2271 comprising data for center of mass of the end effector. However, Shelton ‘311 teaches an embodiment of surgical instrument 8002 (FIGs 59, 81) where the manufacturing process of an ultrasonic transducer and ultrasonic waveguide contain RFID tags 8006 that store a first and second datum indicative of mass variations (broadly interpreted as center of mass), material density variations, and/or assembly variations (¶491). Shelton ‘311 teaches that control circuit 1210 can be configured 8706 to determine adjustments to the natural frequency of surgical instrument 8002 (¶492), which can be identified by any suitable identification information including device number, type, or manufacturer transmitted (¶492).
Accordingly, Shelton ‘311 provides a suggestion and motivation for a system controller to process end-effector-stored information related to center of mass of the end effector for ultrasonic end-effector systems where Shelton ‘311 expressly teaches a known problem of mass variations, material density variations, and/or assembly variations can affect the performance of the end-effector (¶¶491, 492). Additionally, Shelton ‘311 teaches that the control circuits and processors can be configured to check compatibility of the end effector components by accessing memory 1212 (¶255).
It would have been obvious to one having ordinary skill in the art as of the effective filing date of the invention to combine the teachings of Shelton ‘458, Harris, and Shelton ‘311 given that the prior art included each element claimed, although not necessarily in a single reference. Shelton ‘458, Harris, and Shelton ‘311 teach in the same field of endeavor, robotic surgical systems comprising processors, controllers, and end-effectors.
Although, Shelton ‘458 teaches the claimed base robotic surgical system (robotic arm, end effectors, end effectors comprising memory and data including serial numbers, processors, and system controllers), Shelton ‘458 does not expressly disclose the end effector comprising a memory storing a mechanical parameter of the end effector comprising at least one of a size dimension, a weight, or a center of mass of the end effector.
Harris specifically addresses end effector assemblies comprising end effector memory and that the response of the physical system takes into account properties like mass, inertial, viscous friction, inductance resistance, etc., to predict what the states and outputs of the physical system will be by knowing the input.
Shelton ‘311 (embodiment FIGs 19-22, ¶303) teaches an end effector comprising a memory storing a mechanical parameter of the end effector comprising a size dimension (FIG 18, end-effector size of 25mm or 31mm; ¶303) and a weight (FIG 22, load in lbs; ¶303).
Embodiment FIGs 59 and 81 of Shelton ‘311 teaches surgical instrument 8002 with known manufacturing process differentials of mass variations, material density variations, and/or assembly variations (¶491) that need to be accounted for in the operation of an ultrasonic transducer and ultrasonic waveguide. Shelton’s solution for the differential mass of these end-effector instrument 8002 is that they contain RFID tags 8006 that store a first and second datum indicative of mass variations, material density variations, and/or assembly variations (¶491) such that control circuit 1210 can be configured 8706 to determine adjustments to the natural frequency of surgical instrument 8002 (¶492) based on the identified corresponding devices (¶492). Accordingly, Shelton ‘311 provides a suggestion and motivation for a system controller to process end-effector-stored information related to center of mass of the end effector for ultrasonic end-effector systems where Shelton ‘311 expressly teaches a known problem of mass variations, material density variations, and/or assembly variations can affect the performance of the end-effector (¶¶491, 492).
Because Shelton ‘458 includes an end-to-end connected surgical robotic system with end effectors comprising memory (¶¶1972, 1975) that store at least serial numbers of the end effectors, a person of ordinary skill in the art, seeking to provide more detailed data in a real-time setting would reasonably consult Harris’s more detailed end effector local-memory storage solution, which takes into account properties including mass, inertial, viscous friction, and inductance resistance to predict what the states and outputs of the physical system will be by knowing the input. Moreover, the similar and overlapping teachings of Shelton ‘311 (embodiment FIGs 19-22, ¶303) expressly teach end effectors comprising local memory that stores mechanical parameters of the end effector comprising a size dimension (FIG 18, end-effector size of 25mm or 31mm; ¶303) and weight (FIG 22, load in lbs; ¶303). Additionally, embodiment FIGs 59 and 81 (¶¶491, 492) of Shelton ‘311 suggest end effectors comprising mass identification such that the controller can make adjustments based on the information provided on the unique end-effector device, in light of the express teachings that differences in mass, for example, can impact the controllability of an ultrasonic blade when the blade is guided by ultrasonic transducers and ultrasonic waveguides that may contain mass and related variations due to manufacturing processes. Shelton ‘311 provides express motivation for providing center of mass data specific to each end effector unit.
Harris’ and Shelton ‘311’s end effector local memory storage solutions can be incorporated alongside Shelton ‘458’s robotic surgical system (same general robotic surgical system, robotic arm, end effector retaining a tool, the end effector comprising a memory storing data, and an algorithmically configurable system controller) using known assembly methods without redesigning Shelton ‘458’s core device components. Shelton ‘311 teaches that the control circuits and processors can be configured to check compatibility of the end effector components by accessing memory 1212 (¶255).
A person of ordinary skill in the art attempting to render Harris’ and Shelton ‘311’s local end effector memory and stored data robotic controller-compatible, configurable, and executable would look for established designs to avoid creating a novel robot interface. Harris’ and Shelton ‘311’s end effector local memory storage provides solutions along with Shelton 311’s express examples as applied to at least one of a size dimension of the end effector, a weight of the end effector, and suggestions regarding the center of mass of the end effector, particularly as applied to ultrasonic blades, such that the solutions are modular and can be adapted to the system of Shelton ‘458 to enable robotic actuation and control for end-effector-specific adaptations.
Because the references address the same engineering problem (robotically integrated hardware and software within the boundaries of a given operating suite) and the proposed modifications are mechanically compatible and implemented by routine engineering practices (adding more data to an on-device memory in an end effector), a person of ordinary skill in the art before the effective filing date of the claimed invention would have had a reasonable expectation of success in combining these teachings.
Regarding currently amended claim 2, Shelton ‘458 modified by Harris and Shelton ‘311 teaches the system of claim 1, as set forth above, for the reasons set forth above.
Shelton ‘458 teaches wherein the data received from the end effector further comprises at least one of a unique identifier, a version identifier, a class identifier, manufacturing information, duration of use, lifetime information, service history, or sterilization history (¶¶1972, 1975).
Regarding currently amended claim 3, Shelton ‘458 modified by Harris and Shelton ‘311 teaches the system of claim 1, as set forth above, for the reasons set forth above.
Shelton ‘458 teaches wherein the system controller is further configured to determine a parameter regarding the end effector, provided that the determined parameter is not received from the end effector (¶1966). See also, Harris (¶734).
Regarding claim 4, Shelton ‘458 modified by Harris and Shelton ‘311 teaches the system of claim 3, as set forth above, for the reasons set forth above.
Harris wherein the determined parameter is retrieved from a database (¶734).
Regarding currently amended claim 5, Shelton ‘458 modified by Harris and Shelton ‘311 teaches the system of claim 3, as set forth above, for the reasons set forth above.
Harris teaches wherein the determined parameter is detected by the system controller (¶734).
Regarding currently amended claim 7, Shelton ‘458 modified by Harris and Shelton ‘311 teaches the system of claim 3, as set forth above, for the reasons set forth above.
Shelton ‘458 teaches wherein the system controller is further configured to determine the determined parameter regarding the end effector before the end effector is coupled to the robot arm (¶¶1972, 1975).
Regarding currently amended claim 8, Shelton ‘458 modified by Harris and Shelton ‘311 teaches the system of claim 3, as set forth above, for the reasons set forth above.
Shelton ‘458 teaches wherein the system controller is further configured to determine the determined parameter regarding the end effector after the end effector is coupled to the robot arm (¶¶1972, 1975).
Regarding currently amended claim 9, Shelton ‘458 modified by Harris and Shelton ‘311 teaches the system of claim 1, as set forth above, for the reasons set forth above.
Shelton ‘458 teaches wherein the operation adjustment by the system controller is at least one of an adjustment to the robot arm based on an estimated load and/or force exerted by the end effector weight or center of mass, an adjustment to the robot arm to allow for optimal joint configuration of the robot arm based on tool trajectory, or an adjustment to calibration data (¶1973).
Regarding claim 10, Shelton ‘458 modified by Harris and Shelton ‘311 teaches the system of claim 9, as set forth above, for the reasons set forth above.
Harris wherein the adjustment to calibration data (¶655) reflects a size dimension of the end effector (¶734).
Regarding currently amended claim 11, Shelton ‘458 modified by Harris and Shelton ‘311 teaches the system of claim 1, as set forth above, for the reasons set forth above.
Shelton ‘458 teaches a related embodiment (FIG 204) wherein the system controller (¶1684) is further configured to determine manufacturing information of the end effector (¶1685), obtain one or more of end effector or tool geometry (¶1681), and adjust the robot arm using the one or more of end effector or tool geometry to improve accuracy of the surgical system (FIG 204; ¶1687).
Shelton ‘458 teaches multiple embodiments of connected robotic surgical systems (¶5). One of ordinary skill in the art would be able to utilize various aspects of the different embodiments and incorporate them into the base system components, particularly when the components are software-based, as they are in configured system controllers. Additionally, Harris supports these additional embodiments by teaching that the end effector assembly may be further configured to receive data from an internal database and/or an external database, such as a cloud or surgical hub database, throughout the course of the surgical procedure (¶734).
Regarding claim 12, Shelton ‘458 modified by Harris and Shelton ‘311 teaches the system of claim 11, as set forth above, for the reasons set forth above.
Shelton ‘458 teaches wherein the tool geometry is at least one of a distance of a tool from a distal end of the robot arm, an angle of a tool axis relative to the distal end of the robot arm, or a depth a tool tip will protrude from the end effector (¶1155).
Regarding currently amended claim 13, Shelton ‘458 modified by Harris and Shelton ‘311 teaches the system of claim 1, as set forth above, for the reasons set forth above.
Shelton ‘458 teaches wherein the system controller is further configured to, based on the data received from the end effector, determine at least one of use, service, or replacement data for the end effector (¶¶1972, 1975).
Regarding currently amended claim 14, Shelton ‘458 modified by Harris teaches the system of claim 13, as set forth above, for the reasons set forth above.
Shelton ‘458 teaches wherein the system controller is further configured to determine if a lifetime maximum duration of use will be exceeded during a procedure (¶¶1678-1679).
Shelton ‘458 teaches multiple embodiments of connected robotic surgical systems (¶5). One of ordinary skill in the art would be able to utilize various aspects of the different embodiments and incorporate them into the base system components, particularly when the components are software-based, as they are in configured system controllers. Additionally, Harris supports these additional embodiments by teaching that the end effector assembly may be further configured to receive data from an internal database and/or an external database, such as a cloud or surgical hub database, throughout the course of the surgical procedure (¶734).
Regarding currently amended claim 15, Shelton ‘458 modified by Harris and Shelton ‘311 teaches the system of claim 1, as set forth above, for the reasons set forth above.
Shelton ‘458 teaches wherein the system controller is further configured to write updated data to the memory of the end effector (¶1585).
Shelton ‘458 teaches multiple embodiments of connected robotic surgical systems (¶5). One of ordinary skill in the art would be able to utilize various aspects of the different embodiments and incorporate them into the base system components, particularly when the components are software-based, as they are in configured system controllers. Additionally, Harris supports these additional embodiments by teaching that the end effector assembly may be further configured to receive data from an internal database and/or an external database, such as a cloud or surgical hub database, throughout the course of the surgical procedure (¶734).
Regarding claim 16, Shelton ‘458 modified by Harris and Shelton ‘311 teaches the system of claim 15, as set forth above, for the reasons set forth above.
Shelton ‘458 teaches wherein the updated data is based on at least one of use of the end effector, a unique identifier of the robot arm to which the end effector was coupled, a most recently performed procedure, sterilization history, or service update (¶1592).
Regarding currently amended independent claim 21, Shelton ‘458 teaches a robotic surgical system (FIGs 255 and 256, system 13800; ¶1954), comprising:
a robot arm (13830/13840/13850; FIGs 255, 256);
an end effector (tool, ¶1954) configured to be coupled to the robot arm at a first end and to retain a tool at a second end (¶1954),
the end effector (tool, ¶1954),
a processor (13822) configured to send data (¶1956),
a system controller (FIG 261, controller 13862, ¶1968) operably connected to the robot arm (FIG 261, 13861; ¶1968) for positioning the tool with respect to a patient (FIGs 258),
the system controller (FIG 261, 13862) being configured to (FIG 258, algorithm 13500):
determine whether the end effector is coupled to the robot arm (¶1969, tool attachment);
receive the data including the mechanical parameter from the end effector (¶1972, for example, identification of the tool attachment)
identify the end effector (¶1972); and
adjust operation of the surgical system based on the data received from the end effector (¶1973),
wherein the operation adjustment by the system controller is at least one of an adjustment to the robot arm based on:
an estimated load and/or force exerted by the end effector weight;
an estimated load and/or force exerted by the end effector center of mass; or
a tool trajectory related to the end effector size dimension (¶1973).
Shelton ‘458 at embodiment 13800 does not expressly teach the end effector comprising a memory storing data about the end effector, a mechanical parameter of the end effector comprising a size dimension of the end effector, a weight of the end effector, or a center of mass of the end effector.
However, embodiment FIG 204 of Shelton ‘458 teaches the memory unit of each modular component stores its usage parameter and/or usable life metric (¶1684).
Shelton ‘458 at embodiment 13800 does not expressly teach that the end effector is coupled to the robot arm via completion of an electrical circuit between mating contacts of the end effector and the robot arm.
However, Shelton ‘458 teaches embodiment FIGs 213 and 214 where “tool mounting portion 12250 also includes electrical contacts 12262, and the robotic tool 12226 includes electrical contacts 12264 positioned and structured to mate with the electrical contacts 12262 on the tool mounting portion 12250. Electrical signals can be communicated between the robotic tool 12226 and the robot 12222 (FIG 213) via the mating electrical contacts 12262, 12264. Mechanical control parameters from the robotic tool 12262 can be communicated to the robot 12222 via the electrical contacts 12262, 12264” (¶1765).
Shelton ‘458 at embodiment 13800 does not expressly teach that data including the mechanical parameter from the end effector is received via wired communication between the contacts.
However, Shelton ‘458 teaches embodiment FIG 215 where electrical contacts 12262 of the tool mounting portion 12250 facilitate data transmission (¶1766).
Shelton ‘458 teaches multiple embodiments of connected robotic surgical systems (¶5). One of ordinary skill in the art would be able to utilize various aspects of the different embodiments and incorporate them into the base system components, particularly when the components are software-based, as they are in configured system controllers. Additionally, Harris supports these additional embodiments by teaching that the end effector assembly may be further configured to receive data from an internal database and/or an external database, such as a cloud or surgical hub database, throughout the course of the surgical procedure (¶734).
Shelton ‘458 does not teach wherein the data about the end effector stored in the memory of the end effector includes at least one of a mechanical parameter comprising a size dimension, a weight, or a center of mass.
However, another embodiment of Shelton ‘458 expressly teach that “each modular component (e.g., handle 8204, modular adapter 8206, end effector 8208, staple cartridge 8210, etc.) may comprise a processor and a memory unit (not shown) that stores its respective serial number”) (¶1681).
Harris teaches end effector assemblies comprising end effector memory (23136) internal databases where the data may comprise sizes (¶734).
Harris also teaches that the computed response of the physical system takes into account properties like mass, inertial, viscous friction, inductance resistance, etc., to predict what the states and outputs of the physical system will be by knowing the input (¶452).
Shelton ‘311 teaches an end effector (FIG 19, instrument 2200, ¶303) comprising a memory (FIG 19, memory 1212 comprising a local memory of instrument 2200, ¶303) storing a mechanical parameter of the end effector (FIG 22, “end-effector information 2271”, ¶303) comprising at least one of a size dimension (FIG 18, end-effector size of 25mm or 31mm; ¶303; see also FIG 19, “identifies an end effector 2210”; ¶303), a weight (FIG 22, load in lbs; ¶303). Shelton ‘311 teaches default and adjustment values of end effectors based on size differences and potential adjustment values (¶304). “Adjustments to the default minimum closer stroke or distance 2282 are based on end effector information 2271” (¶305).
Embodiment FIGs 19-22 of Shelton ‘311 do not expressly teach the end-effector information 2271 comprising data for center of mass of the end effector. However, Shelton ‘311 teaches an embodiment of surgical instrument 8002 (FIGs 59, 81) where the manufacturing process of an ultrasonic transducer and ultrasonic waveguide contain RFID tags 8006 that store a first and second datum indicative of mass variations (broadly interpreted as center of mass), material density variations, and/or assembly variations (¶491). Shelton ‘311 teaches that control circuit 1210 can be configured 8706 to determine adjustments to the natural frequency of surgical instrument 8002 (¶492), which can be identified by any suitable identification information including device number, type, or manufacturer transmitted (¶492).
Accordingly, Shelton ‘311 provides a suggestion and motivation for a system controller to process end-effector-stored information related to center of mass of the end effector for ultrasonic end-effector systems where Shelton ‘311 expressly teaches a known problem of mass variations, material density variations, and/or assembly variations can affect the performance of the end-effector (¶¶491, 492). Additionally, Shelton ‘311 teaches that the control circuits and processors can be configured to check compatibility of the end effector components by accessing memory 1212 (¶255).
It would have been obvious to one having ordinary skill in the art as of the effective filing date of the invention to combine the teachings of Shelton ‘458, Harris, and Shelton ‘311 given that the prior art included each element claimed, although not necessarily in a single reference. Shelton ‘458, Harris, and Shelton ‘311 teach in the same field of endeavor, robotic surgical systems comprising processors, controllers, and end-effectors.
Although, Shelton ‘458 teaches the claimed base robotic surgical system (robotic arm, end effectors, end effectors comprising memory and data including serial numbers, processors, and system controllers), Shelton ‘458 does not expressly disclose the end effector comprising a memory storing a mechanical parameter of the end effector comprising at least one of a size dimension, a weight, or a center of mass of the end effector.
Harris specifically addresses end effector assemblies comprising end effector memory and that the response of the physical system takes into account properties like mass, inertial, viscous friction, inductance resistance, etc., to predict what the states and outputs of the physical system will be by knowing the input.
Shelton ‘311 (embodiment FIGs 19-22, ¶303) teaches an end effector comprising a memory storing a mechanical parameter of the end effector comprising a size dimension (FIG 18, end-effector size of 25mm or 31mm; ¶303) and a weight (FIG 22, load in lbs; ¶303).
Embodiment FIGs 59 and 81 of Shelton ‘311 teaches surgical instrument 8002 with known manufacturing process differentials of mass variations, material density variations, and/or assembly variations (¶491) that need to be accounted for in the operation of an ultrasonic transducer and ultrasonic waveguide. Shelton’s solution for the differential mass of these end-effector instrument 8002 is that they contain RFID tags 8006 that store a first and second datum indicative of mass variations, material density variations, and/or assembly variations (¶491) such that control circuit 1210 can be configured 8706 to determine adjustments to the natural frequency of surgical instrument 8002 (¶492) based on the identified corresponding devices (¶492). Accordingly, Shelton ‘311 provides a suggestion and motivation for a system controller to process end-effector-stored information related to center of mass of the end effector for ultrasonic end-effector systems where Shelton ‘311 expressly teaches a known problem of mass variations, material density variations, and/or assembly variations can affect the performance of the end-effector (¶¶491, 492).
Because Shelton ‘458 includes an end-to-end connected surgical robotic system with end effectors comprising memory (¶¶1972, 1975) that store at least serial numbers of the end effectors, a person of ordinary skill in the art, seeking to provide more detailed data in a real-time setting would reasonably consult Harris’s more detailed end effector local-memory storage solution, which takes into account properties including mass, inertial, viscous friction, and inductance resistance to predict what the states and outputs of the physical system will be by knowing the input. Moreover, the similar and overlapping teachings of Shelton ‘311 (embodiment FIGs 19-22, ¶303) expressly teach end effectors comprising local memory that stores mechanical parameters of the end effector comprising a size dimension (FIG 18, end-effector size of 25mm or 31mm; ¶303) and weight (FIG 22, load in lbs; ¶303). Additionally, embodiment FIGs 59 and 81 (¶¶491, 492) of Shelton ‘311 suggest end effectors comprising mass identification such that the controller can make adjustments based on the information provided on the unique end-effector device, in light of the express teachings that differences in mass, for example, can impact the controllability of an ultrasonic blade when the blade is guided by ultrasonic transducers and ultrasonic waveguides that may contain mass and related variations due to manufacturing processes. Shelton ‘311 provides express motivation for providing center of mass data specific to each end effector unit.
Harris’ and Shelton ‘311’s end effector local memory storage solutions can be incorporated alongside Shelton ‘458’s robotic surgical system (same general robotic surgical system, robotic arm, end effector retaining a tool, the end effector comprising a memory storing data, and an algorithmically configurable system controller) using known assembly methods without redesigning Shelton ‘458’s core device components. Shelton ‘311 teaches that the control circuits and processors can be configured to check compatibility of the end effector components by accessing memory 1212 (¶255).
A person of ordinary skill in the art attempting to render Harris’ and Shelton ‘311’s local end effector memory and stored data robotic controller-compatible, configurable, and executable would look for established designs to avoid creating a novel robot interface. Harris’ and Shelton ‘311’s end effector local memory storage provides solutions along with Shelton 311’s express examples as applied to at least one of a size dimension of the end effector, a weight of the end effector, and suggestions regarding the center of mass of the end effector, particularly as applied to ultrasonic blades, such that the solutions are modular and can be adapted to the system of Shelton ‘458 to enable robotic actuation and control for end-effector-specific adaptations.
Because the references address the same engineering problem (robotically integrated hardware and software within the boundaries of a given operating suite) and the proposed modifications are mechanically compatible and implemented by routine engineering practices (adding more data to an on-device memory in an end effector), a person of ordinary skill in the art before the effective filing date of the claimed invention would have had a reasonable expectation of success in combining these teachings.
Regarding claim 23, Shelton ‘458 modified by Harris and Shelton ‘311 teaches the system of claim 21, as set forth above, for the reasons set forth above.
Shelton ‘458 teaches wherein the controller is further configured to send updated data (¶1585) comprising duration of use, lifetime information, service history, or sterilization history to the end effector (¶1592).
Harris teaches data to be stored in internal databases in end effector memory (¶734).
Regarding claim 24, Shelton ‘458 modified by Harris and Shelton ‘311 teaches the system of claim 21, as set forth above, for the reasons set forth above.
Shelton ‘458 teaches wherein the system controller is further configured to send updated data comprising at least one of use of the end effector, a unique identifier of a robot arm to which the end effector was coupled, a most recently performed procedure, sterilization history, or service update(¶¶1972, 1975).
Harris teaches data to be stored in internal databases in end effector memory (¶734).
Regarding claim 27, Shelton ‘458 modified by Harris and Shelton ‘311 teaches the system of claim 21, as set forth above, for the reasons set forth above.
Shelton ‘458 teaches (FIG 204) wherein the system controller (¶1684) is further configured to determine manufacturing information of the end effector (¶1685), obtain one or more of end effector or tool geometry (¶1681), and adjust the robot arm using the one or more of end effector or tool geometry to improve accuracy of the surgical system (FIG 204; ¶1687).
Regarding new independent claim 28, Shelton ‘458 teaches a robotic surgical system (FIGs 255 and 256, system 13800; ¶1954), comprising:
a robot arm (13830/13840/13850; FIGs 255, 256);
an end effector (tool, ¶1954) configured to be coupled to the robot arm at a first end and to retain a tool at a second end (¶1954),
the end effector (tool, ¶1954),
a processor (13822) configured to send data (¶1956),
a system controller (FIG 261, controller 13862, ¶1968) operably connected to the robot arm (FIG 261, 13861; ¶1968) for positioning the tool with respect to a patient (FIGs 258),
the system controller (FIG 261, 13862) being configured to (FIG 258, algorithm 13500):
determine whether the end effector is coupled to the robot arm (¶1969, tool attachment);
receive the data including the mechanical parameter from the end effector (¶1972, for example, identification of the tool attachment)
identify the end effector (¶1972); and
adjust operation of the surgical system based on the data received from the end effector (¶1973).
Shelton ‘458 does not expressly teach the end effector comprising a memory storing a mechanical parameter of the end effector comprising at least one of a size dimension, a weight, or a center of mass of the end effector; and determine, based on the received mechanical parameter, at least one of an estimated load or an estimated force exerted by the end effector on the robot arm; and adjust operation of the robot arm based on at least one of the estimated load or the estimated force.
However, Shelton ‘458 expressly teach that “each modular component (e.g., handle 8204, modular adapter 8206, end effector 8208, staple cartridge 8210, etc.) may comprise a processor and a memory unit (not shown) that stores its respective serial number”) (¶1681). Shelton ‘458 also teaches wherein the operation adjustment by the system controller is at least one of an adjustment to the robot arm based on: an estimated load and/or force exerted by the end effector weight; an estimated load and/or force exerted by the end effector center of mass; or a tool trajectory related to the end effector size dimension (¶1973).
Harris teaches end effector assemblies comprising end effector memory (23136) internal databases where the data may comprise sizes (¶734).
Harris also teaches that the computed response of the physical system takes into account properties like mass, inertial, viscous friction, inductance resistance, etc., to predict what the states and outputs of the physical system will be by knowing the input (¶452).
Shelton ‘311 teaches an end effector (FIG 19, instrument 2200, ¶303) comprising a memory (FIG 19, memory 1212 comprising a local memory of instrument 2200, ¶303) storing a mechanical parameter of the end effector (FIG 22, “end-effector information 2271”, ¶303) comprising at least one of a size dimension (FIG 18, end-effector size of 25mm or 31mm; ¶303; see also FIG 19, “identifies an end effector 2210”; ¶303), a weight (FIG 22, load in lbs; ¶303). Shelton ‘311 teaches default and adjustment values of end effectors based on size differences and potential adjustment values (¶304). “Adjustments to the default minimum closer stroke or distance 2282 are based on end effector information 2271” (¶305).
Embodiment FIGs 19-22 of Shelton ‘311 do not expressly teach the end-effector information 2271 comprising data for center of mass of the end effector. However, Shelton ‘311 teaches an embodiment of surgical instrument 8002 (FIGs 59, 81) where the manufacturing process of an ultrasonic transducer and ultrasonic waveguide contain RFID tags 8006 that store a first and second datum indicative of mass variations (broadly interpreted as center of mass), material density variations, and/or assembly variations (¶491). Shelton ‘311 teaches that control circuit 1210 can be configured 8706 to determine adjustments to the natural frequency of surgical instrument 8002 (¶492), which can be identified by any suitable identification information including device number, type, or manufacturer transmitted (¶492).
Accordingly, Shelton ‘311 provides a suggestion and motivation for a system controller to process end-effector-stored information related to center of mass of the end effector for ultrasonic end-effector systems where Shelton ‘311 expressly teaches a known problem of mass variations, material density variations, and/or assembly variations can affect the performance of the end-effector (¶¶491, 492). Additionally, Shelton ‘311 teaches that the control circuits and processors can be configured to check compatibility of the end effector components by accessing memory 1212 (¶255).
It would have been obvious to one having ordinary skill in the art as of the effective filing date of the invention to combine the teachings of Shelton ‘458, Harris, and Shelton ‘311 given that the prior art included each element claimed, although not necessarily in a single reference. Shelton ‘458, Harris, and Shelton ‘311 teach in the same field of endeavor, robotic surgical systems comprising processors, controllers, and end-effectors.
Although, Shelton ‘458 teaches the claimed base robotic surgical system (robotic arm, end effectors, end effectors comprising memory and data including serial numbers, processors, and system controllers), Shelton ‘458 does not expressly disclose the end effector comprising a memory storing a mechanical parameter of the end effector comprising at least one of a size dimension, a weight, or a center of mass of the end effector.
Harris specifically addresses end effector assemblies comprising end effector memory and that the response of the physical system takes into account properties like mass, inertial, viscous friction, inductance resistance, etc., to predict what the states and outputs of the physical system will be by knowing the input.
Shelton ‘311 (embodiment FIGs 19-22, ¶303) teaches an end effector comprising a memory storing a mechanical parameter of the end effector comprising a size dimension (FIG 18, end-effector size of 25mm or 31mm; ¶303) and a weight (FIG 22, load in lbs; ¶303).
Embodiment FIGs 59 and 81 of Shelton ‘311 teaches surgical instrument 8002 with known manufacturing process differentials of mass variations, material density variations, and/or assembly variations (¶491) that need to be accounted for in the operation of an ultrasonic transducer and ultrasonic waveguide. Shelton’s solution for the differential mass of these end-effector instrument 8002 is that they contain RFID tags 8006 that store a first and second datum indicative of mass variations, material density variations, and/or assembly variations (¶491) such that control circuit 1210 can be configured 8706 to determine adjustments to the natural frequency of surgical instrument 8002 (¶492) based on the identified corresponding devices (¶492). Accordingly, Shelton ‘311 provides a suggestion and motivation for a system controller to process end-effector-stored information related to center of mass of the end effector for ultrasonic end-effector systems where Shelton ‘311 expressly teaches a known problem of mass variations, material density variations, and/or assembly variations can affect the performance of the end-effector (¶¶491, 492).
Because Shelton ‘458 includes an end-to-end connected surgical robotic system with end effectors comprising memory (¶¶1972, 1975) that store at least serial numbers of the end effectors, a person of ordinary skill in the art, seeking to provide more detailed data in a real-time setting would reasonably consult Harris’s more detailed end effector local-memory storage solution, which takes into account properties including mass, inertial, viscous friction, and inductance resistance to predict what the states and outputs of the physical system will be by knowing the input. Moreover, the similar and overlapping teachings of Shelton ‘311 (embodiment FIGs 19-22, ¶303) expressly teach end effectors comprising local memory that stores mechanical parameters of the end effector comprising a size dimension (FIG 18, end-effector size of 25mm or 31mm; ¶303) and weight (FIG 22, load in lbs; ¶303). Additionally, embodiment FIGs 59 and 81 (¶¶491, 492) of Shelton ‘311 suggest end effectors comprising mass identification such that the controller can make adjustments based on the information provided on the unique end-effector device, in light of the express teachings that differences in mass, for example, can impact the controllability of an ultrasonic blade when the blade is guided by ultrasonic transducers and ultrasonic waveguides that may contain mass and related variations due to manufacturing processes. Shelton ‘311 provides express motivation for providing center of mass data specific to each end effector unit.
Harris’ and Shelton ‘311’s end effector local memory storage solutions can be incorporated alongside Shelton ‘458’s robotic surgical system (same general robotic surgical system, robotic arm, end effector retaining a tool, the end effector comprising a memory storing data, and an algorithmically configurable system controller) using known assembly methods without redesigning Shelton ‘458’s core device components. Shelton ‘311 teaches that the control circuits and processors can be configured to check compatibility of the end effector components by accessing memory 1212 (¶255).
A person of ordinary skill in the art attempting to render Harris’ and Shelton ‘311’s local end effector memory and stored data robotic controller-compatible, configurable, and executable would look for established designs to avoid creating a novel robot interface. Harris’ and Shelton ‘311’s end effector local memory storage provides solutions along with Shelton 311’s express examples as applied to at least one of a size dimension of the end effector, a weight of the end effector, and suggestions regarding the center of mass of the end effector, particularly as applied to ultrasonic blades, such that the solutions are modular and can be adapted to the system of Shelton ‘458 to enable robotic actuation and control for end-effector-specific adaptations.
Because the references address the same engineering problem (robotically integrated hardware and software within the boundaries of a given operating suite) and the proposed modifications are mechanically compatible and implemented by routine engineering practices (adding more data to an on-device memory in an end effector), a person of ordinary skill in the art before the effective filing date of the claimed invention would have had a reasonable expectation of success in combining these teachings.
Conclusion
No claim is allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHERIE M POLAND whose telephone number is (703)756-1341. The examiner can normally be reached M-F 9am-6pm (CST).
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/CHERIE M POLAND/Examiner, Art Unit 3771
/SHAUN L DAVID/Primary Examiner, Art Unit 3771
