DETAILED ACTION
This is a non-final rejection in response to amendments filed 8/26/2025. Claims 1-15, 17-18, and 20 are pending.
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 .
Response to Arguments
Applicant’s arguments with respect to the prior art rejections have been considered but are moot because the new grounds of rejection do 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.
Claim 7 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 7 is rejected as being indefinite. As written, the scope of this claim is unclear. The claim cites “wherein the operating region of the surgical robotic system comprises different operating regions for different arms of the surgical robotic system, wherein optimizing comprises identifying one of the different arms with a largest part of the one or more internal volumes outside of the different operating region and changing the port location to others of the different locations for the identified arm in the optimizing”. It’s unclear if the optimizing identifies an arm with a largest part of the internal volume in which the internal volume needs to be avoided or if the internal volume is the target volume. Further it is unclear if the “largest part of the one or more volumes outside of the different operating region” is referring to the volumes being physically outside of the operating region or if this is referring to identifying the arm with access to the largest volume independent of the operating region.
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.
Claims 1, 3-6, and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Stricko (US 20220287776) in view of Singh (US 20180228460).
Regarding claim 1, as best understood in view of indefiniteness rejection explained above, Stricko teaches a method for port placement recommendation for a surgical robotic system ([0107] discusses a method for placement recommendation), the method comprising:
inputting, by a processor a plurality of external patient measurements of a patient ([0108] discusses generating a patient model based on input external body wall data);
in response to the inputting, outputting by the ([0108] discusses generating a patient model based on input external body wall data);
optimizing, by the processor, overlap of (1) an operating region of the surgical robotic system as an operating volume with (2) the one or more internal volumes based on different port locations, the optimizing identifying the port placement as the port location of the different port locations based on the overlap ([0102]-[0105] discuss determining a port location based on external body wall patient data where the system is optimized by a reachability metric with [0115] discussing the optimization including how much of the target tissue is within the reachable swept volume (overlap between operating region and internal volume)); and
controlling the surgical robotic system to use the port placement or a port derived from the port placement ([0133]-[0136] discuss the surgical robotic system using the port placement).
Stricko teaches determining a port placement of a patient based on external data as described above but does not explicitly teach using a machine-learned model to output internal volume data of a patient based on external patient data.
Singh teaches inputting, by a processor a plurality of external patient measurements of a patient to a machine-learned model as implemented by the processor ([0032] discusses a processor using machine learning to learn a correlation model between body surface data of a patient (external patient measurements) and internal structures where the body surface data is received by the processor);
in response to the inputting, outputting by the machine-learned model one or more internal volumes of the patient ([0032] discusses a processor using machine learning to learn a correlation model between body surface data of a patient (external patient measurements) and internal structures);
Stricko teaches determining a port location based on external patient measurements and Singh teaches using machine-learned model to correlate external patient data with internal volumes as discussed above. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the system of Stricko with the machine-learned model of Singh as Singh teaches that this reduces the need for unnecessary radiation to the patient [0003] making the system safer for the patient.
Regarding claim 3, Stricko teaches correlating patient input data to determine an output using processors but does not explicitly teach wherein the machine-learned model comprises a regression trained model, and wherein outputting comprises outputting by the regression trained model in response to the inputting of the external patient measurements.
Singh teaches wherein the machine-learned model comprises a regression trained model, and wherein outputting comprises outputting by the regression trained model in response to the inputting of the external patient measurements ([0026] discusses the machine learning algorithm being a sparse linear regression trained model).
Stricko teaches determining a port location based on external patient measurements and Singh teaches using machine-learned model to correlate external patient data with internal volumes as discussed above. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the system of Stricko with the machine-learned model of Singh as Singh teaches that this reduces the need for unnecessary radiation to the patient [0003] making the system safer for the patient.
Regarding claim 4, Stricko teaches correlating patient input data to determine an output using processors but does not explicitly teach wherein the machine-learned model comprises a multi-layer perceptron neural network, and wherein outputting comprises outputting by the multi-layer perceptron neural network in response to the inputting of the external patient measurements.
Singh teaches wherein the machine-learned model comprises a multi-layer perceptron neural network, and wherein outputting comprises outputting by the multi-layer perceptron neural network in response to the inputting of the external patient measurements ([0041] discusses the machine learning model comprising a multi-layer neural network).
Stricko teaches determining a port location based on external patient measurements and Singh teaches using machine-learned model to correlate external patient data with internal volumes as discussed above. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the system of Stricko with the machine-learned model of Singh as Singh teaches that this reduces the need for unnecessary radiation to the patient [0003] making the system safer for the patient.
Regarding claim 5, Stricko teaches determining port location but does not explicitly teach wherein outputting the one or more internal volumes comprises outputting a centroid and radius for each of the one or more internal volumes.
Singh teaches wherein outputting the one or more internal volumes comprises outputting a centroid and radius for each of the one or more internal volumes ([0105] discusses the output internal volume as a normalized volume with a volumetric center where this is interpreted as a centroid and as the volume is normalized it is interpreted as having a radius).
Stricko teaches determining a port location based on external patient measurements and Singh teaches using machine-learned model to correlate external patient data with internal volumes as discussed above. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the system of Stricko with the machine-learned model of Singh as Singh teaches that this reduces the need for unnecessary radiation to the patient [0003] making the system safer for the patient.
Regarding claim 6, Stricko teaches wherein optimizing comprises identifying the operating region of the surgical robotic system with inverse kinematics ([0077]-[0079] discuss utilizing any type of kinematics information such as joints of the robotic system to indicate position and orientation of the surgical robotic system).
Regarding claim 8, Stricko teaches displaying an indication of the port placement as part of a pre-operative plan ([0128] discusses displaying the indication of the port placement).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Stricko in view of Singh and further in view of McDonald (US 20140148816).
Regarding claim 2, Stricko teaches wherein inputting comprises inputting the external patient measurements as height, ([0038]-[0043] discuss the external body wall data which is interpreted as including height, gender, abdominal width, and pelvic location).
Stricko does not explicitly teach the external patient measurements including weight and body mass index.
McDonald teaches wherein inputting comprises inputting the external patient measurements as height, weight, gender, body mass index, abdominal width, and pelvic location ([0019] discusses patient characteristics for a machine learning model to determine internal patient volumes as including weight, body mass index (BMI), patient height, and operation region dimensions, such as patient chest width and height, as well as the particular organ/organs being treated and the location of a specific tumor to be operated on are to be included).
Stricko teaches external patient measurements based on external surface data of the patient. McDonald teaches external patient measurements based on height, weight and body mass index. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the system of Stricko with the measurements of McDonald as utilizing additional patient characteristics will allow for a more accurate estimation of patient internal volumes making the system safer for the patient.
Claims 9, 15, 17-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Stricko in view of Singh and further in view of Mumaw (US 20210196382).
Regarding claim 9, Stricko teaches wherein inputting further comprises inputting a profile of a surgeon to the machine-learned model, and wherein outputting comprises outputting the one or more internal volumes in response to the inputting of the profile and the external patient measurements, the one or more internal volumes accounting for the profile of the surgeon.
Stricko does not teach inputting a profile of a surgeon to the machine-learned model, and wherein outputting comprises outputting the one or more internal volumes in response to the inputting of the profile, the one or more internal volumes accounting for the profile of the surgeon.
Mumaw teaches wherein inputting further comprises inputting a profile of a surgeon to the machine-learned model, and wherein outputting comprises outputting the one or more internal ([0321] discusses selecting the optimal trocar placement based on a characteristic of the surgeon including hand dominance, patient-side preference, or physical characteristics of the user with [0245] discussing the surgical hub operating using a machine learning system).
Modified Stricko teaches the user being able to input data to determine an optimal port placement and Mumaw teaches using surgeon characteristics in determining an optimal port placement as discussed above. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the port placement of Stricko with the surgeon characteristics of Mumaw as Mumaw teaches that this reduces user fatigue and increases efficiency [0321].
While Mumaw does not explicitly teach the output being in volumetric form of the internal regions of the patient, Singh teaches the output being in volumetric form of the internal regions of the patient ([0029] discusses the output being in volumetric form).
Modified Stricko teaches determining a port location based on external patient measurements and surgeon information and Singh teaches using machine-learned model to correlate external patient data with internal volumes as discussed above. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the system of Stricko with the machine-learned model of Singh as Singh teaches that this reduces the need for unnecessary radiation to the patient [0003] making the system safer for the patient.
Regarding claim 15, Stricko teaches a surgical robotic system for port placement recommendation, the surgical robotic system (computer-assisted surgical system 204) comprising:
a robotic arm configured to hold and operate a surgical tool (Fig. 14 shows a robotic arm configured to hold and operate a surgical tool); and
a processor ([0028] discusses the system including processors) configured to determine a location of a port for the surgical tool to enter a patient, the location determined from an output of ;
wherein the output ([0102]-[0105] discuss determining a port location based on external body wall patient data where the system is optimized by a reachability metric with [0115] discussing the optimization including how much of the target tissue is within the reachable swept volume (overlap between operating region and internal volume)), the volumetric operating region based on inverse kinematics ([0077]-[0079] discuss utilizing any type of kinematics information such as joints of the robotic system to indicate position and orientation of the surgical robotic system); and a control system of the surgical robotic system, the control system configured to control the robotic arm to enter the patient at the port ([0133]-[0136] discuss the surgical robotic system using the port placement).
Stricko teaches determining a port placement of a patient based on external data as described above but does not explicitly teach using artificial intelligence to output internal volume data of a patient based on external patient data.
Singh teaches using artificial intelligence to output internal volume data of a patient based on external patient data ([0041] discusses a processor using an artificial neural network to learn a correlation model between body surface data of a patient (external patient measurements) and internal structures where the body surface data is received by the processor);
Stricko teaches determining a port location based on external patient measurements and Singh teaches using machine-learned model to correlate external patient data with internal volumes as discussed above. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the system of Stricko with the machine-learned model of Singh as Singh teaches that this reduces the need for unnecessary radiation to the patient [0003] making the system safer for the patient.
Stricko teaches using external patient data but does not explicitly teach using surgeon information in the port location determination. Mumaw teaches using surgeon information in the port location determination ([0321] discusses selecting the optimal trocar placement based on a characteristic of the surgeon including hand dominance, patient-side preference, or physical characteristics of the user with [0245] discussing the surgical hub operating using a machine learning system).
Modified Stricko teaches the user being able to input data to determine an optimal port placement and Mumaw teaches using surgeon characteristics in determining an optimal port placement as discussed above. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the port placement of modified Stricko with the surgeon characteristics of Mumaw as Mumaw teaches that this reduces user fatigue and increases efficiency [0321].
Regarding claim 17, Stricko teaches using external patient data but does not explicitly teach wherein the surgeon information comprises a handedness of a surgeon controlling the robotic arm, the one or more internal regions being positioned based on the handedness.
Mumaw teaches wherein the surgeon information comprises a handedness of a surgeon controlling the robotic arm, the one or more internal regions being positioned based on the handedness ([0321] discusses selecting the optimal trocar placement based on a characteristic of the surgeon including hand dominance, patient-side preference, or physical characteristics of the user).
Modified Stricko teaches the user being able to input data to determine an optimal port placement and Mumaw teaches using surgeon characteristics in determining an optimal port placement as discussed above. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the port placement of Stricko with the surgeon characteristics of Mumaw as Mumaw teaches that this reduces user fatigue and increases efficiency [0321].
Regarding claim 18, Stricko teaches correlating patient input data to determine an output using processors but does not explicitly teach wherein the surgeon information comprises a trajectory of a surgeon controlling the robotic arm, the trajectory being a sequence or route to be used by the surgeon during operation on the patient with the surgical tool, the one or more internal regions being positioned based on the trajectory.
Mumaw teaches wherein the surgeon information comprises a trajectory of a surgeon controlling the robotic arm, the trajectory being a sequence or route to be used by the surgeon during operation on the patient with the surgical tool, the one or more internal regions being positioned based on the trajectory ([0321]-[0322] discuss utilizing surgeon information and visualization information in determining trocar placement with [0215] discusses identifying the desired forward path of the robotic arm as being included in visualization information).
Modified Stricko teaches the user being able to input data to determine an optimal port placement and Mumaw teaches using surgeon characteristics in determining an optimal port placement as discussed above. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the port placement of Stricko with the surgeon characteristics of Mumaw as Mumaw teaches that this reduces user fatigue and increases efficiency [0321].
Regarding claim 20, Stricko teaches correlating patient input data to determine an output using processors but does not explicitly teach wherein the artificial intelligence comprises a machine-learned regression model or a machine- learned neural network.
Singh teaches wherein the artificial intelligence comprises a machine-learned regression model or a machine- learned neural network ([0026] discusses the machine learning algorithm being a sparse linear regression trained model).
Stricko teaches determining a port location based on external patient measurements and Singh teaches using machine-learned model to correlate external patient data with internal volumes as discussed above. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the system of Stricko with the machine-learned model of Singh as Singh teaches that this reduces the need for unnecessary radiation to the patient [0003] making the system safer for the patient.
Claims 10-13 are rejected under 35 U.S.C. 103 as being unpatentable over Mumaw in view of Singh.
Regarding claim 10, Mumaw teaches a method for port placement recommendation for a surgical robotic system ([0321] discusses steps for recommending an optimal port placement for a surgical robotic system), the method comprising:
inputting, by a processor, a profile of a surgeon to a machine-learned model as implemented by the processor ([0321] discusses the surgical hub utilizing user characteristics (profile of surgeon) with [0245] discussing the surgical hub utilizing machine learning systems to correlate input data);
in response to the inputting, outputting by the machine-learned model one or more internal ([0321] discusses selecting an optimal trocar placement based on the user characteristics with [0245] discussing the correlation of the input data being done using machine learning systems with Fig. 27 further showing the internal volume of the patient using the optimal trocar);
determining, by the processor, the port placement recommendation by volumetric calculation using the one or more internal volumes ([0321] discusses determining the optimal trocar placement with Fig. 27 showing that this includes critical structure of the patient); and
displaying an indication of port placement recommendation ([0319]-[0320] discuss displaying the optimal port placement to the user using a visualization system such as cameras or light);
and controlling the surgical robotic system by the surgeon using the port placement or a port derived from the port placement ([0311] discusses controlling the robotic system to use the port placement).
Mumaw does not explicitly teach the output being in volumetric form of the internal regions of the patient, Singh teaches the output being in volumetric form of the internal regions of the patient ([0029] discusses the output being in volumetric form).
Mumaw teaches determining a port location based on surgeon information and Singh teaches using machine-learned model to correlate external patient data with internal volumes as discussed above. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the system of Mumaw with the machine-learned model of Singh as Singh teaches that this reduces the need for unnecessary radiation to the patient [0003] making the system safer for the patient.
Regarding claim 11, Mumaw teaches wherein outputting comprises outputting the internal ([0321] discusses selecting the optimal trocar placement based on a characteristic of the surgeon including hand dominance, patient-side preference, or physical characteristics of the user).
Mumaw does not explicitly teach the output being in volumetric form of the internal regions of the patient, Singh teaches the output being in volumetric form of the internal regions of the patient ([0029] discusses the output being in volumetric form).
Mumaw teaches determining a port location based on surgeon information and Singh teaches using machine-learned model to correlate external patient data with internal volumes as discussed above. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the system of Mumaw with the machine-learned model of Singh as Singh teaches that this reduces the need for unnecessary radiation to the patient [0003] making the system safer for the patient.
Regarding claim 12, Mumaw teaches wherein the characteristic is handedness of the surgeon or surgical trajectory used by the surgeon, and wherein outputting comprises outputting the internal ([0321] discusses selecting the optimal trocar placement based on a characteristic of the surgeon including hand dominance, patient-side preference, or physical characteristics of the user).
Mumaw does not explicitly teach the output being in volumetric form of the internal regions of the patient, Singh teaches the output being in volumetric form of the internal regions of the patient ([0029] discusses the output being in volumetric form).
Mumaw teaches determining a port location based on surgeon information and Singh teaches using machine-learned model to correlate external patient data with internal volumes as discussed above. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the system of Mumaw with the machine-learned model of Singh as Singh teaches that this reduces the need for unnecessary radiation to the patient [0003] making the system safer for the patient.
Regarding claim 13, Mumaw teaches a machine-learned model utilizing the user characteristics but does not explicitly teach wherein inputting further comprises inputting external measurements of the patient to the machine-learned model, and wherein outputting comprises outputting the one or more internal volumes based on the external measurements of the patient.
Singh teaches inputting, by a processor a plurality of external patient measurements of a patient to a machine-learned model as implemented by the processor ([0032] discusses a processor using machine learning to learn a correlation model between body surface data of a patient (external patient measurements) and internal structures where the body surface data is received by the processor);
in response to the inputting, outputting by the machine-learned model one or more internal volumes of the patient ([0032] discusses a processor using machine learning to learn a correlation model between body surface data of a patient (external patient measurements) and internal structures);
Mumaw teaches determining a port location based on surgeon information and Singh teaches using machine-learned model to correlate external patient data with internal volumes as discussed above. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the system of Mumaw with the machine-learned model of Singh as Singh teaches that this reduces the need for unnecessary radiation to the patient [0003] making the system safer for the patient.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Mumaw in view of Singh and further in view of Stricko.
Regarding claim 14, Mumaw teaches utilizing the user characteristics but does not explicitly teach but does not explicitly teach optimizing overlap of an operating region of the surgical robotic system with the one or more internal volumes based on different port locations, the optimizing identifying the port placement recommendation as one of the different port locations based on the overlap, and wherein displaying comprises displaying the indication as the port placement recommendation.
Stricko teaches optimizing overlap of an operating region of the surgical robotic system with the one or more internal volumes based different port locations ([0102]-[0105] discuss determining a port location based on external body wall patient data where the system is optimized by a reachability metric with [0115] discussing the optimization including how much of the target tissue is within the reachable swept volume (overlap between operating region and internal volume)), and wherein displaying comprises displaying the indication as the port placement from the optimized overlap ([0128] discusses displaying the indication of the port placement).
Mumaw teaches a machine-learned model utilizing the user characteristics to determine an optimal port placement and Stricko teaches using patient measurements in determining an optimal port placement as discussed above. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the port placement of Mumaw with the patient measurements of Stricko as this allows the system to account for the patient’s size and/or unique anatomy making the system safer for all patients.
Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIELLE M JACKSON whose telephone number is (303)297-4364. The examiner can normally be reached Monday-Friday 7:00-4:30 MT.
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/D.M.J./ Examiner, Art Unit 3657 /ABBY LIN/Supervisory Patent Examiner, Art Unit 3657