DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant’s submission filed on April 10, 2026 has been entered and made of record.
Claim Objections
Claims 1 and 25 are objected to because of the following informalities:
Claim 1 line 12: “the machine learning model” should read -- the trained machine learning model --
Claim 25 line 11: “the machine learning model” should read -- the trained machine learning model --
Appropriate correction is required.
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 1-4, 11, 17, 19, 20, 22, and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Sanchez-Matilla et al. (U.S. Pub. No. 2024/0303984) in view of Owen et al. (U.S. Pub. No. 2025/0143806).
As to claim 1, Sanchez-Matilla et al. teaches a surgical system (i.e., “system 100”, Paragraph [0051]) comprising
a processing device configured to (i.e., “machine-learning processing system 110”, Paragraph [0051]) implement a trained machine learning model, the processing device being configured to:
receive first data (i.e., FIG. 5A, Paragraph [0091]) having a first data format (i.e., “receive, as input, surgical data 147 to be processed … the surgical data 147 can include data streams (e.g., an array of intensity, depth, and/or RGB values) for a single image or for each of a set of frames representing a temporal window of fixed or variable length in a video”, Paragraph [0060]) from a sensing device (See for example, “The surgical data 147 that is input can be received from a real-time data collection system 145”, Paragraph [0060]);
receive additional data indicating a condition of the surgical system (i.e., “Task-specific decoding of the feature space 432 in the detection model 430 can result in detecting a phase 434 and a structure 436, such as a phase of surgery and one or more surgical instruments being used”, Paragraph [0082]); and
based on the additional data (i.e., “The feature decoder 406 can also receive phase 434 along with fused features as previously described with respect to FIG. 4. Feature fusion can be performed to combine one or more task-specific features of the surgical procedure with one or more temporally aligned features spanning two or more images of frames 720”, Paragraph [0099]),
input the first data or data derived therefrom to the trained machine learning model (i.e., Paragraph [0096]; and “The feature encoder 738 can receive the frame 720 and temporal input 422 as input”, Paragraph [0099]);
transform, using the machine learning model, the first data or data derived therefrom to second data having a second data format (i.e., “The one or more machine-learning models 702 can also include a second machine-learning model 706 that can synthesize an image adjustment based on the proposed region of interest 440 and the image”, Paragraph [0096]); and
output the second data having a second data format (i.e., Paragraph [0099]; and “The outputs the one or more machine-learning models 702 can be used by the output generator 160 to provide augmented visualization via the augmented reality devices 180. The augmented visualization can include the graphical overlays being overlaid on the corresponding features (anatomical structure, surgical instrument, etc.) in the image(s)”, Paragraph [0104]).
However, Sanchez-Matilla et al. does not explicitly disclose based on the additional data, perform a determination of whether to process the first data or data derived therefrom by inputting the first data or data derived therefrom to the trained machine learning model; if it is determined that the first data or data derived therefrom is to be processed, the processing device is configured to: input the first data or data derived therefrom to the trained machine learning model; transform, using the machine learning model, the first data or data derived therefrom to second data having a second data format; and output the second data having a second data format; or if it is determined that the first data or data derived therefrom is not to be processed, the processing device is configured to: output the first data having a first data format.
Owen et al. teaches based on additional data (i.e., “The machine learning model, during inference, operates on the input window 320 to detect a surgical phase represented by the images 302 in the input window 320 (block 202)”, Paragraph [0067]), perform a determination of whether to process the first data or data derived therefrom by inputting the first data or data derived therefrom to the trained machine learning model (i.e., “determine when to generate an alert based on one or more metrics (e.g., certainty, accuracy, etc.) associated with the detections”, Paragraph [0085]);
if it is determined that the first data or data derived therefrom is to be processed, the processing device is configured to: input the first data or data derived therefrom to the trained machine learning model (i.e., “A set of such temporally synchronized inputs from the surgical data 300 that are analyzed together by the machine learning model can be referred to as an “input window” 320. The machine learning model, during inference, operates on the input window 320 to detect a surgical phase represented by the images 302 in the input window 320 (block 202)”, Paragraph [0067]; and Paragraph [0102]); transform, using the machine learning model, the first data or data derived therefrom to second data having a second data format (i.e., “The graphical overlays 502 that are used to overlay the images 302 to represent the predicted features (surgical instruments, anatomical structures, etc.) are accordingly adjusted, if required, based on the predicted locations”, Paragraph [0108]); and output the second data having a second data format (i.e., “generating the overlays 502 and/or other types of user feedback when an alert is to be provided (e.g., instrument within predetermined vicinity of an anatomical structure)”, Paragraph [0108]); or
if it is determined that the first data or data derived therefrom is not to be processed, the processing device is configured to: output the first data having a first data format (i.e., “In some aspects, the graphical overlays 502 can be configured to be switched off by the user, for example, the surgeon, and the system works without overlays 502, rather only generating the overlays 502 and/or other types of user feedback when an alert is to be provided”, Paragraph [0108]).
Sanchez-Matilla et al. and Owen et al. are analogous art because they are from the field of digital image processing for medical imaging.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Sanchez-Matilla et al. by incorporating the performing of a determination of whether to process the first data or data derived therefrom by inputting the first data or data derived therefrom to the trained machine learning model based on the additional data, if it is determined that the first data or data derived therefrom is to be processed, the processing device is configured to: input the first data or data derived therefrom to the trained machine learning model, transform, using the machine learning model, the first data or data derived therefrom to second data having a second data format, and output the second data having a second data format, or if it is determined that the first data or data derived therefrom is not to be processed, the processing device is configured to: output the first data having a first data format, as taught by Owen et al.
The suggestion/motivation for doing so would have been to improve surgical safety and workflow by generating alerts to the user only when needed.
Therefore, it would have been obvious to combine Owen et al. with Sanchez-Matilla et al. to obtain the invention as specified in claim 1.
As to claim 2, Sanchez-Matilla et al. teaches wherein the second data format comprises an additional aspect to the first data format (i.e., “augmented visualization of the surgical view”, Paragraph [0091]).
As to claim 3, Sanchez-Matilla et al. teaches wherein the processing device is configured to generate the additional aspect in dependence on the first data using the trained machine learning model (i.e., “augmented visualization of the surgical view … According to one or more aspects, when adaptive visualization is activated, the one or more machine-learning models 702 of FIG. 7 can predict the proposed region of interest 440 in image 500B to generate a modified visualization of the surgical procedure by incorporating an image adjustment in a real-time output of the video of the surgical procedure … The image adjustment may appear, for example, as a change in contrast, brightness, focus, or other such parameters to enhance visibility for the surgeon”, Paragraph [0091]).
As to claim 4, Sanchez-Matilla et al. teaches wherein the trained machine learning model is a generative model (See for example, “The machine-learning models can include a fully convolutional network adaptation (FCN) and/or conditional generative adversarial network model”, Paragraph [0048]).
As to claim 11, Sanchez-Matilla et al. teaches wherein the surgical system further comprises a surgical instrument (i.e., “The surgical data 147 can include additional data streams, such as audio data, RFID data, textual data, measurements from one or more surgical instruments/sensors, etc., that can represent stimuli/procedural state from the operating room”, Paragraph [0060]).
As to claim 17, Sanchez-Matilla et al. teaches wherein the sensing device is an imaging device comprising one or more image sensors (i.e., “surgical data 147 can include data streams (e.g., an array of intensity, depth, and/or RGB values) for a single image or for each of a set of frames representing a temporal window of fixed or variable length in a video”, Paragraph [0060]; and see also, Paragraph [0113]).
As to claim 19, Sanchez-Matilla et al. teaches wherein the first data format and the second data format are respective representations of an image or a video (See for example, Figures 5A and 5B, Paragraph [0091]).
As to claim 20, Sanchez-Matilla et al. teaches wherein the first data represents a complete field of view or a fraction of a field of view of the imaging device (See for example, “image 500A depicts a surgical instrument 502 proximate to an anatomical structure 504. In this example, the surgical instrument 502 and the anatomical structure 504 are located in close physical proximity to a centroid 505 of the image 500A, while background structures 506 are further separated from the centroid 505”, Paragraph [0091]).
As to claim 22, Sanchez-Matilla et al. teaches wherein the second data output by the trained machine learning model is a predicted output (i.e., “The one or more machine-learning models, during execution, can receive, as input, surgical data 147 to be processed and generate one or more inferences according to the training”, Paragraph [0060]) and
wherein the sensing device is further configured to acquire true data (i.e., “labeled ground truth data”, Paragraph [0128]) having the second data format (i.e., Paragraph [0061]; “user input 148 may also support the addition of labels/annotations”, Paragraph [0092]; and Paragraph [0128]).
As to claim 25, Sanchez-Matilla et al. teaches a method for data processing in a surgical system (i.e., “computer-implemented method that predicts a proposed region of interest in an image from a video of a surgical procedure based on one or more contextual targets”, Abstract), the method comprising:
receiving first data (i.e., FIG. 5A, Paragraph [0091]) having a first data format (i.e., “receive, as input, surgical data 147 to be processed … the surgical data 147 can include data streams (e.g., an array of intensity, depth, and/or RGB values) for a single image or for each of a set of frames representing a temporal window of fixed or variable length in a video”, Paragraph [0060]) from a sensing device (See for example, “The surgical data 147 that is input can be received from a real-time data collection system 145”, Paragraph [0060]; and Paragraph [0113]);
receiving additional data indicating a condition of the surgical system (i.e., “state detector 150 can use the output from the execution of the machine-learning model to identify a state within a surgical procedure (“procedure”). A procedural tracking data structure can identify a set of potential states that can correspond to part of a performance of a specific type of procedure. Different procedural data structures (e.g., and different machine-learning-model parameters and/or hyperparameters) may be associated with different types of procedures”, Paragraph [0063]); and
based on the additional data (i.e., “The feature decoder 406 can also receive phase 434 along with fused features as previously described with respect to FIG. 4. Feature fusion can be performed to combine one or more task-specific features of the surgical procedure with one or more temporally aligned features spanning two or more images of frames 720”, Paragraph [0099]),
inputting the first data or data derived therefrom to the trained machine learning model (i.e., Paragraph [0096]; and “The feature encoder 738 can receive the frame 720 and temporal input 422 as input”, Paragraph [0099]);
transforming, using the machine learning model, the first data or data derived therefrom to second data having a second data format (i.e., “The one or more machine-learning models 702 can also include a second machine-learning model 706 that can synthesize an image adjustment based on the proposed region of interest 440 and the image”, Paragraph [0096]); and
outputting the second data having a second data format (i.e., Paragraph [0099]; and “The outputs the one or more machine-learning models 702 can be used by the output generator 160 to provide augmented visualization via the augmented reality devices 180. The augmented visualization can include the graphical overlays being overlaid on the corresponding features (anatomical structure, surgical instrument, etc.) in the image(s)”, Paragraph [0104]).
However, Sanchez-Matilla et al. does not explicitly disclose based on the additional data, performing a determination of whether to process the first data or data derived therefrom by inputting the first data or data derived therefrom to the trained machine learning model; if it is determined that the first data or data derived therefrom is to be processed, the method comprises: inputting the first data or data derived therefrom to the trained machine learning model; transforming, using the machine learning model, the first data or data derived therefrom to second data having a second data format; and outputting the second data having a second data format; or if it is determined that the first data or data derived therefrom is not to be processed, the method comprises: outputting the first data having a first data format.
Owen et al. teaches based on additional data (i.e., “The machine learning model, during inference, operates on the input window 320 to detect a surgical phase represented by the images 302 in the input window 320 (block 202)”, Paragraph [0067]), perform a determination of whether to process the first data or data derived therefrom by inputting the first data or data derived therefrom to the trained machine learning model (i.e., “determine when to generate an alert based on one or more metrics (e.g., certainty, accuracy, etc.) associated with the detections”, Paragraph [0085]);
if it is determined that the first data or data derived therefrom is to be processed, the method comprises: input the first data or data derived therefrom to the trained machine learning model (i.e., “A set of such temporally synchronized inputs from the surgical data 300 that are analyzed together by the machine learning model can be referred to as an “input window” 320. The machine learning model, during inference, operates on the input window 320 to detect a surgical phase represented by the images 302 in the input window 320 (block 202)”, Paragraph [0067]; and Paragraph [0102]); transform, using the machine learning model, the first data or data derived therefrom to second data having a second data format (i.e., “The graphical overlays 502 that are used to overlay the images 302 to represent the predicted features (surgical instruments, anatomical structures, etc.) are accordingly adjusted, if required, based on the predicted locations”, Paragraph [0108]); and output the second data having a second data format (i.e., “generating the overlays 502 and/or other types of user feedback when an alert is to be provided (e.g., instrument within predetermined vicinity of an anatomical structure)”, Paragraph [0108]); or
if it is determined that the first data or data derived therefrom is not to be processed, the method comprises: output the first data having a first data format (i.e., “In some aspects, the graphical overlays 502 can be configured to be switched off by the user, for example, the surgeon, and the system works without overlays 502, rather only generating the overlays 502 and/or other types of user feedback when an alert is to be provided”, Paragraph [0108]).
Therefore, in view of Owen et al., it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sanchez-Matilla et al. by incorporating the performing of a determination of whether to process the first data or data derived therefrom by inputting the first data or data derived therefrom to the trained machine learning model based on the additional data, if it is determined that the first data or data derived therefrom is to be processed, the method comprises: inputting the first data or data derived therefrom to the trained machine learning model, transforming, using the machine learning model, the first data or data derived therefrom to second data having a second data format, and outputting the second data having a second data format, or if it is determined that the first data or data derived therefrom is not to be processed, the method comprises: outputting the first data having a first data format, as taught by Owen et al., in order to improve surgical safety and workflow by generating alerts to the user only when needed.
Claims 5 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Sanchez-Matilla et al. in view of Owen et al. as applied to claim 1 above, and further in view of Souza (U.S. Pub. No. 2022/0240879). The teachings of Sanchez-Matilla et al. and Owen et al. have been discussed above.
As to claim 5, Sanchez-Matilla et al. and Owen et al. do not explicitly disclose wherein the first data format represents data having a reduced quality relative to the second data format.
Souza teaches the first data format (i.e., “sparse image set”, Paragraph [0073]) represents data having a reduced quality relative to the second data format (See for example, “The low-dose sinogram—which may reflect, for example, an 87.5% reduction in dose as compared to a normal-dose sinogram—has inferior image quality to an up-sampled sinogram 208 such as that shown in FIG. 2B”, Paragraph [0068]).
Sanchez-Matilla et al., Owen et al. and Souza are combinable because they are from the field of digital image processing for medical imaging.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to further modify Sanchez-Matilla et al. and Owen et al. by incorporating the first data format represents data having a reduced quality relative to the second data format, as taught by Souza.
The suggestion/motivation for doing so would have been to expose the patient to a lower radiation when imaging (i.e., Paragraph [0060]).
Therefore, it would have been obvious to combine Souza with Sanchez-Matilla et al. and Owen et al. to obtain the invention as specified in claim 5.
As to claim 6, Sanchez-Matilla et al. and Owen et al. do not explicitly disclose where the second data format has a higher spatial, frequency or temporal resolution than the first data format.
Souza teaches the second data format (i.e., “up-sampled sinogram”, Paragraph [0076]) has a higher spatial, frequency or temporal resolution (i.e., Paragraph [0046]; and “applying a high-resolution filter kernel”, Paragraph [0079]) than the first data format (i.e., “sparse image set”, Paragraph [0073]).
Therefore, it would have been obvious to a person having ordinary skill in the art to which the claimed invention pertains before the effective filing date of the claimed invention to further modify Sanchez-Matilla et al. and Owen et al. to include the second data format has a higher spatial, frequency or temporal resolution than the first data format, as taught by Souza, in order to enhance soft tissue edges represented in the image set (See for example, Souza at Paragraph [0079]).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Sanchez-Matilla et al. in view of Owen et al. as applied to claim 1 above, and further in view of Kim et al. (U.S. Pub. No. 2021/0272339). The teachings of Sanchez-Matilla et al. and Owen et al. have been discussed above.
As to claim 7, Sanchez-Matilla et al. and Owen et al. do not explicitly disclose wherein first data format has fewer data channels than the second data format.
Kim et al. teaches a first data format that has fewer data channels than a second data format (i.e., “reconstructing an image from Ls (1≤LS<L) channels, where the reconstructed image is comparable with an image reconstructed from beamformed data using L channels. As an example, an image can be reconstructed and beamformed using 1 channel, where the resulting image has image quality similar to, or better than, an image reconstructed from 64 or 128 channel data, without requiring increased complexity and significant change in hardware or front-end architecture”, Paragraph [0022]).
Sanchez-Matilla et al., Owen et al. and Kim et al. are combinable because they are from the field of digital image processing for medical imaging.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to further modify Sanchez-Matilla et al. and Owen et al. by incorporating the first data format has fewer data channels than the second data format, as taught by Kim et al.
The suggestion/motivation for doing so would have been to reduce the size and cost for imaging systems without degrading image quality and performance (See for example, Kim et al. at Paragraph [0039]).
Therefore, it would have been obvious to combine Kim et al. with Sanchez-Matilla et al. and Owen et al. to obtain the invention as specified in claim 7.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Sanchez-Matilla et al. in view of Owen et al. as applied to claim 1 above, and further in view of Krishnan et al. (U.S. Pub. No. 2025/0191124). The teachings of Sanchez-Matilla et al. and Owen et al. have been discussed above.
As to claim 8, Sanchez-Matilla et al. and Owen et al. do not explicitly disclose wherein the second data format comprises a greater number of frequency bands than the first data format.
Krishnan et al. teaches a second data format (i.e., “high resolution reconstructed image 268”, Paragraph [0063]) that comprises a greater number of frequency bands than a first data format (i.e., “low resolution image 264”, Paragraph [0063]; and “machine learning enabled restoration of low resolution images and improving the reconstruction of high frequency information”, Paragraph [0070]).
Sanchez-Matilla et al., Owen et al. and Krishnan et a. are combinable because they are from the field of digital image processing for medical imaging.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to further modify Sanchez-Matilla et al. and Owen et al. by incorporating the second data format comprises a greater number of frequency bands than the first data format, as taught by Krishnan et al.
The suggestion/motivation for doing so would have been to alleviate the degradation of high frequency details when increasing the resolution of low resolution images.
Therefore, it would have been obvious to combine Krishnan et al. with Sanchez-Matilla et al. and Owen et al. to obtain the invention as specified in claim 8.
Claims 9, 10, and 12-15 are rejected under 35 U.S.C. 103 as being unpatentable over Sanchez-Matilla et al. in view of Owen et al. as applied to claims 1 and 11 above, and further in view of Meglan (U.S. Pub. No. 2023/0024362). The teachings of Sanchez-Matilla et al. and Owen et al. have been discussed above.
As to claim 9, Sanchez-Matilla et al. and Owen et al. do not explicitly disclose wherein the surgical system further comprises a robot arm having one or more joints.
Meglan teaches the surgical system (i.e., “surgical robotic system 10”, Paragraph [0027]) further comprises a robot arm having one or more joints (i.e., “each of the robotic arms 40 may include a plurality of links 42a, 42b, 42c, which are interconnected at joints 44a, 44b, 44c, respectively”, Paragraph [0033]; and Paragraphs [0035]-[0036]).
Sanchez-Matilla et al., Owen et al. and Meglan are combinable because they are from the field of digital image processing for medical imaging.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to further modify Sanchez-Matilla et al. and Owen et al. by incorporating the surgical system further comprises a robot arm having one or more joints, as taught by Meglan.
The suggestion/motivation for doing so would have been to enable rapid and effective completion of tissue manipulations tasks.
Therefore, it would have been obvious to combine Meglan with Sanchez-Matilla et al. and Owen et al. to obtain the invention as specified in claim 9.
As to claim 10, Sanchez-Matilla et al. and Owen et al. do not explicitly disclose wherein the additional data indicating the condition of the surgical system comprises data indicating a state of the robot arm.
Meglan teaches the additional data indicating the condition of the surgical system comprises data indicating a state of the robot arm (See for example, Paragraph [0038]; and “sensor data”, Paragraph [0047]).
Therefore, it would have been obvious to a person having ordinary skill in the art to which the claimed invention pertains before the effective filing date of the claimed invention to further modify Sanchez-Matilla et al. and Owen et al. to include the additional data indicating the condition of the surgical system comprises data indicating a state of the robot arm, as taught by Meglan, in order to produce force feedback commands and provide haptic feedback to a surgeon (See for example, Meglan at Paragraph [0038]).
As to claim 12, Sanchez-Matilla et al. and Owen et al. do not explicitly disclose wherein the additional data indicating the condition of the surgical system comprises data indicating a state of the surgical instrument.
Meglan teaches the additional data (i.e., “sensor data”, Paragraph [0047]) indicating the condition of the surgical system comprises data indicating a state of the surgical instrument (i.e., “the sensor data may be transmitted by the surgical instrument 50 to the learning system 100”, Paragraph [0048]).
Therefore, it would have been obvious to a person having ordinary skill in the art to which the claimed invention pertains before the effective filing date of the claimed invention to further modify Sanchez-Matilla et al. and Owen et al. to include the additional data indicating the condition of the surgical system comprises data indicating a state of the surgical instrument, as taught by Meglan, in order to enable the detection of surgical instrument failure (See for example, Meglan at Paragraph [0046]).
As to claim 13, Sanchez-Matilla et al. and Owen et al. do not explicitly disclose wherein the surgical system is configured to input the first data into the trained machine learning model to output the second data if the data indicating the condition of the surgical system indicates that the surgical instrument is in operation.
Meglan teaches the surgical system is configured to input the first data into the trained machine learning model to output the second data (i.e., “failure probability”, Paragraph [0049]) if the data indicating the condition of the surgical system indicates that the surgical instrument is in operation (i.e., “The system 10 utilizes the learning system 100, once it has been trained, to determine an operational status of the surgical instrument 50 being used during the surgical procedure … The learning system 100 receives as input the following real-time data: video data, sensor data, user input data, service life data, and procedure data”, Paragraph [0047]).
Therefore, it would have been obvious to a person having ordinary skill in the art to which the claimed invention pertains before the effective filing date of the claimed invention to further modify Sanchez-Matilla et al. and Owen et al. to include the surgical system is configured to input the first data into the trained machine learning model to output the second data if the data indicating the condition of the surgical system indicates that the surgical instrument is in operation, as taught by Meglan, in order to prevent tissue damage when a failure is detected (See for example, Meglan at Paragraph [0052]).
As to claim 14, Sanchez-Matilla et al. and Owen et al. do not explicitly disclose wherein the sensing device is configured to sense data relating to the position of the one or more joints or forces at the one or more joints.
Meglan teaches the sensing device is configured to sense data relating to the position of the one or more joints or forces at the one or more joints (i.e., “The controller 21a also receives back the actual joint angles”, Paragraph [0038]).
Therefore, it would have been obvious to a person having ordinary skill in the art to which the claimed invention pertains before the effective filing date of the claimed invention to further modify Sanchez-Matilla et al. and Owen et al. to include the sensing device is configured to sense data relating to the position of the one or more joints or forces at the one or more joints, as taught by Meglan, in order to produce force feedback commands and provide haptic feedback to a surgeon (See for example, Meglan at Paragraph [0038]).
As to claim 15, Sanchez-Matilla et al. and Owen et al. do not explicitly disclose wherein the sensing device comprises a torque sensor and/or a position sensor.
Meglan teaches wherein the sensing device comprises a torque sensor and/or a position sensor (See for example, Paragraphs [0038] and [0040]).
Therefore, it would have been obvious to a person having ordinary skill in the art to which the claimed invention pertains before the effective filing date of the claimed invention to further modify Sanchez-Matilla et al. and Owen et al. to include the sensing device comprises a torque sensor and/or a position sensor, as taught by Meglan, in order to produce force feedback commands and provide haptic feedback to a surgeon (See for example, Meglan at Paragraph [0038]).
Response to Arguments
Claim Rejections - 35 USC §§ 102 and 103
With respect to claims 1-15, 17, 19, 20, 22, and 25, Applicant’s arguments (Remarks dated April 10, 2026, pages 6-9) have been fully considered, but they are moot in view of the new ground(s) of rejections (Refer to Claim Rejections - 35 USC § 103 Section above).
Conclusion
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/JOSE M TORRES/Examiner, Art Unit 2664 09/22/2026
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