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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 11/03/2025 has been entered.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-4, 8, 10-15 & 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Camarillo et al. (U.S. Patent Application 2020/0297444 A1) and further in view of Viswanathan (U.S. Patent Application 2007/0021742 A1).
Claim 1: Camarillo teaches –
An endoscopic system [FIG. 22 illustrates a schematic block diagram of an example localization/navigation system 300 in conjunction with a modeling system 320 as described herein. Using the system 300,…to provide…location/orientation information of a medical instrument (e.g., the endoscope)] (Para 0127 and Figure 22), comprising:
one or more processors [one or more processors] (Para 0128); and
memory [memory] (Para 0128), with instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to: [a set of computer-readable instructions, stored in a memory, and one or more processors configured by the instructions for performing the features described] (Para 0128)
receive patient information [representing the anatomical luminal network of the patient] (Para 0130) including an image [images] (Para 0130) of a target anatomy of a patient [the processor of the robotic system accesses information regarding a target location within the luminal network] (Para 0186) [configured to receive data from a medical imaging system…The received data can include, for example, a series of 2D images representing the anatomical luminal network of the patient] (Para 0130);
Examiner’s Note: The process disclosed in Fig. 27B from Fig. 27A (See Para 0186) is disclosed being used with the localization/navigation system 300 (See Para 0186). Thus, there is no improper combination of embodiments.
apply the target anatomy image [the navigation path may be identified programmatically by analysis of the…an identified target site] (Para 0186) [data representing a…navigation path through the luminal network to a target tissue site] (Para 0151) to a trained machine-learning (ML) model [using the machine learning model] (Para 0186) to recognize the target anatomy [first algorithm can classify the visible airways by their anatomical index (e.g., trachea, right bronchus, etc.)… first algorithm may utilize a neural network modeled after ResNet, a convolutional neural network (CNN)] (Para 0121) and to estimate one or more cannulation or navigation parameters [for navigating the medical instrument …using the machine learning model] (Para 0186) for maneuvering a steerable elongate instrument [to navigate the elongate body of the instrument toward the target location] (Para 0186); and
Examiner’s Note: It is understood that the navigation path (within the 3D model made from 2D imaging (See Para 0010, 0130 & 0151) includes the target image.
provide a control signal [command data instructing the instrument tip to reach a specific anatomical site] (Para 0150) to an actuator [motion of one or more pull wires, tendons or shafts of the endoscope that drive the actual movement of the endoscope] (Para 0150) to robotically facilitate operation of the steerable elongate instrument in the target anatomy [the endoscope that drive the actual movement of the endoscope within the luminal network] (Para 0150) in accordance with the estimated one or more cannulation or navigation parameters [the processor can cause the localization/navigation system 300 to navigate the elongate body of the instrument toward the target location] (Para 0186)
Camarillo fails to teach the one or more cannulation or navigation parameter of force applied to the steerable elongated instrument. However, Viswanathan teaches –
wherein the one or more cannulation or navigation parameters include a force applied [the contact of the distal end of the medical device against the tissue surface is driven by an externally applied magnetic field that applies a magnetic torque to the medical device] (Claim 7) to the steerable elongate instrument [medical device, such as a catheter] (Para 0004)
wherein the force [applying a control variable suitable for establishing a torque] (Claim 8) is based on a distance from a specified distal portion of the steerable elongate instrument to the target anatomy [incrementally moving a minimum distance towards the tissue surface] (Claim 8),
wherein the force is adjusted [determining an external magnetic field to be applied to the subject body and distal end of the medical device for providing a desired estimated contact force against the tissue surface within the subject body] (Claim 5) based on a change in the distance [medical device may be moved in incremental steps towards the target location at an increment of about 1-5 millimeters] (Para 0021) from the distal portion of the steerable elongate instrument to the target anatomy [The method of estimating the contact force can be used to predict and drive navigation controls] in order to determine and control the contact force of the distal tip of the medical device against the tissue surface (Abstract)
Examiner’s Note: The torque applied to the catheter of Viswanathan is the claimed “force”, as torque is a type of force. The control variable of Viswanathan is varied to steer the catheter to the target by adjusting the torque based on the estimated force to be applied when the medical device contacts the target.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Camarillo with the one or more cannulation or navigation parameter of force applied to the steerable elongated instrument as taught by Viswanathan in order to determine and control the contact force of the distal tip of the medical device against the tissue surface (Abstract)
Claim 2/1: Camarillo teaches –
further comprising:
a robot arm [robotic arm] (Figure 16, Element 76) detachably engaging [physically connected, latched, and/or coupled] (Para 0089 and as shown in Figure 16) the steerable elongate instrument [medical instrument] (Figure 16, Element 70),
the robot arm [robotic arm] (Figure 16, Element 76 and Figure 22, Element 300 & 370) configured to automatically adjust position or navigation of the steerable elongate instrument [the processor can cause the localization/navigation system 300 to navigate the elongate body of the instrument toward the target location] (Para 0186) via the actuator [motion of one or more pull wires, tendons or shafts of the endoscope that drive the actual movement of the endoscope] (Para 0150) in response to the control signal [command data instructing the instrument tip to reach a specific anatomical site] (Para 0150).
Claim 3/1: Camarillo teaches –
further comprising: an imaging system, wherein the one or more processors being further configured to:
receive, from the imaging system, real-time imaging data [in real-time from images of the anatomical luminal network] (Para 0136 and Figure 22, Element 300); and
robotically [robotic arm] (Figure 16, Element 76 and Figure 22, Element 300 & 370) adjust operation of the steerable elongate instrument based on the received real-time imaging data [The navigation controller 360 can cause display…of the real-time images received from the scope imaging data repository 380…in order to facilitate user-guided navigation] (Para 0155 and Figure 22, Element 300).
Claim 4/1: Camarillo teaches –
wherein the one or more cannulation or navigation parameters include at least one of:
a projected navigation path toward the target anatomy [The navigation path data repository 345 is a data storage device that stores data representing a pre-planned navigation path through the luminal network to a target tissue site. Navigating to a particular point in a luminal network of a patient's body may require certain steps to be taken preoperatively in order to generate the information needed to create the 3D model of the tubular network and to determine a navigation path within it].
Examiner’s Note: The projected navigation path is the pre-planned navigation path within the 3D model.
Claim 8/1: Camarillo teaches –
wherein the trained machine-learning (ML) model includes a trained deep-learning (DL) network [the machine learning model 310 can include a deep learning architecture like an artificial neural network, such as, for example, a CNN] (Para 0137).
Claim 10/1: Camarillo teaches –
wherein the steerable elongate instrument includes an imaging sensor configured to generate the image of the target anatomy when positioned in the target anatomy [the sensors (e.g., imaging device, EM sensor)] (Para 0146) [an imaging device positioned on a distal portion of the elongate body] (Abstract).
Claim 11/1: Camarillo teaches –
wherein the image of the target anatomy [the navigation path may be identified programmatically by analysis of the…an identified target site] (Para 0186) [data representing a…navigation path through the luminal network to a target tissue site] (Para 0151) includes one or more of a computer-tomography (CT) scan image, a magnetic resonance imaging (MRI) scan image, or an endoscopic ultrasonography (EUS) image [a CT imaging system or magnetic resonance imaging system] (Para 0130 & 0158).
Examiner’s Note: It is understood that the navigation path (within the 3D model made from 2D imaging (See Para 0010, 0130 & 0151)) includes the target image.
Claim 12/1: Camarillo teaches –
wherein:
the steerable elongate instrument includes a sensor [EM sensor (or tracker) comprising one or more sensor coils embedded in one or more locations and orientations in a medical instrument (e.g., an endoscopic tool)] (Para 0114) configured to sense a proximity of a distal end of the steerable elongate instrument [embedded EM tracker in one or more positions of the medical instrument (e.g., the distal tip of an endoscope)] (Para 0114) to the target anatomy [distances and orientations may be intraoperatively “registered” to the patient anatomy (e.g., the preoperative model)] (Para 0114); and
the one or more processors being configured to recognize the target anatomy [first algorithm can classify the visible airways by their anatomical index (e.g., trachea, right bronchus, etc.)… first algorithm may utilize a neural network modeled after ResNet, a convolutional neural network (CNN)] (Para 0121) and to determine one or more cannulation or navigation parameters [to navigate the elongate body of the instrument toward the target location] (Para 0186) further using the sensed proximity [machine learning model 310 can localize a bronchoscope in real-time by predicting an offset…An accuracy of the offset can be improved by comparing EM sensor data] (Para 0140).
Claim 13/1: Camarillo teaches –
wherein the one or more processors being configured to recognize the target anatomy [first algorithm can classify the visible airways by their anatomical index (e.g., trachea, right bronchus, etc.)… first algorithm may utilize a neural network modeled after ResNet, a convolutional neural network (CNN)] (Para 0121) and to determine one or more cannulation or navigation parameters [for navigating the medical instrument …using the machine learning model] (Para 0186) further using one or more of:
a habit or preference of an operating physician using the steerable elongate instrument [to adjust the reach of the robotic arms 12 to meet a variety of…physician preferences] (Para 0058);
Examiner’s Note: The claim limitation, using, is being interpreted in its BRI as being related to the processors while in use. Not being interpreted that the preference is being used in the determination steps.
Claim 14/1: Camarillo teaches –
further comprising:
a user interface configured to receive a user input for controlling the steerable elongate instrument [an input device or controller for manipulating an instrument attached to a robotic arm] (Para 0102) based on the estimated one or more cannulation or navigation parameters [the processor can cause the localization/navigation system 300 to navigate the elongate body of the instrument toward the target location] (Para 0186).
Examiner’s Note: The claim limitation of based on is being interpreted in its BRI as related to the navigation parameters.
Claim 15/1: Camarillo teaches –
further comprising:
an output unit [display] configured to display the target anatomy and a positioning and navigation of the steerable elongate instrument in the target anatomy [a two-dimensional (2D) display of a three-dimensional (3D) luminal network model as described herein, or a cross-section of a 3D model, can resemble FIG 21. Estimated position information regarding the medical instrument 202 or component(s) thereof can be overlaid onto such a representation] (Para 0126).
Examiner’s Note: It is understood that the navigation path (within the 3D model made from 2D imaging (See Para 0010, 0130 & 0151) includes the target image (See rejection of Claim 1 above).
Claim 21/1: Camarillo teaches –
further comprising:
the steerable elongate instrument [medical instrument 70 comprises an elongated shaft 71 (or elongate body) and an instrument base 72] (Para 0089 and Figure 16, Element 70), wherein the steerable elongate instrument is configured to be robotically [robotic arm] (Figure 16, Element 76 and Figure 22, Element 300 & 370) positioned and navigated in a target anatomy of a patient [the processor can cause the localization/navigation system 300 to navigate the elongate body of the instrument toward the target location] (Para 0186) via the actuator [motion of one or more pull wires, tendons or shafts of the endoscope that drive the actual movement of the endoscope] (Para 0150).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Camarillo et al. (U.S. Patent Application 2020/0297444 A1) and Viswanathan (U.S. Patent Application 2007/0021742 A1) and further in view of Hiroo (JP 2020/062218 A; enclosed herein and English translation referenced).
Claim 5/1: Camarillo teaches –
wherein the steerable elongate instrument [medical instrument 70 comprises an elongated shaft 71 (or elongate body) and an instrument base 72] (Para 0089 and Figure 16, Element 70) is configured to be robotically positioned [robotic arm] (Figure 16, Element 76 and Figure 22, Element 300 & 370) and navigated [the processor can cause the localization/navigation system 300 to navigate the elongate body of the instrument toward the target location] (Para 0186) in the target anatomy [may also be utilized when performing a gastro-intestinal (GI) procedure with a gastroscope] (Para 0046)
Camarillo and Viswanathan fail to teach including a duodenal papilla or a portion of a pancreaticobiliary system. However, Hiroo teaches including a duodenal papilla or a portion of a pancreaticobiliary system [Endoscopic retrograde cholangiopancreatography (ERCP) is a procedure in which a special endoscope is inserted from the mouth and advanced to the duodenum, and a thin tube is inserted from the outlet (papillary) of the bile duct / pancreatic duct to give an image. It is an inspection / treatment in which a drug is injected for inspection, stones are removed, and a stent is inserted] (Page 1 of English Translation of Hirro) in order to treat the duodenal papilla and improve patient outcomes of patients suffering with stones through the treatment of those stones (Page 1 of English Translation of Hirro). It is noted that Camarillo teaches general GI procedures which is a genus to the more specific GI procedure of ERCP of Hiroo.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to replace the GI procedure and gastroscope of Camarillo in the device of Camarillo and Viswanathan with the ERCP procedure and special endoscope as taught by Hiroo in order to treat the duodenal papilla and improve patient outcomes of patients suffering with stones through the treatment of those stones (Page 1 of English Translation of Hirro). One of ordinary skill in the art having a patient with bile ducts stones would using the GI procedure of Camarillo would have the understanding that the general GI procedure can be modified to a specific GI procedure to treat a specific aliment of papilla stones.
Claim(s) 6-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Camarillo et al. (U.S. Patent Application 2020/0297444 A1) and Viswanathan (U.S. Patent Application 2007/0021742 A1) and further in view of Chi et al. (W. Chi et al., "Trajectory Optimization of Robot-Assisted Endovascular Catheterization with Reinforcement Learning," 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, 2018, pp. 3875-3881; enclosed prior).
Claim 6/1: Camarillo teaches –
further comprising:
a training component [a neural network trained on simulated data can track a bronchoscope's movement through a human cadaver lung] (Para 0138) configured to train the ML model [machine learning model] (Para 0137 and Figure 22, Element 310) using supervised learning [CNN] (Para 0137; Para 0077 of the Specification as originally filed cites CNN, which is a type of supervised learning) and a training dataset comprising stored endoscopic procedure data of a patient [a neural network trained on simulated data can track a bronchoscope's movement through a human cadaver lung] (Para 0138),
the stored endoscopic procedure data including (i) images of target anatomy of the patients [a neural network trained on simulated data can track a bronchoscope's movement through a human cadaver lung] (Para 0138) and
Examiner’s Note: The training dataset is disclosed as simulated data through a human cadaver lung. The Examiner contends that the tracked movement through the human cadaver lung would include an end point or target point.
(ii) one or more corresponding target anatomy identifications or values for one or more cannulation or navigation parameters [the processor receives additional inputs, such as tagged coordinate positions of certain features within an image. For example, a branching may be provided with an x,y,z coordinate during the training process 600… relative position information (e.g., a distance from another feature, an angle from a center of axis, etc. may be provided] (Para 0170)
Examiner’s Note: The distance from another feature is being interpreted as corresponding target anatomy values.
Camarillo and Viswanathan fail to teach a training set comprising a plurality of patients and multiple images of target anatomies of the plurality of patients. However, Chi teaches a training set comprising a plurality of patients and multiple images [simulated environments with preoperative 3D images] (Page 3876, Section I. Introduction) of target anatomies of the plurality of patients [Catheter tip motion trajectories and vessel centerlines within different Type-I aortic arches under different simulation conditions. Catheter paths obtained from human demonstrations and the proposed robotic approach after optimization] (Figure 5) [Six catheter tip paths obtained from human demonstrations of both LSA and BCA cannulation tasks (dry) were recorded in all three vascular phantoms, performed by the same expert surgeon] (Page 3878, Section II. Materials and Methods; Part C. Experiments) in order to have the training set include diseased states in order for the machine learning to account for collision-free pathing at the diseased regions, where excessive collisions may increase risk of complications for the patient (Page 3878, Section II. Materials and Methods; Part C. Experiments)
Examiner’s Note: The Examiner contends that patient simulated data and patient model data reads on patient data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include a plurality of patients and multiple images of target anatomies as taught by Chi within the training set of Camarillo and Viswanathan in order to have the training set include diseased states in order for the machine learning to account for collision-free navigation of the diseased regions, where excessive collisions may increase risk of complications for the patient (Page 3878, Section II. Materials and Methods; Part C. Experiments). Camarillo is silent with respect to whether or not the training set includes diseased states. It is understood that including diseased states within the training set would improve the training set. The training set would be improved by taking into account diseased states in order to have the machine learning be trained to avoid the diseased states during the medical instrument navigation plan formation because it is understood that it would be harmful to a patient to collide the catheter into the diseased regions.
Claim 7/1: Camarillo teaches –
further comprising:
a training component [a neural network trained on simulated data can track a bronchoscope's movement through a human cadaver lung] (Para 0138) configured to train the ML model [machine learning model] (Para 0137 and Figure 22, Element 310)
a training dataset comprising stored endoscopic procedure data of a plurality of patients, the stored endoscopic procedure data including (i) one or more cannulation or navigation parameters [the processor receives additional inputs, such as tagged coordinate positions of certain features within an image. For example, a branching may be provided with an x,y,z coordinate during the training process 600… relative position information (e.g., a distance from another feature, an angle from a center of axis, etc. may be provided] (Para 0170)
Camarillo and Viswanathan fail to teach reinforcement learning. However, Chi teaches using reinforcement learning [LfD Reinforcement learning] (Page 3876, Section I. Introduction; LfD states for learning from demonstration) and a training dataset comprising procedure data of a plurality of patients [Catheter tip motion trajectories and vessel centerlines within different Type-I aortic arches under different simulation conditions. Catheter paths obtained from human demonstrations and the proposed robotic approach after optimization] (Figure 5) [Six catheter tip paths obtained from human demonstrations of both LSA and BCA cannulation tasks (dry) were recorded in all three vascular phantoms, performed by the same expert surgeon] (Page 3878, Section II. Materials and Methods; Part C. Experiments), the procedure data including (ii) one or more respective rewards [LfD Reinforcement learning] (Page 3876, Section I. Introduction; it is understood that rewards are a part of how reinforcement learning functions) associated with the one or more cannulation or navigation parameters [The human demonstrations by the same expert surgeon were collected and were used to train DMPs] (Page 3878, Section II. Materials and Methods; Part C. Experiments) in order to optimize the policies (training sets) for dynamic flow simulations, adaptation to different vascular phantoms and catheterization tasks (Page 3876, Section I. Introduction)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to replace supervised learning of one patient set of images of Camarillo and Viswanathan with the reinforcement learning of Chi in order to optimize the policies (training sets) for dynamic navigation simulations, adaptation to different anatomical phantoms and different navigation tasks (Page 3876, Section I. Introduction), which would be capable of handling different patients. The motivation for using reinforcement learning is the algorithm’s ability to handle differences in anatomy such as between diseased states, for example, aneurysm aortic arch and stenosis aortic arch.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Camarillo et al. (U.S. Patent Application 2020/0297444 A1) and Viswanathan (U.S. Patent Application 2007/0021742 A1) and further in view of Simon (U.S. Patent Application 2014/0148808 A1).
Claim 9/1: Camarillo teaches –
wherein:
the trained MIL model [machine learning model] (Para 0137 and Figure 22, Element 310) is trained [a neural network trained on simulated data can track a bronchoscope's movement through a human cadaver lung] (Para 0138)
and the one or more processors being configured to select, for the target anatomy image [The navigation controller 360 can cause display of side-by-side views of a slice of the 3D model at the estimated position and of the real-time images received from the scope imaging data repository 380 in some embodiments in order to facilitate user-guided navigation] (Para 0155)
Examiner’s Note: The prior art disclosure of displaying an image is being interpreted as reading on the select step of the claim.
Camarillo and Viswanathan fail to teach reference control patterns and success rates. However, Simon teaches –
to determine a plurality of reference control patterns [Various candidate plans can be determined or found in the search strategy in block 180] (Para 0108 and Figure 9, Element 180) for maneuvering the steerable elongate instrument [a catheter] (Para 0103)
wherein each pattern of the plurality of reference control patterns are each associated with respective success rates [plan optimizer 154 can use a search strategy 180 to determine various points through which a catheter can pass to achieve the destination X' from the entry point X] (Para 0103) [A plan or a plurality of plans that have an appropriate plan goodness value can then be proposed in block 156] (Para 0110); and
the one or more processors [processor can carry out a set of computer executable instructions] (Para 0112) being configured to select [the plan optimizer 154 can be designed to disregard all plans save for the best plan] (Para 0111), for the target anatomy image [the plan can include a final position of an instrument, a path to reach the final destination] (Para 0092 and Figure 6) [The plan from block 156 can be displayed on the image data] (Para 0092), at least one reference control pattern of the plurality of reference control patterns based on the respective success rates [Therefore, the path X-X' can be a path that is determined based upon the search strategy and the various optimization criteria and constraints that have been put into the plan optimizer 154 to provide or plot a path to the brain tumor 170] (Para 0103) in order to position the catheter relative to the target area, which can be difficult depending on the position of the affected area and path necessary to reach the affected area (Para 0005). It is desirable to optimize a therapy procedure that can include the precise positioning of a delivery device relative to a selected area of a patient (Para 0005).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the ML of Camarillo and Viswanathan to include the reference control patterns and success rates as taught by Simon in order to position the catheter relative to the target area, which can be difficult depending on the position of the affected area and path necessary to reach the affected area (Para 0005 of Simon). It is desirable to optimize a therapy procedure that can include the precise positioning of a delivery device relative to a selected area of a patient (Para 0005 of Simon).
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Camarillo et al. (U.S. Patent Application 2020/0297444 A1) and Viswanathan (U.S. Patent Application 2007/0021742 A1) and further in view of Inkpen et al. (U.S. Patent Application 2014/0148808 A1).
Claim 16/15/1: Camarillo teaches –
wherein the one or more processors being configured to automatically adjust the display of the target anatomy on the output unit [The navigation controller 360 can cause display of side-by-side views of a slice of the 3D model at the estimated position and of the real-time images received from the scope imaging data repository 380 in some embodiments in order to facilitate user-guided navigation] (Para 0155)
Examiner’s Note: Wherein the real-time images being received would case the display to be automatically adjust to the facilitate the user-guided navigation.
Camarillo and Viswanathan fail to teach including auto-centering the target anatomy in a viewing area. However, Inkpen teaches auto-centering the target anatomy in a viewing area [the interface may automatically adjust field of view and magnification based on detected conditions of alignment] (Para 0335) [then centers aligns with and zooms in on a particular target axis](Para 0335) in order to enable optimization of view parameters without additional input from the user, such as a requirement to press a zoom in or zoom out command or to have a separate selection dialog to specify a target to zoom in on [enables optimization of view parameters without additional input from the user, such as a requirement to press a zoom in or zoom out command or to have a separate selection dialog to specify a target to zoom in on] (Para 0335), which would improve the efficiency of the system.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the auto-centering as taught by Inkpen within the automatic display adjustment as taught by Camarillo and Viswanathan in order to enable optimization of view parameters without additional input from the user, such as a requirement to press a zoom in or zoom out command or to have a separate selection dialog to specify a target to zoom in on [enables optimization of view parameters without additional input from the user, such as a requirement to press a zoom in or zoom out command or to have a separate selection dialog to specify a target to zoom in on] (Para 0335 of Camarillo), which would improve the efficiency of the system.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-16 & 21 have been considered but are moot because the new ground of rejection does not rely on the manner in which the references were applied in the prior rejection of record. No pertinent arguments remain as the arguments of the Applicant were directed to the newly claimed subject matter. The arguments are unconvincing.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Govari et al. (U.S. Patent Application 2009/0138007 A1) – Govari teaches a medical probe includes an insertion tube, having a longitudinal axis and having a distal end. A distal tip is disposed at the distal end of the insertion tube and is configured to be brought into contact with a body tissue. A joint couples the distal tip to the distal end of the insertion tube. A joint sensor, contained within the probe, senses a position of the distal tip relative to the distal end of the insertion tube. The joint sensor includes first and second subassemblies, which are disposed within the probe on opposite, respective sides of the joint and each include one or more magnetic transducers.
Govari et at. (U.S. Patent Application 2011/0153252 A1) – Govari teaches a calibration apparatus includes a fixture coupled to hold a distal end of a medical probe. An actuator is configured to press against the distal tip of the probe and apply to the distal tip multiple force vectors having respective magnitudes and angles with respect to the distal end, so as to cause a deformation of the distal tip relative to the distal end. A sensing device is configured to measure the magnitudes of the force vectors applied by the actuator. A calibration processor is configured to receive from the probe first measurements indicative of the deformation of the distal tip in response to the force vectors, to receive from the sensing device second measurements indicative of the magnitudes of the force vectors, and to compute, based on the angles and the first and second measurements, calibration coefficients for assessing the force vectors as a function of the first measurements.
Viswanathan et al. (U.S. Patent Application 2006/0009735 A1) – Viswanathan teaches a method involves storing information representative of a magnetic field that is applied to a magnetically orientable medical device at a point. A location of the medical device is determined using a computational model of the medical device and the stored information representative of the magnetic field. The magnetic field applied to the medical device includes a position and orientation of a magnet.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HELENE C BOR whose telephone number is (571)272-2947. The examiner can normally be reached Mon - Fri 10:30 - 6:30.
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/Helene Bor/Examiner, Art Unit 3797
/CHRISTOPHER KOHARSKI/Supervisory Patent Examiner, Art Unit 3797