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 .
Application Status
Present office action is in response to application filed 09/05//2024. Claims 1-15 are currently pending in the application.
Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claims 1-3 and 8-15 are rejected under 35 U.S.C. 102(a)(1) as obvious over Simaan et al. (US 20110066160 A1) (Simaan) in view of Narang et al. (US 20220318459 A1) (Narang) and Lindkvist et al. (US 20190340956 A1) (Lindkvist).
Re claims 1-3 and 8-15:
[Claims 1-2] Simaan teaches or at least suggests a system comprising: a 3D model of the scala tympani of a cochlea; a processor; and software that, when executed by the processor, causes the system to: receive data from one or more sensing elements of an instrumented electrode array of a cochlear implant during insertion of the electrode array in the 3D model (at least ¶ 4: The cochlear implant system consists of the microphone, micro-processor, transmitter, receiver, and electrode array. FIG. 1 … illustrates the cochlear implant system in relation to the inner and outer ear of the patient), ([Claim 2]) wherein the 3D model is transparent to allow observation of an actual position and state of the electrode array at discrete points during the insertion of the electrode array into the 3D model (at least ¶ 124: Models of the scala tympani have been discussed in previous studies which have provided three-dimensional modeling of the scala tympani. Internet-based 3D visualization tools for the cochlea based on a 3D generalization of Cohen's 2D spiral template have also been created; ¶ 169: Based on this present model, Cochlea Inc. created planar scala tympani models that are used for training surgeons. Inventors have conducted experiments on one of these models to measure insertion forces. Because the model is transparent from the top, it provides good conditions for imaging afterwards).
Simaan appears to be silent on but Narang teaches or at least suggests estimate a normal force vector comprising forces acting along a length of the electrode array; estimate a position vector comprising a position of one or more segments of the electrode array (at least ¶ 148: two tactile features (i.e., 3D contact location and 3D net force vector) are estimated from tactile sensor electrode values using neural networks; ¶ 157: two tactile features (i.e., 3D contact location and 3D net force vector) are estimated using 3 different network architectures: MLP, 3D voxel-grid-based CNN (referred to in figures as Voxel), and PointNet++ (referred to in figures as PointNet). It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have utilized Narang’s 3D contact location and 3D net force vector features to modify Simaan as claimed because this would amount to no more than applying known techniques to a known device (method, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Simaan in view of Narang appears to be silent on but Lindkvist which “relates generally to medical simulators that are configured to utilize metric-based training techniques to assist medical practitioners with developing and/or maintaining skill sets associated with performing medical procedures” (¶ 2), teaches or at least suggests provide a feedback score (at least: ¶ 30: By precisely defining the metrics, the medical simulators are able to reliably compute scores for each of the metrics during the simulations based on the medical practitioner's performance across a variety of functions for skills training, and also across a variety of different experience levels). It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have utilized the feedback based on defined metrics feature of Lindkvist to modify Simaan in view of Narang where the feedback score comprises one or more metrics derived from the estimated normal force and position vectors as claimed because this would amount to no more than applying known techniques to a known device (method, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
[Claim 3] Simaan as modified by Narang and Lindkvist teaches or at least suggests wherein the observed position and state of the electrode array and the data received from the one or more sensing elements to output the normal force and position vectors (at least Simaan: ¶ 72: FIG. 59 is a system diagram for an example system for training, adapting, instantiating and deploying machine learning models in an advanced computing pipeline; ¶ 76: FIG. 62A illustrates a data flow diagram for a process to train a machine learning model; ¶ 224: FIG. 28 illustrates an example of a process that, as a result of being performed by a computer system, trains a machine-learned model to estimate behavior of a tactile sensor; ¶ 251: data center 3100 may include tools, services, software or other resources to train one or more machine learning models or predict or infer information using one or more machine learning models according to one or more embodiments described herein; ¶ 675: training pipeline 5904 similar to a first example described with respect to FIG. 58 may be used for a first machine learning model, training pipeline 5904 similar to a second example described with respect to FIG. 58 may be used for a second machine learning model, and training pipeline 5904 similar to a third example described with respect to FIG. 58 may be used for a third machine learning model … any combination of tasks within training system 5804 may be used depending on what is required for each respective machine learning model).
Simaan as modified by Narang and Lindkvist appears to be silent on but Narang teaches or at least suggests the observed and received data are used as training data for a first machine learning model used (at least ¶ 72: FIG. 59 is a system diagram for an example system for training, adapting, instantiating and deploying machine learning models in an advanced computing pipeline; ¶ 76: FIG. 62A illustrates a data flow diagram for a process to train a machine learning model; ¶ 224: FIG. 28 illustrates an example of a process that, as a result of being performed by a computer system, trains a machine-learned model to estimate behavior of a tactile sensor; ¶ 251: data center 3100 may include tools, services, software or other resources to train one or more machine learning models or predict or infer information using one or more machine learning models according to one or more embodiments described herein; ¶ 675: training pipeline 5904 similar to a first example described with respect to FIG. 58 may be used for a first machine learning model, training pipeline 5904 similar to a second example described with respect to FIG. 58 may be used for a second machine learning model, and training pipeline 5904 similar to a third example described with respect to FIG. 58 may be used for a third machine learning model … any combination of tasks within training system 5804 may be used depending on what is required for each respective machine learning model). It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have incorporated the machine learning and training pipeline features of Narang within the teachings of Simaan as modified by Narang and Lindkvist because this would amount to no more than applying known techniques to a known device (method, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
[Claims 8-9] Simaan as modified by Narang and Lindkvist appears to be silent on but Lindkvist teaches or at least suggests wherein a surgeon performs a practice insertion of the electrode array without observing the actual position of the electrode array via the transparent model, ([Claim 9)] wherein the surgeon receives no feedback from the system during practice insertion of the electrode array (at least ¶ 85: In the testing mode 670, some or all of the training assistance is turned off or deactivated, and the medical practitioners are permitted to perform the simulated medical procedures 630 on their own without guidance or assistance). It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have incorporated the testing mode of Lindkvist within the teachings of Simaan as modified by Narang and Lindkvist because this would amount to no more than applying known techniques to a known device (method, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
[Claim 10] Simaan in view of Narang and Lindkvist teaches or at least suggests wherein the one or more sensing elements of the electrode array comprise strain sensors (at least ¶ 83: The primary measurement device within the sensor is an array of 19 sensing electrodes and four excitation electrodes located on the outer surface of the core. In at least one embodiment, a tactile sensor can produce).
[Claim 11] Simaan in view of Narang and Lindkvist teaches or at least suggests wherein the software further causes the system to: provide feedback to a user when the force vector indicates that one or more portions of the electrode array exhibit forces that exceed predetermined thresholds (at least Narang: ¶ 105: … filtering, initial experimental data points corresponded to force magnitudes slightly higher than 0.5 N; to facilitate later alignment with simulation data …; ¶ 111: the maximum force magnitudes (not pictured) for tactile sensor 1, 2, and 3 were 23.4, 32.1, and 29.0 N, respectively; ¶ 135: simulation data corresponding to net force magnitudes below 0.5 N were filtered out; ¶ 162: 1) removing all data corresponding to force magnitudes below 8 N, and 2) removing all data corresponding to force magnitudes above 2 N. The high-force dataset has a lower error compared to the low-force dataset as shown in at least one embodiment, in real-world applications, location estimation may be improved by applying greater force to the object …).
Alternatively, and/or additionally, in the event the above interpretation is viewed as not being reasonable, it would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have modified Simaan in view of Narang and Lindkvist as claimed because this would amount to no more than applying known techniques to a known device (method, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
[Claim 12] Simaan in view of Narang and Lindkvist teaches or at least suggests wherein the software further causes the system to: provide feedback to a user when the position vector indicates that one or more segments of the electrode array exhibit a position deviation (at least Narang: ¶ 30: … For each procedure step, the metrics may also describe what should not be done (e.g., by characterizing performance that deviates from optimal, sufficient or acceptable performance levels) and to identify errors …; ¶ 40: precision metrics may indicate whether a correctly sized tool (e.g., stent) was place at or near an optimal position, whether a medical practitioner ablated material precisely along a predefined path, and/or whether fluids were injected exactly or close to required amounts (e.g., whether precise amounts of embolization liquids were inject into an arteriovenous malformation).
Alternatively, and/or additionally, in the event the above interpretation is viewed as not being reasonable, it would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have modified Simaan in view of Narang and Lindkvist as claimed because this would amount to no more than applying known techniques to a known device (method, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
[Claim 13] Simaan in view of Narang and Lindkvist teaches or at least suggests wherein the software performs the further function of: providing a visualization of the position and state of the electrode array as it is inserted into the model (at least Simaan: ¶ 19: … displaying the performance information to the user; ¶ 81: Force and location measurements can be displayed to the user on monitor 420. If an active-bending electrode array is used, controller 425 can deflect the active-bending electrode array by applying force (e.g., tension on an actuation thread) to the active-bending electrode array; ¶ 88: the surgeon controls the motion of the insertion module in all directions using the input device and relies on information displayed on monitor 420 …; ¶ 90: … monitor 420 can display the location of the active-bending electrode array in the body (e.g., the inner ear) and can also display a graph of the force being applied to the active-bending electrode array 300; Narang: ¶ 88: … analyses and detailed visualizations of the experimental and simulation datasets are provided, including the distributions of contact locations, forces, electrode values, and FE deformations…; ¶ 40: precision metrics may indicate whether a correctly sized tool (e.g., stent) was place at or near an optimal position, whether a medical practitioner ablated material precisely along a predefined path, and/or whether fluids were injected exactly or close to required amounts (e.g., whether precise amounts of embolization liquids were inject into an arteriovenous malformation; Lindkvist: ¶ 19: The feedback system provides a visualization of the simulated environment during the performance of the medical procedures by the medical practitioners (e.g., by showing a simulated anatomy and tools being manipulated in the simulated anatomy) and corresponds to what is visualized when performing a real procedure on a real patient).
Alternatively, and/or additionally, in the event the above interpretation is viewed as not being reasonable, it would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have modified Simaan in view of Narang and Lindkvist as claimed because this would amount to no more than applying known techniques to a known device (method, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
[Claim 14] Simaan in view of Narang and Lindkvist teaches or at least suggests wherein the 3D model is created using a 3D printing process (at least Simaan: ¶ 123: The 2D template of the scala tympani model was first provided by Cohen (Cohen, L., Xu, J., Xu, S. A., Clark, G. M., 1996, "Improved and Simplified Methods for Specifying Positions of the Electrode Bands of a Cochlear Implant Array," The American Journal of Otology, 17, the entire contents of which are herein incorporated by reference) to aid surgeons with an estimation of the insertion angle…).
Alternatively, and/or additionally, the Examiner takes official notice that patient specific markers or templates can be manufactured/produced using stereolithography or 3D printing techniques known in the art. In view of the foregoing, it would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have modified Simaan in view of Narang, and Lindkvist as claimed because this would amount to no more than applying known techniques to a known device (method, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
[Claim 15] Simaan in view of Narang and Lindkvist teaches or at least suggests wherein the model is created based on imaging data from an actual recipient of the implant (at least Simaan: ¶ 123: The 2D template of the scala tympani model was first provided by Cohen (Cohen, L., Xu, J., Xu, S. A., Clark, G. M., 1996, "Improved and Simplified Methods for Specifying Positions of the Electrode Bands of a Cochlear Implant Array," The American Journal of Otology, 17, the entire contents of which are herein incorporated by reference) to aid surgeons with an estimation of the insertion angle…).
Alternatively, and/or additionally, the Examiner takes official notice that patient specific markers or templates can be manufactured/produced using stereolithography or 3D printing techniques known in the art. In view of the foregoing, it would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have modified Simaan in view of Narang, and Lindkvist as claimed because this would amount to no more than applying known techniques to a known device (method, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Claims 4-6 rejected under 35 U.S.C. 102(a)(1) as obvious over Simaan in view of Narang and Lindkvist, as applied to claim 1, further in view of MORINAGA et al. (US 20170032017 A1) (MORINAGA).
Re claims 4-6:
[Claim 4] Simaan in view of Narang and Lindkvist teaches or at least suggests wherein the normal force and position vectors are used as training data for a second machine learning model used (least Narang: ¶ 72: FIG. 59 is a system diagram for an example system for training, adapting, instantiating and deploying machine learning models in an advanced computing pipeline; ¶ 76: FIG. 62A illustrates a data flow diagram for a process to train a machine learning model; ¶ 224: FIG. 28 illustrates an example of a process that, as a result of being performed by a computer system, trains a machine-learned model to estimate behavior of a tactile sensor; ¶ 251: data center 3100 may include tools, services, software or other resources to train one or more machine learning models or predict or infer information using one or more machine learning models according to one or more embodiments described herein; ¶ 675: training pipeline 5904 similar to a first example described with respect to FIG. 58 may be used for a first machine learning model, training pipeline 5904 similar to a second example described with respect to FIG. 58 may be used for a second machine learning model, and training pipeline 5904 similar to a third example described with respect to FIG. 58 may be used for a third machine learning model … any combination of tasks within training system 5804 may be used depending on what is required for each respective machine learning model).
Simaan in view of Narang and Lindkvist appears to be silent on to perform dimensionality reduction of the normal force and position vectors to produce a lower-dimensional, higher-order state vector representation of the electrode array. However, the concept and advantages of the claimed features are old and well-known, as evident in MORINAGA (at least ¶ 28: from multidimensional data, a plurality of PCPs that are lower-dimensional than the number of dimensions of the multidimensional data (hereafter such PCPs are also referred to as “low-dimensional PCPs” or “low-dimensional parallel coordinates plots”); ¶ 51: The coordinate optimization device 105 projects the inter-PCP feature value vector in a direction of a designated principal component vector (e.g. higher-order two-dimensional principal component vector), thereby computing the optimum coordinates of the low-dimensional PCP). It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have incorporated the multidimensional data visualization of MORINAGA within the teachings of Simaan in view of Narang and Lindkvist as claimed because this would amount to no more than applying known techniques to a known device (method, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
([Claims 5-6]) Simaan in view of Narang, Lindkvist and MORINAGA teaches or at least suggests wherein the higher-order state vector represents features of the force and position vectors indicative of a high probability of a positive clinical outcome, ([Claim 6]) wherein the higher-order state vector representation to is used as training data for a third machine learning model to produce an action space indicative of surgical actions that increase a probability of a positive clinical outcome (at least Narang: abstract: The performance evaluation information can provide feedback and recommendations for improving and/or maintaining skill sets; ¶¶ 224, 251, 675; … train one or more machine learning models or predict or infer information using one or more machine learning models … a first machine learning model … a second machine learning model … a third machine learning model … any combination of tasks within training system 5804 may be used depending on what is required for each respective machine learning model).
Alternatively, and/or additionally, it is common knowledge that machine learning models may be trained on events/outcomes from past procedures and strategies more likely to result in successful outcome. In view of the foregoing, it would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have modified Simaan in view of Narang, Lindkvist and MORINAGA as claimed because this would amount to no more than applying known techniques to a known device (method, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Claim 7 is rejected under 35 U.S.C. 102(a)(1) as obvious over Simaan in view of Narang and Lindkvist, as applied to claim 1, further in view of Rau et al. (US 20240424309 A1) (Rau) and Smith et al. (US 20210234265 A1) (Smith).
Re claim 7:
[Claim 7] Simaan in view of Narang and Lindkvist appears to be silent on but Rau teaches or at least suggests wherein the metrics comprising the feedback score include peak insertion force (at least ¶ 25: critical insertion forces and thresholds can be identified so that the cochlear implant insertion procedure can be improved in general). Additionally, Smith teaches or at least suggests wrapping factor
(at least ¶ 40: The inductor assembly 146a defines one or more turns (or “loops” or “windings”), the number of which is determined by the intended application). It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have incorporated the identifying critical insertion forces and thresholds feature of Rau and the one or more turns (or “loops” or “windings”) determination feature of Smith within the teachings of Simaan in view of Narang and Lindkvist because the addition of each of these features would amount to no more than applying known techniques to a known device (method, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Conclusion
The prior art made of record and not relied upon is listed in the attached PTO Form 892 and is considered pertinent to applicant's disclosure.
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/EDDY SAINT-VIL/Primary Examiner, Art Unit 3715
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