DETAILED ACTION
This action is in response to the RCE filed March 6, 2026 and the amendment filed on March 6, 2026. Claims 1-27 and 30 are currently pending with no claims amended.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 March 6, 2026 has been entered.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
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.
Claims 1-8 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by WO 2017151778 A1 as filed by Devam et al. (hereinafter “Devam”).
Regarding claim 1 (Previously presented), Devam discloses:
A surgical training system, comprising:
a model of an anatomical part of a human or animal (Devam, para 00117, “In augmented reality, the rendered image is seen by the user or users as a three-dimensional model of the patient's morphology overlaid atop the physical patient.”);
one or more sensors attached to said model, said one or more sensors operable to emit a signal in response to receiving an activation input (Devam, para 0004, “FIG. 1 is a block diagram of a system for tracking, in an immersive environment, a positional sensor ingested by, injected into, or inserted into a patient in accordance with some embodiments;”);
an augmented reality headset having one or more electronic or optical input and output channels adapted to receive electronic signals from said one or more sensors (Devam, para 0326, “Data is transmitted from the sensors to the immersion device controlling the immersive environment.”);
a computer processor operable to receive signals from said augmented reality headset or said one or more sensors (Devam, Fig 5 illustrates “Processor(s)” receiving input from the “Display Device (AR/VR/tablet/screen/project).”);
a computer database having surgical training curriculum having one or more individual surgical subject matter components stored therein (Devam, para 0111, “In some embodiments, the system is connected to a database of symptoms, diagnoses, and treatment options.” and para 0246, “By using artificial intelligence and a databank of information to be taught…”); and
a software program running on said computer processor and connected to said database (Devam, Fig 5 illustrates all display devices connected to the “Program Store & Processor(s)” and the “Data Store”.),
said software program operable to correlate said one or more sensor signals to said one or more individual surgical subject matter components, said software program being further operable to provide as an output the individual surgical subject matter component which is correlated to a received signal (Devam, para 0046, “Various techniques and apparatuses for training and testing of surgical and diagnostic skills using AR or VR and display of real patient data gathered by magnetic resonance imaging (MRI) are also disclosed. In a number of embodiments, real patient data (e.g., composed from an MRI, CT scan, x-ray, or any other patient data source) is displayed to a practitioner/trainee and further enhanced through AR or VR to simulate a variety of conditions for testing and training.”).
Regarding claim 2 (Previously presented), Devam discloses:
The surgical training system of claim 1, wherein a machine learning model receives visual or electronic signal cues from one or more sensors attached to the model or the augmented reality headset (Devam, para 0429, “By using artificial intelligence (for example machine learning) to analyze video [recorded by the augmented reality headset]…”).
Regarding claim 3 (Original), Devam discloses:
The surgical training system of claim 2, wherein the software program tracks the state of progress of steps of a surgical procedure from beginning to end (Devam, para 0158, “Procedural directions and information are also available from pre-created sources. These procedures and methods can be stepped through using various forms of user interaction such as voice control, gesture control or other control method.”).
Regarding claim 4 (Original), Devam discloses:
The surgical training system of claim 3, wherein each step of the surgical procedure has one or more machine learning models having two or more machine learning classes which determine transition to a next step of the surgical procedure (Devam, para 0195, “…artificial intelligence algorithms can be applied in order to test whether the material has been learned by the user, and to adjust the rate and style of teaching to match the needs and preferences of the user.” In other words, Devam discloses a machine learning model that classifies the student’s actions into at least two categories to identify whether to increase/decrease the rate or change the style of learning when transitioning to a next step.).
Regarding claim 5 (Original), Devam discloses:
The surgical training system of claim 4, wherein the one or more machine learning models predict a class which corresponds to a correct action for a particular step of the surgical procedure (Devam, para 0200, “IQ testing is done using a variety of tests involving different aspects of intelligence. These tests can be administered in an immersive environment [utilizing augmented reality and artificial intelligence], and the results evaluated automatically…”) and,
an affirmative instruction is displayed to the student through a projected display waveguide with see through optics of the augmented reality headset for the particular step (Devam, Fig 25 discloses a workflow where the user selects a target area. If the target area is correct, the “Target area is confirmed.”).
Regarding claim 6 (Original), Devam discloses:
The surgical training system of claim 5, wherein the one or more machine learning models predict a class which corresponds to an incorrect action for a particular step of the surgical procedure (Devam, para 0200, “IQ testing is done using a variety of tests involving different aspects of intelligence. These tests can be administered in an immersive environment [utilizing augmented reality and artificial intelligence], and the results evaluated automatically…”),
and a corrective instruction is displayed to the student through the projected display waveguide with see through optics of the augmented reality headset for the particular step (Devam, para 0206, “Simulations can also be prepared for dissection, allowing a student to interact using a…user interface in order to attempt to perform a dissection, with feedback given to tell the user if they've made a mistake.”).
Regarding claim 7 (Original), Devam discloses:
The surgical training system of claim 6, wherein at the end of the surgical procedure,
the software program is operable to tally all occurrences of correct actions and incorrect actions in order to produce a quantitative score which tracks the state of progress of each step of the surgical procedure (Devam, para 0237, “The simulation can also be used for testing and grading of users.”).
Regarding claim 8 (Previously presented), Devam discloses:
The surgical training system of claim 7, wherein at the end of the surgical procedure,
a recording of the surgical procedure is saved so that it may be reviewed by the student or instructor for instructional or proficiency scoring purposes (Devam, para 0435, “Recording of a…surgical procedure…using a wearable device…allows for later playback. This recording can be used for, but is not limited to,…training of students, review of procedures, and/or audits.”).
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.
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.
Claims 9-24, 26-27, and 30 are rejected under 35 U.S.C. 103 as being unpatentable over US 9595208 B2 as filed by Ottensmeyer, et al. (hereinafter “Ottensmeyer”) in view of Devam.
Regarding claim 9 (Previously presented), Ottensmeyer discloses:
A procedure training system comprising:
a physical model (Ottensmeyer, col 7, line 32, “…employs a physical model…”) associated with performance of one or more procedures by a trainee (Ottensmeyer, col 24, lines 12-14, “Such segmentation [of procedures into steps] is based on the analysis of system and environmental events generated in tracking user's performance.”), each of the one or more procedures having a finite number of defined steps, each step having one or more cues associated therewith (Ottensmeyer, col 23, lines 10-15, “…the methodology of the present invention facilitates the identification of specific patterns [from cue receivers] of events/states that clearly correlate with specific gestures, subtasks and tasks (e.g., cues) of a simulated medical procedure. This event-driven approach may be extended to a wider range of Surgical procedures.”);
one or more cue receivers operable to receive one or more cues during performance of the one or more procedures associated with the physical model, and generate a signal in response to receiving the one or more cues (Ottensmeyer, col 2, lines 25-27, “…the incision sensor (i.e., cue receiver) is structured to detect incision of an instrument (i.e., cue) into the trauma module and, in response to such incision, generate data…”);
a computer processor operable to receive signals from said one or more cue receivers (Ottensmeyer, col 7, lines 36-40, “…hand-motion sensors (i.e., cue receivers) configured to assess hand motions not associated with instrument motions, and a computer-processor specifically programmed to generate an output representing statistical score and/or assessment of performance of the user…”);
a computer database having training curriculum having one or more individual subject matter components stored therein for each of the one or more procedures (Ottensmeyer, col 7, lines 43-49, “A simulator structured according to embodiment of the invention…enables surgical task/sequence detection…with a simulated surgical procedure effectuated at the simulator (optionally, in reference to expert knowledge/curriculum).”); and
a software program running on said computer processor and connected to said database,
said software program operable to correlate said received signals to said one or more individual subject matter components for a particular procedure, and provide as an output the individual subject matter component which is correlated to a received signal (Ottensmeyer, col 29, lines 59-63, “…program code for creating a multi-level hierarchy of descriptors representing changes in the operational status of the instrument by determining identifiable portions of the motion based on combination of multiple event outputs.”),
wherein the one or more cue receivers includes an augmented reality (AR) headset having one or more electronic or optical input and output channels adapted to send and receive said signals corresponding to said received cues (Ottensmeyer, col 13, lines 58-63, “…the augmented reality microscope system of the invention is structured to produce (i.e., output) a stereoscopic view of the generated graphics, and to optically fuse the object image (i.e., input) and the system display image thereby allowing for the illusion of three dimensional objects projected into the same field as the image of the real objects.”).
Ottensmeyer does not explicitly disclose the augmented reality system capable of being positioned on the user’s head.
Devam, however, discloses:
an augmented reality (AR) headset (Devam, para 0119, “…the system is comprised of a pair of augmented reality glasses…”).
It would have been obvious to one of ordinary skill in the art before the effect filing date of the claimed invention to mobilize via a headset, as taught by Devam, the augmented reality system of Ottensmeyer with the motivation of offering a more mobile and versatile system as taught by Devam over Ottensmeyer.
Regarding claim 10 (Previously presented), Ottensmeyer/Devam discloses:
The procedure training system of claim 9, wherein the one or more cues include any one or combination of model cues for detecting discrete elements or conditions associated with the physical model itself (Ottensmeyer, col 2, lines 25-27, “…the incision sensor (i.e., cue receiver) is structured to detect incision of an instrument (i.e., cue) into the trauma module and, in response to such incision, generate data…”),
identification or motion (ID-motion) cues for detecting physical presence or motions by the trainee or an instrument used by the trainee (Ottensmeyer, col 23, lines 10-15, “…the methodology of the present invention facilitates the identification of specific patterns [from cue receivers] of events/states that clearly correlate with specific gestures, subtasks and tasks (e.g., cues) of a simulated medical procedure. This event-driven approach may be extended to a wider range of Surgical procedures.”), or
still picture/video cues including image capture and video feeds (Ottensmeyer, col 13, lines 31-32, “…equipped with an output channel for video/still image recording…”).
Regarding claim 11 (Previously presented), Ottensmeyer/Devam discloses:
The procedure training system of claim 10, wherein the one or more cue receivers further include any one or combination of:
one or more image scanners (Ottensmeyer, col 8, line 5, “…a 3D-camera (Kinect)…”),
one or more cameras operable to capture images or videos (Ottensmeyer, col 13, lines 31-32, “…equipped with an output channel for video/still image recording…”), and
one or more sensors in, on, near, or associated with the physical model (Ottensmeyer, col 2, lines 25-27, “…the incision sensor (i.e., cue receiver) is structured to detect incision of an instrument (i.e., cue) into the trauma module and, in response to such incision, generate data…”).
Regarding claim 12 (Previously presented), Ottensmeyer/Devam discloses:
The procedure training system of claim 11, wherein the one or more cue receivers generate said signals based on any one or combination of
a change in the physical model,
a movement of the trainee (Ottensmeyer, col 23, lines 10-15, “…the methodology of the present invention facilitates the identification of specific patterns [from cue receivers] of events/states that clearly correlate with specific gestures, subtasks and tasks (e.g., cues) of a simulated medical procedure. This event-driven approach may be extended to a wider range of Surgical procedures.”),
use of an instrument by the trainee (Ottensmeyer, col 2, lines 25-27, “…the incision sensor (i.e., cue receiver) is structured to detect incision of an instrument into the trauma module and, in response to such incision, generate data…”), or
a particular field of view during the course of the procedure.
Regarding claim 13 (Original), Ottensmeyer/Devam discloses:
The procedure training system of claim 10, wherein the software program is programmed with one or more machine learning (ML) models operable to identify, process, and classify the received signals for each step of the one or more procedures (Devam, para 0429, “By using artificial intelligence (for example machine learning) to analyze video, a subject can be analyzed to determine if differences in the responses to injury amount to a real injury or not. For example, if a subject is claiming compensation for an injured knee, the software is able to determine whether the subject's gait is consistent. While an uninjured patient may be able to convince a person that the injury is real, the software can analyze the motions involved and determine whether they are consistent. Inconsistencies in the response to the injury are a strong indicator that the injury is exaggerated or fake.” In other words, Devam discloses a machine learning model that identifies a subject in a video, processes the video to analyze gait, then classifies the subject as injured or not injured.),
wherein the one or more ML models have two or more classes (e.g., injured versus not injured) which determine a transition to a next step from a current step of a particular procedure (i.e., transitioning from diagnostic step to post-diagnostic step.), and
wherein the one or more ML models enable the software program to determine and communicate through the AR headset or a computer if actions performed by the trainee for each step of the particular procedure are correct or incorrect (Devam, para 0494, “The immersive environment [using augmented or virtual reality combined with artificial intelligence] can also be used for testing of the pre-programmed material. A student is asked to respond to questions, or to perform tasks, or otherwise interact with the immersive environment as defined in the program. Based on the success or failure of the responses, a grade can be assigned and areas of improvement can be identified.”).
Regarding claim 14 (Previously presented), Ottensmeyer/Devam discloses:
The procedure training system of claim 13, wherein the one or more ML models are operable to detect specific performance of the trainee during the course of the procedure associated with the physical model based on
the received signals associated with one or more of the model cues,
the ID-motion cues or
the still picture/video cues (Devam, para 0429, “By using artificial intelligence (for example machine learning) to analyze video…”), and
the software program is operable to output corresponding
training curriculum (Devam, para 0111, “In some embodiments, the system is connected to a database of symptoms, diagnoses, and treatment options.”),
alerts (Devam, para 0129, “While using a surgical overlay, audio, and/or visual cues are given to the surgeon if they are approaching an area that has either been noted as an area to avoid or use caution.”), or
related instructions to the trainee based on the specific performance of the trainee (Devam, para 0206, “Simulations can also be prepared for dissection, allowing a student to interact using a…user interface in order to attempt to perform a dissection, with feedback given to tell the user if they've made a mistake.”).
Regarding claim 15 (Original), Ottensmeyer/Devam discloses:
The procedure training system of claim 14, wherein the software program is operable to interpret the received signals as a specific action or reference point of the physical model within the context of each step of the particular procedure using the one or more ML models (Ottensmeyer, col 25, lines 36-39, “One more level of recognition is performed on the subtask list at step 670 and, at step 674, tasks are recognized that aggregately serve to identify an entire simulated surgical procedure.” See Figs 6A, 6B, and 6E for further detail.), and
cause a state change during the performance of the procedure by the trainee that will advance the procedure from the current step to the next step (Ottensmeyer, col 26, lines 56-62, “This example [pictured in Fig 7] illustrates the sequential re-composition of the raw data, events detected, and gestures into a subtask. Longer sequences are further composed into tasks making up a procedure. Similar observations of sets of 60 expert performances are parsed into the broad taxonomic library that the system employs to segment automatically trainee performance data sets…” and col 23, lines 1-2, “An event is defined as an action that can change the state of a state machine…”).
Regarding claim 16 (Original), Ottensmeyer/Devam discloses:
The procedure training system of claim 15, wherein the software program is programmed to deliver tutorials and guidance to the trainee that correspond to ongoing progress of the steps of the procedure (Devam, para 0158, “Procedural directions and information are also available from pre-created sources. These procedures and methods can be stepped through using various forms of user interaction such as voice control, gesture control or other control method.”).
Regarding claim 17 (Previously presented), Ottensmeyer/Devam discloses:
The procedure training system of claim 15, wherein the software program is programmed with an ordered sequence of actions on the physical model which are indicative of a successful procedure (Ottensmeyer, col 27, lines 2-4, “Depending on the training goals, this may be evaluation of decision processes, confirmation of correct sequencing of sub-tasks or gestures…”), and
wherein the model cues or the ID-motion cues or the still picture/video cues are correlated to the programmed ordered sequence of actions for the particular procedure (Ottensmeyer, col 27, lines 4-5, “…when required, detailed analysis of a subset of the motion data within a gesture.”).
Regarding claim 18 (Original), Ottensmeyer/Devam discloses:
The procedure training system of claim 17, wherein the software program is operable to identify whether the trainee performs any actions on the physical model that are not in agreement with an expected protocol or performance standard as identified in the software program based on the one or more ML models (Devam, para 0195, “…artificial intelligence algorithms can be applied in order to test whether the material has been learned by the user, and to adjust the rate and style of teaching to match the needs and preferences of the user.” In other words, Devam discloses a machine learning model that identifies ill performance of a student and adjusts the rate and style of training accordingly.) and
inform the trainee of a detected digression from the expected protocol or performance standard (Devam, para 0206, “Simulations can also be prepared for dissection, allowing a student to interact using a…user interface in order to attempt to perform a dissection, with feedback given to tell the user if they've made a mistake.”).
Regarding claim 19 (Previously presented), Ottensmeyer/Devam discloses:
The procedure training system of claim 18, wherein the software program is operable to deliver corresponding
training curriculum (Devam, para 0111, “In some embodiments, the system is connected to a database of symptoms, diagnoses, and treatment options.”),
an alert (Devam, para 0129, “While using a surgical overlay, audio, and/or visual cues are given to the surgeon if they are approaching an area that has either been noted as an area to avoid or use caution.”), or
corrective instructions to the trainee at the time of the detected digression from the expected protocol or performance standard during the performance of the procedure or at the conclusion of the particular procedure (Devam, para 0206, “Simulations can also be prepared for dissection, allowing a student to interact using a…user interface in order to attempt to perform a dissection, with feedback given to tell the user if they've made a mistake.”).
Regarding claim 20 (Original), Ottensmeyer/Devam discloses:
The procedure training system of claim 15, wherein the software program is operable to analyze the received signals from the one or more cue receivers using the one or more ML models corresponding to the current step of the particular procedure (Devam, para 0158, “Procedural directions and information are also available from pre-created sources. These procedures and methods can be stepped through using various forms of user interaction such as voice control, gesture control or other control method.”), and
determine whether the received signals indicate that the trainee performed the current step of the procedure properly (Devam, para 0494, “The immersive environment [using augmented or virtual reality combined with artificial intelligence] can also be used for testing of the pre-programmed material. A student is asked to respond to questions, or to perform tasks, or otherwise interact with the immersive environment as defined in the program. Based on the success or failure of the responses, a grade can be assigned and areas of improvement can be identified.”).
Regarding claim 21 (Previously presented), Ottensmeyer/Devam discloses:
The procedure training system of claim 20, wherein the software program is operable to:
output corresponding training curriculum or affirmative instructions associated with the next step in the particular procedure in response to the received signals indicating that the trainee performed the current step of the procedure properly (Devam, para 0159, “FIG. 10 shows a HUD identical to FIG. 9, however on the left below the temperature stats a guide can be shown giving instructions to the user on how to perform a procedure. As each step is completed, the guide is updated either automatically or with user interaction.”), or
output corresponding training curriculum or corrective instructions associated with the current step in the particular procedure in response to the received signals indicating that the trainee did not perform the current step properly (Devam, para 0206, “Simulations can also be prepared for dissection, allowing a student to interact using a…user interface in order to attempt to perform a dissection, with feedback given to tell the user if they've made a mistake.”).
Regarding claim 22 (Previously presented), Ottensmeyer/Devam discloses:
The procedure training system of claim 13, wherein the software program is operable to generate and output a performance score or figure of merit for individual steps taken by the trainee or for the entire procedure based on the number of correct actions and incorrect actions detected using the one or more ML models (Devam, para 0200, “IQ testing is done using a variety of tests involving different aspects of intelligence. These tests can be administered in an immersive environment [utilizing augmented reality and artificial intelligence], and the results evaluated automatically…”).
Regarding claim 23 (Original), Ottensmeyer/Devam discloses:
The procedure training system of claim 13, wherein the software program is operable to log, timestamp, and store the detected signals in the database based on recognized classifications using the one or more ML models (Devam, para 0119, “The data retrieved from the database is herein referenced as "procedural data," which can include, but is not limited to, the patient morphological data, patient information, procedural instructions, procedure time/date, and/or procedure location.”), and
provide the ability to playback recordings of the procedures for training debrief purposes after completion of the particular procedure (Devam, para 0435, “Recording of a…surgical procedure…using a wearable device…allows for later playback. This recording can be used for, but is not limited to,…training of students, review of procedures, and/or audits.”).
Regarding claim 24 (Previously presented), Ottensmeyer/Devam discloses:
The procedure training system of claim 10, wherein the software program is programmed with direct visual detection algorithms (e.g., video analysis) including
machine learning (Devam, para 0429, “By using artificial intelligence (for example machine learning) to analyze video...),
deep learning, or
reinforcement learning to develop cue detection functions.
Regarding claim 26 (Previously presented), Ottensmeyer/Devam discloses:
The procedure training system of claim 10, wherein the software program is trained for machine and deep learning using neural networks (Ottensmeyer, col 23, lines 18-22, “appropriately structured neural networks and other algorithms known to those skilled in the art would also perform the necessary processing. Where "state machine" is used in the text, other algorithms [such as machine and deep learning using neural networks] may be substituted.”) for detection of user technique during performance of the steps of the procedure (Ottensmeyer, col 23, lines 5-7, “An event [(an action that can change the state of a state machine)] is generally accompanied by an occurrence of realignment of an object with which a particular gesture or task is performed.”),
wherein a neural network classifies patterns of images or signals based on a learned features database defining two or more classes for each step of the procedure (Ottensmeyer, col 4, lines 53-54, “…for each motion from a set of motions that have been tabulated for a surgical procedure…”), and
outputs a performance score or figure of merit for each step of the procedure based on the classified patterns (Devam, para 0228, “A student is asked to respond to questions, or to perform tasks, or otherwise interact with the immersive environment as defined in the program. Based on the success or failure of the responses, a grade can be assigned and areas of improvement can be identified.”).
Regarding claim 27 (Previously presented), Ottensmeyer/Devam discloses:
The procedure training system of claim 10, wherein the software program is operable to detect a current step of the particular procedure based on one or more detected model cues or ID-motion cues or still video cues (Ottensmeyer, col 23, lines 10-15, “Based on registration of such events, the methodology of the present invention facilitates the identification of specific patterns of events/states that clearly correlate with specific gestures, subtasks and tasks of a simulated medical procedure.”).
Regarding claim 30 (Previously presented), Ottensmeyer/Devam discloses:
The procedure training system of claim 9 wherein each of the procedures includes a set of activatable cues pertaining thereto, and wherein selecting one or more procedures activates the activatable cues pertaining to the one or more procedures (See citations recited above).
Claim 25 is rejected under 35 U.S.C. 103 as being unpatentable over Ottensmeyer and Devam as applied to claims 5-24, 26-27, and 30 above, and further in view of US 9589374 B1 as filed by Gao, et al. (hereinafter “Gao”).
Regarding claim 25 (Original), Ottensmeyer/Devam discloses using machine learning and cues of interest within a procedure training system to identify quality of performance, but Ottensmeyer/Devam does not disclose an offline training process.
Gao, however, discloses:
The procedure training system of claim 10, wherein training of the software program is an offline (Gao, col 13, line 14, “…one or more off-line trained DCNN (Deep Convolution Neural Network) models…”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to perform training of the machine learning models of Ottensmeyer/Devam via an offline process as taught by Gao. As in Gao, it is within the capabilities of one of ordinary skill in the art to train machine learning models offline for the training system of Ottensmeyer/Devam with the predicted result of uploading the pre-prepared models to execute the models’ intended purpose as needed in Ottensmeyer/Devam.
Response to Arguments
Applicant's arguments filed March 6, 2026 have been fully considered but they are not persuasive.
Applicant’s remarks accompanying the Request for Continued Examination filed March 6, 2026 contend that the rejection of November 6, 2025 under 35 U.S.C. 102 and 35 U.S.C. 103 failed to satisfy the clarity requirements imposed upon the Office. The Examiner respectfully traverses this contention.
Regarding the rejection of claims 1-8 under 35 U.S.C. 102, the Final Rejection dated November 6, 2025 mapped each limitation of claims 1-8 to specifically enumerated portions of Devam – including paragraphs [0004], [0111], [0117], [0158], [0195], [0196], [0200], [0206], [0237], [0246], [0326], [0429], and [0435] and, where applicable, figures 1, 5, and 25. As stated in MPEP 2131, regarding the rejections of claims 1-8 under 35 U.S.C. 102, “To reject a claim as anticipated by a reference, the disclosure must teach every element required by the claim under its broadest reasonable interpretation.” This requirement has been satisfied in detail.
Regarding the rejection of claims 9-27 and 30 under 35 U.S.C. 103, the Office Action expressly identified the secondary references, stated the limitations for which each reference was relied upon, and articulated the rationale supporting the combination consistent with MPEP 2143. The analysis was made explicit and was supported by articulated reasoning with rationale underpinning, as required by KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 415-421, 82 USPQ2d 1385, 1395-97 (2007) and In re Kahn, 441 F.3d 977, 988, 78 USPQ2d 1329, 1336 (Fed. Cir. 2006).
Applicant’s contention that the rejection was deficient in clarity is not well-taken. The standard set forth in MPEP 707.07 – that the ground of rejection be “fully and clearly stated” – has been satisfied. To the extent Applicant disagrees with specific mapping of references to claim limitations, or with the rationale supporting the obviousness combination, such disagreements are substantive in nature and are properly addresses through arguments directed to the merits of the rejection or through claim amendment. They are not deficiencies in the clarity of the rejection itself. The Examiner further notes that the structure and organization of the Final Rejection dated November 6, 2025 matches the structure and organization of the Non-Final Rejection dated February 11, 2025, and Applicant’s response to the Non-Final Rejection dated February 11, 2025 substantively engaged with the cited mapping on a limitation-by-limitation basis, which is itself probative of the fact that the rejection was sufficiently clear to permit a reasoned response.
The Examiner observes that 37 CFR 1.133 and MPEP 713.01 and 713.02 afford an applicant the opportunity to request an interview with the Examiner at any point during prosecution, including prior to the filing of a written response to an Office Action. Interviews are routinely employed for the precise purpose now raised by Applicant – namely, to clarify claim mapping, reference citations, and rejection rationale before the Applicant commits a position to the written record.
In the present prosecution, no interview was requested during the response period following the Non-Final Rejection of February 11, 2025. The first interview of record within the instant application was requested after the Final Rejection had issued. To the extent the Applicant perceives the rejection of record to have been unclear, that perception could have been addressed promptly through the interview practice available throughout the response period. Given the interview that took place on February 11, 2026 did not contain inquiry by the Applicant regarding the Examiner’s “Response to Arguments” section, it is understood that the Applicant did not find clarity issues within said section; therefore, Applicant’s argument that the rejections under 35 U.S.C. 102 and 103 within the Final Rejection of November 6, 2025, which follows the same structure and organization of the Non-Final Rejection of February 11, 2025, regarding the clarity of the Final Rejection is moot. The Examiner remains available to schedule an interview at Applicant’s convenience should any aspect of the present Office Action warrant further discussion.
Applicant further contends that, during the interview on February 11, 2026, the Examiner identified paragraphs within the references that were not expressly enumerated in the Non-Final Rejection of February 11, 2025 or Final Rejection of November 6, 2025. Applicant appears to suggest that this constitutes a deficiency in the written rejection. The Examiner respectfully disagrees.
The regulatory standard set forth in 37 CFR 1.104(c)(2) requires that “the examiner must cite the best references at his or her command.” The standard is one of fair notice and pinpoint citation – not exhaustive reproduction of every supporting passage. The citations provided in an Office Action are intended to direct an applicant to representative portions of the cited reference(s) that establish the disclosure of the claim limitation; they do not constitute an undertaking by the Office to catalog every passage within the reference that may also support the rejection. A reference, once properly applied, stands for everything it teaches to a person of ordinary skill in the art, not merely the specific passages cited. As stated in MPEP 2123, “"The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain." In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)).” and MPEP 2144.01, “"[I]n considering the disclosure of a reference, it is proper to take into account not only specific teachings of the reference but also the inferences which one skilled in the art would reasonably be expected to draw therefrom." In re Preda, 401 F.2d 825, 826, 159 USPQ 342, 344 (CCPA 1968)”.
Correspondingly, 37 CFR 1.111(b) places upon the Applicant the obligation to consider the cited references in their entirety when formulating a response on the merits. The identification, during the course of an interview, of additional paragraphs within a reference already of record does not constitute a new ground of rejection; the reference, the rejection, and the claim-limitation correspondence remain the same. The additional paragraphs serve only to further illustrate the disclosure that was already part of the record by virtue of the reference’s citation. Such disclosure would have been apparent to Applicant upon a complete reading of the reference, as 37 CFR 1.111(b) contemplates.
To the extent the Applicant suggests that the Office bears an affirmative duty to enumerate, within the written Office Action itself, every paragraph of every cited reference that may support the rejection, that suggestion is not supported by 35 U.S.C. 132, 37 CFR 1.104, the MPEP, or controlling precedent. The statutory and regulatory duty is to provide notice sufficient to enable a reasoned response on the merits. That duty was discharged in the Non-Final Rejection of February 11, 2025 and the Final Rejection of November 6, 2025. The duty to read the cited references is, and remains, the Applicant’s.
For the reasons set forth in this Action and as detailed in the rejections within the corresponding sections, the rejections under 35 U.S.C. 102 and 103 are maintained as modified, as updated to address Applicant’s substantive arguments filed March 6, 2026.
Regarding the rejection of claim 1 under 35 U.S.C. 102(a)(2), in the Remarks filed March 6, 2026 on page 5 of 14, para 2, the Applicant argues:
“Applicant respectfully submits that the Devam reference’s use of the term “diagnoses” is inconsistent with the use of term “surgical” in the specification, which relies on the plain and ordinary meaning thereof.”
The Examiner respectfully submits the Examiner has not equated the terms “surgery” and “diagnosis”. Regarding the interview that took place on February 11, 2026, the Examiner merely stated diagnosis is a stage within the process leading up to surgery as no surgery occurs without the identification of the issue. Furthermore, the Examiner does not rely on Devam’s disclosure of diagnosis to map to “surgery” as disclosed within the instant application. Devam discloses “surgery” directly. As disclosed by Devam in [0111] (emphasis added), “[i]n some embodiments, the system is connected to a database of symptoms, diagnoses, and treatment options…[a]s the user and patient speak, their speech is analyzed to identify symptoms and other relevant data...[t]he data is processed, and a ranked or unranked list of possible diagnoses is presented…[t]he diagnoses also include treatment options, such as medications and surgeries, for each particular diagnosis…”. The Examiner views surgery as a “treatment option” as explicitly stated by Devam.
Regarding the rejection of claim 1 under 35 U.S.C. 102(a)(2), in the Remarks filed March 6, 2026 on page 6 of 14, para 4, the Applicant argues:
“a POSITA would conclude that the reference fails to teach surgical courses offered by a medical school (i.e., a surgical training curriculum) since the term “curriculum” is defined as “the courses offered by an educational institution” or “a set of courses constituting an area of specialization.” “Curriculum.” Merriam-Webster.com Dictionary, Merriam-Webster”
The Examiner respectfully disagrees with the Applicant’s sentiment that surgical courses must be offered explicitly by a medical school to be categorized as a curriculum. As seen in the second definition provided by the Applicant of “curriculum” from Merriam-Webster’s Dictionary, a curriculum is “a set of courses constituting an area of specialization”. Further, a “course” is defined as “the act or action of moving in a path from point to point” (Merriam-Webster’s Dictionary) and “an ordered process or succession” (Merriam-Webster’s Dictionary). Neither definition, nor any other definition provided by Merriam-Webster for the term “course”, places a limitation on the term “course” to require a formal educational institution.
Regarding the rejection of claim 1 under 35 U.S.C. 102(a)(2), in the Remarks filed March 6, 2026 on page 7 of 14, paras 2-3, the Applicant argues:
“The Examiner further asserts that the Devam reference’s disclosure of “sensor data may be recorded for the feeling of a normal vs. enlarged spleen in the detection of mononucleosis” teaches “said software program operable to correlate said one or more sensor signals to said one or more individual surgical subject matter components, said software program being further operable to provide as an output the individual surgical subject matter component which is correlated to a received signal” as required by Claim 1. Final Office Action, p. 4 (emphasis added). The above analysis of the Devam reference further applies here.
Given that the detection of mononucleosis is a diagnostic procedure and the Devam’s reference’s use of the term “diagnoses” is inconsistent with the use of term “surgical” in the specification, a POSITA would conclude that the Devam reference does not teach “said software program operable to correlate said one or more sensor signals to said one or more individual surgical subject matter components, said software program being further operable to provide as an output the individual surgical subject matter component which is correlated to a received signal” as required by Claim 1.”
The Examiner respectfully submits the cited portion illustrates Devam’s disclosure of utilizing sensor data, which is correlated with the software program of Devam during various training experiences. Devam also contains various other disclosures regarding the use of sensor data correlated to surgical subject matter components, such as [0046], which states, “Various techniques and apparatuses for training and testing of surgical and diagnostic skills using AR or VR and display of real patient data gathered by magnetic resonance imaging (MRI) are also disclosed. In a number of embodiments, real patient data (e.g., composed from an MRI, CT scan, x-ray, or any other patient data source) is displayed to a practitioner/trainee and further enhanced through AR or VR to simulate a variety of conditions for testing and training.” In [0046], “real patient data” is gathered via an MRI/CT scan/X-Ray/etc. (these systems encompass sensor signals to collect the data), then the “real patient data” is correlated with “a variety of conditions for testing and training” (i.e., one or more surgical subject matter components); therefore, Devam teaches the argued limitation.
Regarding the rejection of claim 9 under 35 U.S.C. 103, in the Remarks filed March 6, 2026 on page 9 of 14, para 3, the Applicant argues:
“Rather than providing a reasoned explanation for the rejection, the Examiner merely appended a citation to the end of each paragraph of the claims with no reasoning or association with the claimed features. As such, Applicant cannot reasonably determine which “element” of the claim is believed to correspond with which section, figure or feature cited in the rejection. In other words, Applicant is not put on notice as to the rationale behind the rejection and cannot adequately determine whether such basis is proper.”
The Examiner respectfully submits a reasoned explanation for the combination of Devam and Ottensmeyer was included within each Office Action previously provided for the instant application. For the Non-Final Rejection of February 11, 2025, the reasoned explanation appears on page 18 as the first full paragraph. For the Final Rejection of November 6, 2025, the reasoned explanation appears on page 11 – directly preceding the rejection of claim 10. The Applicant has been put on notice as to the rationale behind the rejection.
Regarding the rejection of claim 9 under 35 U.S.C. 103, in the Remarks filed March 6, 2026 on pages 10-11 of 14, paras 3 and 1-2, respectively, the Applicant argues:
“Applicant respectfully submits that the Ottensmeyer reference’s use of the term “data” is inconsistent with the use of term “signal” in the specification, which does not include a specific definition of the term and thereby relies on the plain and ordinary meaning thereof. Here, a POSITA would conclude that the specification uses the plain and ordinary meaning of the term “signal.” As such, a POSITA would interpret the term “signal” to mean “a detectable physical quantity or impulse (such as a voltage, current, or magnetic field strength) by which messages or information can be transmitted.” “Signal.” Merriam-Webster.com Dictionary, Merriam-Webster, https://www.merriam-webster.com/dictionary/signal. Accessed 5 Mar. 2026. In other words, signals are the physical, time-varying carriers of information (e.g., voltage, radio waves), acting as the medium of transmission.
On the contrary, a POSITA would interpret the term “data” to mean “information in digital form that can be transmitted or processed.” “Data.” Merriam-Webster.com Dictionary, Merriam-Webster, https://www.merriam-webster.com/dictionary/data. Accessed 5 Mar. 2026. In other words, data represents the structured, encoded content or raw facts (e.g., bits, symbols) conveyed by that signal. Given that a signal conveys data and data is the information conveyed by that signal, a POSITA would conclude that the meaning of the term “data” as used by the Ottensmeyer reference is inconsistent with the use of term “signal” in the specification, which relies on the plain and ordinary meaning thereof.”
The Examiner respectfully submits that a reading of Ottensmeyer’s discussions on “data” and “signals” would make apparent that, as also stated by the Applicant in the quotation above, “a signal conveys data and data is the information conveyed by that signal”. In addition to the citation quoted by the Applicant, Ottensmeyer discloses, “The output signals received from instrument-specific gages of different instruments formed inputs to and were amplified with the use of a dedicated amplifier 260 (miniature circuit board, actual size 10 mm×16 mm), as required, and form instrument-specific output signals facilitating instrument identification by the data acquisition board.” (Ottensmeyer, col 11, lines 26-31). In other words, the data acquisition board (i.e., a cue receiver) receives outputs signals from instrument-specific gages (i.e., a signal generated in response to receiving one or more cues); therefore, Ottensmeyer discloses the argued limitation, “one or more receivers operable to receive one or more cues during performance of the one or more procedures associated with the physical model, and generate a signal in response to receiving the one or more cues” as recited in claim 9 of the instant application.
Regarding the rejection of claim 9 under 35 U.S.C. 103, in the Remarks filed March 6, 2026 on page 11-12 of 14, paras 3-5 and 1, respectively, the Applicant argues:
“On page 10 of the Final Office Action, the Examiner asserts that the Ottensmeyer reference’s disclosure of “[a] simulator structured according to embodiment of the invention…enables surgical task/sequence detection…with a simulated surgical procedure effectuated at the simulator (optionally, in reference to expert knowledge/curriculum)” discloses “a computer database having training curriculum having one or more individual subject matter components stored therein for each of the one or more procedures” as required by Claim 9. Final Office Action, p. 10. Applicant does not agree with the Examiner’s assertion.
To be sure, the Ottensmeyer reference discloses “[a] simulator structured according to embodiment of the invention includes performance scoring algorithms, enables surgical task/sequence detection, identifies differences in operational performance of a novice vs. an expert and provides feedback contemporaneously with a simulated surgical procedure effectuated at the simulator (optionally, in reference to expert knowledge/curriculum)…[i]n one implementation, feedback output created by the simulator represents a performance assessment ranking compared to that for an average expert for a given procedure.” Ottensmeyer, col. 7, 43-53 (emphasis added).
Here, a POSITA would conclude that the Ottensmeyer reference discloses the steps of detecting a surgical task or sequence, identifying differences thereof with an expert performance of the task or sequence, and providing a performance assessment ranking in reference to expert knowledge or curriculum. In other words, the expert curriculum refers to the knowledge an expert would have as opposed to that of a novice (i.e., trainee).”
The Examiner respectfully submits Ottensmeyer discloses more than just “the knowledge an expert would have as opposed to that of a novice” as the system of Ottensmeyer may operate without the presence of an expert. Ottensmeyer discloses, “In absence of the expert, the relevant training images such as graphical arrows, texts, video clips can be projected onto the object of training 150 by the projector 134.” (Ottensmeyer, col 12, lines 44-46); therefore, Ottensmeyer, discloses comparing various performance data points as indicated by the Applicant, but Ottensmeyer also discloses the storage of the curriculum.
Regarding the rejection of claim 9 under 35 U.S.C. 103, in the Remarks filed March 6, 2026 on page 12 of 14, paras 2-3, the Applicant argues:
“The Examiner further posits that the Ottensmeyer reference’s disclosure of “program code for creating a multi-level hierarchy of descriptors representing changes in the operational status of the instrument by determining identifiable portions of the motion based on combination of multiple event outputs” discloses “said software program operable to correlate said received signals to said one or more individual subject matter components for a particular procedure, and provide as an output the individual subject matter component which is correlated to a received signal” as required under Claim 9. Final Office Action, p. 10. Applicant disagrees.
Assuming, arguendo, that the changes in the operational status of the instrument are signals, which Applicant does not, the Ottensmeyer reference merely teaches the creation of descriptors that represent changes in the operational status of the instrument as opposed to correlating those changes in the operation status of the instrument to individual subject matter components for a particular procedure as required by Claim 9.”
The Examiner respectfully submits the cited passage of Ottensmeyer describes the correlation between Ottensmeyer’s changes in the operational status of the instrument (i.e., received signals) and the surgical training simulator system (i.e., individual subject matter components). Ottensmeyer’s disclosure of “combination of multiple event outputs” refers to the surgical training simulator system when using the individual subject matter components; therefore, the Examiner respectfully submits that the argued limitation is taught by Ottensmeyer as cited.
Regarding the rejection of claim 9 under 35 U.S.C. 103, in the Remarks filed March 6, 2026 on page 12 of 14, para 4, the Applicant argues:
“Even assuming, arguendo, the Examiner's proposed combination is possible, a POSITA would not have had a reasonable expectation of success in combining the references to arrive at the claimed invention because neither the Ottensmeyer reference nor the Devam reference teach or suggest “one or more cue receivers operable to receive one or more cues during performance of the one or more procedures associated with the physical model, and generate a signal in response to receiving the one or more cues,” “a computer database having training curriculum having one or more individual subject matter components stored therein for each of the one or more procedures,” and “said software program operable to correlate said received signals to said one or more individual subject matter components for a particular procedure, and provide as an output the individual subject matter component which is correlated to a received signal” as required by Claim 9.”
The Examiner respectfully submits Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references. Specifically, Applicant lists claim limitations with a general assertion that the said claim limitations are not taught by either of the references, Ottensmeyer or Devam, despite the presence of citations by the Examiner from the references indicating the presence of said claim limitations. The Examiner acknowledges that some of the said claim limitations listed were argued earlier in the Remarks. For those limitations, the Examiner directs the Applicant to the corresponding sections for the Examiner’s rebuttal. For the limitations that are not acknowledged earlier in the Remarks, the Applicant must clearly distinguish the instant application from the prior art references by arguing how said citations do not teach the Applicant’s intended disclosure.
Applicant does not argue claims 2-8, 10-27, and 30 separately with particularity.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZACHARY JOSEPH POLLOCK whose telephone number is (703)756-5952. The examiner can normally be reached Monday-Friday 10:00am-8:00pm ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, XUAN THAI can be reached at (571) 272-7147. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Z.J.P./Examiner, Art Unit 3715
/XUAN M THAI/Supervisory Patent Examiner, Art Unit 3715
Prosecution Insights