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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/03/2025 has been entered.
Status of the Application
Claims 1-2, 4-12, and 14-23 are currently pending in this case and have been examined and addressed below.
Claims 1, 4-5, 8, 10-11, 15, 18, and 21 are currently amended.
Claims 3 and 13 are cancelled.
Claims 22 and 23 are added.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 5-6, 9-12, 15-16, and 19-21 are rejected under 35 U.S.C. 103 as being unpatentable over FLEXMAN (US-20200126661-A1)[hereinafter Flexman], in view of Hsieh (US-10438354-B2)[hereinafter Hsieh], in view of Harlev (US-20120184865-A1)[hereinafter Harlev], in view of Spahn (US-8355928-B2)[hereinafter Spahn].
As per Claim 1, Flexman discloses a system for real-time adaptive graphical-user- interface generation that reduces procedure time and streamlines workflow during a cardiac electrophysiology medical procedure in paragraphs [0015-0018] and [0046] (a system for augmented reality learning and customization (synonymous to real-time adaptive graphical-user-interface generation) that reduces procedure time and improves workflow during a cathlab medical procedure (synonymous to a cardiac electrophysiology medical procedure) (Examiner notes that the cathlab medical procedure is a procedure for medical treatment or diagnoses for the heart performed in a cathlab, indicating a cardiac electrophysiology medical procedure)), the system comprising: a memory in paragraphs [0021] and [0025] (a memory); a display in paragraphs [0047] and Figure 1 (a display), an eye-tracking apparatus configured to be worn by a first user performing the cardiac electrophysiology medical procedure and to output gaze-tracking data indicative of present gaze behavior in paragraphs [0015-0016] and [0027-0028] and [0031] and [0049] (a head-mounted display (synonymous to an eye-tracking apparatus) worn by the operator (synonymous to a first user), referred to as user, wherein the head-mounted display tracks the user's view, gestures, and interaction, performing the cathlab medical procedure and to provide gaze-tracking feedback (synonymous to gaze-tracking data), as to a current action or activity of the user (Examiner notes that the current action or activity of the user is synonymous to the present gaze behavior)); and one or more processors that are communicatively coupled to the memory, the display, and the eye-tracking apparatus in paragraphs [0019] and [0025] and [0048] and Figure 1 (one or more processors that are connected to the memory, display and the head-mounted display), wherein the one or more processors are collectively configured to: (a) receive information with respect to initiating the cardiac electrophysiology medical procedure in paragraphs [0058-0059] (receive gathered information with respect to a current condition of the cathlab procedure), and further receive the gaze-tracking data from the eye- tracking apparatus in real time in paragraphs [0058-0059] (receive eye movement and eye focus data from the head-mountable augmented reality platform); (b) predict workflow preferences for the first user from the gaze-tracking data by applying a trained machine-learning model in paragraphs [0016] and [0025] and [0048-0050] and [0060] (predict the operator's way of working (synonymous to workflow preferences for the first user) from the navigation information by applying a trained model).
Flexman discloses receiving information with respect to initiating the cardiac electrophysiology medical procedure and predicting workflow preferences for the user from the gaze-tracking data. Flexman does not disclose receiving information including health demographics and biometric data that includes unique physiological characteristics of an arrhythmia of the patient. Also, Flexman does not disclose predicting workflow preferences from the information. However, Hsieh discloses (a) receive information with respect to initiating the cardiac electrophysiology medical procedure for a patient, the information comprising health demographics and biometric data of the patient in column 17 lines 24-49 (acquire information regarding a patient being examined by the imaging system, the information includes population health information (synonymous to health demographics) and patient context (synonymous to biometric data of the patient) (Examiner notes that a patient being examined by the imaging system is considered to be a medical procedure for patient)); (b) predict workflow preferences for the first user from the information by applying a trained machine-learning model in column 5 lines 5-12 and column 9 lines 26-column 10 lines 10 and column 38 lines 11-23 (determine image quality (workflow) preferences for the users performing the examination using a imaging system using a deep learning network of the analysis engine (synonymous to a model of the mapping engine), wherein the analysis engine includes a deep learning network that takes input information and generates a resulting image).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a system for real-time adaptive graphical-user-interface generation that reduces procedure time and streamlines workflow during a cardiac electrophysiology medical procedure, as disclosed by Flexman, to be combined with receive information including health demographics and biometric data of the patient and predict workflow preferences for the user from the information, as disclosed by Hsieh, for the purpose of improving deep learning medical systems and methods for medical procedures [column 1 lines 15-36].
The combination of Flexman and Hsieh discloses the concept of receiving information with respect to initiating the cardiac electrophysiology procedure and predicting workflow preferences for the user from the information and gaze tracking data. The combination of Flexman and Hsieh does not disclose the biometric data including unique physiological characteristics of one or more arrhythmias of the patient. However, Harlev discloses (a) receive information with respect to initiating the cardiac electrophysiology medical procedure for a patient, the information comprising biometric data of the patient that comprise unique physiological characteristics of one or more arrhythmias of the patient in paragraphs [0309] and [0314] and [0318-0319] and [0381] (receive physiological information of cardiac excitation (synonymous to unique physiological characteristics) of the arrhythmia).
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of the physiological information of cardiac excitation of Harlev for the biometric data of the Flexman and Hsieh. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Flexman, Hsieh, and Harlev do not disclose the following limitations. However, Spahn discloses (c) generate, prior to manual selection by the first user and based on the workflow preferences predicted, image-display options comprising at least one graphical control element that is repositioned, highlighted, or otherwise modified in column 4 lines 5-9 and column 6 lines 50-55 and column 7 lines 44-53 and column 8 lines 38-66 (generate, prior to manual selection by the user and based on workflow preferences, image presentation options including image elements (synonymous to graphical control elements) that are modified); and (d) present the image-display options on the display such that an average time required for the first user to locate the graphical control element is reduced in column 2 lines 14-39 and column 6 lines 50-55 and column 8 lines 24-66 (present the image presentation options on the user interface (synonymous to a display) for bi-plane operation mode or single plane operation (Examiner notes that displaying image presentation options in prompt menu prompts the user to select an operation mode indicating a reduction in time of locating the image elements to select the operation mode)).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a system for real-time adaptive graphical-user-interface generation that reduces procedure time and streamlines workflow during a cardiac electrophysiology medical procedure, as disclosed by Flexman, Hsieh, and Harlev, to be combined with generating image display options with graphical control elements and presenting the image display options such that locating the graphical control element is reduced, as disclosed by Spahn, for the purpose of improving workflow and reducing the number of user interaction steps by predicting a next workflow task [column 1 lines 41-61].
As per Claim 2, Flexman, Hsieh, Harlev, and Spahn disclose the system of claim 1.
Flexman, Hsieh, and Harlev do not disclose the following limitations. However, Spahn discloses wherein the image- display options further comprise an intelligent toolbar in column 3 lines 6-33, column 8 lines 24-49 ( the image presentation options include a prompt menu (synonymous to the intelligent tool bar) (Examiner notes that the prompt menu is considered an intelligent toolbar based on enabling the user to select a display operation mode or a display tool)).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a system for real-time adaptive graphical-user-interface generation that reduces procedure time and streamlines workflow during a cardiac electrophysiology medical procedure, as disclosed by Flexman, Hsieh, and Harlev, to be combined with the image display options including an intelligent toolbar, as disclosed by Spahn, for the purpose of improving workflow and reducing the number of user interaction steps by predicting a next workflow task [column 1 lines 41-61].
As per Claim 5, Flexman, Hsieh, Harlev, and Spahn disclose the system of claim 1.
Flexman and Hsieh do not disclose the following limitations. However, Harlev discloses wherein the unique physiological characteristics of the one or more arrhythmias comprise electrophysiological features derived from intracardiac electrograms and/or surface electrocardiograms associated with the one or more arrhythmias in paragraphs [0309] and [0314] and [0318-0319] and [0381] (physiological information of cardiac excitation (synonymous to unique physiological characteristics) of the arrhythmia includes signals from intracardiac electrograms and surface electrocardiograms).
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of the physiological information of cardiac excitation of Harlev for the biometric data of the Flexman and Hsieh. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
As per Claim 6, Flexman, Hsieh, Harlev, and Spahn disclose the system of claim 1.
Flexman does not disclose the following limitations. However, Hsieh discloses wherein the trained machine-learning model comprises a trained deep learning architecture using a recurrent neural network in column 9 lines 26-49 and column 17 lines 24-49 and column 19 lines 66-column 20 lines 20 (deep learning network includes a training deep learning network model that learns the connections and processes feedback to establish connections and identify patterns (Examiner notes that a neural network the processes feedback to establish connections is considered a recurrent neural network)).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a system for real-time adaptive graphical-user-interface generation that reduces procedure time and streamlines workflow during a cardiac electrophysiology medical procedure, as disclosed by Flexman, to be combined with the trained machine-learning model including a trained deep learning architecture using a recurrent neural network, as disclosed by Hsieh, for the purpose of improving deep learning medical systems and methods for medical procedures [column 1 lines 15-36].
As per Claim 9, Flexman, Hsieh, Harlev, and Spahn disclose the system of claim 1, Flexman also discloses wherein the trained machine-learning model is further trained to: predict next events to be performed by the first user conducting the cardiac electrophysiology medical procedure on the patient in paragraphs [0018] and [0025-0026] and [0032-0033] (predict next steps to be performed by the user conducting the medical procedure on the patient).
As per Claim 10, Flexman, Hsieh, Harlev, and Spahn disclose the system of claim 9, Flexman also discloses wherein the one or more processors are further collectively configured to: predict probabilities of the next events using the trained machine-learning model in paragraphs [0035-0036] (generate possibility scores (synonymous to probabilities) of the next steps using the model).
Flexman, Hsieh, and Harlev do not disclose the following limitations. However, Spahn discloses and generate, within the image-display options, input/output elements for the next events based on the probabilities for selection by the first user in column 2 lines 14-39 and column 8 lines 24-49 (generate, within the image presentation options, a prompt menu (synonymous to input/output elements) for the next task based on the most probable next task).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a system for real-time adaptive graphical-user-interface generation that reduces procedure time and streamlines workflow during a cardiac electrophysiology medical procedure, as disclosed by Flexman, Hsieh, and Harlev, to be combined with generate input/output elements for the next events based on the probabilities for selection by the user, as disclosed by Spahn, for the purpose of improving workflow and reducing the number of user interaction steps by predicting a next workflow task [column 1 lines 41-61].
As per Claim 11, Flexman discloses a method for real-time adaptive graphical-user- interface generation that reduces procedure time and streamlines workflow during a cardiac electrophysiology medical procedure in paragraphs [0015-0018] (a method for augmented reality learning and customization (synonymous to real-time adaptive graphical-user-interface generation) that reduces procedure time and improves workflow during a medical procedure (synonymous to a cardiac electrophysiology medical procedure) (Examiner notes that the medical procedure is a procedure for medical treatment or diagnoses for the heart)), the method comprising: receiving (ii) gaze-tracking data streamed in real time from an eye-tracking apparatus worn by a first user who is performing the cardiac electrophysiology medical procedure in paragraphs [0058-0059] (receive eye movement and eye focus data from the head-mountable augmented reality platform (Examiner notes that augmented reality occurs in real time)); (b) predicting, by applying a trained machine-learning model, workflow preferences of the first user from the gaze-tracking data in paragraphs [0016] and [0025] and [0048-0050] and [0060] (predict the operator's way of working (synonymous to workflow preferences for the first user) from the navigation information by applying a trained model).
Flexman discloses receiving information with respect to initiating the cardiac electrophysiology medical procedure and predicting workflow preferences for the user from the gaze-tracking data. Flexman does not disclose receiving information including health demographics and biometric data that includes unique physiological characteristics of an arrhythmia of the patient. Also, Flexman does not disclose predicting workflow preferences from the information. However, Hsieh discloses (a) receiving (i) information with respect to initiating the cardiac electrophysiology medical procedure for a patient, the information comprising health demographics and biometric data of the patient in column 17 lines 24-49 (acquire information regarding a patient being examined by the imaging system, the information includes population health information (synonymous to health demographics) and patient context (synonymous to biometric data of the patient) (Examiner notes that a patient being examined by the imaging system is considered to be a medical procedure for patient)); (b) predicting, by applying a trained machine-learning model, workflow preferences of the first user from the combination of the information in column 5 lines 5-12 and column 9 lines 26-column 10 lines 10 and column 38 lines 11-23 (determine image quality (workflow) preferences for the users performing the examination using a imaging system using a deep learning network of the analysis engine (synonymous to a model of the mapping engine), wherein the analysis engine includes a deep learning network that takes input information and generates a resulting image).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a method for real-time adaptive graphical-user-interface generation that reduces procedure time and streamlines workflow during a cardiac electrophysiology medical procedure, as disclosed by Flexman, to be combined with receive information including health demographics and biometric data of the patient and predict workflow preferences for the user from the information, as disclosed by Hsieh, for the purpose of improving deep learning medical systems and methods for medical procedures [column 1 lines 15-36].
The combination of Flexman and Hsieh discloses the concept of receiving information with respect to initiating the cardiac electrophysiology procedure and predicting workflow preferences for the user from the information and gaze tracking data. The combination of Flexman and Hsieh does not disclose the biometric data including unique physiological characteristics of one or more arrhythmias of the patient. However, Harlev discloses (a) receiving (i) information with respect to initiating the cardiac electrophysiology medical procedure for a patient, the information comprising biometric data of the patient that comprise unique physiological characteristics of one or more arrhythmias of the patient in paragraphs [0309] and [0314] and [0318-0319] and [0381] (receive physiological information of cardiac excitation (synonymous to unique physiological characteristics) of the arrhythmia).
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of the physiological information of cardiac excitation of Harlev for the biometric data of the Flexman and Hsieh. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Flexman, Hsieh, and Harlev do not disclose the following limitations. However, Spahn discloses (c) generating, prior to manual selection by the first user and based on the workflow preferences predicted, image-display options that include at least one graphical control element repositioned, highlighted, or otherwise modified in paragraphs column 4 lines 5-9 and column 6 lines 50-55 and column 7 lines 44-53 and column 8 lines 38-66 (generate, prior to manual selection by the user and based on workflow preferences, image presentation options including image elements (synonymous to graphical control elements) that are modified); and (d) presenting the image-display options on a display such that an average time required for the first user to locate the graphical control element is reduced in column 2 lines 14-39 and column 6 lines 50-55 and column 8 lines 24-66 (present the image presentation options on the user interface (synonymous to a display) for bi-plane operation mode or single plane operation (Examiner notes that displaying image presentation options in prompt menu prompts the user to select an operation mode indicating a reduction in time of locating the image elements to select the operation mode)).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a method for real-time adaptive graphical-user-interface generation that reduces procedure time and streamlines workflow during a cardiac electrophysiology medical procedure, as disclosed by Flexman, Hsieh, and Harlev, to be combined with generating image display options with graphical control elements and presenting the image display options such that locating the graphical control element is reduced, as disclosed by Spahn, for the purpose of improving workflow and reducing the number of user interaction steps by predicting a next workflow task [column 1 lines 41-61].
As per Claim 12, Flexman, Hsieh, Harlev, and Spahn disclose the method of claim 11.
Flexman, Hsieh, and Harlev do not disclose the following limitations. However, Spahn discloses wherein the image- display options comprise an intelligent toolbar in column 3 lines 6-33, column 8 lines 24-49 ( the image presentation options include a prompt menu (synonymous to the intelligent tool bar) (Examiner notes that the prompt menu is considered an intelligent toolbar based on enabling the user to select a display operation mode or a display tool)).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a method for real-time adaptive graphical-user-interface generation that reduces procedure time and streamlines workflow during a cardiac electrophysiology medical procedure, as disclosed by Flexman, Hsieh, and Harlev, to be combined with the image display options including an intelligent toolbar, as disclosed by Spahn, for the purpose of improving workflow and reducing the number of user interaction steps by predicting a next workflow task [column 1 lines 41-61].
As per Claim 15, Flexman, Hsieh, Harlev, and Spahn disclose the method of claim 11.
Flexman and Hsieh do not disclose the following limitations. However, Harlev discloses wherein the unique physiological characteristics of the one or more arrhythmias comprise electrophysiological features derived from intracardiac electrograms and/or surface electrocardiograms associated with the one or more arrhythmias in paragraphs [0309] and [0314] and [0319] and [0381] (physiological information of cardiac excitation (synonymous to unique physiological characteristics) of the arrhythmia includes signals from intracardiac electrograms and surface electrocardiograms).
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of the physiological information of cardiac excitation of Harlev for the biometric data of the Flexman and Hsieh. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
As per Claim 16, Flexman, Hsieh, Harlev, and Spahn disclose the method of claim 11.
Flexman does not disclose the following limitations. However, Hsieh discloses wherein the trained machine-learning model comprises a trained deep learning architecture using a recurrent neural network in column 9 lines 26-49 and column 17 lines 24-49 and column 19 lines 66-column 20 lines 20 (deep learning network includes a training deep learning network model that learns the connections and processes feedback to establish connections and identify patterns (Examiner notes that a neural network the processes feedback to establish connections is considered a recurrent neural network)).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a method for real-time adaptive graphical-user-interface generation that reduces procedure time and streamlines workflow during a cardiac electrophysiology medical procedure, as disclosed by Flexman, to be combined with the trained machine-learning model including a trained deep learning architecture using a recurrent neural network, as disclosed by Hsieh, for the purpose of improving deep learning medical systems and methods for medical procedures [column 1 lines 15-36].
As per Claim 19, Flexman, Hsieh, Harlev, and Spahn disclose the method of claim 11, Flexman also discloses wherein the trained machine-learning model is further trained to: predict next events to be performed by the first user conducting the cardiac electrophysiology medical procedure on the patient in paragraphs [0018] and [0025-0026] and [0032-0033] (predict next steps to be performed by the user conducting the medical procedure on the patient).
As per Claim 20, Flexman, Hsieh, Harlev, and Spahn disclose the method of claim 19, Flexman also discloses further comprising: predicting probabilities of the next events using the trained machine-learning model in paragraphs [0035-0036] (generate possibility scores (synonymous to probabilities) of the next steps using the model).
Flexman, Hsieh, and Harlev do not disclose the following limitations. However, Spahn discloses and generating, within the image-display options, input/output elements for the next events based on the probabilities for selection by the first user in column 2 lines 14-39 and column 8 lines 24-49 (generate, within the image presentation options, a prompt menu (synonymous to input/output elements) for the next task based on the most probable next task).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a method for real-time adaptive graphical-user-interface generation that reduces procedure time and streamlines workflow during a cardiac electrophysiology medical procedure, as disclosed by Flexman, Hsieh, and Harlev, to be combined with generate input/output elements for the next events based on the probabilities for selection by the user, as disclosed by Spahn, for the purpose of improving workflow and reducing the number of user interaction steps by predicting a next workflow task [column 1 lines 41-61].
As per Claim 21, Flexman discloses a non-transitory computer readable storage medium storing instructions for real-time adaptive graphical-user-interface generation that reduces procedure time and streamlines workflow during a cardiac electrophysiology medical procedure, the instructions when executed by a processor of a surgical console cause the surgical console to perform a method in paragraphs [0015-0018] and [0021] and [0025] (a computer-readable storage medium (synonymous to a non-transitory computer readable storage medium) storing instructions for augmented reality learning and customization (synonymous to real-time adaptive graphical-user-interface generation) that reduces procedure time and improves workflow during a cathlab medical procedure (synonymous to a cardiac electrophysiology medical procedure), the instructions executed by a processor of a workstation (synonymous to a surgical console)(Examiner notes that the cathlab medical procedure is a procedure for medical treatment or diagnoses for the heart performed in a cathlab, indicating a cardiac electrophysiology medical procedure)): comprising: (a) receiving (ii) gaze-tracking data streamed in real time from an eye-tracking apparatus worn by a first user who is performing the cardiac electrophysiology medical procedure in paragraphs [0058-0059] (receive eye movement and eye focus data from the head-mountable augmented reality platform (Examiner notes that augmented reality occurs in real time)); (b) predicting, by applying a trained machine-learning model, workflow preferences of the first user from the gaze-tracking data in paragraphs [0016] and [0025] and [0048-0050] and [0060] (predict the operator's way of working (synonymous to workflow preferences for the first user) from the navigation information by applying a trained model).
Flexman discloses receiving information with respect to initiating the cardiac electrophysiology medical procedure and predicting workflow preferences for the user from the gaze-tracking data. Flexman does not disclose receiving information including health demographics and biometric data that includes unique physiological characteristics of an arrhythmia of the patient. Also, Flexman does not disclose predicting workflow preferences from the information. However, Hsieh discloses (a) receiving (i) information with respect to initiating the cardiac electrophysiology medical procedure for a patient, the information comprising health demographics and biometric data of the patient in paragraphs column 17 lines 24-49 (acquire information regarding a patient being examined by the imaging system, the information includes population health information (synonymous to health demographics) and patient context (synonymous to biometric data of the patient) (Examiner notes that a patient being examined by the imaging system is considered to be a medical procedure for patient)); (b) predicting, by applying a trained machine-learning model, workflow preferences of the first user from the combination of the information in paragraphs column 5 lines 5-12 and column 9 lines 26-column 10 lines 10 and column 38 lines 11-23 (determine image quality (workflow) preferences for the users performing the examination using a imaging system using a deep learning network of the analysis engine (synonymous to a model of the mapping engine), wherein the analysis engine includes a deep learning network that takes input information and generates a resulting image).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a non-transitory computer readable storage medium for real-time adaptive graphical-user-interface generation that reduces procedure time and streamlines workflow during a cardiac electrophysiology medical procedure, as disclosed by Flexman, to be combined with receive information including health demographics and biometric data of the patient and predict workflow preferences for the user from the information, as disclosed by Hsieh, for the purpose of improving deep learning medical systems and methods for medical procedures [column 1 lines 15-36].
The combination of Flexman and Hsieh discloses the concept of receiving information with respect to initiating the cardiac electrophysiology procedure and predicting workflow preferences for the user from the information and gaze tracking data. The combination of Flexman and Hsieh does not disclose the biometric data including unique physiological characteristics of one or more arrhythmias of the patient. However, Harlev discloses (a) receiving (i) information with respect to initiating the cardiac electrophysiology medical procedure for a patient, the information comprising biometric data of the patient that comprise unique physiological characteristics of one or more arrhythmias of the patient in paragraphs [0309] and [0314] and [0318-0319] and [0381] (receive physiological information of cardiac excitation (synonymous to unique physiological characteristics) of the arrhythmia).
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of the physiological information of cardiac excitation of Harlev for the biometric data of the Flexman and Hsieh. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Flexman, Hsieh, and Harlev do not disclose the following limitations. However, Spahn discloses (c) generating, prior to manual selection by the first user and based on the workflow preferences predicted, image-display options that include at least one graphical control element repositioned, highlighted, or otherwise modified in column 4 lines 5-9 and column 6 lines 50-55 and column 7 lines 44-53 and column 8 lines 38-66 (generate, prior to manual selection by the user and based on workflow preferences, image presentation options including image elements (synonymous to graphical control elements) that are modified); and (d) presenting the image-display options on a display such that an average time required for the first user to locate the graphical control element is reduced in column 2 lines 14-39 and column 6 lines 50-55 and column 8 lines 24-66 (present the image presentation options on the user interface (synonymous to a display) for bi-plane operation mode or single plane operation (Examiner notes that displaying image presentation options in prompt menu prompts the user to select an operation mode indicating a reduction in time of locating the image elements to select the operation mode)).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a non-transitory computer readable storage medium for real-time adaptive graphical-user-interface generation that reduces procedure time and streamlines workflow during a cardiac electrophysiology medical procedure, as disclosed by Flexman, Hsieh, and Harlev, to be combined with generating image display options with graphical control elements and presenting the image display options such that locating the graphical control element is reduced, as disclosed by Spahn, for the purpose of improving workflow and reducing the number of user interaction steps by predicting a next workflow task [column 1 lines 41-61].
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over FLEXMAN (US-20200126661-A1)[hereinafter Flexman], in view of Hsieh (US-10438354-B2)[hereinafter Hsieh], in view of Harlev (US-20120184865-A1)[hereinafter Harlev], in view of Spahn (US-8355928-B2)[hereinafter Spahn], in view of Ilkin (US-20080221830-A1)[hereinafter Ilkin] .
As per Claim 4, Flexman, Hsieh, Harlev, and Spahn disclose the system of claim 1.
Flexman, Hsieh, Harlev, and Spahn do not disclose the following limitations. However, Ilkin discloses wherein the one or more processors are further configured to: initiate storage of the information relating to the cardiac electrophysiology medical procedure to a storage device in advance of a predicted time for completion of the cardiac electrophysiology medical procedure in paragraphs [0008] and [0019] and [0023] and [0035] (store patient information, wherein the information includes information associated with the perioperative process (synonymous to information relating to the medical procedure), in a database prior to the predicted duration of the medical procedure in a catheterization department (Examiner notes that cardiac electrophysiology medical procedure is a medical procedure performed in the catheterization department)).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a system for real-time adaptive graphical-user-interface generation that reduces procedure time and streamlines workflow during a cardiac electrophysiology medical procedure, as disclosed by Flexman, Hsieh, Harlev, and Spahn, to be combined with initiate storage of the information relating to the medical cardiac electrophysiology procedure to a storage device in advance of a predicted time for completion of the cardiac electrophysiology medical procedure, as disclosed by Ilkin, for the purpose of improving the workflow process [0003-0004].
As per Claim 14, Flexman, Hsieh, Harlev, and Spahn disclose the method of claim 11.
Flexman, Hsieh, Harlev, and Spahn do not disclose the following limitations. However, Ilkin discloses further comprising: initiating storage of the information relating to the cardiac electrophysiology medical procedure to a storage device in advance of a predicted time for completion of the cardiac electrophysiology medical procedure in paragraphs [0008] and [0019] and [0023] and [0035] (store patient information, wherein the information includes information associated with the perioperative process (synonymous to information relating to the medical procedure), in a database prior to the predicted duration of the medical procedure in a catheterization department (Examiner notes that cardiac electrophysiology medical procedure is a medical procedure performed in the catheterization department)).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a method for real-time adaptive graphical-user-interface generation that reduces procedure time and streamlines workflow during a cardiac electrophysiology medical procedure, as disclosed by Flexman, Hsieh, Harlev, and Spahn, to be combined with initiate storage of the information relating to the medical cardiac electrophysiology procedure to a storage device in advance of a predicted time for completion of the cardiac electrophysiology medical procedure, as disclosed by Ilkin, for the purpose of improving the workflow process [0003-0004].
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over FLEXMAN (US-20200126661-A1)[hereinafter Flexman], in view of Hsieh (US-10438354-B2)[hereinafter Hsieh], in view of Harlev (US-20120184865-A1)[hereinafter Harlev], in view of Spahn (US-8355928-B2)[hereinafter Spahn], in view of Muthalaly (“Applications of Machine Learning in Cardiac Electrophysiology”)[hereinafter Muthalaly] .
As per Claim 7, Flexman, Hsieh, Harlev, and Spahn disclose the system of claim 1.
Flexman, Hsieh, Harlev, and Spahn do not disclose the following limitations. However, Muthalaly discloses wherein the trained machine-learning model is trained based upon an analysis of a plurality of prior cardiac electrophysiology cases in paragraphs 4 and 9 in Surface Electrocardiography and Machine Learning Applications in Cardiac Electrophysiology on page 74 (the models are trained using features of delta wave polarity and R-wave duration as a proportion of the QRS complex (Examiner notes that the feature data indicates an analysis of a plurality of prior cardiac electrophysiology cases was performed)).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a system for real-time adaptive graphical-user-interface generation that reduces procedure time and streamlines workflow during a cardiac electrophysiology medical procedure, as disclosed by Flexman, Hsieh, Harlev, and Spahn, to be combined with the trained machine-learning model is trained based on an analysis of a plurality of prior cardiac electrophysiology cases, as disclosed by Muthalaly, for the purpose of improving the understanding and efficacy of electrogram-based AF ablation [1st paragraph of Intracardiac Mapping on page 75].
As per Claim 17, Flexman, Hsieh, Harlev, and Spahn disclose the method of claim 11.
Flexman, Hsieh, Harlev, and Spahn do not disclose the following limitations. However, Muthalaly discloses wherein the trained machine-learning model is trained based upon an analysis of a plurality of prior cardiac electrophysiology cases in paragraphs 4 and 9 in Surface Electrocardiography and Machine Learning Applications in Cardiac Electrophysiology on page 74 (the models are trained using features of delta wave polarity and R-wave duration as a proportion of the QRS complex (Examiner notes that the feature data indicates an analysis of a plurality of prior cardiac electrophysiology cases was performed)).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a method for real-time adaptive graphical-user-interface generation that reduces procedure time and streamlines workflow during a cardiac electrophysiology medical procedure, as disclosed by Flexman, Hsieh, Harlev, and Spahn, to be combined with the trained machine-learning model is trained based on an analysis of a plurality of prior cardiac electrophysiology cases, as disclosed by Muthalaly, for the purpose of improving the understanding and efficacy of electrogram-based AF ablation [1st paragraph of Intracardiac Mapping on page 75].
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over FLEXMAN (US-20200126661-A1)[hereinafter Flexman], in view of Hsieh (US-10438354-B2)[hereinafter Hsieh], in view of Harlev (US-20120184865-A1)[hereinafter Harlev], in view of Spahn (US-8355928-B2)[hereinafter Spahn], in view of Stiller (US-11977998-B2)[hereinafter Stiller].
As per Claim 8, Flexman, Hsieh, Harlev, and Spahn disclose the system of claim 1, Flexman also discloses wherein the trained machine-learning model is trained by: receiving historical data regarding past medical procedures on a plurality of patients, the historical data including respective workflow preferences of a plurality of users performing the past medical procedures in paragraphs [0026] and [0032] and [0042] (receiving historic data regarding previous performances, referred to as medical procedures, on a plurality of patients, the historic data includes respective workflow preferences of a multiple users performing the previous medical procedures); and training the machine-learning model using the historical data in paragraphs [0032] and [0042] and [0057] (training by the model using the historic data).
Flexman discloses receiving historical data including workflow preferences, but does not disclose the historical data including biometric data and health demographics of the plurality of patients. However, Stiller discloses wherein the trained machine-learning model is trained by: receiving historical data regarding past medical procedures on a plurality of patients, the historical data including respective workflow preferences of a plurality of users performing the past medical procedures, respective biometric data of the plurality of patients, and respective health demographics of the plurality of patients in column 6 lines 1-17 and column 9 lines 39-42 and column 14 lines 14-34 (receive clinical information regarding previous surgeries (synonymous to historical data regarding medical procedures on a plurality of patients), including surgeon's workflow preferences (synonymous to workflow preferences of a plurality of users performing the past medical procedures), a subset of clinical information regarding patients' blood pressure and heart rate (synonymous to biometric data), and patient information (synonymous to health demographics of the plurality of patients), wherein the patient information is grouped by various patients, wherein the patients may be grouped by age (Examiner notes that patients grouped by age indicates that health demographics of the plurality of patients were received)); and training the machine-learning model using the historical data in column 14 lines 14-34 and column 17 lines 20-32 (generate and train the machine learning module (synonymous to the model) using the clinical information regarding previous surgeries).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a system for real-time adaptive graphical-user-interface generation that reduces procedure time and streamlines workflow during a cardiac electrophysiology medical procedure, as disclosed by Flexman, Hsieh, Harlev, and Spahn, to be combined with receiving historical data including biometric data and health demographics of the plurality of patients and training the trained machine-learning model using the historical data, as disclosed by Stiller, for the purpose of improving the workflow of a medical procedure [column 1 lines 58-61].
As per Claim 18, Flexman, Hsieh, Harlev, and Spahn disclose the method of claim 11, Flexman also discloses wherein the trained machine-learning model is trained by: receiving historical data regarding past medical procedures on a plurality of patients, the historical data including respective workflow preferences of a plurality of users performing the past medical procedures in paragraphs [0026] and [0032] and [0042] (receiving historic data regarding previous performances, referred to as medical procedures, on a plurality of patients, the historic data includes respective workflow preferences of a multiple users performing the previous medical procedures); and training the machine-learning model using the historical data in paragraphs [0032] and [0042] and [0057] (training by the model using the historic data).
Flexman discloses receiving historical data including workflow preferences, but does not disclose the historical data including biometric data and health demographics of the plurality of patients. However, Stiller discloses wherein the trained machine-learning model is trained by: receiving historical data regarding past medical procedures on a plurality of patients, the historical data including respective workflow preferences of a plurality of users performing the past medical procedures, respective biometric data of the plurality of patients, and respective health demographics of the plurality of patients in column 6 lines 1-17 and column 9 lines 39-42 and column 14 lines 14-34 (receive clinical information regarding previous surgeries (synonymous to historical data regarding medical procedures on a plurality of patients), including surgeon's workflow preferences (synonymous to workflow preferences of a plurality of users performing the past medical procedures), a subset of clinical information regarding patients' blood pressure and heart rate (synonymous to biometric data), and patient information (synonymous to health demographics of the plurality of patients), wherein the patient information is grouped by various patients, wherein the patients may be grouped by age (Examiner notes that patients grouped by age indicates that health demographics of the plurality of patients were received)); and training the machine-learning model using the historical data in column 14 lines 14-34 and column 17 lines 20-32 (generate and train the machine learning module (synonymous to the model) using the clinical information regarding previous surgeries).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a method for real-time adaptive graphical-user-interface generation that reduces procedure time and streamlines workflow during a cardiac electrophysiology medical procedure, as disclosed by Flexman, Hsieh, Harlev, and Spahn, to be combined with receiving historical data including biometric data and health demographics of the plurality of patients and training the trained machine-learning model using the historical data, as disclosed by Stiller, for the purpose of improving the workflow of a medical procedure [column 1 lines 58-61].
Claims 22 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over FLEXMAN (US-20200126661-A1)[hereinafter Flexman], in view of Hsieh (US-10438354-B2)[hereinafter Hsieh], in view of Harlev (US-20120184865-A1)[hereinafter Harlev], in view of Spahn (US-8355928-B2)[hereinafter Spahn], in view of Stiller (US-11977998-B2)[hereinafter Stiller], in view of Feeny et al.(“ Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology”)[hereinafter Feeny].
As per Claim 22, Flexman, Hsieh, Harlev, and Spahn disclose the system of claim 1.
Flexman, Hsieh, Harlev, and Spahn do not disclose the following limitations. However, Stiller discloses wherein the trained machine-learning model is trained to learn associations between (i) the biometric data and (ii) historical workflow preferences of users performing medical procedures in column 14 lines 14-34 and column 17 lines 20-65 (the machine-learning model learns associations between the clinical information and historical workflow preferences performing medical procedures).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a system for real-time adaptive graphical-user-interface generation that reduces procedure time and streamlines workflow during a cardiac electrophysiology medical procedure, as disclosed by Flexman, Hsieh, Harlev, and Spahn, to be combined with the trained machine learning model is trained to learn association between biometric data and historical workflow preferences of users performing medical procedures, as disclosed by Stiller, for the purpose of improving the workflow of a medical procedure [column 1 lines 58-61].
The combination of Flexman, Hsieh, Harlev, Spahn, and Stiller discloses the trained machine-learning model learning associations between biometric data and historical workflow preferences of users performing medical procedures. The combination does not teach the biometric data including the unique physiological characteristics of the one or more arrhythmias of the patient during cardiac electrophysiology medical procedures. However, Feeny discloses wherein the trained machine-learning model is trained to learn associations between (i) the unique physiological characteristics of the one or more arrhythmias of the patient included in the biometric data cardiac electrophysiology medical procedures in paragraphs Deep Learning section on pages 874-877 (the deep learning model learns patterns of raw ECGs of the multiple arrhythmias of the patient during cardiac electrophysiology medical procedures).
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself that is in the substitution of the raw ECGs of Feeny for the biometric data of the combination of Flexman, Hsieh, Harlev, Spahn, and Stiller. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
As per Claim 23, Flexman, Hsieh, Harlev, and Spahn disclose the method of claim 11.
Flexman, Hsieh, Harlev, and Spahn do not disclose the following limitations. However, Stiller discloses wherein the trained machine-learning model is trained to learn associations between (i) the biometric data and (ii) historical workflow preferences of users performing the medical procedures in column 14 lines 14-34 and column 17 lines 20-65 (the machine-learning model learns associations between the clinical information and historical workflow preferences performing medical procedures).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a method for real-time adaptive graphical-user-interface generation that reduces procedure time and streamlines workflow during a cardiac electrophysiology medical procedure, as disclosed by Flexman, Hsieh, Harlev, and Spahn, to be combined with the trained machine learning model is trained to learn association between biometric data and historical workflow preferences of users performing medical procedures, as disclosed by Stiller, for the purpose of improving the workflow of a medical procedure [column 1 lines 58-61].
The combination of Flexman, Hsieh, Harlev, Spahn, and Stiller discloses the trained machine-learning model learning associations between biometric data and historical workflow preferences of users performing medical procedures. The combination does not teach the biometric data including the unique physiological characteristics of the one or more arrhythmias of the patient during cardiac electrophysiology medical procedures. However, Feeny discloses wherein the trained machine-learning model is trained to learn associations between (i) the unique physiological characteristics of the one or more arrhythmias of the patient included in the biometric data the cardiac electrophysiology medical procedures in paragraphs Deep Learning section on pages 874-877 (the deep learning model learns patterns of raw ECGs of the multiple arrhythmias of the patient during cardiac electrophysiology medical procedures).
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself that is in the substitution of the raw ECGs of Feeny for the biometric data of the combination of Flexman, Hsieh, Harlev, Spahn, and Stiller. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Response to Arguments
Applicant’s arguments, see Page 11, “Claim Objections”, filed 12/03/2025, with respect to claim 10 have been fully considered and are persuasive. The claim objection of claim 10 has been withdrawn.
Applicant’s arguments, see Pages 12-20, “Claim Rejections - 35 U.S.C. & 103” filed 12/03/2025 with respect to claims 1-20 have been fully considered. With regards to independent claims 1, 11, and 21, Applicant argues that Flexman, Hsieh, and Spahn do not teach or suggest the amended limitations of the claims. Examiner finds this persuasive. Therefore, the rejection of 11/13/2025 has been withdrawn. However, upon further consideration a new grounds of rejection is made over Flexman, in view of Hsieh, in view of Harlev, in view of Spahn. In response to the argument that the references do not teach arrhythmia-specific biometric data and feeding the arrhythmia specific biometric data in a model that predicts electrophysiology console workflow preferences, Examiner points out that Flexman and Hsieh in combination with Harlev discloses the concept of receiving information with respect to initiating the cardiac electrophysiology procedure and predicting workflow preferences for the user from the information and gaze tracking data, wherein the biometric data disclosed in column 17 lines 24-49 of Hsieh can be substituted with the physiological information of cardiac excitation of the arrhythmia disclosed in paragraphs [0314] and [0318-0319] of Harlev. Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious. Additionally, the Applicant argues that there is no articulated motivation to combine Spahn’s disclosure of image elements associated with the next task to the concept of receiving information with respect to initiating the cardiac electrophysiology procedure and predicting workflow preferences for the user from the information and gaze tracking data disclosed by the combination of Flexman and Hsieh. Examiner notes that it would have been obvious to one of ordinary still in the art to include in the system and method of real-time adaptive graphical user-interface generation that reduces procedure time and streamlines workflow during a cardiac electrophysiology medical procedure of Flexman, Hsieh, and Harlev with the generation of image display options and presenting the image-display options on the display so that the average time required to locate the graphical control element is reduced as taught by Spahn since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately. One of ordinary skill in the art would have recognized that the results of the combination were predictably a system or a method that treat arrhythmia-specific physiological characteristics as a distinct category of biometric data, to compute or extract such characteristics, or to feed them into a model that predicts electrophysiology console workflow preferences.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Corrado C. A work flow to build and validate patient specific left atrium electrophysiology models from catheter measurements teaches on a workflow to capture clinically measured patient specific electrophysiological heterogeneity.
Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/K.N.W./Examiner, Art Unit 3682
/FONYA M LONG/Supervisory Patent Examiner, Art Unit 3682