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 .
Response to Arguments
Applicant’s arguments, see amendment and Remarks, filed 6/24/26, with respect to the rejection of claims 1-20 under 35 USC 101 have been fully considered and are persuasive. The rejection of claims 1-20 under 35 USC 101 has been withdrawn.
Applicant’s arguments with respect to claim(s) 1, 13, 17-18, and 20 were rejected under 35 U.S.C. § 102(a)(1) have been considered but are moot in view of the new grounds of rejection.
Applicant’s arguments with respect to claim(s) 1-2 and 13-20 were rejected under 35 U.S.C. § 103 have been considered but are moot in view of the new grounds of rejection.
Applicant’s arguments with respect to claim(s) 3-4 were rejected under 35 U.S.C. § 103 have been considered but are moot in view of the new grounds of rejection.
Applicant’s arguments with respect to claim(s) 5-12 were rejected under 35 U.S.C. § 103 have been considered but are moot in view of the new grounds of rejection.
The examiner rejected the claims as set forth below upon consideration of a new reference to LODDENKEMPER et al.( US 20230386025) and previously applied reference Stafstrom and a reconsideration of Alves et al.(WO2020006271) and Stafstrom in view of a new reference to Ray et al.( US 10485471). It is noted that LODDENKEMPER et al.( US 20230386025) has priority to at least a continuation to PCT/US2022/014212 with a filing date of Jan. 28, 2022 which is prior to applicants filing date of Aug. 30, 2022. It appears that applicants currently amended claims include subject matter that is not supported in the provisional applications ( 63/239,158 and 63/243,896) to afford the earlier dates.
LODDENKEMPER et al. teaches a System and method to determine whether the patient experiences a grand tonic clonic seizures using video recordings or sensor data or both. The systems and methods receive data from a device that continuously records video and/or sensor data, continuously analyzes the data with a processing unit utilizing machine learning to classify segments of the data as seizure or no seizure, and to classify seizure types. An alarm is produced using an output unit when an epileptic data segment is detected. The processing unit thus provides continuous and in real-time monitoring of an epilepsy patient in the home or hospital setting.
Ray et al.( US 10485471) teaches a system and method for measuring, via a sensor associated with the patient monitoring device, a breathing rate of the patient and obtaining, via a camera associated with the patient monitoring device, video data of the patient's face for identifying ictal states in a patient. Ray et al. also teaches interacting with the patient to convey information. Alves et al. teaches monitoring brain health and predicting and detecting seizures via a wearable device, a plurality of sensors, at least one camera, a wireless communication element, and a frame. The at least one camera records image data of a user's face. The wireless communication element transmits sensor data from the plurality of sensors to an external computing device. And Stafstrom et al. teaches diagnosing and managing absence epilepsy by telemedicine in which the patient is instructed over an audio/video communication device to breath in a way to provoke hyperventilation to better diagnose the likelihood of absence epilepsy.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A 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.
Claim(s) 1,3,8,9,11,14, and 16-26 is/are rejected under 35 U.S.C. 103 as being unpatentable over LODDENKEMPER et al.( US 20230386025) hereinafter LODDENKEMPER et al. in view of Stafstrom et al.
LODDENKEMPER et al. teaches a System and method to determine whether the patient experiences a grand tonic clonic seizures using video recordings or sensor data or both. The systems and methods receive data from a device that continuously records video and/or sensor data, continuously analyzes the data with a processing unit utilizing machine learning to classify segments of the data as seizure or no seizure, and to classify seizure types. An alarm is produced using an output unit when an epileptic data segment is detected. The processing unit thus provides continuous and in real-time monitoring of an epilepsy patient in the home or hospital setting, e.g. while the patient is sleeping in bed. [0097] A seizure may be considered a “symptom . . . of an occasional, an excessive and a disorderly discharge of nerve tissue”. The definition of epileptic seizure may include, “a transient occurrence of signs and/or symptoms due to abnormal excessive or synchronous neuronal activity in the brain”. Specific seizure types have varying features that may enable detection. In some embodiments, the multi-sensor monitoring system may include one or more sensors for the detection of various types of seizures. Indeed, some seizures are more effectively identified using particular sensors. [0101] In some embodiments, a seizure early-warning system 100 may automatically determine whether a patient experiences a GTCS using video recordings. The seizure early-warning system 100 may include a video capture device 161 to continuously record video of a patient location 162, a processing unit 110 to continuously analyze the video data and an alarm unit 150 that provides an alarm 154 when an epileptic video is detected. [0102] In some embodiments, the processing unit 110 may include any type of data processing capacity, such as a hardware logic circuit, for example an application specific integrated circuit (ASIC) and a programmable logic, or such as a computing device, for example, a microcomputer or microcontroller that include a programmable microprocessor. In some embodiments, the processing unit 110 may include data-processing capacity provided by the microprocessor. In some embodiments, the microprocessor may include memory, processing, interface resources, controllers, and counters. In some embodiments, the microprocessor may also include one or more programs stored in memory.
[0103] In some embodiments, the processing unit 110 may be a part of a computing device associated with the patient, a caregiver of the patient, a family member of the patient, a healthcare facility or service associated with the patient or where the patient location 162 is located, among other related systems responsible for monitoring health of the patient. In some embodiments, the processing unit 110 may embodied is a user computer, laptop computer, mobile computing device, server or server system, cloud computing system, or other computing system. [0104] In some embodiments, the processing unit 110 may include a database 140 for receiving and storing the received video from the video capture device 161. In some embodiments, the database 140 may be configured to maintain a record of the video for a predetermined period of time, e.g., about a week, a month, 3 months, 6 months, a year, or other suitable length of time. As such, the database 140 may provide video data related to a patient for a period of time to allow a user to review the data, to update or train seizure recognition models of the seizure recognition engine 120, among other uses. As such, the length of time may be any suitable length of time to review the data, update the models, or any other use, subject to the pertinent patient privacy requirements (e.g., HIPPA or other laws and standards). Note also paragraphs [0105] – [0111]. [0207] Alternatively, or in addition, in some embodiments, the signal processor 822 may process the biometric sensor data 803 by, e.g., analyzing the biometric sensor measurement segments of the biometric sensor data 803 for movements performed by a person, such as, e.g., breathing, pulse, limb or other bodily movement, among other data. For example, in some embodiments, the signal processor 822 may include, e.g., a respiration detection model to analyze a time-series of the biometric sensor data 803 to identify breathing motions (e.g., using accelerometer, NIRS, video frames, mattress sensors, etc.), e.g., by outputting a time-series of respiration statuses and/or measurements, and/or a respiration detection model to analyze a time-series of the biometric sensor data 803 to calculate an inferred respiration rate based on heart rate and blood flow as measured by a PPG signal, or by any other suitable respiration detection technique or any combination thereof. In another example, in some embodiments, the signal processor 822 may include, e.g., a pulse detection model to analyze a time-series of the biometric sensor data 803 and/or video data 802 and identify heart rate-related motions, e.g., by outputting a time-series of pulse measurements. In another example, in some embodiments, the signal processor 822 may include, e.g., a tremor detection model to analyze a time-series of the biometric sensor data 803 and/or video data 802 and identify bodily motions indicate of a tremor, e.g., by outputting a time-series of tremor measurements for each body part.
Regarding claims 1 and 22, LODDENKEMPER et al. does teach monitoring the patient via video and a microphone to monitor the breathing of the patient and a user interface to communicate to a patient and obtaining patient data regarding neurological events such as seizures. Note above and paragraphs [0126], [0154], and [0158].
However LODDENKEMPER et al. does not specifically teach the interface to guide the patient to provoke an event by instructing the patient to induce hyperventilation or giving the patient feedback regarding the breathing rate relative to a target or whether they are in the video or not.
Stafstrom et al. teaches diagnosing and managing absence epilepsy by telemedicine in which the patient is instructed over an audio/video communication device to breath in a way to provoke hyperventilation to better diagnose the likelihood of absence epilepsy. Stafstrom et al. further teaches hyperventilation is a reliable way to provoke an absence seizure. Stafstrom et al. further teaches a number of methods to help the patient to hyperventilate including the use of apps for cell phones with animations to aid in the process. It is noted that and recognized in Stafstrom et al., in order for the patient to achieve hyperventilation it would be necessary for the patient to breath at a fast enough rate( threshold) to ensure hyperventilation and if the patient is not breathing at the desired rate instructions for the patient to adjust their breathing pattern would be needed.
Therefore It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the device of LODDENKEMPER et al. the option of provoking the patient to hyperventilate to diagnose seizures including absence seizures and to give feedback to breath at an appropriate rate to ensure hyperventilation is achieved as taught by Stafstrom et al.
LODDENKEMPER et al. as modified by Stafstrom et al. does not specifically teach wherein evaluating the suitability of the patient data comprises detecting whether the patient's face is sufficiently visible in the video data or wherein, if the patient's face is not sufficiently visible in the video data, the feedback comprises instructions to reposition the patient's face.
It is noted that in order for the cameras of LODDENKEMPER et al. as modified by Stafstrom with video telemedicine of induced seizures to be able to detect patient motion which LODDENKEMPER et al. teaches indicate seizures, the patient’s face must be properly visible within the field of view of the camera and there are a limited number of choices available to a person of ordinary skill in the art correct for situations where the patient’s face is not sufficiently visible within the field of view of the camera. Therefore, It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the device of LODDENKEMPER et al. as modified by Stafstrom the option of determining if the camera includes a sufficient view of a patient’s face and if not sufficient enough to detect eye movement or facial twitches, instruct the patient to better position their face within the cameras with a reasonable expectation of successfully obtaining better facial detection and thus better seizure detection. See KSR Int’l Co. v. Teleflex Inc., 127 S.Ct. 1727, 1742, 82 USPQ2d 1385, 1396 (2007).
Regarding claim 3, LODDENKEMPER et al. as modified by Stafstrom et al. teaches wherein the hyperventilation-triggered seizure comprises an absence seizure.
Regarding claims 8, LODDENKEMPER et al. as modified by Stafstrom et al. teaches the sensor comprises a microphone configured to capture audio data, and wherein the breathing rate is measured based on the audio data.
Regarding claims 9, LODDENKEMPER et al. as modified by Stafstrom et al. teaches wherein the microphone is part of a mobile device. Note paragraphs [0124] – [0125], [0158].
Regarding claims 11, LODDENKEMPER et al. as modified by Stafstrom et al. does not specifically teach if the breathing rate of the patient is less than the target breathing rate, the first feedback comprises instructions to increase the breathing rate, and if the breathing rate of the patient is greater than the target breathing rate, the first feedback comprises instructions to decrease the breathing rate.
It is noted that and recognized in Stafstrom et al., in order for the patient to achieve hyperventilation it would be necessary for the patient to breath at a fast enough rate( threshold) to ensure hyperventilation and if the patient is not breathing at the desired rate instructions for the patient to adjust their breathing pattern would be needed.
Therefore It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the device of LODDENKEMPER et al. as modified by Stafstrom et al. the option of provoking the patient to hyperventilate to diagnose seizures including absence seizures and to give feedback to breath at an appropriate rate including increasing or decreasing the breathing rate to ensure hyperventilation is achieved as taught by Stafstrom et al.
Regarding claim 14, LODDENKEMPER et al. as modified by Stafstrom et al. teaches wherein the video data is generated by an imaging device of a mobile device. Note paragraphs [0124] – [0125], [0158].
Regarding claim 16, LODDENKEMPER et al. as modified by Stafstrom et al. teaches in paragraph [0160] In some embodiments, an automatic video detection system may use velocity, area, duration, rotation, oscillation, angular speed, and/or displacement (motion trajectory) to detect seizures based on changes in position and pose of the patient and/or body parts of the patient in the patient location 763. The underlying concept is to detect complex motor patterns by automatic interpretation of video data. In some embodiments, automatic video detection may include as marker-based or marker-free types of detection, depending on whether the cameras track detectable markers placed in relevant places. In some embodiments, automatic video detection may detect seizure types such as focal, hypermotor, myoclonic, and clonic. Myoclonic seizures are detected with good sensitivity and specificity with a marker-based system using spatio-temporal interest points. Reference markers may be placed on the head, trunk and extremities to asses for movement when evaluated with infrared light by a video system. In some embodiments, marker-based devices present with the shortcoming that sensors can be uncomfortable or dislocate over time. In some embodiments, marker-free systems detect seizures with a motor component but may have difficulty in detecting seizures without a motor component and may be more limited to the area covered by video: the patient must be visible and properly placed. Seizure detection based on video is feasible, but it recognizes mainly seizures with large movements.
However LODDENKEMPER et al. as modified by Stafstrom et al. does not specifically teach wherein evaluating the suitability of the patient data comprises detecting whether the patient's face is sufficiently visible in the video data or wherein, if the patient's face is not sufficiently visible in the video data, the feedback comprises instructions to reposition the patient's face.
It is noted that in order for the cameras of LODDENKEMPER et al. as modified by Stafstrom with video telemedicine of induced seizures to be able to detect patient motion which LODDENKEMPER et al. teaches indicate seizures, the patient’s face must be properly visible within the field of view of the camera and there are a limited number of choices available to a person of ordinary skill in the art correct for situations where the patient’s face is not sufficiently visible within the field of view of the camera. Therefore, It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the device of LODDENKEMPER et al. as modified by Stafstrom the option of determining if the camera includes a sufficient view of a patient’s face and if not sufficient enough to detect eye movement or facial twitches, instruct the patient to better position their face within the cameras with a reasonable expectation of successfully obtaining better facial detection and thus better seizure detection. See KSR Int’l Co. v. Teleflex Inc., 127 S.Ct. 1727, 1742, 82 USPQ2d 1385, 1396 (2007).
Regarding claims 17, and 24-26, LODDENKEMPER et al. as modified by Stafstrom et al. does not specifically teach wherein the provocation sequence includes a baseline period, a provocation period, and a cooldown period nor giving feedback throughout each period.
It is noted that and recognized in Stafstrom et al. that the patient is instructed to increase the breathing rate to achieve hyperventilation and that hyperventilation was performed for a period of time. This would include a time prior to the patient increasing their breathing rate ( baseline), the time while hyperventilation is performed ( provocation period) and the time after hyperventilation is completed ( which would be a return to baseline or “cooldown” period).
Therefore It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the device of LODDENKEMPER et al. as modified by Stafstrom et al. the option of provoking the patient to hyperventilate for a period of time and include the period prior to hyperventilating as a baseline, the induced hyperventilation as the provocation period and the time after hyperventilation as the return to baseline or “cooldown” period and to provide feedback to the patient throughout the process to better diagnose seizures including absence seizures as taught by Stafstrom et al. and It is further noted that there are a limited number of choices available to a person of ordinary skill in the art for initiating hyperventilation and providing feedback to the patient during the process. Therefore, it would have been obvious to one of ordinary skill in the art to try to give feedback to the patient before during and after the process with a reasonable expectation of successfully initiating hyperventilation and being able to measure any resulting seizures. See KSR Int’l Co. v. Teleflex Inc., 127 S.Ct. 1727, 1742, 82 USPQ2d 1385, 1396 (2007).
Regarding claim 18, LODDENKEMPER et al. as modified by Stafstrom et al. teaches determining whether the patient experienced the hyperventilation-triggered seizure based on the video data.
Regarding claim 19, LODDENKEMPER et al. as modified by Stafstrom et al. teaches wherein the patient data comprises video data of the patient's face and eyes, and wherein determining whether the patient experienced the neurological event comprises analyzing the video data using a two-stage machine learning algorithm. Note above and paragraph [0182] in LODDENKEMPER et al. In some embodiments, the seizure recognition engine 720 may process each epoch of video data and biometric sensor measurements to determine whether each epoch includes a seizure occurrence. In some embodiments, the seizure recognition engine 720 may employ machine learning models and techniques trained to detect seizure occurrences based on epochs of the video data in tandem with biometric sensor measurements. Thus, in some embodiments, the seizure recognition engine 720 may include a machine learning model for differentiating between video combined with biometric measurements of patient activity that indicates “seizure” and video combined with biometric measurements of patient activity that indicates “no seizure.” To do so, in some embodiments, the seizure recognition engine 720 may include artificial intelligence (AI) or machine learning techniques for generating a binary classification of an epoch of data at each detection period based on the epoch of video data and biometric sensor measurements and, e.g., physiological parameters and physiological data.
Regarding claim 20, LODDENKEMPER et al. as modified by Stafstrom et al. teaches transmitting the patient data to a remote system configured to determine whether the patient experienced the neurological event based on the patient data. Paragraph [0229] – [0238] in LODDENKEMPER et al.
Regarding claim 21, LODDENKEMPER et al. as modified by Stafstrom et al. does not specifically teach wherein the target breathing rate is within a range from 20 breaths per minute to 24 breaths per minute.
It is noted that and recognized in Stafstrom et al., in order for the patient to achieve hyperventilation it would be necessary for the patient to breath at a fast enough rate( threshold) to ensure hyperventilation and if the patient is not breathing at the desired rate instructions for the patient to adjust their breathing pattern would be needed.
Therefore It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the device of LODDENKEMPER et al. as modified by Stafstrom et al. the option of provoking the patient to hyperventilate to diagnose seizures including absence seizures and to give feedback to breath at an appropriate rate including increasing or decreasing the breathing rate and setting the rate from 20 breaths per minute to 24 breaths per minute to ensure hyperventilation is achieved as a matter of design choice and It is further noted that there are a limited number of choices available to a person of ordinary skill in the art for initiating hyperventilation. Therefore, it would have been obvious to one of ordinary skill in the art to try setting the rate from 20 breaths per minute to 24 breaths per minute with a reasonable expectation of successfully initiating hyperventilation. See KSR Int’l Co. v. Teleflex Inc., 127 S.Ct. 1727, 1742, 82 USPQ2d 1385, 1396 (2007).
Regarding claim 23, LODDENKEMPER et al. as modified by Stafstrom et al. teaches wherein the microphone is part of a mobile device. Note paragraphs [0124] – [0125], [0158].
Claim(s) 5, 6, 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over LODDENKEMPER et al.( US 20230386025) hereinafter LODDENKEMPER et al. in view of Stafstrom et al. and further in view of Hirano et al.( WO 2020085108) hereinafter Hirano et al.
LODDENKEMPER et al. as modified by Stafstrom et al. teach the claimed invention as set forth above including in LODDENKEMPER et al. Note paragraphs [0124] – [0125], [0158].
Regarding claims 5-6 and 12 LODDENKEMPER et al. as modified by Stafstrom et al. do not specifically teach wherein the user interface is configured to display a graphical representation of a target breathing rate for the hyperventilation or wherein the graphical representation includes an animation representing the target breathing rate.
Hirano et al. teaches a monitoring device, a monitoring method, and a monitoring program that can reduce the risk relating to the physical condition of the user. The monitoring device includes a control unit 10, a storage unit 12, an acquisition unit 20, and a notification unit 30. The monitoring device 1 may further include an input unit 40 that receives an input from a user. The monitoring device 1 is connected to the external sensor 50 via the acquisition unit 20. The monitoring device 1 may include the sensor 50 inside. The sensor 50 is attached to the user and detects biometric information of the user. The monitoring device 1 can monitor changes in the physical condition of the user based on the biometric information of the user detected by the sensor 50. The sensor 50 may include a device that detects the breathing rate of the subject within a predetermined time period. The sensor 50 may include a device that detects brain waves of a subject. The control unit 10 determines whether the biometric information satisfies the first determination criterion (step S23). The first criterion may include that the respiratory rate of the subject is equal to or higher than a predetermined threshold. If the breathing rate of the subject is greater than or equal to a predetermined threshold, the subject may be hyperventilated. The first criterion may include that the respiratory rate of the subject is less than a predetermined threshold. A subject may be apnea if the subject's respiratory rate is below a predetermined threshold. The control unit 10 may determine whether the subject is in a state of hyperventilation or apnea based on the breathing rate of the subject (step S711). Furthermore, The notification unit 30 may display characters, images, and the like on the display device to notify the content based on the control information acquired from the control unit 10.
It is noted there are a limited number of choices available to display characters and images to users to relay information including animations. Therefore, It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the device of LODDENKEMPER et al. as modified by Stafstrom et al. graphical representation including images and animations of target breathing thresholds as taught by Hirano et al. with a reasonable expectation of success to aid the patient in achieving hyperventilation and to enable an accurate neurologic measurement. See KSR Int’l Co. v. Teleflex Inc., 127 S.Ct. 1727, 1742, 82 USPQ2d 1385, 1396 (2007).
Claim(s) 1,3,8,9,11,14, and 16-26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Claim(s) 1-2 and 13-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Alves et al.(WO2020006271) hereinafter Alves et al. in view of Ray et al.( US 10485471) hereinafter Ray et al. and further in view of Stafstrom et al.
Alves et al. teaches monitoring brain health and predicting and detecting seizures via a wearable device, a plurality of sensors, at least one camera, a wireless communication element, and a frame. The at least one camera records image data of a user's face. The wireless communication element transmits sensor data from the plurality of sensors to an external computing device. The frame houses the at least one camera, the wireless communication element, and the plurality of sensors. The frame is configured to be worn on the head of the user. [0093] Cameras can include video cameras and photographic cameras. These cameras can detect eye movements, blinking, pupil size, skin color, and a heart rate. For example, changes in eye movements, blinking, and pupil size can indicate that a seizure event is occurring. Analysis of camera data can determine normal values and determine how the data differs during a seizure event. [0013] In some examples, each sensor of the plurality of sensors includes at least one of: a light source, an electrical sensor, a microphone, a photometric sensor, a light sensor, an accelerometer, an electroencephalogram (EEG) sensor, an electromyography (EMG) sensor, an electrooculogram (EOG) sensors, an electrocardiography (EKG) sensors, an electro-dermal activity (EDA) sensor, and a kinetic sensor. [0022] In some examples, the external computing device is a smart phone. For example, the smartphone is further configured to receive electroencephalography (EEG) data output by at least one of the plurality of sensors. For example, the EEG data includes electrical signals representing brain activity of the user. The smartphone further provides for processing the EEG data using a machine learning model to identify a time window of a subset of the EEG data representing a seizure. The smartphone further provides for outputting at a display the identified time window representing a seizure. [0095] For example, location 802 can include wide-angle cameras pointing towards the eyes and face of a wearer. Wide-angle cameras can detect eye movement, pupil size, blinking, skin color, pulse, facial movements, and facial twitching. Many of these movements can indicate seizures and general wellness of the wearer. Changes in eye movement and pupil size, or eye lids closing, can indicate that a wearer is losing consciousness due to a seizure episode. Pulse (heart rate), for example, can be derived from the wearer’s skin color. Location 802 can also include visible or non-visible light sources pointing towards the eyes and face of the wearer. This can help detect visual data from the wearer. [00151] The cloud application 540 can also provide for a user interface 550. This allows patients/users, caregivers, and doctors to access the raw sensor data, the model training, and the detected seizure data. Patients/users and caregivers 552 can have separate user interfaces from doctors 554. For example, the interface can provide alerts or notifications 566 sent to a mobile application 530 when a seizure event is detected. In some examples, the user interface 550 can give the patient, caregiver, doctor, and/or any health care provider the ability to confirm or deny that a seizure event took place during a seizure event detected by the real-time machine learning 542. In other examples, the user interface 550 can allow the patient, caregiver, doctor, and/or any caregiver to identify that a seizure did occur during a certain timeframe, when the cloud application 540 did not detect a seizure during that timeframe. In all instances, the user interface 550 can send the corrections to the automated model training 546 to then update the machine learning model. [0094] Microphones can detect sound. Increased background noise can indicate that the user is experiencing shaky movements of a seizure event. The microphone can also detect voices from others indicating alarm or concern for a user. In some instances, a microphone can also indicate a warning to the user by sounding an alarm. The microphone can also record biological data such as breathing. Analysis of microphone data can determine normal values and determine how the data differs during a seizure event.
Regarding claims 1 and 22, Alves et al. does teach monitoring the patient via video and a microphone to monitor the breathing of the patient and a user interface to communicate to a patient and obtaining patient data regarding neurological events such as seizures.
However Alves et al. does not specifically teach the interface to guide the patient to provoke an event by instructing the patient to induce hyperventilation or giving the patient feedback regarding the breathing rate relative to a target or whether they are in the video or not.
Ray et al. teaches in the same field of endeavor measuring, via a sensor associated with the patient monitoring device, a breathing rate of the patient and obtaining, via a camera associated with the patient monitoring device, video data of the patient's face for identifying ictal states in a patient. Ray et al. also teaches interacting with the patient to convey information. Paragraph (15), Recent studies with controlled clinical conditions have shown promising results that ictal states can be statistically separated from normal homeostasis using single sensor modalities other than EEG. Variations in heart rate, breathing rate, and specific movements have been shown to correlate with seizures. Paragraph (16), ( For purposes of this document, a photoplethysmographic (PPG) sensor is an optical sensor adapted to be placed on or near skin and adapted to measure one or more of blood oxygenation, pulse, cardiac arrhythmia, respiration rate, blood pressure, and cardiac pulse waveform. Paragraph (17), machine-learning algorithms that incorporate patient response to queries in state classification, while using multiple sensor types. Traditional machine-learning techniques seek to maximize classification of true positive and negative events and minimize false positive and negative classifications. In the context of seizure detection, the goal of such techniques would be to count seizure events with extremely high accuracy based solely on sensed extra-cerebral signals. In contrast, our machine-learning techniques work in two stages: generate preliminary seizure classifications by processing extra-cerebral signals, and then seek additional input through a patient responsiveness test to increase the classification accuracy. In other words, the device of the invention mimics what a human caregiver would do upon observation of a possible seizure—for example asking the patient “Are you having a seizure?” The lack of response affirms the seizure to high probability owing to seizure amnesia. If the patient is experiencing a partial seizure without alteration of responsiveness, she or he could also confirm this. Paragraph (22), FIG. 1 shows one exemplary system 100 for identifying ictal states in a patient. System 100 includes a signal processing unit 102, and a plurality of sensors that includes at least two of the following sensors: PPG sensor 120, accelerometer 122, electrocardiogram (ECG) sensor 124, a microphone 126, a temperature sensor 128, a galvanic skin response (GSR) (also known as electrodermal activity (EDA) or electrodermal response) sensor 130, electromyography (EMG) sensor 140, an optical facial muscle motion sensor (134), and optical eye motion tracking sensor (141). Signal processing unit 102 includes a memory 104 and at least one processor 106 communicatively coupled with memory 104. In embodiments, the ECG sensor 124 includes a two-electrode sensing unit; in alternative embodiments, the ECG sensors 124 include additional electrodes. (23) In an embodiment, optical eye movement sensors 132 mounted to an eyeglass frame or cap brim are provided. In embodiments, electromyographic sensors 140 adapted to detect movements of muscles known to be involved in a particular patient's seizures, are provided. In some embodiments, an optical sensor 134 or camera is positioned on a cap brim or mounted to an eyeglass frame to observe movements of facial muscles.
Therefore, It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the device of Alves et al. to include monitoring the breathing rate in the patient as taught by Ray et al. to help identify whether the patient is having a seizure or not.
Alves et al. as modified by Ray et al. do not teach guiding the patient to hyperventilate to induce a seizure, giving feedback to the patient with respect to the breathing, or the visibility of the patient in the video.
Stafstrom et al. teaches diagnosing and managing absence epilepsy by telemedicine in which the patient is instructed over an audio/video communication device to breath in a way to provoke hyperventilation to better diagnose the likelihood of absence epilepsy. Stafstrom et al. further teaches hyperventilation is a reliable way to provoke an absence seizure. Stafstrom et al. further teaches a number of methods to help the patient to hyperventilate including the use of apps for cell phones with animations to aid in the process. It is noted that and recognized in Stafstrom et al., in order for the patient to achieve hyperventilation it would be necessary for the patient to breath at a fast enough rate( threshold) to ensure hyperventilation and if the patient is not breathing at the desired rate instructions for the patient to adjust their breathing pattern would be needed.
Therefore It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the device of Alves et al. as modified by Ray et al. the option of provoking the patient to hyperventilate to diagnose seizures including absence seizures and to give feedback to breath at an appropriate rate to ensure hyperventilation is achieved as taught by Stafstrom et al.
Alves et al. as modified by Ray et al. and Stafstrom et al. does not specifically teach wherein evaluating the suitability of the patient data comprises detecting whether the patient's face is sufficiently visible in the video data or wherein, if the patient's face is not sufficiently visible in the video data, the feedback comprises instructions to reposition the patient's face.
It is noted that in order for the cameras of Alves et al. to be able to detect eye movement, pupil size, blinking, skin color, pulse, facial movements, and facial twitching which Alves et al. teaches indicate seizures and general wellness of the wearer, the patient’s face must be properly visible within the field of view of the camera and there are a limited number of choices available to a person of ordinary skill in the art correct for situations where the patient’s face is not sufficiently visible within the field of view of the camera. Therefore, It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the device of Alves et al. as modified by Ray et al. and Safstrom et al. the option of determining if the camera includes a sufficient view of a patient’s face and if not sufficient enough to detect eye movement or facial twitches, instruct the patient to better position their face within the cameras with a reasonable expectation of successfully obtaining better facial detection and thus better seizure detection. See KSR Int’l Co. v. Teleflex Inc., 127 S.Ct. 1727, 1742, 82 USPQ2d 1385, 1396 (2007).
Regarding claim 3, Alves et al. as modified by Ray et al. and Stafstrom et al. teaches wherein the hyperventilation-triggered seizure comprises an absence seizure.
Regarding claims 8, Alves et al. as modified by Ray et al. and Stafstrom et al. teaches the sensor comprises a microphone configured to capture audio data, and wherein the breathing rate is measured based on the audio data.
Regarding claims 9, Alves et al. as modified by Ray et al. and Stafstrom et al. teaches wherein the microphone is part of a mobile device. Note paragraphs [00151] and [0022].
Regarding claims 11, Alves et al. as modified by Ray et al. and Stafstrom et al. does not specifically teach if the breathing rate of the patient is less than the target breathing rate, the first feedback comprises instructions to increase the breathing rate, and if the breathing rate of the patient is greater than the target breathing rate, the first feedback comprises instructions to decrease the breathing rate.
It is noted that and recognized in Stafstrom et al., in order for the patient to achieve hyperventilation it would be necessary for the patient to breath at a fast enough rate( threshold) to ensure hyperventilation and if the patient is not breathing at the desired rate instructions for the patient to adjust their breathing pattern would be needed.
Therefore It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the device of Alves et al. as modified by Ray et al. the option of provoking the patient to hyperventilate to diagnose seizures including absence seizures and to give feedback to breath at an appropriate rate including increasing or decreasing the breathing rate to ensure hyperventilation is achieved as taught by Stafstrom et al.
Regarding claim 14, Alves et al. teaches wherein the video data is generated by an imaging device of a mobile device. Note paragraphs [00151] and [0022].
Regarding claim 16, Alves et al. as modified by Ray et al. and Stafstrom et al. teaches in Alves paragraph [0095] For example, location 802 can include wide-angle cameras pointing towards the eyes and face of a wearer. Wide-angle cameras can detect eye movement, pupil size, blinking, skin color, pulse, facial movements, and facial twitching. Many of these movements can indicate seizures and general wellness of the wearer. Changes in eye movement and pupil size, or eye lids closing, can indicate that a wearer is losing consciousness due to a seizure episode.
However Alves et al. as modified by Ray et al. and Stafstrom et al. does not specifically teach wherein evaluating the suitability of the patient data comprises detecting whether the patient's face is sufficiently visible in the video data or wherein, if the patient's face is not sufficiently visible in the video data, the feedback comprises instructions to reposition the patient's face.
It is noted that in order for the cameras of Alves et al. as modified by Ray et al. and Stafstrom et al. to be able to detect eye movement, pupil size, blinking, skin color, pulse, facial movements, and facial twitching which Alves et al. teaches indicate seizures and general wellness of the wearer, the patient’s face must be properly visible within the field of view of the camera and there are a limited number of choices available to a person of ordinary skill in the art correct for situations where the patient’s face is not sufficiently visible within the field of view of the camera. Therefore, It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the device of Alves et al. as modified by Ray et al. and Stafstrom et al. the option of determining if the camera includes a sufficient view of a patient’s face and if not sufficient enough to detect eye movement or facial twitches, instruct the patient to better position their face within the cameras with a reasonable expectation of successfully obtaining better facial detection and thus better seizure detection. See KSR Int’l Co. v. Teleflex Inc., 127 S.Ct. 1727, 1742, 82 USPQ2d 1385, 1396 (2007).
Regarding claims 17, and 24-26, Alves et al. as modified by Ray et al. and Stafstrom et al. does not specifically teach wherein the provocation sequence includes a baseline period, a provocation period, and a cooldown period nor giving feedback throughout each period.
It is noted that and recognized in Stafstrom et al. that the patient is instructed to increase the breathing rate to achieve hyperventilation and that hyperventilation was performed for a period of time. This would include a time prior to the patient increasing their breathing rate ( baseline), the time while hyperventilation is performed ( provocation period) and the time after hyperventilation is completed ( which would be a return to baseline or “cooldown” period).
Therefore It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the device of Alves et al. as modified by Ray et al. and Stafstrom et al. the option of provoking the patient to hyperventilate for a period of time and include the period prior to hyperventilating as a baseline, the induced hyperventilation as the provocation period and the time after hyperventilation as the return to baseline or “cooldown” period and to provide feedback to the patient throughout the process to better diagnose seizures including absence seizures as taught by Stafstrom et al. and It is further noted that there are a limited number of choices available to a person of ordinary skill in the art for initiating hyperventilation and providing feedback to the patient during the process. Therefore, it would have been obvious to one of ordinary skill in the art to try to give feedback to the patient before during and after the process with a reasonable expectation of successfully initiating hyperventilation and being able to measure any resulting seizures. See KSR Int’l Co. v. Teleflex Inc., 127 S.Ct. 1727, 1742, 82 USPQ2d 1385, 1396 (2007).
Regarding claim 18, Alves et al. as modified by Ray et al. and Stafstrom et al. teaches determining whether the patient experienced the hyperventilation-triggered seizure based on the video data.
Regarding claim 19, Alves et al. as modified by Ray et al. and Stafstrom et al. teaches wherein the patient data comprises video data of the patient's face and eyes, and wherein determining whether the patient experienced the neurological event comprises analyzing the video data using a two-stage machine learning algorithm. Paragraph [0095] in Alves et al. For example, location 802 can include wide-angle cameras pointing towards the eyes and face of a wearer. Wide-angle cameras can detect eye movement, pupil size, blinking, skin color, pulse, facial movements, and facial twitching. Many of these movements can indicate seizures and general wellness of the wearer. [0052] In another exemplary embodiment, the present disclosure provides for a machine learning model which can receive data from the brain health monitoring system. The machine learning model can identify whether a set of data identifies a seizure. Continuous updating of the data available to the machine learning model can ensure that the model will grow in accuracy over time. Additionally, the machine learning model can accept input from the user and/or a caretaker of the user. The user and/or caretaker can identify whether the machine learning model correctly identified a seizure, incorrectly identified a seizure, or failed to identify a seizure. Therefore, this additional closed-loop human verification of the events can further and adaptively increase the accuracy of the machine learning model. [00139] In some examples, each type of data has a separate machine-learning model, including, for example, a first machine learning model for processing EEG data, a second machine learning model for processing audio data, a third machine learning model for processing visual data, and any other machine learning model as needed. In some examples, a machine learning model receives more than one type of input data, including for example, audio data and EEG data; visual data and EEG data; visual data, audio data, and EEG data.
Regarding claim 20, Alves et al. as modified by Ray et al. and Stafstrom et al. teaches transmitting the patient data to a remote system configured to determine whether the patient experienced the neurological event based on the patient data. Paragraph [00151] in Alves et al. The cloud application 540 can also provide for a user interface 550. This allows patients/users, caregivers, and doctors to access the raw sensor data, the model training, and the detected seizure data. Patients/users and caregivers 552 can have separate user interfaces from doctors 554. For example, the interface can provide alerts or notifications 566 sent to a mobile application 530 when a seizure event is detected. In some examples, the user interface 550 can give the patient, caregiver, doctor, and/or any health care provider the ability to confirm or deny that a seizure event took place during a seizure event detected by the real-time machine learning 542.
Regarding claim 21, Alves et al. as modified by Ray et al. and Stafstrom et al. does not specifically teach wherein the target breathing rate is within a range from 20 breaths per minute to 24 breaths per minute.
It is noted that and recognized in Stafstrom et al., in order for the patient to achieve hyperventilation it would be necessary for the patient to breath at a fast enough rate( threshold) to ensure hyperventilation and if the patient is not breathing at the desired rate instructions for the patient to adjust their breathing pattern would be needed.
Therefore It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the device of Alves et al. as modified by Ray et al. the option of provoking the patient to hyperventilate to diagnose seizures including absence seizures and to give feedback to breath at an appropriate rate including increasing or decreasing the breathing rate and setting the rate from 20 breaths per minute to 24 breaths per minute to ensure hyperventilation is achieved as a matter of design choice and It is further noted that there are a limited number of choices available to a person of ordinary skill in the art for initiating hyperventilation. Therefore, it would have been obvious to one of ordinary skill in the art to try setting the rate from 20 breaths per minute to 24 breaths per minute with a reasonable expectation of successfully initiating hyperventilation. See KSR Int’l Co. v. Teleflex Inc., 127 S.Ct. 1727, 1742, 82 USPQ2d 1385, 1396 (2007).
Regarding claim 23, Alves et al. as modified by Ray et al. and Stafstrom et al. teaches wherein the microphone is part of a mobile device. Note paragraphs [00151] and [0022].
Claim(s) 5, 6, 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Alves et al.(WO2020006271) hereinafter Alves et al.(WO2020006271) hereinafter Alves et al. in view of Ray et al.( US 10485471) hereinafter Ray et al. and further in view of Stafstrom et al. and further in view of Hirano et al.( WO 2020085108) hereinafter Hirano et al.
Alves et al. as modified by Ray et al. and Stafstrom et al. teach the claimed invention as set forth above including in Alves et al. [0099] Location 810 can include a microphone. The microphone can detect sound from the nasal airflow of the wearer to measure respiration rates. Other respiratory sounds, such as snoring, can also be collected for analysis.
Regarding claims 5-6 and 12 Alves et al. as modified by Ray et al. and Stafstrom et al. do not specifically teach wherein the user interface is configured to display a graphical representation of a target breathing rate for the hyperventilation or wherein the graphical representation includes an animation representing the target breathing rate.
Hirano et al. teaches a monitoring device, a monitoring method, and a monitoring program that can reduce the risk relating to the physical condition of the user. The monitoring device includes a control unit 10, a storage unit 12, an acquisition unit 20, and a notification unit 30. The monitoring device 1 may further include an input unit 40 that receives an input from a user. The monitoring device 1 is connected to the external sensor 50 via the acquisition unit 20. The monitoring device 1 may include the sensor 50 inside. The sensor 50 is attached to the user and detects biometric information of the user. The monitoring device 1 can monitor changes in the physical condition of the user based on the biometric information of the user detected by the sensor 50. The sensor 50 may include a device that detects the breathing rate of the subject within a predetermined time period. The sensor 50 may include a device that detects brain waves of a subject. The control unit 10 determines whether the biometric information satisfies the first determination criterion (step S23). The first criterion may include that the respiratory rate of the subject is equal to or higher than a predetermined threshold. If the breathing rate of the subject is greater than or equal to a predetermined threshold, the subject may be hyperventilated. The first criterion may include that the respiratory rate of the subject is less than a predetermined threshold. A subject may be apnea if the subject's respiratory rate is below a predetermined threshold. The control unit 10 may determine whether the subject is in a state of hyperventilation or apnea based on the breathing rate of the subject (step S711). Furthermore, The notification unit 30 may display characters, images, and the like on the display device to notify the content based on the control information acquired from the control unit 10.
It is noted there are a limited number of choices available to display characters and images to users to relay information including animations. Therefore, It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the device of Alves et al. as modified by Ray et al. and Stafstrom et al. graphical representation including images and animations of target breathing thresholds as taught by Hirano et al. with a reasonable expectation of success to aid the patient in achieving hyperventilation and to enable an accurate neurologic measurement. See KSR Int’l Co. v. Teleflex Inc., 127 S.Ct. 1727, 1742, 82 USPQ2d 1385, 1396 (2007).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Girouard(US 20160220169) teaches method of monitoring a patient for seizures with motor manifestations may comprise monitoring a patient using one or more EMG and acoustic sensors and determining whether the collected data is indicative of seizure activity.
ANNALA et al.( WO 2019158824) teaches receiving video data of a patient (105). First anomaly is detected in the video data as anomaly in movement of the patient. A video frame stack is determined includes the first anomaly. The video frame stack is classified using a pre-trained neural network to obtain a first classification. A motor seizure type is determined based on the first classification. An actual feature is determined over a video data segment, the actual feature representing actual movement of the patient. A predicted feature is determined over the video data segment using a pre-trained neural network, the predicted feature representing predicted movement of the patient.
CHAN et al.( WO 2015076752) teaches a system (500) able to detect seizures automatically and reliably in unsupervised conditions and has division module (550) that is configured to divide a video input region into sub-windows. A sub-window flow module (560) determines a sub-window flow field magnitude for each of the sub-windows. A local motion magnitude module (570) obtains a magnitude of local motion based on the sub-window flow field magnitude for each of the sub-windows. A local motion comparison module (580) compares magnitude of local motion with a local motion threshold for preset number of observation frames to obtain a local motion reference value.
Singh et al.( US 20170061074) teaches a system and method for monitoring and tracking health is provided that comprises temperature sensing devices communicatively connectable to at least one computing device and configured to calculate temperature information. When executed by a processor, the computing device is configured to: access at least one data repository that stores health-related information and provider information; receive health-related information in response to an event; generate and transmit at least one prompt for regarding the health-related information; receive a response to the at least one prompt; match the provider information with the response to the prompt and/or the received health-related information; and provide, in response to the step of matching, at least one option for: scheduling a meeting with a provider; communicating with a provider; and sending information associated with the received health-related information. Note paragraphs [0134] – [0136].
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN L CASLER whose telephone number is (571)272-4956. The examiner can normally be reached M-Th 6:30 to 4:30.
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/BRIAN L CASLER/Primary Examiner, Art Unit 3791
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