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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claims are directed to an abstract idea without significantly more.
With Respect to claim 1, the claim recites the following limitation(s):
Claim 1: outputting a user interface configured to guide the patient in performing a provocation sequence for a neurological event;
obtaining patient data indicative of a state of the patient during the provocation sequence;
evaluating suitability of the patient data for detecting an occurrence of the neurological event; and
outputting feedback via the user interface based on the evaluation.
Step 1- Claim 1 is directed to a method for monitoring a patient for neurological events such as seizures.
Step 2a Prong 1 – The claimed invention is directed to non-statutory subject matter. The above limitations, under their broadest reasonable interpretation, fall within the “methods of organizing human activity"/"managing personal behavior or relationships or interactions between people" and “mental processes” grouping of abstract ideas, enumerated in MPEP 2106.04(a)(2)(II)(III), in that they recite a series of steps to monitor a patient while instructing the patient to perform an activity and obtain and evaluate data and providing feedback to the patient regarding their neural activity or seizure activity based on the evaluation. When given their BRI, the limitations are considered an abstract idea of being certain methods of organizing human activity and mental processes.
With respect to claim 1, the claim merely prompts the patient to perform an activity in sequence while observing the patient or obtaining data of the patient’s neural activity, evaluating the patient and providing feedback to the patient based on the evaluation. All of which are mental steps that could be done by a human with or without a physical aid or the aid of a computer. The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid. Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer.
Step 2a Prong 2 - The recitation of the additional elements of a user interface merely invokes such additional element(s) as a tool to perform the abstract idea. MPEP 2106.05(f).
Further, the recitation of these additional element(s) in the claim generally links the use of the abstract idea to a particular technological environment or field of use, i.e., a computerized environment. MPEP 2106.05(h).
As such, under Prong 2 of Step 2A, when considered both individually and as a whole, the limitations of claim 1 is not indicative of integration into a practical application (Prong 2, Step 2A: NO). MPEP 2106.04(d)
With respect to claim 1, The user interface is the only additional element provided which represent insignificant data gathering and outputting. Nothing further is set forth in the claim where the abstract idea is positively or actively integrated into a practical application.
As such, these additional elements do not integrate the abstract idea into a practical application and therefore the claim is directed to the judicial exception.
Step 2B - The recitation of the additional elements is acknowledged, as identified above with respect to Prong 2 of Step 2A. These additional elements do not add significantly more to the abstract idea for the same reasons as addressed above with respect to Prong 2 of Step 2A.
Even when considered as an ordered combination, the additional element of claim 1 does not add anything that is not already present when they are considered individually. Therefore, under Step 2B, there are no meaningful limitations in claim 1 that transform the judicial exception into a patent eligible application such that the claim amounts to significantly more than the judicial exception itself (Step 2B: NO). MPEP 2106.05.
Accordingly, under the Subject Matter Eligibility test, claim 1 is ineligible.
Furthermore, the dependent claims, 2-20 do not add significantly more to the
abstract idea for the same reasons as addressed above with respect to Prong 2 of Step 2A.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 13, 17, 18, 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Geva et al.( WO 2016046830) hereinafter Geva et al.
Geva et al. teaches a method and device for detecting and treating brain disorders including epilepsy in which a reference brain network activity (BNA) pattern, and a BNA pattern describing a neurophysiological state of the subject, each of the BNA patterns having a plurality of nodes and each node representing a brain location and at least one brain wave frequency. The method further comprises comparing the BNA patterns; and configuring the local brain stimulation tool to apply local brain stimulation at a frequency selected based on the comparison. Implementation of the method and/or system of embodiments of the invention can involve having the user performing or completing selected tasks manually, automatically, or a combination thereof. Moreover, according to actual instrumentation and equipment of embodiments of the method and/or system of the invention, several selected tasks could be implemented by hardware, by software or by firmware or by a combination thereof using an operating system. For example, hardware for performing selected tasks according to embodiments of the invention could be implemented as a chip or a circuit. As software, selected tasks according to embodiments of the invention could be implemented as a plurality of software instructions being executed by a computer using any suitable operating system. In an exemplary embodiment of the invention, one or more tasks according to exemplary embodiments of method and/or system as described herein are performed by a data processor, such as a computing platform for executing a plurality of instructions. Optionally, the data processor includes a volatile memory for storing instructions and/or data and/or a non-volatile storage, for example, a magnetic hard-disk and/or removable media, for storing instructions and/or data. Optionally, a network connection is provided as well. A display and/or a user input device such as a keyboard or mouse are optionally provided as well. A display device 440 is shown in communication with computer 433, typically via I/O circuit 434. Computer 433 issues to display device 440 graphical and/or textual output images generated by CPU 436. A keyboard 442 is also shown in communication with computer 433, typically I/O circuit 434. Note fig. 37, abstract, and page 9, lines 1-20, page 22, lines 22-32, page 25, lines 14-32, page 26 lines 1-30.
Regarding claim 1, Geva et al. teaches outputting a user interface configured to guide the patient in performing a provocation sequence for a neurological event;
obtaining patient data indicative of a state of the patient during the provocation sequence;
evaluating suitability of the patient data for detecting an occurrence of the neurological event; and
outputting feedback via the user interface based on the evaluation. Claim 1 is broad and not limited to any specific neurological event or provoked task. Geva et al. teaches having the user perform a task before during and after neurological measurements are taken and further teaches the use of a user input/output device which is interpreted as a user interface for instructing the user to perform a task. Geva et al. teaches comparing the before and after Brain activity to determine the presence of impaired neural activity and also teaches this device could be used for Epilepsy which would include seizure related data. Note fig. 37, abstract, and page 9, lines 1-20, page 22, lines 22-32, page 25, lines 14-32, page 26 lines 1-30, page 29, lines 1-31 discuss measuring EEG data.
Regarding claim 13, Geva et al. teaches wherein the patient data comprises video data. A display device 440 is shown in communication with computer 433, typically via I/O circuit 434. Computer 433 issues to display device 440 graphical and/or textual output images generated by CPU 436. Note fig. 37, abstract, and page 9, lines 1-20, page 22, lines 22-32, page 25, lines 14-32, page 26 lines 1-30.
Regarding claim 17, Geva et al. teaches wherein the provocation sequence includes a baseline period, a provocation period, and a cooldown period. The data collection is on-going such that neurophysiological data are collected continuously before, during and after performance or conceptualization of the task and/or action and the neurophysiological data are acquired during particular activities, but the acquisition is not synchronized with a stimulus. Geva et al. also teaches having the user perform a task before during and after neurological measurements are taken which is interpreted as a baseline period, a provocation period, and a cooldown period. Note fig. 37, abstract, and page 9, lines 1-20, page 22, lines 22-32, page 25, lines 14-32, page 26 lines 1-30, page 29, lines 1-31 and page 30 line 1-9.
Regarding claim 18, Geva et al. teaches determining whether the patient experienced the neurological event based on the patient data. The data collection is on-going such that neurophysiological data are collected continuously before, during and after performance or conceptualization of the task and/or action and the neurophysiological data are acquired during particular activities, but the acquisition is not synchronized with a stimulus. Geva et al. also teaches having the user perform a task before during and after neurological measurements are taken. After the task is completed, a BNA pattern is reconstructed for the subject. The reconstructed BNA pattern can be compared to the BNA pattern before the task or the reference BNA pattern, and the parameters of the tasks can be varied based on the comparison. Note fig. 37, abstract, and page 9, lines 1-20, page 22, lines 22-32, page 25, lines 14-32, page 26 lines 1-30, page 29, lines 1-31 and page 30 line 1-9.
Regarding claim 20, Geva 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. It will be appreciated by one of ordinary skill in the art that system 431 can be part of a larger system. For example, system 431 can also be in communication with a network, such as connected to a local area network (LAN), the Internet or a cloud computing resource of a cloud computing facility. Note fig. 37, abstract, and page 9, lines 1-20, page 22, lines 22-32, page 25, lines 14-32, page 26 lines 1-30, page 27, lines 15-18, page 29, lines 1-31 and page 30 line 1-9.
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) 1-2 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 Geva et al.( Geva et al.( WO 2016046830) hereinafter Geva 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.
Regarding claim 1, Alves et al. does teach using 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 using the interface to provoke an event via instructing the patient to perform a task.
Geva et al. teaches in the same field of endeavor as set forth above a method and device for detecting and treating brain disorders including epilepsy in which a reference brain network activity (BNA) pattern, and a BNA pattern describing a neurophysiological state of the subject by having the subject perform a task and measuring the subject’s neural activity before, during and after the task is performed to identify brain neural activity that is impaired due to conditions like epilepsy. Geva et al. also teaches A display and/or a user input device such as a keyboard or mouse are optionally provided as well. A display device 440 is shown in communication with computer 433, typically via I/O circuit 434. Computer 433 issues to display device 440 graphical and/or textual output images generated by CPU 436. A keyboard 442 is also shown in communication with computer 433, typically I/O circuit 434.
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 instructing the patient to perform a task to better detect and measure neurological events as taught by Geva et al.
Regarding claim 2, Alves et al. teaches monitoring brain health and predicting and detecting seizures via a wearable device. Note the abstract and figure 4.
Regarding claim 13, Alves et al. teaches wherein the patient data comprises video data. [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.
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 claims 15-16, Alves et al. teaches [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. 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. 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 18, Alves et al. does not specifically teach wherein the provocation sequence includes a baseline period, a provocation period, and a cooldown period.
However Geva et al. teaches in the same field of endeavor wherein the provocation sequence includes a baseline period, a provocation period, and a cooldown period. The data collection is on-going such that neurophysiological data are collected continuously before, during and after performance or conceptualization of the task and/or action and the neurophysiological data are acquired during particular activities, but the acquisition is not synchronized with a stimulus. Geva et al. also teaches having the user perform a task before during and after neurological measurements are taken which is interpreted as a baseline period, a provocation period, and a cooldown period. Note fig. 37, abstract, and page 9, lines 1-20, page 22, lines 22-32, page 25, lines 14-32, page 26 lines 1-30, page 29, lines 1-31 and page 30 line 1-9.
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. instructing the patient to perform a task to better detect and measure neurological events and to include a baseline period, a provocation period, and a cooldown period as taught by Geva et al. to obtain better brain neural activity results and be able to compare those results to a reference before and after the activity.
Regarding claim 19, Alves 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. [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. [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. teaches transmitting the patient data to a remote system configured to determine whether the patient experienced the neurological event based on the patient data. [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.
Claim(s) 3 and 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Alves et al.(WO2020006271) hereinafter Alves et al. in view of Geva et al.( Geva et al.( WO 2016046830) hereinafter Geva et al. and further in view of applicant cited reference to Stafstrom et al.
Alves et al. in view of Geva et al. teach the claimed invention as set forth above including detecting brain neurological events for epilepsy including seizure detection.
Alves et al. in view of Geva et al. does not specifically teach wherein the seizure comprises an absence seizure or wherein the provocation sequence is configured to induce the neurological event in the patient via hyperventilation.
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.
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 Geva et al. the option of provoking the patient to hyperventilate to diagnose seizures including absence seizures as taught by Stafstrom et al.
Claim(s) 5-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Alves et al.(WO2020006271) hereinafter Alves et al. in view of Geva et al.( Geva et al.( WO 2016046830) hereinafter Geva et al. and in view of applicant cited reference to Stafstrom et al. and further in view of Hirano et al.( WO 2020085108) hereinafter Hirano et al.
Alves et al. in view of Geva 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. in view of Geva 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. in view of Geva 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).
Regarding claims 7 - 9 Alves et al. in view of Geva et al. and Stafstrom et al. teach 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. However, Alves et al. in view of Geva et al. and Stafstrom et al. do not specifically teach measuring a breathing rate of the patient, based on the patient data.
Regarding claim 10-11 , The importance of achieving hyperventilation in a patient is recognized in Alves et al. in view of Geva et al. and Stafstrom et al. to better diagnose the patient with respect to seizure events. However, Alves et al. in view of Geva et al. and Stafstrom et al. do not specifically teach comparing the breathing rate of the patient to the target breathing rate or if the breathing rate of the patient is less than the target breathing rate, the feedback comprises instructions to increase the breathing rate, and if the breathing rate of the patient is greater than the target breathing rate, the 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.
Hirano et al. teaches 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). The notification unit 30 notifies the subject of the content based on the control information acquired from the control unit 10. The notification unit 30 notifies the subject of the content based on the control information acquired from the control unit 10.
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. in view of Geva et al. and Stafstrom et al. a comparison of the breathing rates to the target and a display as to whether the patient is above or below the target threshold as taught by Hirano et al. to encourage the patient to achieve hyperventilation and allow seizures to be initiated and recorded.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
WINTER(WO 03001359) teaches a system for simultaneously recording measuring data (mes) and video and/or audio data (vid, aud), comprising at least one data processing device (COM), at least one measuring data recording device (MES) that is connected to the data processing device (COM) by at least one interface (SCH1, SCH2).
RAO et al.( WO 2019079475) teaches system and methods of diagnosing and monitoring neurological disorders in a patient utilizing an artificial intelligence based system. The system may comprise a plurality of sensors, a collection of trained machine learning based diagnostic and monitoring tools, and an output device. The plurality of sensors may collect data relevant to neurological disorders. The trained diagnostic tool will learn to use the sensor data to assign risk assessments for various neurological disorders.
Bogdan et al.( GB 2591581) teaches a method of seizure logging comprising continuously detecting 35 electroencephalogram (EEG) waveforms of a patient using a portable EEG recording device. Video data is periodically recorded 37 using a camera and the recorded video and EEG data is synchronized 39 and logged 41 as a seizure event.
Kadambi(US 10561333) teaches systems and methods for seizure diagnosis by video electroencephalography (Video-EEG). There is disclosed a fully automated, portable, point-of-care diagnostic video EEG device. In an embodiment, the device includes a tracker configured for placement on a patient.
Silberstein(US 6792304) teaches a mass communication assessment system (2) communicating a cognitive task to selected remote test sites (12) via a network (10) for providing a two-way communication between the central control site and the remote sites, detecting brain response signals from the subject (40) to the task, and having processing means (16) for computing variations in the brain activity for the subjects at each of the selected sites.
ZHOU(CN 104667486) teaches a neurophysiologic feedback system, through a single sensing signal generation source of the brain activity information and respiration guidance signal provide real time, so that the user can follow the breathing guide signal to improve attention, further improving the neurophysiologic realized by the feedback effect.
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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles Marmor can be reached at (571)272-4730. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/BRIAN L CASLER/Primary Examiner, Art Unit 3791
Prosecution Insights