DETAILED ACTION
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 § 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C 102(a)(2) as being anticipated by Errico et al. (hereinafter ‘Errico’, U.S. PGPub No. 2022/0044828).
In regards to claim 1, Errico discloses a method, comprising receiving a motion signal from a motion sensor of an implantable device implanted in a subject ([0107]: "The electrodes used to stimulate a vagus nerve can be implanted about the nerve during open neck surgery."), generating, from the motion signal, a calculated biomarker ([0301]: "The medical device 208 can include one or more sensors, such as, for example, biosensors, feedback sensors, chemical sensors, optical sensors, acoustic sensors, vibration sensors, motion sensors, fluid sensors, radiation sensors, temperature sensors, motion sensors, proximity sensors, fluid sensors, or others."), detecting an epileptic seizure, the detecting being based on the calculated biomarker ([0097]: "For example, the system and methods of the present disclosure may also be configured to prevent, diagnose, monitor, ameliorate, or treat a neurological condition, such as epilepsy, headache/migraine, whether primary or secondary, whether cluster or tension, neuralgia, seizures..."), and in response to the detecting of the epileptic seizure, applying, by the implantable device, vagus nerve stimulation ([0303]: "For example, the neurostimulator can be enable spinal cord stimulation to provide therapy for intractable pain and refractory angina; occipital nerve stimulation to provide therapy for occipital neuralgia and transformed migraine; afferent vagus nerve modulation to provide therapy for a host of neurological and neuropsychiatric disorders, such as epilepsy,...").
In regards to claim 2, Errico discloses that the calculated biomarker comprises a calculated heart rate or a calculated respiration rate ([0275]: "For example, the sensors may comprise those used in conventional Holter and bedside monitoring applications, for monitoring heart rate and variability, ECG, respiration depth and rate…") and the detecting, based on the calculated biomarker, of the epileptic seizure, comprises detecting the epileptic seizure based on an increase or decrease in the calculated heart rate or based on an increase or decrease in the calculated respiration rate ([0097]: "For example, the system and methods of the present disclosure may also be configured to prevent, diagnose, monitor, ameliorate, or treat a neurological condition, such as epilepsy, headache/migraine, whether primary or secondary, whether cluster or tension, neuralgia, seizures...").
In regards to claim 3, Errico discloses that the calculated biomarker comprises a calculated respiration rate ([0275]: "For example, the sensors may comprise those used in conventional Holter and bedside monitoring applications, for monitoring heart rate and variability, ECG, respiration depth and rate…") and the detecting, based on the calculated biomarker, of the epileptic seizure, comprises detecting the epileptic seizure based on an increase or decrease in the calculated respiration rate ([0097]: "For example, the system and methods of the present disclosure may also be configured to prevent, diagnose, monitor, ameliorate, or treat a neurological condition, such as epilepsy, headache/migraine, whether primary or secondary, whether cluster or tension, neuralgia, seizures...").
In regards to claim 4, Errico discloses that the detecting of the epileptic seizure comprises detecting a decrease in the calculated respiration rate, and the detecting of the decrease in the calculated respiration rate comprises performing frequency tracking of the calculated respiration rate with an infinite impulse response adaptive notch filter tuned to the calculated respiration rate ([0097]: "For example, the system and methods of the present disclosure may also be configured to prevent, diagnose, monitor, ameliorate, or treat a neurological condition, such as epilepsy, headache/migraine, whether primary or secondary, whether cluster or tension, neuralgia, seizures...").
In regards to claim 5, Errico discloses that the generating of the calculated biomarker comprises calculating a heart rate ([0152]: "In other embodiments, a low-pass filter may be used instead of the electrically conductive fluid to filter out the undesirable high frequency components of the signal. The low-pass filter may comprise a digital or analog filter or simply a capacitor placed in series between the signal generator and the electrode/interface.") and the detecting, based on the calculated biomarker, of the epileptic seizure, comprises detecting the epileptic seizure based on an increase in the calculated heart rate ([0097]: "For example, the system and methods of the present disclosure may also be configured to prevent, diagnose, monitor, ameliorate, or treat a neurological condition, such as epilepsy, headache/migraine, whether primary or secondary, whether cluster or tension, neuralgia, seizures...").
In regards to claim 6, Errico discloses that the calculating of the heart rate comprises performing a method selected from the group consisting of linear filtering, numerical differentiation, application of a memoryless nonlinear transform, low pass filtering, peak detection, and combinations thereof ([0152]: "In other embodiments, a low-pass filter may be used instead of the electrically conductive fluid to filter out the undesirable high frequency components of the signal. The low-pass filter may comprise a digital or analog filter or simply a capacitor placed in series between the signal generator and the electrode/interface.").
In regards to claim 7, Errico discloses detecting an increase in a heart rate of the subject, determining that the subject is engaged in exercise, and determining, based on the increase in the heart rate, and based on the determining that the subject is engaged in exercise, that an epileptic seizure is not occurring ([0125]: "Different stimulation parameters may also be selected as the course of the patient's condition changes. In some embodiments, some methods and devices do not produce clinically significant side effects, such as agitation or anxiety, or changes in heart rate or blood pressure.").
In regards to claim 8, Errico discloses detecting, based on the motion sensor, muscle movements characteristic of an epileptic seizure, wherein the detecting of the epileptic seizure is further based on the detecting of the muscle movements ([0301]: "The medical device 208 can include one or more sensors, such as, for example, biosensors, feedback sensors, chemical sensors, optical sensors, acoustic sensors, vibration sensors, motion sensors, fluid sensors, radiation sensors, temperature sensors, motion sensors, proximity sensors, fluid sensors, or others. The one sensor can be used to sense and detect various properties, conditions and/or characteristics or variations to same or lack thereof.").
In regards to claim 9, Errico discloses that the detecting of the muscle movements comprises detecting a period of high amplitude signals in a frequency band characteristic of shaking encountered during clonic seizures ([0305]: "For example, the first mode can be associated with a first prevention, diagnosis, monitoring, amelioration, or treatment signal/energy output and the second mode can be associated with a second prevention, diagnosis, monitoring, amelioration, or treatment signal/energy output, wherein the first signal/energy output is identical to or differs from the second signal/energy output in various parameters, such as a content, a format, an amplitude, a frequency, a time period, or others.").
In regards to claim 11, Errico discloses detecting, based on the motion sensor, motion characteristic of poor sleep quality, wherein the detecting of the epileptic seizure is further based on the detecting of the motion characteristic of poor sleep quality ([0118]: "For certain disorders, the time of day can be more important than the time interval between treatments. For example, the locus correleus has periods of time during a 24 hour day wherein it has inactive periods and active periods. Typically, the inactive periods can occur in the late afternoon or in the middle of the night when the patient is asleep. It is during the inactive periods that the levels of inhibitory neurotransmitters in the brain that are generated by the locus correleus are reduced.").
In regards to claim 12, Errico discloses that the detecting of the motion characteristic of poor sleep quality comprises detecting motion corresponding to a position change of the subject while the subject is lying down ([0118]: "For certain disorders, the time of day can be more important than the time interval between treatments. For example, the locus correleus has periods of time during a 24 hour day wherein it has inactive periods and active periods. Typically, the inactive periods can occur in the late afternoon or in the middle of the night when the patient is asleep. It is during the inactive periods that the levels of inhibitory neurotransmitters in the brain that are generated by the locus correleus are reduced.").
In regards to claim 13, Errico discloses the detecting of the epileptic seizure comprises detecting the calculated biomarker passing a threshold, and the threshold is based on a history of the calculated biomarker ([0314]: "When the system 200A is used to at least one of prevent, diagnose, monitor, ameliorate, or treat the medical condition, disease, or the disorder of the patient, the processor 204 tracks such use and can take an action when a predetermined threshold is satisfied or not satisfied, such as via the logic stored via the memory 206.").
In regards to claim 17, Errico discloses that the implantable device comprises a housing having a biocompatible outer surface and containing the motion sensor and a vagus nerve stimulation circuit ([0301]: "The medical device 208 can include one or more sensors, such as, for example, biosensors, feedback sensors, chemical sensors, optical sensors, acoustic sensors, vibration sensors, motion sensors, fluid sensors, radiation sensors, temperature sensors, motion sensors...").
In regards to claim 18, Errico discloses that the motion sensor is a micro-electromechanical systems (MEMS) sensor ([0301]: "The medical device 208 can include one or more sensors, such as, for example, biosensors, feedback sensors, chemical sensors, optical sensors, acoustic sensors, vibration sensors, motion sensors, fluid sensors, radiation sensors, temperature sensors, motion sensors...").
In regards to claim 19, Errico discloses that the motion sensor comprises an accelerometer ([0275]: "A noninvasive accelerometer may also be included among the ambulatory sensors, in order to identify motion artifacts.").
In regards to claim 20, Errico discloses that the motion sensor comprises a gyroscope ([0212]: "However, they may be monitored by accelerometers and gyroscopes within the smartphone, which may be transmitted as movement data from the stimulator to the patient interface computer program (in the mobile phone or laptop computer).").
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 10 is rejected under 35 U.S.C. 103 as being unpatentable over Errico in view of Wen (U.S. PGPub No. 2023/0172528).
In regards to claim 10, Errico discloses the invention substantially as described in claim 1.
However, Errico does not disclose receiving a magnetic field signal from a magnetometer of the implantable device, wherein the detecting of the epileptic seizure is further based on the detecting of the magnetic field signal.
Wen teaches a therapy device that also detects sleep quality to prevent disorders or seizures, which also uses a magnetometer to detect magnetic field signals ([0043]: "FIG. 2 is the block diagram of one embodiment of a motion sensor 101-105 applicable to the system for detection of onset and ictal phases of an epileptic seizure 1 of the present invention. The motion sensor 101 as shown in the figure includes a motion sensing element 10. The motion sensing element 10 is preferably a three-axis inertial sensor, and more preferably includes at least one of a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer."). Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the stimulation device to use a magnetometer, as taught by Wen, as doing so would provide a common addition to sensing systems that further output measurable, useful data for sleep quality determination or epilepsy or seizure prevention and treatment ([0043]: "The magnetometer measures the geomagnetism and outputs the three-axial components of the sensed value. In most applications, only a three-axis accelerometer would be sufficient. However, as the motion sensor has become a popular commodity, motion sensing components available in the market have already provided a three-axis accelerometer, a gyroscope, and a three-axis magnetometer.").
Claims 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Errico in view of Mirmomeni et al. (hereinafter ‘Mirmomeni’, U.S. PGPub No. 2023/0259811).
In regards to claim 14, Errico discloses the invention substantially as described in claim 1.
However, Errico does not disclose that the detecting of the epileptic seizure comprises detecting of the epileptic seizure by a machine learning model, based on a plurality of signals including the motion signal.
Mirmomeni teaches the development of deep learning models for epileptic seizure analysis and treatment my machine learning ([0033]: "This task may encompass the bidding participants to develop deep learning models for automatic annotation of epileptic seizure signals in raw EEG data with maximum sensitivity and minimum false alarm rates. Such task will then include generating a machine learning (ML)-based automatic EEG annotation system that can include a deep-learning model that can learn to automatically recognize different seizure patterns for individual patients based on raw EEG data which allows to calibrate these detection algorithms to patient-specific disease expressions."). Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the stimulation device to use machine learning to detect epileptic seizures, as taught by Mirmomeni, as doing so would provide an automated or supplementary detection of epileptic seizures through modern technology ([0033]: "This task may encompass the bidding participants to develop deep learning models for automatic annotation of epileptic seizure signals in raw EEG data with maximum sensitivity and minimum false alarm rates. Such task will then include generating a machine learning (ML)-based automatic EEG annotation system that can include a deep-learning model that can learn to automatically recognize different seizure patterns for individual patients based on raw EEG data which allows to calibrate these detection algorithms to patient-specific disease expressions.").
In regards to claim 15, Errico discloses that the plurality of signals further includes a magnetic field signal ([0092]: "Another form of non-invasive electrical stimulation is magnetic stimulation. It involves the induction, by a time-varying magnetic field, of electrical fields and current within tissue, in accordance with Faraday's law of induction.").
In regards to claim 16, Errico discloses the invention substantially as described in claim 14.
However, Errico does not disclose training the machine learning model by performing supervised training with training data comprising a plurality of labeled data elements, each labeled data element being labeled with an indicator of whether a seizure was occurring when the data element was collected.
Mirmomeni teaches the development of deep learning models for epileptic seizure analysis and treatment my machine learning with labelled training data ([0033]: "The challenge task description can include example model parameters that can be used to empower a challenge team to build the model. Such model example parameters can include but are not limited to: data type (imaging, time series, multi-modal), data sizes, labels or signal types that are to be detected or predicted, sample images with labels, etc., without providing direct access to the actual proprietary enterprise data."). Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the stimulation device to use labelled training data for epileptic seizure detection machine learning, as taught by Mirmomeni, as doing so would provide an automated or supplementary detection of epileptic seizures through modern technology ([0033]: " The challenge task description can include example model parameters that can be used to empower a challenge team to build the model. Such model example parameters can include but are not limited to: data type (imaging, time series, multi-modal), data sizes, labels or signal types that are to be detected or predicted, sample images with labels, etc., without providing direct access to the actual proprietary enterprise data.").
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRYAN M LEE whose telephone number is (703)756-1789. The examiner can normally be reached 9:00 am - 6:00 pm.
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/B.M.L./Examiner, Art Unit 3796
/CARL H LAYNO/Supervisory Patent Examiner, Art Unit 3796
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