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 .
Response to Arguments
The Applicant filed Amendments to the Claims and Remarks on January 31, 2025 prior to examination.
Amendments to the Claims
At this time, claims 1-15 and 25-29 are pending. Claims 16-24 and 30-35 have been cancelled. The Applicant asserts that no new matter is added. Claims 1, 11, and 25 are in independent form. (Remarks, pg. 1)
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, 3-11, 13-15, 25-26, and 28-29 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mental process of calculating and analyzing data to ultimately make a judgement/determination) without significantly more.
Step 1
Independent claims 1 and 25 are directed to an apparatus (system) and independent claims 11 is directed to a method, and thus meet the requirements for step 1.
Step 2A, Prong 1
Regarding independent claims 1, 11, and 25, the following steps recite an abstract idea:
“to determine a calculated heart rate of the subject based on a signal from the motion sensor” is a mental process when given its broadest reasonable interpretation. As discussed in MPEP 2106.04(a)(2)(III), the mental process grouping includes observations, evaluation, judgements, and opinions. In this case, a human (e.g., doctor, cardiologist) could evaluate a motion sensor signal and could mentally determine a calculated heart rate of the subject.
“to determine whether a seizure is imminent or occurring, based on: the calculated heart rate; and a subject-specific classifier” is a mental process when given its broadest reasonable interpretation. As discussed in MPEP 2106.04(a)(2)(III), the mental process grouping includes observations, evaluation, judgements, and opinions. In this case, a human (e.g., doctor, cardiologist) could evaluate the calculated heart rate and a subject specific classifier and could mentally make a judgement of whether a seizure is imminent or occurring.
Step 2A, Prong 2
Regarding independent claims 1 and 25, the claims do not include any additional elements that integrate the abstract idea into a practical application. The following elements do not add any meaningful limitation to the abstract idea:
“a motion sensor configured to be secured to a subject” – insignificant pre-solution activity, i.e. mere data gathering
Regarding independent claim 11, the claim does not include any additional elements that integrate the abstract idea into a practical application.
Step 2B
The additional elements of independent claims 1 and 15, when considered either individually or in an ordered combination, are not enough to qualify as significantly more than the abstract idea. As discussed above with respect to the integration of the abstract idea into a practical application, the “a motion sensor configured to be secured to a subject”, along with their associated functions and components, are recited with a high level of generality and simply amount to implementing the abstract idea on a computer. The additional elements that were considered insignificant extra-solution activity have been re-analyzed and do not amount to anything more than what is well-understood, routine, and conventional. Also, simply appending well-understood, routine, and conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception is not indicative of an inventive concept [MPEP 2106.05(d)].
“a sensing arrangement for sensing electrocardiogram signals” -– Osorio et al. (WO 2013056099), [0084]: “The sensor(s) 212 are capable of receiving signals related to a physiological parameter, such as the patient's heart beat, blood pressure, and/or temperature, and delivering the signals to the medical device 200. The sensor 212 may also be capable of detecting kinetic signal associated with a patient's motor activity.”; [0085]: “…the medical device 200 may comprise a body data collection module 275 that is capable of collecting body data, e.g., cardiac data comprising fiducial time markers of each of a plurality of heart beats or arterial or venous pulsations; …kinetic signals indicative of the patient's motor activity …”.
Hence, the claims are directed to an abstract idea without a practical application and without significantly more.
Dependent claims
Regarding dependent claims 2, 12 and 27, the limitations include a specific prophylaxis, i.e. closed loop vagus nerve stimulation, and thus would integrate the mental process into practical application. Therefore, the dependent claims are not rejected under 35 U.S.C. 101.
Regarding dependent claims 3-10, 13-15, 26, and 28-29, the limitations only further define insignificant extra-solution activity of generic computer implementation of the abstract idea.
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)(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 1-15 and 25-29 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Osorio et al. (WO 2013056099, hereinafter referred to as Osorio).
Regarding independent claim 1, Osorio discloses methods, systems, and apparatus for determining probabilistic measures of seizure activity (PMSA) values based on a plurality of seizure detection algorithms and/or body signals used as inputs by the seizure detection algorithms and use of the PMSA values to detect seizure activity based on a consensus of the algorithms and/or body signals, and/or warn, log, administer a therapy, or assess the efficacy of a therapy. Osorio further discloses a system (medical device 200 in Fig. 2), comprising:
a motion sensor configured to be secured to a subject (sensor(s) 212 in Fig. 2; [0084]: “The sensor(s) 212 are capable of receiving signals related to a physiological parameter, such as the patient's heart beat, blood pressure, and/or temperature, and delivering the signals to the medical device 200. The sensor 212 may also be capable of detecting kinetic signal associated with a patient's motor activity. The sensor 212, in one embodiment, may be an accelerometer.”; [0085]: “…the medical device 200 may comprise a body data collection module 275 that is capable of collecting body data, e.g., cardiac data comprising fiducial time markers of each of a plurality of heart beats or arterial or venous pulsations; …kinetic signals indicative of the patient's motor activity …”; [0086]: “The body signal data may be provided by the sensor(s) 212.”); and
a processing circuit (controller 210 with processor 215 and memory 217 in Fig. 2),
the processing circuit being configured:
to determine a calculated heart rate of the subject based on a signal from the motion sensor ([0084]: “The sensor(s) 212 are capable of receiving signals related to a physiological parameter, such as the patient's heart beat, blood pressure, and/or temperature, and delivering the signals to the medical device 200.”; [0085]: “…the medical device 200 may comprise a body data collection module 275 that is capable of collecting body data, e.g., cardiac data comprising fiducial time markers of each of a plurality of heart beats or arterial or venous pulsations…”), and
to determine whether a seizure is imminent or occurring ([0033]: “…a probabilistic measure of seizure activity (PMSA)…”; [0088]: “…a medical device system may comprise a storage unit to store an indication of at least one of seizure or an increased risk of a seizure.”), based on:
the calculated heart rate ([0084]; [0085]); and
a subject-specific classifier ([0092]: “The database unit 250 and/or the local database unit 255 are capable of storing various patient data. These data may comprise patient parameter data acquired from a patient's body, therapy parameter data, seizure severity data, and/or therapeutic efficacy data. …The database unit 250 and/or the local database unit 255 may store various patient data.”; [0104]: “The seizure onset/termination unit 280 may also comprise a plurality of algorithm units 521, 522, 523... Each algorithm unit 521, etc. may apply an algorithm to body signal data to determine an occurrence of a seizure…”; [0108]-[0109]; Fig. 5; The Examiner notes that the database unit 250 and/or the local database unit 255, in communication with medical device 200 via line 277 in Fig. 2, stores data that would be specific to a subject via the patient data stored regarding their particular body, and discussed in paras. [0091]-[0092].).
Regarding claim 2, Osorio discloses that the processing circuit (controller 210 with processor 215 and memory 217 in Fig. 2) is further configured:
to determine that a seizure is imminent or occurring ([0033]: “…a probabilistic measure of seizure activity (PMSA)…”; [0088]: “…a medical device system may comprise a storage unit to store an indication of at least one of seizure or an increased risk of a seizure.”); and
in response to the determining that a seizure is imminent or occurring, to apply closed loop vagus nerve stimulation (therapy unit 235 in Fig. 2; [0080]: “The controller 210 …is capable of causing a therapy unit 235 to generate and deliver an electrical signal …to one or more target tissues of the patient's body for treating a medical condition. For example, the controller 210 …may cause an electrical signal to be generated and delivered based on internal calculations and programming.”; [0151]: “It should be especially apparent that the principles of the disclosure may be applied to selected cranial nerves other than, or in addition to, the vagus nerve to achieve particular results in treating patients having epilepsy, depression, or other medical conditions.”).
Regarding claim 3, Osorio discloses that the subject-specific classifier comprises a parameter calculator (body signal processing unit 510 in Fig. 5; [0103]: “The seizure onset/termination unit 280 may comprise a body signal processing unit 510 adapted to process collected body data from the body data collection module 275. For example, the seizure onset/termination unit 280 may be adapted to receive a time series of collected body data.”), the parameter calculator being configured to fit a calculated heart rate history ([0041]: “One or more of these algorithms may be used to detected seizures from one or more body data streams including, but not limited to, a brain activity (e.g., EEG) data stream, a cardiac (e.g., a heart beat) data stream, and a kinetic (e.g., body movement as measured by an accelerometer) data stream.”; The Examiner notes that a cardiac data stream would be analogous to heart beat over time, i.e. a heart rate history.) with a parametric model ([0100]: “Body data collection module 275 may use body data from memory 350 and/or interface 310 to calculate one or more body indices. A wide variety of body indices may be determined, including a variety of autonomic indices such as heart rate, blood pressure, respiration rate, blood oxygen saturation, neurological indices such as maximum acceleration, patient position (e.g., standing or sitting), and other indices derived from body data acquisition units 360, 370, 373, 374, 375, 376, 377, etc.”), and to generate parameter values ([0084]: “The sensor(s) 212 are capable of receiving signals related to a physiological parameter, such as the patient's heart beat, blood pressure, and/or temperature, and delivering the signals to the medical device 200.”; [0085]: “…the medical device 200 may comprise a body data collection module 275 that is capable of collecting body data, e.g., cardiac data comprising fiducial time markers of each of a plurality of heart beats or arterial or venous pulsations…”; [0100]).
Regarding claim 4, Osorio discloses that the subject-specific classifier ([0092]: “The database unit 250 and/or the local database unit 255 are capable of storing various patient data. These data may comprise patient parameter data acquired from a patient's body, therapy parameter data, seizure severity data, and/or therapeutic efficacy data. …The database unit 250 and/or the local database unit 255 may store various patient data.”; [0104]: “The seizure onset/termination unit 280 may also comprise a plurality of algorithm units 521, 522, 523... Each algorithm unit 521, etc. may apply an algorithm to body signal data to determine an occurrence of a seizure…”; [0108]-[0109]; Fig. 5) comprises a seizure detector configured to determine whether a seizure is imminent or occurring ([0033]: “…a probabilistic measure of seizure activity (PMSA)…”; [0088]: “…a medical device system may comprise a storage unit to store an indication of at least one of seizure or an increased risk of a seizure.”), based on the parameter values (seizure onset/termination unit 280 in Figs. 2, 5; [0088]: “The seizure onset/termination unit 280 is capable of detecting an onset and/or a termination of an epileptic event based upon at least one body signal provided by body data collection module 275.”).
Regarding claim 5, Osorio discloses that the seizure detector (seizure onset/termination unit 280 in Figs. 2, 5; [0088]) is configured to determine whether a seizure is imminent or occurring ([0033]: “…a probabilistic measure of seizure activity (PMSA)…”; [0088]: “…a medical device system may comprise a storage unit to store an indication of at least one of seizure or an increased risk of a seizure.”), based on whether a parameter value exceeds a threshold ([0109]: “The seizure onset/termination unit 280 may also comprise an indicator-threshold comparison unit 540. The indicator-threshold comparison unit 540 may be adapted to compare a value of an IF output by the IF unit 530 to a detection threshold value.”).
Regarding claim 6, Osorio discloses that the seizure detector (seizure onset/termination unit 280 in Figs. 2, 5; [0088]) comprises a machine-learning classifier ([0061]; [0072]: “The Auto-Regressive model (r2), sensitive mainly to changes in spectral shape, was chosen as the simplest and most general method, with which to provide a statistical description of oscillations (ECoG) that may be regarded as generated by the stochastic analogue of a linear oscillator.”; [0113]; The Examiner notes that the mentioned paragraphs discuss adaptive data processing to output a classifier determining the probability of seizure activity in a subject.; Figs. 5, 15-18), configured to classify a set of one or more parameter values as corresponding to either (i) the absence of a seizure or (ii) a seizure being imminent or occurring ([0033]: “…a probabilistic measure of seizure activity (PMSA)…”; [0088]: “…a medical device system may comprise a storage unit to store an indication of at least one of seizure or an increased risk of a seizure.”).
Regarding claim 7, Osorio discloses that the machine-learning classifier ([0061]; [0072]: “The Auto-Regressive model (r2), sensitive mainly to changes in spectral shape, was chosen as the simplest and most general method, with which to provide a statistical description of oscillations (ECoG) that may be regarded as generated by the stochastic analogue of a linear oscillator.”; [0113]; The Examiner notes that the mentioned paragraphs discuss adaptive data processing to output a classifier determining the probability of seizure activity in a subject.; Figs. 5, 15-18) is a classifier selected from the group consisting of adaptive boosting classifiers, artificial neural network learning algorithms, Bayesian belief networks, Bayesian classifiers, Bayesian neural networks, boosted trees, case-based reasoning classifiers, classification trees, convolutional neural networks, decisions trees, deep learning classifiers, elastic nets, fully convolutional networks, genetic algorithms, gradient boosting trees, k-nearest neighbor classifiers, least absolute shrinkage and selection operator classifiers, linear classifiers, naive Bayes classifiers, neural networks, logistic regression, random forests, ridge regression, support vector machines ([0061]: “Tools such as those available through cluster analysis of multidimensional vectors of relevant features would aid in the pursuit of automated seizure detection and quantification. To even have a modicum of success, this approach should not ignore the non-stationarity of seizures and strike some sort of balance between supervised (human) and unsupervised machine-learning) approaches. The resulting multidimensional parameter space, expected to be broad and intricate, may also foster discovery of hypothesized (e.g. pre-ictal) brain sub-states.”), and combinations thereof ([0113]: “The at least first and second seizure detection algorithms may be selected from an autoregression algorithm, a wavelet transform maximum modulus (WTMM) algorithm, or a short-term-average to long-term-average (STALTA) algorithm, such as those described above. Any other algorithms may be applied to a time series to generate indicator functions to compute [an average indicator function (AIF) or a product indicator function (PIF)].”).
Regarding claim 8, Osorio discloses that the subject-specific classifier comprises a machine-learning classifier ([0061]; [0072]; [0113]; The Examiner notes that the mentioned paragraphs discuss adaptive data processing to output a classifier determining the probability of seizure activity in a subject.; Figs. 5, 15-18) configured to determine whether a seizure is imminent or occurring ([0033]: “…a probabilistic measure of seizure activity (PMSA)…”; [0088]: “…a medical device system may comprise a storage unit to store an indication of at least one of seizure or an increased risk of a seizure.”) based on a calculated heart rate history ([0041]: “One or more of these algorithms may be used to detected seizures from one or more body data streams including, but not limited to, a brain activity (e.g., EEG) data stream, a cardiac (e.g., a heart beat) data stream, and a kinetic (e.g., body movement as measured by an accelerometer) data stream.”; The Examiner notes that a cardiac data stream would be analogous to heart beat over time, i.e. a heart rate history.; [0084]; [0085]: “…body data, e.g., cardiac data comprising fiducial time markers of each of a plurality of heart beats or arterial or venous pulsations…”).
Regarding claim 9, Osorio discloses that the motion sensor comprises an accelerometer (sensor(s) 212 in Fig. 2; [0084]: “The sensor 212, in one embodiment, may be an accelerometer.”).
Regarding claim 10, Osorio discloses that the motion sensor comprises a gyroscope ([0084]: “The sensor 212 may also be capable of detecting kinetic signal associated with a patient's motor activity. … The sensor 212, in another embodiment, may be an inclinometer. In another embodiment, the sensor 212 may be an actigraph.”).
Regarding independent claim 11, Osorio discloses a method (Abstract: “Methods, systems, and apparatus for determining probabilistic measures of seizure activity (PMSA) values based on a plurality of seizure detection algorithms and/or body signals used as inputs by the seizure detection algorithms. Use of the PMSA values to detect seizure activity based on a consensus of the algorithms and/or body signals, and/or warn, log, administer a therapy, or assess the efficacy of a therapy.”), comprising:
determining a calculated heart rate of a subject ([0084]: “The sensor(s) 212 are capable of receiving signals related to a physiological parameter, such as the patient's heart beat, blood pressure, and/or temperature, and delivering the signals to the medical device 200.”), based on a signal from a motion sensor secured to the subject (sensor(s) 212 in Fig. 2; [0084]: “The sensor(s) 212 are capable of receiving signals related to a physiological parameter, such as the patient's heart beat, blood pressure, and/or temperature, and delivering the signals to the medical device 200. The sensor 212 may also be capable of detecting kinetic signal associated with a patient's motor activity. The sensor 212, in one embodiment, may be an accelerometer.”; [0085]: “…the medical device 200 may comprise a body data collection module 275 that is capable of collecting body data, e.g., cardiac data comprising fiducial time markers of each of a plurality of heart beats or arterial or venous pulsations; …kinetic signals indicative of the patient's motor activity …”; [0086]: “The body signal data may be provided by the sensor(s) 212.”); and
determining, by a subject-specific classifier ([0092]: “The database unit 250 and/or the local database unit 255 are capable of storing various patient data. These data may comprise patient parameter data acquired from a patient's body, therapy parameter data, seizure severity data, and/or therapeutic efficacy data. …The database unit 250 and/or the local database unit 255 may store various patient data.”; [0104]: “The seizure onset/termination unit 280 may also comprise a plurality of algorithm units 521, 522, 523... Each algorithm unit 521, etc. may apply an algorithm to body signal data to determine an occurrence of a seizure…”; [0108]-[0109]; Fig. 5; The Examiner notes that the database unit 250 and/or the local database unit 255, in communication with medical device 200 via line 277 in Fig. 2, stores data that would be specific to a subject via the patient data stored regarding their particular body, and discussed in paras. [0091]-[0092].), whether a seizure is imminent or occurring ([0033]: “…a probabilistic measure of seizure activity (PMSA)…”; [0088]: “…a medical device system may comprise a storage unit to store an indication of at least one of seizure or an increased risk of a seizure.”), based on the calculated heart rate ([0084]: “The sensor(s) 212 are capable of receiving signals related to a physiological parameter, such as the patient's heart beat, blood pressure, and/or temperature, and delivering the signals to the medical device 200.”).
Regarding claim 12, Osorio discloses determining that a seizure is imminent or occurring ([0033]: “…a probabilistic measure of seizure activity (PMSA)…”; [0088]: “…a medical device system may comprise a storage unit to store an indication of at least one of seizure or an increased risk of a seizure.”); and
in response to the determining that a seizure is imminent or occurring, applying closed loop vagus nerve stimulation (therapy unit 235 in Fig. 2; [0080]: “The controller 210 …is capable of causing a therapy unit 235 to generate and deliver an electrical signal …to one or more target tissues of the patient's body for treating a medical condition. For example, the controller 210 …may cause an electrical signal to be generated and delivered based on internal calculations and programming.”; [0151]: “It should be especially apparent that the principles of the disclosure may be applied to selected cranial nerves other than, or in addition to, the vagus nerve to achieve particular results in treating patients having epilepsy, depression, or other medical conditions.”).
Regarding claim 13, Osorio discloses that the subject-specific classifier comprises a parameter calculator (body signal processing unit 510 in Fig. 5), the parameter calculator being configured to fit a calculated heart rate history ([0041]: “One or more of these algorithms may be used to detected seizures from one or more body data streams including, but not limited to, a brain activity (e.g., EEG) data stream, a cardiac (e.g., a heart beat) data stream, and a kinetic (e.g., body movement as measured by an accelerometer) data stream.”; The Examiner notes that a cardiac data stream would be analogous to heart beat over time, i.e. a heart rate history.; [0084]; [0085]: “…body data, e.g., cardiac data comprising fiducial time markers of each of a plurality of heart beats or arterial or venous pulsations…”) with a parametric model ([0100]: “Body data collection module 275 may use body data from memory 350 and/or interface 310 to calculate one or more body indices. A wide variety of body indices may be determined, including a variety of autonomic indices such as heart rate, blood pressure, respiration rate, blood oxygen saturation, neurological indices such as maximum acceleration, patient position (e.g., standing or sitting), and other indices derived from body data acquisition units 360, 370, 373, 374, 375, 376, 377, etc.”), and to generate parameter values ([0100]; [0103]: “The seizure onset/termination unit 280 may comprise a body signal processing unit 510 adapted to process collected body data from the body data collection module 275. For example, the seizure onset/termination unit 280 may be adapted to receive a time series of collected body data.”).
Regarding claim 14, Osorio discloses that the subject-specific classifier comprises a seizure detector configured to determine whether a seizure is imminent or occurring, based on the parameter values (seizure onset/termination unit 280 in Figs. 2, 5; [0033]: “…a probabilistic measure of seizure activity (PMSA)…”; [0088]: “The seizure onset/termination unit 280 is capable of detecting an onset and/or a termination of an epileptic event based upon at least one body signal provided by body data collection module 275. …a medical device system may comprise a storage unit to store an indication of at least one of seizure or an increased risk of a seizure.”).
Regarding claim 15, Osorio discloses that the seizure detector is configured to determine whether a seizure is imminent or occurring ([0033]: “…a probabilistic measure of seizure activity (PMSA)…”; [0088]: “…a medical device system may comprise a storage unit to store an indication of at least one of seizure or an increased risk of a seizure.”), based on whether a parameter value exceeds a threshold ([0109]: “The seizure onset/termination unit 280 may also comprise an indicator-threshold comparison unit 540. The indicator-threshold comparison unit 540 may be adapted to compare a value of an IF output by the IF unit 530 to a detection threshold value.”).
Regarding independent claim 25, Osorio discloses a system (medical device 200 in Fig. 2), comprising:
a motion sensor configured to be secured to a subject (sensor(s) 212 in Fig. 2; [0084]: “The sensor(s) 212 are capable of receiving signals related to a physiological parameter, such as the patient's heart beat, blood pressure, and/or temperature, and delivering the signals to the medical device 200. The sensor 212 may also be capable of detecting kinetic signal associated with a patient's motor activity. The sensor 212, in one embodiment, may be an accelerometer.”; [0085]: “…the medical device 200 may comprise a body data collection module 275 that is capable of collecting body data, e.g., cardiac data comprising fiducial time markers of each of a plurality of heart beats or arterial or venous pulsations; …kinetic signals indicative of the patient's motor activity …”; [0086]: “The body signal data may be provided by the sensor(s) 212.”); and
a processing circuit (controller 210 with processor 215 and memory 217 in Fig. 2),
the processing circuit being configured to determine whether a seizure is imminent or occurring ([0033]: “…a probabilistic measure of seizure activity (PMSA)…”; [0088]: “…a medical device system may comprise a storage unit to store an indication of at least one of seizure or an increased risk of a seizure.”), based on:
a signal from the motion sensor ( [0084]: “The sensor(s) 212 are capable of receiving signals related to a physiological parameter… and delivering the signals to the medical device 200.”; [0086]: “The body signal data may be provided by the sensor(s) 212.”); and
a subject-specific classifier ([0092]: “The database unit 250 and/or the local database unit 255 are capable of storing various patient data. These data may comprise patient parameter data acquired from a patient's body, therapy parameter data, seizure severity data, and/or therapeutic efficacy data. …The database unit 250 and/or the local database unit 255 may store various patient data.”; [0104]: “The seizure onset/termination unit 280 may also comprise a plurality of algorithm units 521, 522, 523... Each algorithm unit 521, etc. may apply an algorithm to body signal data to determine an occurrence of a seizure…”; [0108]-[0109]; Fig. 5; The Examiner notes that the database unit 250 and/or the local database unit 255, in communication with medical device 200 via line 277 in Fig. 2, stores data that would be specific to a subject via the patient data stored regarding their particular body, and discussed in paras. [0091]-[0092].),
the subject-specific classifier comprising a machine learning model selected from the group consisting of fully convolutional networks, recurrent neural networks, and combinations thereof ([0061]; [0072]: “The Auto-Regressive model (r2), sensitive mainly to changes in spectral shape, was chosen as the simplest and most general method, with which to provide a statistical description of oscillations (ECoG) that may be regarded as generated by the stochastic analogue of a linear oscillator.”; [0113]: “The at least first and second seizure detection algorithms may be selected from an autoregression algorithm, a wavelet transform maximum modulus (WTMM) algorithm, or a short-term-average to long-term-average (STALTA) algorithm, such as those described above. Any other algorithms may be applied to a time series to generate indicator functions to compute [an average indicator function (AIF) or a product indicator function (PIF)].”; The Examiner notes that the mentioned paragraphs discuss adaptive data processing to output a classifier determining the probability of seizure activity in a subject.; Figs. 5, 15-18).
Regarding claim 26, Osorio discloses that the subject-specific classifier comprises a long short-term memory neural network ([0113]: “The at least first and second seizure detection algorithms may be selected from an autoregression algorithm, a wavelet transform maximum modulus (WTMM) algorithm, or a short-term-average to long-term-average (STALTA) algorithm, such as those described above. Any other algorithms may be applied to a time series to generate indicator functions to compute [an average indicator function (AIF) or a product indicator function (PIF)].”; Figs. 5, 15-18).
Regarding claim 27, Osorio discloses that the processing circuit (controller 210 with processor 215 and memory 217 in Fig. 2) is further configured:
to determine that a seizure is imminent or occurring ([0033]: “…a probabilistic measure of seizure activity (PMSA)…”; [0088]: “…a medical device system may comprise a storage unit to store an indication of at least one of seizure or an increased risk of a seizure.”); and
in response to the determining that a seizure is imminent or occurring ([0033]; [0088]), to apply closed loop vagus nerve stimulation (therapy unit 235 in Fig. 2; [0080]: “The controller 210 …is capable of causing a therapy unit 235 to generate and deliver an electrical signal …to one or more target tissues of the patient's body for treating a medical condition. For example, the controller 210 …may cause an electrical signal to be generated and delivered based on internal calculations and programming.”; [0151]: “It should be especially apparent that the principles of the disclosure may be applied to selected cranial nerves other than, or in addition to, the vagus nerve to achieve particular results in treating patients having epilepsy, depression, or other medical conditions.”).
Regarding claim 28, Osorio discloses that the subject-specific classifier comprises a parameter calculator (body signal processing unit 510 in Fig. 5), the parameter calculator being configured to fit a calculated heart rate history ([0041]: “One or more of these algorithms may be used to detected seizures from one or more body data streams including, but not limited to, a brain activity (e.g., EEG) data stream, a cardiac (e.g., a heart beat) data stream, and a kinetic (e.g., body movement as measured by an accelerometer) data stream.”; The Examiner notes that a cardiac data stream would be analogous to heart beat over time, i.e. a heart rate history.; [0084]; [0085]: “…body data, e.g., cardiac data comprising fiducial time markers of each of a plurality of heart beats or arterial or venous pulsations…”) with a parametric model ([0100]: “Body data collection module 275 may use body data from memory 350 and/or interface 310 to calculate one or more body indices. A wide variety of body indices may be determined, including a variety of autonomic indices such as heart rate, blood pressure, respiration rate, blood oxygen saturation, neurological indices such as maximum acceleration, patient position (e.g., standing or sitting), and other indices derived from body data acquisition units 360, 370, 373, 374, 375, 376, 377, etc.”), and to generate parameter values ([0100]; [0103]: “The seizure onset/termination unit 280 may comprise a body signal processing unit 510 adapted to process collected body data from the body data collection module 275. For example, the seizure onset/termination unit 280 may be adapted to receive a time series of collected body data.”).
Regarding claim 29, Osorio discloses that the subject-specific classifier ([0092]: “The database unit 250 and/or the local database unit 255 are capable of storing various patient data. These data may comprise patient parameter data acquired from a patient's body, therapy parameter data, seizure severity data, and/or therapeutic efficacy data. …The database unit 250 and/or the local database unit 255 may store various patient data.”; [0104]: “The seizure onset/termination unit 280 may also comprise a plurality of algorithm units 521, 522, 523... Each algorithm unit 521, etc. may apply an algorithm to body signal data to determine an occurrence of a seizure…”; [0108]-[0109]; Fig. 5) comprises a seizure detector configured to determine whether a seizure is imminent or occurring, based on the parameter values (seizure onset/termination unit 280 in Figs. 2, 5; [0033]: “…a probabilistic measure of seizure activity (PMSA)…”; [0088]: “…a medical device system may comprise a storage unit to store an indication of at least one of seizure or an increased risk of a seizure.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Sullivan et al. (US 2016/0135706);
Osorio et al. (US 2017/0157402);
Wang et al. (US 10,398,319); and
Narayan et al. (US 11,564,591).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARY G SCHLUETER whose telephone number is (703)756-4601. The examiner can normally be reached M-F 9:00am-5:30pm EST.
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/M.G.S./Examiner, Art Unit 3796
/LYNSEY C Eiseman/Primary Examiner, Art Unit 3796
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