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, Amendments to the Abstract, Amendments to the Specification, Amendments to the Drawings, and Remarks on November 13, 2025 in response to the Examiner’s Non-Final Office Action, mailed August 15, 2025.
Amendments to the Claims
At this time, claims 1-3, 5, and 7-18 are pending. Claims 1, 5, 7-10, and 13-18 have been amended. The Applicant has cancelled claims 4 and 6. The Applicant asserts that no new matter is added. (Remarks, pg. 8)
Objections to the Abstract
The Abstract has been amended to address the objection to it previously exceeding 150 words. Applicant’s arguments, see Remarks, pg. 8, with respect to the Abstract objection have been fully considered and are persuasive. The objection of the Abstract from August 15, 2025 has been withdrawn.
Objections to the Drawings
The Drawing of Fig. 2 has been amended to address that reference character 14 in FIG. 2 is not described in the specification. Applicant’s arguments, see Remarks, pg. 8, with respect to the Drawing objection have been fully considered and are persuasive. The objection of the Drawings from August 15, 2025 has been withdrawn.
Objections to the Claims
Claims 4-18 were previously objected to as being in improper multiple dependent claim form. Applicant has addressed this issue by amending the claim dependencies. Applicant’s arguments, see Remarks, pg. 8, with respect to the claim objections have been fully considered and are persuasive, excepting claim 12 (see Claim Objections section below). The objections of the claims 4-11 and 13-18 from August 15, 2025 have been withdrawn.
Rejections under 35 U.S.C. §112
Claim 1 was previously rejected under 35 U.S.C. §112 for indefiniteness. As a basis for this rejection the Examiner asserted that the "step b) recitation of "identifying neurological activity in the EEG data, which corresponds to a neurological condition, based upon neurological condition identification tags included in the EEG data and/or input in the computer" leaves questions as that what defines "neurological condition identification tags". Applicant’s arguments, see Remarks, pg. 9, with respect to the 35 U.S.C. 112(b) rejection have been fully considered and are persuasive. The indefiniteness rejection of claim 1 from August 15, 2025 has been withdrawn.
Rejections under 35 U.S.C. §101
Claims 1-3 were previously rejected under 35 U.S.C. §101 as being directed to non-statutory subject matter. As a basis for this rejection, the Examiner asserts that the claims read on transitory forms of signal transmission. Applicant’s amendments and arguments, see Remarks, pg. 9, with respect to the 35 U.S.C. 101 rejection have been fully considered and are persuasive. The 35 U.S.C. 101 rejection of claims 1-3 from August 15, 2025 has been withdrawn.
Rejections under 35 U.S.C. §102
Claims 1-3 were previously rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kremen et al. (US 2020/0337645, hereinafter referred to as Kremen). The claims have been extensively amended at this time. Applicant’s arguments, see Remarks, pg. 9, with respect to the 35 U.S.C. 102(a)(1) rejection have been fully considered and are persuasive. The 35 U.S.C. 102(a)(1) rejection of claims 1-3 from August 15, 2025 has been withdrawn.
The Examiner would like to address the Applicant’s statement of “Kremen describes a system for classifying brain states (awake, light sleep, deep sleep) using adaptive sensor selection and signal analysis, and as such, does not teach the detection of pathological events (e.g., seizures) and delivering stimulation to prevent or mitigate the symptoms. Kremen does not use pattern recognition or predictive modelling to identify abnormal brain activity, but instead uses threshold- based classifications of EEG features to determine a sleep stage.” (Remarks, pg. 9) The Examiner respectfully disagrees.
Though Kremen focuses on classifying brain states (awake, light sleep, deep sleep), the system can also be used to detect and deliver stimulation to prevent or mitigate the symptoms of pathological events (e.g., seizures), as shown in paras. not limited to: [0023]-[0025], [0030], [0032], [0036], [0053], and [0070]-[0076].
Nonetheless, 35 U.S.C. 102(a)(1) rejection of claims 1-3 from August 15, 2025 has been withdrawn to address the amendments to the claims.
Claim Objections
Claim 3 is objected to under 37 CFR 1.75(c) as being in improper form because a multiple dependent claim language in claim 3 (for example, but not limited to, " any of the preceding claims..."). See MPEP § 608.01(n). Claim 3 was previously and is currently examined, but the Examiner requests that this be remedied.
Claim 12 is additionally objected to under 37 CFR 1.75(c) as being in improper form because a multiple dependent claim language in claim 3 (for example, but not limited to, " any of the preceding claims..."). See MPEP § 608.01(n). Claim 12 is currently examined, but the Examiner requests that this be remedied.
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.
Claim 8 is rejected under 35 U.S.C. 102(a)(2) as being anticipated by Kremen et al. (US 2020/0337645, hereinafter referred to as Kremen).
Regarding amended, independent claim 8, Kremen discloses a computer program stored on a non-transitory computer-readable medium (a method 300 in Fig. 3 for classifying the behavioral state of the brain; [0035]: “Note that the method 300 can be implemented using a non-transitory computer readable medium that when executed causes the one or more processors to perform the method.”) for training a neurological condition detection algorithm to be used for neurological condition detection in an implantable neurostimulation device (implanted device 900 in Fig. 9 ; [0030]: “…machine learning and/or supervision can be used to configure, adjust, fine tune or otherwise operate the system in block 212 [in Fig. 2].”; [0066]: “The control algorithm in this case would use behavioral state classifications determined from EEG or other sensors as the input and electrical stimulation is used to modulate and drive the brain to the prescribed state. This algorithm can run on the implanted device…”) having a target electrode arrangement ([0031], [0037]: “…one or more suitable sensor configurations…”), the computer program comprising the following steps:
a) inputting EEG data in a computer ([0028]: “The user interface 104 can be any component or device in which information is transmitted to and/or received from the one or more processors 108. …the user interface 104 can be a connector in which a separate device can be attached, a network interface, a wireless transceiver, a display, a keyboard, a keypad, etc. For example, …computer, can wirelessly connect to and control/monitor the system 102 via the user interface 104.”) which executes the computer program ([0066]: “The control algorithm in this case would use behavioral state classifications determined from EEG or other sensors as the input and electrical stimulation is used to modulate and drive the brain to the prescribed state.”), the EEG data being recorded by at least one EEG from at least one patient (multi-channel iEEG data 402, 418 in Fig. 4) using an electrode system with a plurality of electrode channels ([0062]: “…selects one electrodes from array of available electrodes…”),
b) identifying neurological activity in the EEG data ([0006]; [0029]: “The one or more processors 108 receive a signal from each of a plurality of sensors 118 via the sensor and/or electrode interface 102. The sensors 118 are configured to detect an electrical activity of the brain.”), which corresponds to a neurological condition ([0030]: “…behavioral state classification is performed in block 208 [in Fig. 2]…”; [0032]: “The one or more processors 108 can pre-process the signals by: detecting an abnormal amplitude distortion in the signals; or detecting a seizure or an abnormal electrophysiological condition using the signals; or detecting a high 60 or 50 Hz line interference in the signals; or other desired process.”; [0036]), based upon neurological condition identification tags included in the EEG data and/or input in the computer ([0062]: “The foregoing testing investigated behavioral state classification (wake & N2, and slow wave sleep) using intracranial EEG spectral power features in an unsupervised machine learning method. The method automatically selects one electrodes from array of available electrodes based on unsupervised score of the data and deploys cascade of classifiers using features extracted from selected electrode to classify into AW, N2, and N3 stages.”),
c) selecting a subset of electrode channels out of the available electrode channels in the EEG data ([0032]: “…the one or more processors 108 can select or restrict a number of channels of the sensors 118…”; [0038]; [0052]: “The method 400 is also referred to as the Behavioral State Classifier (BSC). Sleep scoring and multi-channel iEEG data 402 are used in subsequent automated steps for feature extraction or selection (if turned on). The single electrode assesed to yield in the best performance is selected and used for classification and classifier uses features extracted from the selected electrode and then supplied to inputs of hierarchical clustering methods that returns an AW, N2, and N3 classification. The user can select and restrict the number of channels, number of features, and whether automated feature selection and electrode selection is used.”; [0064]: “…FIG. 8 shows a training method 800 and an application of trained classifier 850. In training 800, scalp EEG data 802 and multi-channel iEEG data 804 is provided for manual sleep scoring (e.g., 10 min Awake, 10 min SWS) 806. Automated classification 808 uses the score 810 and iEEG data 812 with channel constriction 814 for feature extraction selection 816.”) depending
c1) on the identified neurological activity and/or
c2) on characteristic data of the target electrode arrangement ([0062]: “…selects one electrodes from array of available electrodes based on unsupervised score of the data and deploys cascade of classifiers using features extracted from selected electrode to classify into AW, N2, and N3 stages.”),
d) training a neurological condition detection algorithm by using the EEG data only of the selected subset of electrode channels ([0031]: “The one or more processors 108 can also use training signal processing and a machine learning system to identify one or more suitable sensor configurations for an automated or semi-automated classification of the behavioral state.”),
and wherein the computer program comprises at least two training cycles of the neurological condition detection algorithm (method 400 in Fig. 4 for classifying the behavioral state of the brain):
e) in a first training cycle a general training of the neurological condition detection algorithm is done using the EEG data of one or more patients ([0052:]: “The top part 404 of Figure 4 shows its unsupervised method that was trained and tested here in the study and it doesn't require training on gold standard data for each patient. …Here a day and night of multichannel iEEG recording were used as an input of the method for each patient.”), and
f) in a second training cycle a patient specific training of the neurological condition detection algorithm is done using the EEG data only of the patient to which the neurological condition detection algorithm shall be applied and/or using the EEG data of another patient having similar neurological condition onset pattern as the patient to which the neurological condition detection algorithm shall be applied ([0052]: “The bottom part 406 of Figure 4 shows how another user input and/or a supervision and active learning can be implemented if needed or if training data are being available… In the supervised version 406,multi-channel scalp EEG data 418 is processed using gold standard sleep scoring (AW, N1, N2, N3, REM) 420 to define and select the features, and/or automated sleep scoring (AW, N2, N3)N3) 422 from the cascade classification is used to define and select the features.”).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 5, 7, and 9-18 are rejected under 35 U.S.C. 103 as being unpatentable over Kremen in view of Remmert (US 2018/0117308) and further in view of Lee et al. (US 2011/0137381).
Regarding amended, independent claim 1, Kremen discloses tracking human brain activity, and more particularly, to a system and method for classifying and modulating brain behavioral states ([0002]). Kremen further discloses a computer program stored on a non-transitory computer-readable medium (a method 300 in Fig. 3 for classifying the behavioral state of the brain; [0035]: “Note that the method 300 can be implemented using a non-transitory computer readable medium that when executed causes the one or more processors to perform the method.”) for training a neurological condition detection algorithm to be used for neurological condition detection ([0024]; [0030]: “…in FIG. 2, the sensor signals/data are received in block 202, the sensor signals/data are preprocessed in block 204 (optional), one or more sensors are selected in block 206, and behavioral state classification is performed in block 208. A user or other device can be used to configure the system 100 in block 210. In addition, machine learning and/or supervision can be used to configure, adjust, fine tune or otherwise operate the system in block 212.”) in an implantable neurostimulation device (implanted device 900 in Fig. 9 ; [0030]: “…machine learning and/or supervision can be used to configure, adjust, fine tune or otherwise operate the system in block 212 [in Fig. 2].”; [0066]: “The control algorithm in this case would use behavioral state classifications determined from EEG or other sensors as the input and electrical stimulation is used to modulate and drive the brain to the prescribed state. This algorithm can run on the implanted device…”) having a target electrode arrangement ([0031], [0037]: “…one or more suitable sensor configurations…”), the computer program comprising the following steps:
a) inputting EEG data in a computer ([0028]: “The user interface 104 can be any component or device in which information is transmitted to and/or received from the one or more processors 108. …the user interface 104 can be a connector in which a separate device can be attached, a network interface, a wireless transceiver, a display, a keyboard, a keypad, etc. For example, …computer, can wirelessly connect to and control/monitor the system 102 via the user interface 104.”) which executes the computer program ([0066]: “The control algorithm in this case would use behavioral state classifications determined from EEG or other sensors as the input and electrical stimulation is used to modulate and drive the brain to the prescribed state.”), the EEG data being recorded by at least one EEG (multi-channel iEEG data 402, 418 in Fig. 4) from at least one patient,
b) identifying neurological activity in the EEG data ([0006]; [0029]: “The one or more processors 108 receive a signal from each of a plurality of sensors 118 via the sensor and/or electrode interface 102. The sensors 118 are configured to detect an electrical activity of the brain.”), which corresponds to a neurological condition ([0030]: “…behavioral state classification is performed in block 208 [in Fig. 2]…”; [0032]: “The one or more processors 108 can pre-process the signals by: detecting an abnormal amplitude distortion in the signals; or detecting a seizure or an abnormal electrophysiological condition using the signals; or detecting a high 60 or 50 Hz line interference in the signals; or other desired process.”; [0036]), based upon neurological condition identification tags comprising information identifying the time and location of a neurological condition ([0030]: “…automatically map one or more spatial and temporal patterns of the classified behavioral state…”) included in the EEG data and/or input in the computer ([0062]: “The foregoing testing investigated behavioral state classification (wake & N2, and slow wave sleep) using intracranial EEG spectral power features in an unsupervised machine learning method. The method automatically selects one electrodes from array of available electrodes based on unsupervised score of the data and deploys cascade of classifiers using features extracted from selected electrode to classify into AW, N2, and N3 stages.”),
c) selecting a subset of five electrode channels out of the available electrode channels in the EEG data ([0032]: “…the one or more processors 108 can select or restrict a number of channels of the sensors 118…”; [0038]; [0052]: “The method 400 is also referred to as the Behavioral State Classifier (BSC). Sleep scoring and multi-channel iEEG data 402 are used in subsequent automated steps for feature extraction or selection (if turned on). The single electrode assesed to yield in the best performance is selected and used for classification and classifier uses features extracted from the selected electrode and then supplied to inputs of hierarchical clustering methods that returns an AW, N2, and N3 classification. The user can select and restrict the number of channels, number of features, and whether automated feature selection and electrode selection is used.”; [0064]: “…FIG. 8 shows a training method 800 and an application of trained classifier 850. In training 800, scalp EEG data 802 and multi-channel iEEG data 804 is provided for manual sleep scoring (e.g., 10 min Awake, 10 min SWS) 806. Automated classification 808 uses the score 810 and iEEG data 812 with channel constriction 814 for feature extraction selection 816.”) depending
i) on the identified neurological activity ([0062]: “…selects one electrodes from array of available electrodes based on unsupervised score of the data and deploys cascade of classifiers using features extracted from selected electrode to classify into AW, N2, and N3 stages.”) and
ii) on characteristic data of the target electrode arrangement ([0038]: “For example, the one or more selection criteria can be a K-NN clustering algorithm with Euclidean distance measure where inter and intra-cluster distance are used as parameters for selection of only one sensor.”),
wherein electrode selection is performed to obtain an electrode set with the maximum number of electrodes covering a seizure onset zone at inter-electrode distances ([0031]: “The one or more processors 108 can also use training signal processing and a machine learning system to identify one or more suitable sensor configurations for an automated or semi-automated classification of the behavioral state. Moreover, the one or more processors 108 select target brain locations for the sensors from one or more of a cortex, hippocampus, thalamus, brain stem, basal ganglia, subthalamic nucleus, globus pallidus or other movement circuitry structures and muscles via EMG or ENG or actigraphy.”)
d) training a neurological condition detection algorithm by using the EEG data only of the selected subset of electrode channels ([0031]: “The one or more processors 108 can also use training signal processing and a machine learning system to identify one or more suitable sensor configurations for an automated or semi-automated classification of the behavioral state.”).
Kremen is silent to having five electrodes which are arranged in a pseudo-Laplacian pattern having a center electrode and four stimulation electrodes which surround the center electrode; a)… the EEG data being recorded by at least one EEG from at least one patient using a 10-20 or 10-10 or any other high density EEG electrode system; and wherein electrode selection is performed to obtain an electrode set with the maximum number of electrodes covering a seizure onset zone at inter-electrode distances below a threshold mapping to the pseudo-Laplacian pattern design of the implantable system, wherein
c21) the mean of five selected electrode coordinates is calculated and the nearest scalp electrode to this position is found, wherein this electrode is considered as the center electrode, and any of the selected five electrodes that have a distance from the center electrode greater than the threshold distance is excluded from the list, or
c22) one electrode is selected as the central electrode and the number of electrodes from the initial seizure onset zone electrodes whose distance from the central electrode is smaller than the threshold distance is counted;
this process is repeated for all EEG electrode positions over the scalp, and list of the selected initial seizure onset zone electrodes enclosed with each electrode is generated;
the electrode that contained the maximum number of initial seizure onset zone electrodes is chosen as the central electrode;
if required, electrodes with a minimum distance from the central electrode are added to the list to yield exactly five electrodes for seizure detection.
However, Remmert teaches an electrode for the electrical stimulation of brain tissue or other tissue of a patient is configured for location between skull and scalp of the patient. Remmert further teaches having five electrodes which are arranged in a pseudo-Laplacian pattern having a center electrode and four stimulation electrodes which surround the center electrode ([0053]: “FIG. 2 shows a preferred pseudo-Laplacian arrangement of the electrodes of the neurostimulation system. An electrode pad 2 comprises a stimulation electrode 20 and for secondary electrodes 21, 22, 23, 24.”).
Remmert teaches a similar pursuit to that of the instant application and Kremen in teaching an implantable neurostimulator adapted for a particular treatment, such as epilepsy. It would have been obvious to one having ordinary skill in the art at the effective filing date of the invention to modify the invention of Kremen to further include electrodes configured in a pseudo-Laplacian pattern in order to focalize stimulation and provide optimal treatment to a patient experiencing the onset of a seizure.
The Kremen/Remmert combination is silent to:
a)… the EEG data being recorded by at least one EEG at least one patient using a 10-20 or 10-10 or any other high density EEG electrode system; and
c)… wherein electrode selection is performed to obtain an electrode set with the maximum number of electrodes covering a seizure onset zone at inter-electrode distances below a threshold mapping to the pseudo-Laplacian pattern design of the implantable system, wherein
c21) the mean of five selected electrode coordinates is calculated and the nearest scalp electrode to this position is found, wherein this electrode is considered as the center electrode, and any of the selected five electrodes that have a distance from the center electrode greater than the threshold distance is excluded from the list, or
c22) one electrode is selected as the central electrode and the number of electrodes from the initial seizure onset zone electrodes whose distance from the central electrode is smaller than the threshold distance is counted;
this process is repeated for all EEG electrode positions over the scalp, and list of the selected initial seizure onset zone electrodes enclosed with each electrode is generated;
the electrode that contained the maximum number of initial seizure onset zone electrodes is chosen as the central electrode;
if required, electrodes with a minimum distance from the central electrode are added to the list to yield exactly five electrodes for seizure detection.
However, Lee teaches the prevention and/or treatment of neurological disorders via electrical stimulation. Lee further teaches:
a)… the EEG data being recorded by at least one EEG at least one patient using a 10-20 or 10-10 or any other high density EEG electrode system ([0135]-[0141]; [0136]-[0137]: “As an example, shown in FIG. 10A, is an array 11 of 16.times.14 equally spaced disc electrodes… With a switching network, any combination of electrodes in the n.times.m array can be connected together.”); and
c) …wherein electrode selection is performed to obtain an electrode set with the maximum number of electrodes covering a seizure onset zone at inter-electrode distances below a threshold mapping to the pseudo-Laplacian pattern design of the implantable system ([0106]: “If the position of each electrode is known, the data can be processed to yield a 3-dimensional map of brain electrical activity. Using this map, the appropriate electrodes can be energized and the areas of the brain to be treated can be limited to only those areas in which abnormal electrical activity is present.”), wherein
c21) the mean of five selected electrode coordinates is calculated and the nearest scalp electrode to this position is found ([0136]: “…one example of an epilepsy system of the present invention senses the EEG signal using an array of epicranial discrete electrodes, detects the presence and location of a seizure focus, and stimulates localized said focus by electrically connecting appropriate single discrete electrodes in said array to a targeting electrode… The discs within an array can all be shaped the same or differently. In one embodiment of the invention, one or more arrays can be used, of which some may be implanted subscalp and some cutaneous or some may be implanted intracranial.”), wherein this electrode is considered as the center electrode (Figs. 10A and 10B show inner pole electrodes centered at a seizure focus location in electrodes 22, 24, and 26.), and any of the selected five electrodes that have a distance from the center electrode greater than the threshold distance is excluded from the list ([0137]: “All other electrodes in the array are not used and left uncharged or neutral.”), or
c22) one electrode is selected as the central electrode (Figs. 10A and 10B show inner pole electrodes centered at a seizure focus location in electrodes 22, 24, and 26.) and the number of electrodes from the initial seizure onset zone electrodes whose distance from the central electrode is smaller than the threshold distance is counted (Fig 11 shows “Determine Disc Electrodes within the Array to optimally stimulate Seizure Focus”; [0142]-[0146]);
this process is repeated for all EEG electrode positions over the scalp, and list of the selected initial seizure onset zone electrodes enclosed with each electrode is generated (Fig 11 shows “Determine Disc Electrodes within the Array to optimally stimulate Seizure Focus”; [0142]-[0146]; [0137]: “All other electrodes in the array are not used and left uncharged or neutral.”);
the electrode that contained the maximum number of initial seizure onset zone electrodes is chosen as the central electrode (Figs. 10A and 10B show inner pole electrodes centered at a seizure focus location in electrodes 22, 24, and 26.);
if required, electrodes with a minimum distance from the central electrode are added to the list to yield exactly five electrodes for seizure detection.
Lee teaches a similar pursuit to that of the instant application and the Kremen/Remmert in teaching an implantable neurostimulator adapted for a particular treatment, such as epilepsy. It would have been obvious to one having ordinary skill in the art at the effective filing date of the invention to combine the neurological condition detection algorithm of Kremen and pseudo-Laplacian electrode configuration of Remmert with Lee’s high density EEG electrode system and “Dynamic Adaptation of Targeting Disc-Array Electrodes” ([0142]-[0146] and Fig. 11 of Lee) to establish the optimum stimulation locations of a patient in order to provide therapy to prevent the onset or escalation of seizures.
Regarding claim 2, in view of the Kremen/Remmert/Lee combination, Kremen discloses that such electrode channels are selected out of the available electrode channels ([0062]: “…selects one electrodes from array of available electrodes based on unsupervised score of the data and deploys cascade of classifiers using features extracted from selected electrode to classify into AW, N2, and N3 stages.”) which are in closest proximity to the location of the identified neurological activity which corresponds to the neurological condition ([0031]: “The one or more processors 108 can also use training signal processing and a machine learning system to identify one or more suitable sensor configurations for an automated or semi-automated classification of the behavioral state. Moreover, the one or more processors 108 select target brain locations for the sensors from one or more of a cortex, hippocampus, thalamus, brain stem, basal ganglia, subthalamic nucleus, globus pallidus…”).
Regarding claim 3, in view of the Kremen/Remmert/Lee combination, Kremen discloses that such electrode channels are selected out of the available electrode channels ([0062]: “…selects one electrodes from array of available electrodes…”) which have the closest geometrical match with the electrodes of the target electrode arrangement ([0038]: “For example, the one or more selection criteria can be a K-NN clustering algorithm with Euclidean distance measure where inter and intra-cluster distance are used as parameters for selection of only one sensor.”).
Regarding amended claim 5, in view of the Kremen/Remmert/Lee combination, Kremen discloses that step d) ([0031]: “The one or more processors 108 can also use training signal processing and a machine learning system to identify one or more suitable sensor configurations for an automated or semi-automated classification of the behavioral state.”) comprises the steps:
d2) training the neurological condition detection algorithm ([0030]: “…machine learning and/or supervision can be used to configure, adjust, fine tune or otherwise operate the system in block 212 [in Fig. 2].”; [0066]: “The control algorithm in this case would use behavioral state classifications determined from EEG or other sensors as the input and electrical stimulation is used to modulate and drive the brain to the prescribed state. This algorithm can run on the implanted device…”) by using calculated linear combinations of the EEG data.
The Kremen/Remmert combination is silent to d1) calculating linear combinations of the EEG data of the selected subset of electrode channels, e.g. calculating linear combinations representing bipolar or quadrupolar electrode channels.
However, Lee teaches d1) calculating linear combinations of the EEG data of the selected subset of electrode channels ([0103]; Fig. 11), e.g. calculating linear combinations representing bipolar or quadrupolar electrode channels ([0078]: “FIG. 2K illustrates an elliptical tripolar ring electrode or tripolar Laplace electrode.”; [0081]: “…the ring electrodes may not function as Laplace but rather function as multi bipolar electrodes.”).
It would have been obvious to one having ordinary skill in the art at the effective filing date of the invention to modify the Kremen/Remmert combination with calculating linear combinations of the EEG data of the selected subset of electrode channels and additionally using such calculations in step d2) in order to refine the spatial signal resolution of the electrode pattern, without needing further hardware electrode channels.
Regarding amended claim 7, in view of the Kremen/Remmert/Lee combination, Kremen discloses that the neurological condition detection algorithm ([0030]: “…machine learning and/or supervision can be used to configure, adjust, fine tune or otherwise operate the system in block 212 [in Fig. 2].”; [0066]: “The control algorithm in this case would use behavioral state classifications determined from EEG or other sensors as the input and electrical stimulation is used to modulate and drive the brain to the prescribed state. This algorithm can run on the implanted device…”) is an artificial intelligence algorithm (Support Vector Machine 858 in Fig. 8), e.g. Random Forest, Support Vector Machine ([0064]: “A single lead is selected as an input in block 854, feature vectors are calculated in block 856 and a support vector machine 858 is used in block 858 to classify the behavior state as Awake or SWS 860.”), Multi-layer Perceptron, Convolutional Neural Network, Long Short-Term Memory Network.
Regarding amended claim 9, in view of the Kremen/Remmert/Lee combination, Kremen discloses that the computer program is arranged for evaluating data tags which are assigned to the EEG data which are input in the computer which executes the computer program (a method 300 in Fig. 3 for classifying the behavioral state of the brain; [0035]: “Note that the method 300 can be implemented using a non-transitory computer readable medium that when executed causes the one or more processors to perform the method.”), wherein the data tags are used for selecting the subset of electrode channels out of the available electrode channels ([0062]: “The foregoing testing investigated behavioral state classification (wake & N2, and slow wave sleep) using intracranial EEG spectral power features in an unsupervised machine learning method. The method automatically selects one electrode from array of available electrodes based on unsupervised score of the data and deploys cascade of classifiers using features extracted from selected electrode to classify into AW, N2, and N3 stages.”).
Regarding amended claim 10, in view of the Kremen/Remmert/Lee combination, Kremen discloses a method of programming an implantable neurostimulation device (implanted device 900 in Fig. 9), comprising the following steps:
a) running a computer program of claim 1 on a computer (a method 300 in Fig. 3 for classifying the behavioral state of the brain; [0035]: “Note that the method 300 can be implemented using a non-transitory computer readable medium that when executed causes the one or more processors to perform the method.”),
b) programming the neurological condition detection algorithm trained by the computer program into the implantable neurostimulation device (implanted device 900 in Fig. 9; [0030]: “…machine learning and/or supervision can be used to configure, adjust, fine tune or otherwise operate the system in block 212 [in Fig. 2].”; [0066]: “The control algorithm in this case would use behavioral state classifications determined from EEG or other sensors as the input and electrical stimulation is used to modulate and drive the brain to the prescribed state. This algorithm can run on the implanted device…”).
Regarding claim 11, in view of the Kremen/Remmert/Lee combination, Kremen discloses that the implantable neurostimulation device (implanted device 900 in Fig. 9 ) is a closed-loop neurostimulator which is arranged for recording EEG signals, for calculating stimulation signals based upon the recorded EEG signals and for outputting the stimulation signals ([0065]: “The technology for brain state (behavioral state) determination and tracking described above can be used to dynamically follow and modulate brain state using electrical stimulation. As illustrated in FIGS. 9 and 10, the closed-loop system can modulate sleep and wake states using electrical stimulation, and fine-tune overall sleep-wake dynamics to meet any desired, pre-determined behavioral state patterns by tracking behavioral state and modulating via a control algorithm.”).
Regarding claim 12, in view of the Kremen/Remmert/Lee combination, Kremen discloses that in step g) the computer program is run on an external computer ([0034]: “The system may also include a remote device communicably coupled to the one or more processors 108, in which the one or more processors 108 transmit the classified behavioral state to the remote device, and receive one or more control signals for the electrical stimulation from the remote device. The remote device can be a handheld device, a cloud computing resource, a computer or any other type of control or processing device.”) which is not part of the implantable neurostimulation device (implanted device 900 in Fig. 9).
Regarding amended claim 13, in view of the Kremen/Remmert/Lee combination, Kremen discloses in which a neurological condition detection algorithm or classifier for detecting neurological conditions from EEG data has been trained and/or is being trained by a computer program (a method 300 in Fig. 3 for classifying the behavioral state of the brain; [0035]: “Note that the method 300 can be implemented using a non-transitory computer readable medium that when executed causes the one or more processors to perform the method.”; [0064]: “…FIG. 8 shows a training method 800 and an application of trained classifier 850. In training 800, scalp EEG data 802 and multi-channel iEEG data 804 is provided for manual sleep scoring (e.g., 10 min Awake, 10 min SWS) 806. Automated classification 808 uses the score 810 and iEEG data 812 with channel constriction 814 for feature extraction selection 816.”).
Regarding amended claim 14, in view of the Kremen/Remmert/Lee combination, Kremen discloses that the computer program is configured for implementation on a microcontroller (“one or more processors” further described in [0008]).
Regarding amended claim 15, in view of the Kremen/Remmert/Lee combination, the Kremen/Remmert combination is silent to that the computer program is optimized for lowest power consumption.
However, Lee in teaching “All other electrodes in the array are not used and left uncharged or neutral.” ([0137]) would inherently optimize the power consumption of the system, as minimizing the number of electrodes charged in an array reduces power usage. Therefore, it would have been obvious to one having ordinary skill in the art at the effective filing date of the invention that the Kremen/Remmert/Lee combination teaches that the computer program is optimized for lowest power consumption.
Regarding amended claim 16, in view of the Kremen/Remmert/Lee combination, Kremen discloses that the neurological condition detection algorithm or classifier for detecting neurological conditions ([0030]: “…machine learning and/or supervision can be used to configure, adjust, fine tune or otherwise operate the system in block 212 [in Fig. 2].”; [0066]: “The control algorithm in this case would use behavioral state classifications determined from EEG or other sensors as the input and electrical stimulation is used to modulate and drive the brain to the prescribed state. This algorithm can run on the implanted device…”) is an artificial intelligence algorithm, e.g. Random Forest, Support Vector Machine ([0064]: “A single lead is selected as an input in block 854, feature vectors are calculated in block 856 and a support vector machine 858 is used in block 858 to classify the behavior state as Awake or SWS 860.”), Multi-layer Perceptron, Convolutional Neural Network, Long Short-Term Memory Network.
Regarding amended claim 17, in view of the Kremen/Remmert/Lee combination, Kremen discloses that the computer program (a method 300 in Fig. 3 for classifying the behavioral state of the brain; [0035]: “Note that the method 300 can be implemented using a non-transitory computer readable medium that when executed causes the one or more processors to perform the method.”) is configured to run on an implantable neurostimulation device (implanted device 900 in Fig. 9).
Regarding amended claim 18, in view of the Kremen/Remmert/Lee combination, Kremen discloses a method of treatment of a neurological condition in a subject (a method 300 in Fig. 3 for classifying the behavioral state of the brain; [0035]: “Note that the method 300 can be implemented using a non-transitory computer readable medium that when executed causes the one or more processors to perform the method.”), comprising implanting the implantable neurostimulation device according to claim 17 into the subject (implanted device 900 in Fig. 9; [0067]: “FIG. 9 is an embodiment and application of proposed system that integrates an implanted device 900 with brain electrodes and peripheral nerve electrodes 902 that provides both sensing and electrical stimulation and couples this capability with a bi-directional connectivity with a handheld device 904 and cloud computing environment 906.”).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/M.G.S./Examiner, Art Unit 3796
/CARL H LAYNO/Supervisory Patent Examiner, Art Unit 3796