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 .
Status of Claims
Claims 1, 7, and 13 has been amended.
Claims 6, 12, and 15 are cancelled.
Response to Arguments
Applicant’s arguments, see pages 7-8, filed 2/11/2026, with respect to the rejection(s) of
claim(s) 1-5, 7-11, 13-14, and 16-20 under 35 U.S.C. 102 have been fully considered, but are not persuasive.
35 U.S.C. 102:
Regarding claim 1, the applicant argues Kaemmerer does not teach “wherein the machine
learning model differentiates the first neural signals from the second neural signals based on the first signals displaying lower frequency theta and alpha oscillations, whereas the second signals display beta oscillations and lack gamma oscillations.” The examiner respectfully disagrees and argues Kaemmerer teaches this limitation (paragraph 101-109 and 116). The theta band (3-5 Hz) may also be used to improve classifier performance. Beta oscillations may be related to the location of the active stimulation electrode independently chosen for optimal clinical outcome. Classifiers using one of the high gamma bands above 60 Hz. Alpha band classifier is mainly determine between a frequency oscillation of 8-13Hz. It is disclosed that spectral analysis includes a plurality of frequency bands (e.g., 3-5 Hz, 5-10 Hz, and every 10 Hz between 10-90 Hz). Therefore, a classification of alpha oscillation is disclosed.
After further search and consideration, the examiner will provide further evidence by Wisbey to
teach this limitation (paragraph 45, 52, and 58). Power may be predicted using an Extreme Gradient Boosting (XGB) machine learning model with eight feature variables. The EEG variables are based on brain wave activity categorized into Delta, Theta, Low Alpha, High Alpha, Low Beta, High Beta, Low Gamma and Mid Gamma frequency ranges. Therefore, the learning models may differentiate between a first and second neural signal based on these signal frequency oscillations.
Claim Rejections - 35 USC § 102
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.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable by Kaemmerer et al.
US Pub.: US 20160144186 A1, hereinafter Kaemmerer, further evidence by Wisbey et al. US Pub.: US 20200401222 A1, hereinafter Wisbey.
Regarding claim 1, Kaemmerer teaches a method, comprising:
training a machine learning model to differentiate first neural signals generated by a biological subject that are associated with tremor in the biological subject from second neural signals generated by the biological subject that are associated with bradykinesia in the biological subject (paragraph 27-29); It is disclosed that the device may be used to treat bradykinesia and tremor in a patient. It is further disclosed that “a device may compare a representation of electrical signals for a patient with one or more machine learning models to automatically select electrodes to deliver electrical stimulation to the patient.” Therefore, a way to differentiate between neural signals associated with tremor or bradykinesia is disclosed.
placing a first plurality of deep brain stimulation (DBS) electrodes in a subthalamic nucleus (STN) of the biological subject (fig. 1, 20A; paragraph 41-44);
placing a second plurality of DBS electrodes in the STN of the biological subject (fig. 1, 20B; paragraph 41-44);
placing a plurality of microelectrodes in the STN of the biological subject (paragraph 116);
placing a plurality of macroelectrodes in the STN of the biological subject (paragraph 116);
and in response to identifying, by the machine learning model, a given neural signal observed by the plurality of microelectrodes and the plurality of macroelectrodes as one of the first neural signals or the second neural signals, wherein the machine learning model differentiates the first neural signals from the second neural signals based on the first signals displaying lower frequency theta and alpha oscillations, whereas the second signals display beta oscillations and lack gamma oscillations (paragraph 101-109 and 116). The theta band (3-5 Hz) may also be used to improve classifier performance. Beta oscillations may be related to the location of the active stimulation electrode independently chosen for optimal clinical outcome. Classifiers using one of the high gamma bands above 60 Hz. Alpha band classifier is mainly determine between a frequency oscillation of 8-13Hz. It is disclosed that spectral analysis includes a plurality of frequency bands (e.g., 3-5 Hz, 5-10 Hz, and every 10 Hz between 10-90 Hz). Therefore, a classification of alpha oscillation is disclosed.
Further evidence from Wisbey teaches this limitation (paragraph 45, 52, and 58). Power may be predicted using an Extreme Gradient Boosting (XGB) machine learning model with eight feature variables. The EEG variables are based on brain wave activity categorized into Delta, Theta, Low Alpha, High Alpha, Low Beta, High Beta, Low Gamma and Mid Gamma frequency ranges. Therefore, the learning models may differentiate between a first and second neural signal based on these signal frequency oscillations.
activating a corresponding one of the first plurality of DBS electrodes or the second plurality of DBS electrodes with a therapeutically effective voltage and current to affect deep brain stimulation in the biological subject to treat a neuromotor disorder in the biological subject (paragraph 46-47, 60, and 63-66). A therapeutically effective voltage and current to affect deep brain stimulation is disclosed as therapy parameter values, which may be adjusted based on a sensed brain signal. The therapy device includes separates electrodes located at different regions of the brain to target regions most beneficial for a type of disorder. The combination of selected electrodes uses a machine learning model.
Regarding claims 2 and 8, Kaemmerer teaches wherein the machine learning model is a
support vector analysis model or neural network model (paragraph 25 and 84). The machine learning model is a support vector analysis model.
Regarding claim 3, Kaemmerer teaches wherein the first plurality and the second plurality of
DBS electrodes are implanted to cover multiple functional sub-region in the biological subject (paragraph 63-66). The therapy device includes separates multiple electrodes located at different regions of the brain to target regions most beneficial for a type of disorder.
Regarding claims, 4, 10, and 17, Kaemmerer teaches wherein the plurality of microelectrodes
and the plurality of macroelectrodes are arranged on an anatomical trajectory for neural signals in the biological subject (fig. 1; paragraph 116).
Regarding claims 5, 11, and 18, Kaemmerer teaches wherein the first plurality of DBS electrodes
is located within a dorsolateral region of the STN and the second plurality of DBS electrodes is located within a ventromedial region of the STN, relative to the biological subject, to the first plurality of DBS electrodes (paragraph 39 and 116). Leads 20 may be implanted in the ventromedial region and dorsolateral region.
Regarding claim 7, Kaemmerer teaches a system comprising:
a plurality of electrodes, including: a first plurality of deep brain stimulation (DBS) electrodes (fig. 1, 20A; paragraph 41-44);
a second plurality of DBS electrodes (fig. 1, 20B; paragraph 41-44);
a plurality of microelectrodes (paragraph 116);
and a plurality of macroelectrodes (paragraph 116);
a processor in communication with the plurality of electrodes (fig. 2, 60; paragraph 62-66);
and a memory (fig. 2, 62; paragraph 62-66), storing instructions that when executed by the processor perform operations including: identifying, by a machine learning model, a given neural signal observed by the plurality of microelectrodes and the plurality of macroelectrodes as one of a first neural signal associated with tremor or a second neural signal associated with bradykinesia in a biological subject (paragraph 27-29); It is disclosed that the device may be used to treat bradykinesia and tremor in a patient. It is further disclosed that “a device may compare a representation of electrical signals for a patient with one or more machine learning models to automatically select electrodes to deliver electrical stimulation to the patient.” Therefore, a way to differentiate between neural signals associated with tremor or bradykinesia is disclosed.
wherein the machine learning model differentiates the first neural signals from the second neural signals based on the first signals displaying lower frequency theta and alpha oscillations, whereas the second signals display beta oscillations and lack gamma oscillations (paragraph 101-109 and 116). The theta band (3-5 Hz) may also be used to improve classifier performance. Beta oscillations may be related to the location of the active stimulation electrode independently chosen for optimal clinical outcome. Classifiers using one of the high gamma bands above 60 Hz. Alpha band classifier is mainly determine between a frequency oscillation of 8-13Hz. It is disclosed that spectral analysis includes a plurality of frequency bands (e.g., 3-5 Hz, 5-10 Hz, and every 10 Hz between 10-90 Hz). Therefore, a classification of alpha oscillation is disclosed.
Further evidence from Wisbey teaches this limitation (paragraph 45, 52, and 58). Power may be predicted using an Extreme Gradient Boosting (XGB) machine learning model with eight feature variables. The EEG variables are based on brain wave activity categorized into Delta, Theta, Low Alpha, High Alpha, Low Beta, High Beta, Low Gamma and Mid Gamma frequency ranges. Therefore, the learning models may differentiate between a first and second neural signal based on these signal frequency oscillations.
and activating a corresponding one of the first plurality of DBS electrodes or the second plurality of DBS electrodes with a therapeutically effective voltage and current to affect deep brain stimulation in the biological subject to treat a neuromotor disorder in the biological subject associated with the given neural signal identified (paragraph 46-47, 60, and 63-66). A therapeutically effective voltage and current to affect deep brain stimulation is disclosed as therapy parameter values, which may be adjusted based on a sensed brain signal. The therapy device includes separates electrodes located at different regions of the brain to target regions most beneficial for a type of disorder. The combination of selected electrodes uses a machine learning model.
Regarding claim 9 and 16, Kaemmerer teaches wherein each electrode of the plurality of
macroelectrodes is placed superior, relative to the biological subject, to a corresponding electrode of the plurality of microelectrodes (fig. 1-2; paragraph 116).
Regarding claim 13, Kaemmerer teaches method of treatment for a neuromotor disease,
comprising: placing a first plurality of deep brain stimulation (DBS) electrodes in a subthalamic nucleus (STN) of a biological subject (fig. 1, 20A; paragraph 39 and 41-44);
placing a second plurality of DBS electrodes in the STN of the biological subject (fig. 1, 20B; paragraph 39 and 41-44);
placing a plurality of microelectrodes in the STN of the biological subject (paragraph 116);
placing a plurality of macroelectrodes in the STN of the biological subject (paragraph 116);
and measuring neural signals via the plurality of microelectrodes and the plurality of macroelectrodes (paragraph 116); Beta and theta oscillation are disclosed as neural measurements measured by micro and macroelectrodes.
wherein the machine learning model differentiates the first neural signals from the second neural signals based on the first signals displaying lower frequency theta and alpha oscillations, whereas the second signals display beta oscillations and lack gamma oscillations (paragraph 101-109 and 116). The theta band (3-5 Hz) may also be used to improve classifier performance. Beta oscillations may be related to the location of the active stimulation electrode independently chosen for optimal clinical outcome. Classifiers using one of the high gamma bands above 60 Hz. Alpha band classifier is mainly determine between a frequency oscillation of 8-13Hz. It is disclosed that spectral analysis includes a plurality of frequency bands (e.g., 3-5 Hz, 5-10 Hz, and every 10 Hz between 10-90 Hz). Therefore, a classification of alpha oscillation is disclosed.
Further evidence from Wisbey teaches this limitation (paragraph 45, 52, and 58). Power may be predicted using an Extreme Gradient Boosting (XGB) machine learning model with eight feature variables. The EEG variables are based on brain wave activity categorized into Delta, Theta, Low Alpha, High Alpha, Low Beta, High Beta, Low Gamma and Mid Gamma frequency ranges. Therefore, the learning models may differentiate between a first and second neural signal based on these signal frequency oscillations.
in response to identifying that the neural signals are indicative of bradykinesia or tremor in the biological subject, activating one of the first plurality of DBS electrodes or the second plurality of DBS electrodes with a therapeutically effective voltage and current to affect deep brain stimulation in the biological subject (paragraph 46-47, 60, and 63-66). A therapeutically effective voltage and current to affect deep brain stimulation is disclosed as therapy parameter values, which may be adjusted based on a sensed brain signal. The therapy device includes separates electrodes located at different regions of the brain to target regions most beneficial for a type of disorder. The combination of selected electrodes uses a machine learning model.
Regarding claim 14, Kaemmerer teaches wherein the neural signals are identified as indicative of
bradykinesia or tremor in the biological subject by a machine learning model trained via a data set of neuromotor tasks (paragraph 27-29); It is disclosed that the device may be used to treat bradykinesia and tremor in a patient. It is further disclosed that “a device may compare a representation of electrical signals for a patient with one or more machine learning models to automatically select electrodes to deliver electrical stimulation to the patient.” Therefore, a way to differentiate between neural signals associated with tremor or bradykinesia is disclosed.
Regarding claim 19, Kaemmerer teaches wherein the neural signals are indicative of
bradykinesia, the first plurality of DBS electrodes are activated with the therapeutically effective voltage and current to affect deep brain stimulation in the biological subject (paragraph 29-33). The therapy system 10 may be referred to as a deep brain stimulation (DBS) system because IMD 16 is configured to deliver electrical stimulation therapy directly to tissue within brain 28 at electrode extension 18 connected to leads 20A-B.
Regarding claim 20, Kaemmerer teaches wherein the neural signals are indicative of tremor, the
second plurality of DBS electrodes are activated with the therapeutically effective voltage and current to affect deep brain stimulation in the biological subject (fig. 1-2; paragraph 29-33). The therapy system 10 may be referred to as a deep brain stimulation (DBS) system because IMD 16 is configured to deliver electrical stimulation therapy directly to tissue within brain 28 at electrode extension 18 connected to leads 20A-B.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THIEN J TRAN whose telephone number is (571)272-0486. The examiner can normally be reached M-F. 8:30 am - 5:30 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Benjamin Klein can be reached at 571-270-5213. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/T.J.T./Examiner, Art Unit 3792
/MALLIKA D FAIRCHILD/Primary Examiner, Art Unit 3792
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