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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 16 January 2026 has been entered.
Claims 8-9, 20-21, 23-24 and 27-30 are cancelled; claims 25-26 are withdrawn; claims 1-7, 10-19 and 22 are pending.
Response to Arguments
Applicant’s arguments, see “Remarks”, filed 16 January 2026, with respect to the rejection(s) of claim(s) 1, 11 and their dependent claims under Sackellares have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Leyde.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1- are rejected under 35 U.S.C. 103 as being unpatentable over Sackellares et al (U.S. 2007/0213786) in view of Leyde (U.S. 10,463,270). Sackellares discloses (par. 0074-0082) receiving, by at least one processor, at least one data stream comprising wearable sensor data associated with a user; wherein the at least one data stream comprises biomarker data parameters; utilizing, by the at least one processor, seizure forecasting machine learning model to predict a pre-ictal period probability associated with a forecasted time segment based at least in part on values of the at least one data stream, the seizure forecasting machine learning model comprising at least one neural network having parameters trained on seizure training data (par. 0254); determining, by the at least one processor, a segment for an integration window of a history of pre-ictal period probabilities for the forecasted time segment and at least one previously forecasted time segment; determining, by the at least one processor, a pre-ictal period based at least in part on the segment exceeding a pre-ictal probability threshold; determining, by the at least one processor, a pre-ictal risk indication including a seizure treatment administration responsive to the pre-ictal risk indication; and causing to produce, by the at least one processor, the pre-ictal risk indication at a computing device associated with the user to alert the user of a predicted risk of a seizure.
Sackellares discloses the claimed invention except for seizure forecasting based on cardiac activity data. Leyde, however, discloses (col. 2, line 59-col. 3, line 3) predicting epileptic seizures based on ECG/EKG signals, including via (col. 5, line 43-col. 6, line 6) pre- and post-ictal periods using a neural network. Sackellares and Leyde both disclose systems and methods for predicting epileptic seizures and alerting a patient. Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify Sackellares’ pre-ictal risk indication with Leyde’s cardiac data determination in order to provide many criteria for diagnosis for greater accuracy.
Regarding claim 2, Sackellares discloses (par. 0020) communicating, by the at least one processor, with a wearable device to receive the at least one data stream in real-time.
Regarding claim 3, Sackellares discloses (par. 0020) the wearable device includes a biomarker sensor worn by the user.
Regarding claim 4, Sackellares discloses (par. 0074-0082) the at least one data stream comprises at least electroencephalogram measurements.
Regarding claim 5, Sackellares discloses (par. 0207) the time segment used to calculate forecasts comprises thirty seconds.
Regarding claim 6, Sackellares discloses (par. 0241) the integration window comprises a rolling three hundred second period of the history of pre-ictal period probabilities.
Regarding claim 7, Sackellares discloses (par. 0074-0082) determining, by the at least one processor, an inter-ictal period upon the pre-ictal period probability falling below the pre-ictal probability threshold.
Regarding claim 10, Sackellares discloses (par. 0218) modifying, by the at least one processor, a time-span of the integration window, a time span of the forecasted time segment, the pre-ictal probability threshold, seizure occurrence period, or combinations thereof, based on an accuracy of the pre-ictal risk alert for the user.
Regarding claim 11, Sackellares discloses (par. 0074-0082) at least one sensor; and at least one processor in communication with the at least one sensor and configured to perform steps of instructions stored in a non-transitory memory, the steps comprising: receive from the at least one sensor at least one data stream associated with a user; wherein the at least one data stream comprises biomarker data parameters; utilize seizure forecasting machine learning model to predict a pre-ictal period probability associated with a forecasted time segment based at least in part on values of the at least one data stream, the seizure forecasting machine learning model comprising at least one neural network having parameters trained on seizure training data (par. 0254); determine a segment value for an integration window of a history pre-ictal period probabilities for the forecasted time segment and at least one previously forecasted time segment; determine a pre-ictal period based at least in part on the segment value exceeding a pre-ictal probability threshold; determine a pre-ictal risk indication including a seizure treatment administration responsive to the pre-ictal risk indication; and cause to produce a pre-ictal risk indication at a computing device associated with the user to indicate a predicted risk of a seizure.
Sackellares discloses the claimed invention except for seizure forecasting based on cardiac activity data. Leyde, however, discloses (col. 2, line 59-col. 3, line 3) predicting epileptic seizures based on ECG/EKG signals, including via (col. 5, line 43-col. 6, line 6) pre- and post-ictal periods using a neural network. Sackellares and Leyde both disclose systems and methods for predicting epileptic seizures and alerting a patient. Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify Sackellares’ pre-ictal risk indication with Leyde’s cardiac data determination in order to provide many criteria for diagnosis for greater accuracy.
Regarding claim 12, Sackellares discloses (par. 0074-0082) the at least one processor is further configured to generate a pre-ictal risk alert to alert the user of the predicted seizure.
Regarding claim 13, Sackellares discloses (par. 0074-0082) the at least one processor is further configured to generate a risk profile based on a history of pre-ictal risk indicators associated with the user.
Regarding claim 14, Sackellares discloses (par. 0074-0082) the at least one processor is further configured to generate treatment plan optimizations for mitigating seizures.
Regarding claim 15, Sackellares discloses (par. 0074-0082) the at least one processor is further configured to generate a seizure mitigation suggestion based on the pre-ictal risk indicator and the at least one data stream.
Regarding claim 16, Sackellares discloses (par. 0083) the seizure mitigation suggest comprises at least a release of stimulation.
Regarding claim 17, Sackellares discloses (par. 0020) the at least one processor is further configured to communicate with a wearable device to receive the at least one data stream in real- time.
As to claim 18, the functional language and statement of intended use have been carefully considered but are not considered to impart any further structural limitations over the prior art. Since Sackellares and Leyde utilize external electrodes as claimed by the applicant, Sackellares and Leyde are therefore capable of being used on the wrist. In addition, nothing prevents Leyde and Sackellares’ system from being worn on the wrist. Therefore, they are capable of being worn on the wrist.
Regarding claim 19, Sackellares discloses (par. 0074-0082) the at least one data stream comprises at least electroencephalogram measurements.
Regarding claim 22, Sackellares discloses (par. 0248) the at least one processor is further configured to determine an inter-ictal period upon the pre-ictal period probability falling below the pre-ictal probability threshold.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DEBORAH L MALAMUD whose telephone number is (571)272-2106. The examiner can normally be reached Mon - Fri 1:00-9:30 Eastern.
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/DEBORAH L MALAMUD/Primary Examiner, Art Unit 3792