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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 4/16/2026 has been entered.
Response to Amendment
This office action is responsive to the amendment filed on 4/16/2026. As directed by the amendment, the status of the claim(s) are:
Claim(s) 1, 10, 14, 20 has/have been amended;
Claim(s) 1-20 is/are presently pending.
Response to Arguments
With regard to claim rejections under 35 USC 102 and/or 103, Applicant’s arguments have been fully considered but are moot in light of new grounds of rejection due to claim amendment(s).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ternes (US 20180153460 A1; 6/7/2018; cited in previous office action) in view of Osorio (US 20130096393 A1; 4/18/2013), and further in view of Molero Leon (US 20230377747 A1; Filed 9/29/2021; cited in previous office action), hereinafter Leon.
Regarding claim 1, Ternes teaches a method for detecting epileptic seizures (Abstract; Fig. 3), comprising:
receiving physiological data measured from a user by a wearable device (Fig. 2-5; [0051]);
inputting the physiological data into a machine learning model configured to analyze the physiological data and identify an epileptic seizure event based at least in part on a relationship between the physiological data and a set of features, wherein the set of features comprises at least one of activities associated with the user, or biometrics associated with the user, or both (Fig. 2-6; [0009]; [0054] “machine-learning algorithms”; [0058]; [0064] “computational models…neural network…regression model”; [0077]-[0078]).
Ternes does not teach a biological rhythm adjustment model associated with the user. However, Osorio teaches in the same field of endeavor (Abstract; [0040]) a biological rhythm adjustment model associated with the user ([0130] “at least one parameter of the PMSA (e.g., a weighting between IFs making up the PMSA; a threshold for the PMSA value) based upon one or more of: … a degree of circadian and ultradian fluctuations of the patient's seizure activity…as a function of the patient's sleep/wake cycle…a dependence of the patient's seizure occurrence on circadian or ultradian rhythms…a level of physical activity;”). Thus it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Ternes to include this feature as taught by Osorio because this enables improving detection of seizure ([0040]; [0130]).
In the combination of Ternes and Osorio, Ternes teaches obtaining a result from the machine learning model indicating an occurrence of the epileptic seizure event (Fig. 2-6; [0054] “machine-learning algorithms”; [0058]; [0064] “computational models…neural network…regression model”; [0077]-[0078]).
The combination of Ternes and Osorio does not teach explicitly teach wherein the result is obtained based at least in part on application of one or more weights to the physiological data and the set of features, wherein the one or more weights are applied based at least in part on an activity of the user associated with the wearable device. Note that Ternes teaches using weights with physiological signals and functional signals which include physical activity ([0077]-[0078]) and Osorio teaches weights depending on physical activity ([0130] “a level of physical activity”). However, Leon teaches in the same field of endeavor ([0151]-[0153] “epilepsy and seizures”) wherein the result is obtained based at least in part on application of one or more weights to the physiological data and the set of features, wherein the one or more weights are applied based at least in part on an activity of the user associated with the wearable device (Fig. 10; [0204] “wearable”; [0350] “one or more weights…separate models are trained-where each model is associated with a different type of activity, activity intensity…may then dynamically select the model to use”; [0351] “weights…are to change in response to various…inferred types of activity, inferred activity intensity levels”; [0352]). Thus it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Ternes and Osorio to include these features as taught by Leon because this enables more accuracy (Fig. 10; [0350]).
In the combination of Ternes, Osorio, and Leon, Osorio teaches wherein the biological rhythm adjustment model is configured to adjust the one or more weights based at least in part on a biological rhythm of the user ([0130] “at least one parameter of the PMSA (e.g., a weighting between IFs making up the PMSA; a threshold for the PMSA value) based upon one or more of: … a degree of circadian and ultradian fluctuations of the patient's seizure activity…as a function of the patient's sleep/wake cycle…a dependence of the patient's seizure occurrence on circadian or ultradian rhythms…a level of physical activity;”).
In the combination of Ternes, Osorio, and Leon, Ternes teaches outputting an indication of the epileptic seizure event based at least in part on the obtained result from the machine learning model (Fig. 2-6; [0009]; [0054] “machine-learning algorithms”; [0058]; [0064] “computational models…neural network…regression model”; [0077]-[0078]; [0080]).
Claim 14 is rejected under substantially the same basis as claim 1 above. Note that Ternes teaches using processor(s) and memory/memories (Fig. 2; Fig. 7; [0090]-[0095]).
Claim 20 is rejected under substantially the same basis as claim 1 above. Note that Ternes teaches non-transitory computer-readable medium storing code for detecting epileptic seizures, the code comprising instructions executable by one or more processors (Fig. 2; Fig. 7; [0090]-[0095]).
Regarding claim 2, in the combination of Ternes, Osorio, and Leon, Ternes teaches activating an epileptic seizure mode associated with the wearable device based at least in part on the activity the user associated with the wearable device is engaged in, at least one biometric associated with the user, a user selection to activate the epileptic seizure mode, or a combination thereof ([0013]-[0017]; [0019]; [0022]-[0023]; [0058]-[0059]; [00068]) ,
wherein receiving the physiological data is based at least in part on the activated epileptic seizure mode ([0022]-[0022]; [0068]).
Claim 15 is rejected under substantially the same basis as claim 2 above.
Regarding claim 3, in the combination of Ternes, Osorio, and Leon, Ternes teaches wherein receiving the physiological data is based at least in part on a first periodicity, wherein the first periodicity is based at least in part on the epileptic seizure mode being activated (interpreted in light of instant specification [0094]; Ternes [0019]; [0030]; [0068]; [0075] “change the data resolution”).
Claim 16 is rejected under substantially the same basis as claim 3 above.
Regarding claim 4, in the combination of Ternes, Osorio, and Leon, Ternes teaches wherein the first periodicity associated with the epileptic seizure mode being activated is greater than a second periodicity associated with the epileptic seizure mode being deactivated (interpreted in light of instant specification [0094]; Ternes [0019]; [0030]; [0068]; [0075] “change the data resolution”).
Claim 17 is rejected under substantially the same basis as claim 4 above.
Regarding claim 5, the combination of Ternes, Osorio, and Leon teaches wherein the activity comprises a sleep activity, a physical activity (Ternes [0058]-[0059]; [0077]; Leon [0350]), or both.
Claim 18 is rejected under substantially the same basis as claim 5 above.
Regarding claim 6, in the combination of Ternes, Osorio, and Leon, Ternes teaches wherein the at least one biometric comprises an age of the user, a race of the user, an ethnicity of the user, a gender of the user, a health history of the user ([0064] “patient medical history…epilepsy patient population”), or a combination thereof,
wherein the health history of the user indicates epileptic seizures data related to prior epileptic seizure events associated with the user ([0064] “signals acquired during epileptic events in patient medical history”).
Claim 19 is rejected under substantially the same basis as claim 6 above.
Regarding claim 7, in the combination of Ternes, Osorio, and Leon, Ternes teaches causing the wearable device to output the indication of the epileptic seizure event based at least in part on the obtained result from the machine learning model (Fig. 2; Fig. 5-6; [0051]; [0052]; [0069]; [0080]).
Regarding claim 8, in the combination of Ternes, Osorio, and Leon, Ternes teaches causing a user device associated with the wearable device to output the indication of the epileptic seizure event based at least in part on the obtained result from the machine learning model (Fig. 2; Fig. 5-6; [0051]; [0052]; [0069]; [0080]).
Regarding claim 9, in the combination of Ternes, Osorio, and Leon, Ternes teaches enabling the machine learning model to identify the epileptic seizure event within a threshold period of time of seizure onset for the user based at least in part on the relationship between the physiological data and the set of features (Fig. 2-6; [0054] “individualized prediction…allow the patient to have enough time to react”; [0064]-[0065]; [0066] “early detection of the epileptic seizure”; [0078]).
Regarding claim 10, the combination of Ternes, Osorio, and Leon teaches wherein the one or more weights are applied to the physiological data and the set of features further based at least in part on at least one biometric associated with the user (Ternes [0064] “weight factors”; [0078]; Leon [0350]), and wherein the biological rhythm of the user comprises at least one of an ultradian rhythm, a circadian rhythm (Osorio [0130] “at least one parameter of the PMSA (e.g., a weighting between IFs making up the PMSA; a threshold for the PMSA value) based upon one or more of: … a degree of circadian and ultradian fluctuations of the patient's seizure activity…as a function of the patient's sleep/wake cycle…a dependence of the patient's seizure occurrence on circadian or ultradian rhythms…a level of physical activity;”), anon-endogenous daily rhythm, a weekly rhythm, a multi-day ovarian rhythm, a spermatogenesis rhythm, a lunar rhythm, or a seasonal rhythm.
Regarding claim 11, in the combination of Ternes, Osorio, and Leon, Ternes teaches wherein the physiological data comprises one or more of an oxygen saturation associated with the user ([0077] “blood oxygen saturation”), a heart rate associated with the user ([0077] “heart rate signal”), a temperature associated with the user ([0077] “body temperature signal”), an optical perfusion associated with the user, or a movement pattern associated with the user ([0077] “locomotion pattern”).
Regarding claim 12, in the combination of Ternes, Osorio, and Leon, Ternes teaches wherein the machine learning model comprises a neural network learning model ([0064] “neural network”), a decision tree learning model ([0064] “decision tree”), a support vector machine learning model, or a combination thereof ([0064]).
Regarding claim 13, in the combination of Ternes, Osorio, and Leon, Ternes teaches wherein the indication of the epileptic seizure event comprises an audio output ([0069] “audio”), a haptic output, a message output ([0069] “textual”), or any combination thereof ([0052] “alerts, alarms, emergency calls, or other forms of warnings”; [0069]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jonathan T Kuo whose telephone number is (408)918-7534. The examiner can normally be reached M-F 10 a.m. - 6 p.m. PT.
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/JONATHAN T KUO/Primary Examiner, Art Unit 3792
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