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
This final office action on merits is in response to the communication received on 12/16/2025. Amendments to claims 1, 5-10, and 14-20 are acknowledged and have been carefully considered. Claims 1-20 are pending and considered below.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Under step 1, the analysis is based on MPEP 2106.03, and claims 1-13 are drawn to a method, claims 14-19 are drawn to wearable device, and claim 20 is drawn to one or more non-transitory computer readable storage media. Thus, each claim, on its face, is directed to one of the statutory categories (i.e., useful process, machine, manufacture, or composition of matter) of 35 U.S.C. 101.
Step 2A Prong One
Claim 1, 14, and 20 recite the limitations of detecting that the user is asleep; determining, for each of the plurality of insomnia-related signals, a set of insomnia-indicating signals by comparing each insomnia-related signal in that plurality of insomnia-related signals to a corresponding threshold, each corresponding threshold comprising a user specific threshold determined while the user is asleep; and determining, based on the determined sets of insomnia-indicating signals, an insomnia condition of the user. These limitations, as drafted, is a process that, under their broadest reasonable interpretation, cover performance of the limitations in the mind or by using a pen and paper. But for the “by a trained machine-learning model” language, the claim encompasses a user determining of the user is asleep, comparing insomnia related values to predetermined thresholds, identifying those values that satisfy the thresholds as insomnia indicating signals, and concluding an insomnia condition based on those identified signals in their mind or by using a pen and paper. The mere nominal recitation of a trained machine-learning model does not take the claim limitations out of the mental processes grouping. Thus, the claims recite a mental process which is an abstract idea.
Under Step 2A Prong Two
The claimed limitations, as per claim 1, include:
detecting, by a wearable device worn by a user, that the user is asleep;
detecting, by each of a plurality of sensors of the wearable device worn by the user, a corresponding plurality of insomnia-related signals during at least a predetermined duration while the user is asleep;
determining, for each of the plurality of insomnia-related signals, a set of insomnia-indicating signals by comparing each insomnia-related signal in that plurality of insomnia-related signals to a corresponding threshold, each corresponding threshold comprising a user specific threshold determined while the user is asleep; and
determining, by a trained machine-learning model and based on the determined sets of insomnia-indicating signals, an insomnia condition of the user.
The claimed limitations, as per claim 14, include:
a plurality of sensors, each sensor configured to detect a corresponding plurality of insomnia-related signals of a user during at least a predetermined duration while the user is asleep; and
one or more non-transitory computer readable storage media storing instructions; and
one or more processors coupled to the one or more non-transitory computer readable storage media and operable to execute the instructions to:
detect that the user is asleep;
determine, for each of the plurality of insomnia-related signals, a set of insomnia-indicating signals by comparing each insomnia-related signal in that plurality of insomnia-related signals to a corresponding threshold, each corresponding threshold comprising a user-specific threshold determined while the user is asleep; and
determine, by a trained machine-learning model and based on the determined sets of insomnia-indicating signals, an insomnia condition of the user.
The claimed limitations, as per claim 20, include:
detect that a user is asleep;
access, from each of a plurality of sensors of a wearable device worn by the user, a corresponding plurality of insomnia-related signals during at least a predetermined duration while the user is asleep;
determine, for each of the plurality of insomnia-related signals, a set of insomnia-indicating signals by comparing each insomnia-related signal in that plurality of insomnia-related signals to a corresponding threshold, each corresponding threshold comprising a user-specific threshold determined while the user is asleep; and
determine, by a trained machine-learning model and based on the determined sets of insomnia-indicating signals, an insomnia condition of the user.
Examiner Note: underlined elements indicate additional elements of the claimed invention identified as performing the steps of the claimed invention.
The judicial exception expressed in claims 1, 14, and 20 are not integrated into a practical application. The claim as a whole merely describes how to generally “apply” the concept of comparing insomnia-related signals to predetermined thresholds and classifying the user’s insomnia condition based on those comparisons in a computer environment. The claimed computer components (i.e., by a trained machine-learning model (claim 1, 14, and 20), one or more non-transitory computer readable storage media storing instructions; and one or more processors coupled to the one or more non-transitory computer readable storage media and operable to execute the instructions to (claim 14), one or more non-transitory computer readable storage media storing instructions that are operable when executed to (claim 20) are recited at a high level of generality and are merely invoked as tools to perform an existing process of comparing measure signals to thresholds and making an insomnia classification. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Accordingly, alone and in combination, these additional elements do not integrate the abstract idea into a practical application.
The judicial exception expressed in claims 1, 14, and 20 are not integrated into a practical application. The abstract idea is merely carried out in a technical environment or field (i.e., wearables for sleep or insomnia), however fails to contain meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment (see MPEP 2106.05(h)). The additional elements that are carried out in a technical environment includes by each of a plurality of sensors of a wearable device worn by a user (claim 1), while the wearable device is worn by the user (claim 14), and from each of a plurality of sensors of a wearable device worn by a user (claim 20). Accordingly, alone and in combination, these additional elements do not integrate the abstract idea into a practical application.
The judicial exception expressed in claims 1, 14, and 20 are not integrated into a practical application. The claim recites the additional elements of detecting a corresponding plurality of insomnia-related signals during at least a predetermined duration while the user is asleep (claim 1), a plurality of sensors, each sensor configured to detect a corresponding plurality of insomnia-related signals of a user during at least a predetermined duration, while a user is asleep (claim 14), access a corresponding plurality of insomnia-related signals during at least a predetermined duration while the user is asleep (claim 20). These limitations are recited at a high level of generality (i.e., as a general means of collecting sensor data over a time window), and amounts to merely data gathering, which is a form of insignificant extra-solution activity. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Therefore, under step 2A, the claims are directed to the abstract idea, and require further analysis under Step 2B.
Under step 2B
Claims 1, 14, and 20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A, the claim as a whole merely describes how to generally “apply” the concept of comparing insomnia-related signals to predetermined thresholds and classifying the user’s insomnia condition based on those comparisons in a computer environment. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea.
Claims 1, 14, and 20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A, the abstract idea is merely carried out in a technical environment or field, however fails to contain meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea.
Claims 1, 14, and 20 do not include an additional element that are sufficient to amount to significantly more than the judicial exception. For the providing limitation that was considered extra-solution activity in Step 2A, this has been re-evaluated in Step 2B and determined to be well-understood, routine, conventional activity in the field. The specification does not provide any indication that the limitation of collecting sensor data over a time window is anything other than a conventional action that simply comes before comparing the insomnia-related signals to thresholds and classifying an insomnia condition (see [12]). For these reasons, there is no inventive concept. The claim is not patent eligible.
Claims 2-12, and 15-19 recite no further additional elements, and only further narrow the abstract idea. The previously identified additional elements, individually and as a combination, do not integrate the narrowed abstract idea into a practical application for reasons similar to those explained above, and do not amount to significantly more than the narrowed abstract idea for reasons similar to those explained above.
Claim 13 recites the additional elements of a signal obtained by an accelerometer of the wearable device and by an EEG signal from a head-worn device of the user. However, this additional element amounts to mere linking to a particular environment. As such, these additional elements, when considered individually or in combination with the prior devices, does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea.
Thus, as the dependent claims remain directed to a judicial exception, and as the additional elements of the claims do not amount to significantly more, the dependent claims are not patent eligible.
Therefore, the claims here fail to contain any additional element(s) or combination of additional elements that can be considered as significantly more and the claim is rejected under 35 U.S.C. 101 for lacking eligible subject matter.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 6, 8, 12-13, 14-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Leon et al. (JP Publication 2023544515 A), referred to hereinafter as Leon, in view of Nofzinger (AU Publication 2017268269 A1), referred to hereinafter as Nofzinger.
Regarding claim 1, Leon teaches a method (Leon [0004] “According to some implementations of the present disclosure, a method includes receiving first physiological data associated with a user. The method includes determining a first affective score associated with the user based at least in part on the first physiological data.”) comprising:
by a wearable device worn by a user (Leon [0089] “In some implementations, activity tracker 190 is a wearable device that can be worn by a user, such as a smart watch, wristband, ring or patch. For example, referring to FIG. 2, activity tracker 190 is worn on the wrist of user 210. Additionally, the activity tracker 190 can be coupled to or integrated into the clothing or clothing worn by the user. Further alternatively, activity tracker 190 can be coupled to or integrated with user device 170 (eg, within the same enclosure). More generally, activity tracker 190 can be communicatively coupled to, or physically integrated (e.g., within a housing) with control system 110, memory 114, respiratory therapy system 120, and/or user device 170. be able to.”);
detecting, by each of a plurality of sensors of the wearable device worn by the user, a corresponding plurality of insomnia-related signals(Leon [0089] “In some implementations, activity tracker 190 is a wearable device that can be worn by a user, such as a smart watch, wristband, ring or patch. For example, referring to FIG. 2, activity tracker 190 is worn on the wrist of user 210. Additionally, the activity tracker 190 can be coupled to or integrated into the clothing or clothing worn by the user. Further alternatively, activity tracker 190 can be coupled to or integrated with user device 170 (eg, within the same enclosure). More generally, activity tracker 190 can be communicatively coupled to, or physically integrated (e.g., within a housing) with control system 110, memory 114, respiratory therapy system 120, and/or user device 170. be able to.” and Leon [0088] “Activity tracker 190 is generally used to help generate physiological data for determining activity measurements associated with a user. Activity measurements include, for example, steps taken, distance traveled, steps climbed, duration of physical activity, type of physical activity, intensity of physical activity, time spent standing, respiratory rate, average respiratory rate, resting respiratory rate, Maximum respiratory rate, respiratory rate variability, heart rate, average heart rate, resting heart rate, maximum heart rate, heart rate variability, calorie consumption, blood oxygen saturation (SqO2), electrodermal activity (skin conductance or galvanic (also known as skin response), user position, user posture, or any combination thereof. Activity tracker 190 may include one or more sensors 130 described herein, such as, for example, motion sensor 138 (e.g., one or more accelerometers and/or gyroscopes), PPG sensor 154 and/or ECG sensor 156. can include.”);
determining, for each of the plurality of insomnia-related signals, a set of insomnia-indicating signals by comparing each insomnia-related signal in that plurality of insomnia-related signals to a corresponding threshold, each corresponding threshold comprising a user-specific threshold determined while the user is asleep (Leon [0089] “In some implementations, activity tracker 190 is a wearable device that can be worn by a user, such as a smart watch, wristband, ring or patch. For example, referring to FIG. 2, activity tracker 190 is worn on the wrist of user 210. Additionally, the activity tracker 190 can be coupled to or integrated into the clothing or clothing worn by the user. Further alternatively, activity tracker 190 can be coupled to or integrated with user device 170 (eg, within the same enclosure). More generally, activity tracker 190 can be communicatively coupled to, or physically integrated (e.g., within a housing) with control system 110, memory 114, respiratory therapy system 120, and/or user device 170. be able to.” and Leon [0088] “Activity tracker 190 is generally used to help generate physiological data for determining activity measurements associated with a user. Activity measurements include, for example, steps taken, distance traveled, steps climbed, duration of physical activity, type of physical activity, intensity of physical activity, time spent standing, respiratory rate, average respiratory rate, resting respiratory rate, Maximum respiratory rate, respiratory rate variability, heart rate, average heart rate, resting heart rate, maximum heart rate, heart rate variability, calorie consumption, blood oxygen saturation (SqO2), electrodermal activity (skin conductance or galvanic (also known as skin response), user position, user posture, or any combination thereof. Activity tracker 190 may include one or more sensors 130 described herein, such as, for example, motion sensor 138 (e.g., one or more accelerometers and/or gyroscopes), PPG sensor 154 and/or ECG sensor 156. can include.” and Leon [0146] Physiological parameters can be individual specific. Physiological parameters can be monitored to determine if the physiological parameters are within range. For example, a typical adult's breathing rate is 12-20 breaths per minute (bpm), and a breathing rate greater than 25 bpm may be indicative of anxiety/stress. Similarly, a typical adult heart rate is 60-100 beats/min, and a heart rate above 120 bpm may be indicative of anxiety/stress. Elevated body temperature may indicate anxiety or stress. Furthermore, the value of a physiological parameter changes after a stimulus, such as the initiation of therapy, or after an increase in therapy pressure, but then relatively quickly (e.g., within 5-10 minutes or other predetermined period of time). ) Increases in affective scores can be tolerated if they return to baseline values. Since the slight increase in the affective score is temporary, the increase in the affective score is acceptable and there is no need to adjust the affective score (or a minimum number of prompts are required).”, Leon [0051] As described herein, system 100 generally performs data (e.g., physiological data, flow rate data, pressure data, motion data, acoustic data, etc.). The generated data may be analyzed to determine one or more physiological parameters (e.g., before, during, and/or after a sleep session) and/or which may include any parameters, measurements, etc. related to the user.”). ); and
determining, by a trained machine-learning model and based on the determined sets of insomnia-indicating signals, an insomnia condition of the user (Leon [0151] “In some implementations, the predetermined condition is determined based at least in part on previously recorded physiological data associated with the user. In such implementations, machine learning algorithms may be used to determine the predetermined conditions. The machine learning algorithm can be trained (e.g., using supervised or unsupervised training techniques) to be configured to determine the predetermined condition using previously recorded physiological data associated with the user. can. In such implementations, the previously recorded physiological data may include, for example, sleep onset latency, mid -wake, sleep efficiency, fragmentation index, bedtime, total bedtime, total sleep time, or any combination thereof. Corresponding data regarding the user's ability to fall asleep may be included. Historically recorded data for training machine learning algorithms can include subjective feedback from users, as described herein. The historical data for training the machine learning algorithm can be data from a population of users or a cohort of users who reside in similar demographics as the user. Data from a cohort of users can be used for initialization before learning from physiological data of users.” and Leon [0152] In some implementations, method 600 includes communicating one or more indicators of the determined affective score to the user (eg, in the form of a report). For example, the determined affective score indicator may be communicated to the user via user device 170. The indicators can be communicated to the user before, during, or after the sleep session. Additionally or alternatively, the determined affective score indicator may be communicated to a third party (eg, a health care provider, physician, etc.). Additionally, method 600 can further include communicating to the user an indication of any one or more of the sleep-related parameters described herein. For example, indicators of sleep-related parameters can be communicated to the user via user device 170 before, during, or after a sleep session. Emotions and/or anxiety that are the subject of affective scores can cause insomnia (eg, anxiety-induced insomnia). Therefore, tracking emotions through scoring helps explain the user's insomnia status.”).
Leon fails to explicitly teach detecting that the user is asleep and during at least a predetermined duration.
Nofzinger teaches
detecting that the user is asleep (Nofzinger [000184] “Thus, the device or system may include one or more sensors (electrodes, etc.) that provide at least some indication of sleep cycle, this information may be fed or monitored by the controller, which may modulate the applied dose based on the detected REM/NREM status. The perceived status may be compared to an expected or desired status, which may alter the applied hypothermic therapy.”, and Nofzinger [000185] “In some variations, the system may also or alternatively monitor and/or react to the depth of slow wave sleep, as measured by EEG wave analysis or other mechanism. Similarly, the system may monitor and/or respond to the degree of autonomic arousal as measured by HR variability or other mechanism.”)
during at least a predetermined duration while the user is asleep (Nofzinger [000176] “The device may be adapted by including timing controls adapted for the pre-sleep cooling described herein. In some variations the system may be configured to differentiate between long and short sleep periods; for example, the system may be configured to facilitate “napping” (short sleeps) or longer-duration sleeping. In some variations the system includes controls (and timers) for selecting sleep duration, and may alter the applied hypothermic therapy on the basis of the control.” and Zofzinger [000175] “In some variations the system may be configured to provide hypothermal therapy both before desired sleep time (GNT) and after initially falling asleep. For example, in one arrangement, the thermal transfer pad could be applied 45 minutes to 1 hour prior to getting in to bed to facilitate the sleep onset process and left on throughout a night of sleep to facilitate the sleep process across a night of sleep. Thus, the controller may be configured to initially apply a sleep onset time course (e.g., ramping to a sleeponset temperature such as about 14°C, and maintaining that temperature for a predetermined time period, such as 30 min-1 hr), and then transition to a sleep maintenance time course (e.g., maintaining the temperature at a relatively low temperature such as about 14°C for the first 2-4 hours of sleep or for the rest of the night, or gradually increasing the temperature to a higher level thereafter). The maintenance time course may maintain deeper sleep across the night with lesser degrees of facilitation in higher temperatures up to 30 °C.”).
It would have been obvious to a person having ordinary skill in the art at the time of the invention to modify Leon’s wearable device system for collecting and analyzing physiological signals using machine learning models to determine user conditions by incorporating Nofzinger’s teaching of monitoring physiological states over predetermined sleep durations, and treat the sleep time periods as the relevant monitoring interval for threshold comparison and model input. Leon teaches individualized physiological parameters and the use of user specific ranges or thresholds for evaluating physiological data and for monitoring sleep parameters. Nofzinger teaches that sleep and sleep onset periods are physiologically significant windows for evaluating sleep conditions. Combining these teachings would have led a person skilled in the art to apply user specific thresholds from physiological data collected while the user is asleep, which is a predictable choice to improve the accuracy of sleep conditions. This modification is a predictable use of prior art elements according to their established functions and would have required no more than routine optimization.
Regarding claim 2, Leon and Nofzinger teach the invention in claim 1, as discussed above, and further teach wherein the wearable device comprises a watch (Leon [0089] “In some implementations, activity tracker 190 is a wearable device that can be worn by a user, such as a smart watch, wristband, ring or patch.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to implement the wearable as a smartwatch, as taught in Leon as a routine option for activity trackers.
Regarding claim 3, Leon and Nofzinger teach the invention in claim 1, as discussed above, and further teach wherein: one of the plurality of insomnia-related signals comprises a skin conductance of the user while the user is asleep; and the corresponding threshold comprises a 10% increase relative to a baseline skin conductance of the user while the user is asleep (Leon [0088] Activity tracker 190 is generally used to help generate physiological data for determining activity measurements associated with a user. Activity measurements include, for example, steps taken, distance traveled, steps climbed, duration of physical activity, type of physical activity, intensity of physical activity, time spent standing, respiratory rate, average respiratory rate, resting respiratory rate, Maximum respiratory rate, respiratory rate variability, heart rate, average heart rate, resting heart rate, maximum heart rate, heart rate variability, calorie consumption, blood oxygen saturation (SqO2), electrodermal activity (skin conductance or galvanic (also known as skin response), user position, user posture, or any combination thereof.” And Leon [0056] “Data generated by one or more sensors 130 (eg, physiological data, flow data, pressure data, motion data, acoustic data, etc.) can also be used to determine the respiratory signal. The breathing signal is generally indicative of the user's breathing. The breathing signal can be indicative of a breathing pattern, which can include, for example, breathing rate, breathing rate variability, inspiration amplitude, expiration amplitude, inspiration-to-expiration ratio, and other breathing-related parameters, as well as any combinations thereof. . In some cases, during a sleep session, the breathing signal may include the number of events per hour (eg, during sleep), the pattern of events, the pressure setting of the respiratory therapy device 122, or any combination thereof.”), and Nofzinger [00051] Among body regions, the forehead has unique physiological and neuroanatomical properties that suggest it may play a prominent role in influencing the diving reflex. The distribution of warm and cold spots has been shown to be highest over the face and forehead of all body parts. Thermal sensation has been shown to be highest in the forehead of all body parts. In one study, thermal irradiation was applied to selected skin areas to determine whether particular areas demonstrate a greater thermal sensitivity than others in determination of a physiological thermoregulatory response. Modifications in thigh sweating rate were related to the change in temperature of the irradiated skin and the area of skin irradiated by computing a sensitivity coefficient for each skin area. Thermal sensitivity of the face, as measured by its effect on sweating rate change from the thigh, was found to be approximately three times that of the chest, abdomen men and thigh. Lower legs were found to have about one-half the thermal sensitivity of the thigh. Other studies have reported that thermal sensitivity is highest in the face of all 11 WO 2017/201088 PCT/US2017/032964 body areas. Further, the forehead comprising glabrous (non-hairy) skin has been shown to play a prominent role in the body response to thermoregulation given that the heat transfer function and efficacy of glabrous skin is unique within the entire body based on the capacity for a very high rate of blood perfusion and the novel capability for dynamic regulation of blood flow.)
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to use skin conductance and apply a threshold as a result effective variable to flag arousal related events for insomnia detection, given skin conductance known relationship to arousal and routine threshold optimization.
Regarding claim 4, Leon and Nofzinger teach the invention in claim 1, as discussed above, and further teach wherein one or more of: (1) one of the plurality of insomnia-related signals comprises a heart rate of the user while the user is asleep; and the corresponding threshold comprises a 20% increase, for at least a predetermined period of time, relative to a baseline heart rate of the user while the user is asleep; or (2) one of the plurality of insomnia-related signals comprises a beat-to-beat interval of the user while the user is asleep; and the corresponding threshold comprises a 10% decrease relative to a baseline beat-to-beat interval of the user while the user is asleep (Leon [0088] Activity tracker 190 is generally used to help generate physiological data for determining activity measurements associated with a user. Activity measurements include, for example, steps taken, distance traveled, steps climbed, duration of physical activity, type of physical activity, intensity of physical activity, time spent standing, respiratory rate, average respiratory rate, resting respiratory rate, Maximum respiratory rate, respiratory rate variability, heart rate, average heart rate, resting heart rate, maximum heart rate, heart rate variability, calorie consumption, blood oxygen saturation (SqO2), electrodermal activity (skin conductance or galvanic (also known as skin response), user position, user posture, or any combination thereof.” And Leon [0056] “Data generated by one or more sensors 130 (eg, physiological data, flow data, pressure data, motion data, acoustic data, etc.) can also be used to determine the respiratory signal. The breathing signal is generally indicative of the user's breathing. The breathing signal can be indicative of a breathing pattern, which can include, for example, breathing rate, breathing rate variability, inspiration amplitude, expiration amplitude, inspiration-to-expiration ratio, and other breathing-related parameters, as well as any combinations thereof. . In some cases, during a sleep session, the breathing signal may include the number of events per hour (eg, during sleep), the pattern of events, the pressure setting of the respiratory therapy device 122, or any combination thereof.” And Leon [0074] “PPG sensor 154 may measure one or more of, for example, heart rate, heart rate pattern, heart rate variability, cardiac cycle, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-to-expiratory ratio, estimated blood pressure parameters, or any combination thereof. outputs physiological data associated with the user 210”), and Nofzinger [000288] “In one example, the temperature of the applicator may be controlled based on the heart rate. For example, the processor may monitor the heart rate to identify a change from an initial heart rate to a drop of more than 10% (e.g., between 10-35%) from the initial heart rate within a predetermined time period (e.g., 5 minutes, 4 minute, 3 minutes, 2 minutes, 1 minute, etc.), which may indicate the diving reflex.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to use heart-rate and beat-to-beat interval features with thresholds to indicate hyperarousal during sleep, as heart rates are standard wearable outputs and threshold magnitudes are routinely optimized.
Regarding claim 6, Leon and Nofzinger teach the invention in claim 1, as discussed above, and further teach wherein one or more of: (1) the insomnia-related signals further comprises an amount of average screen time per day; and a corresponding threshold for the amount of average screen time per day comprises 6 hours; or (2) the insomnia-related signals further comprises an amount of screen time before the user’s bedtime; and a corresponding threshold for the amount of screen time before the user’s bedtime comprises 30 minutes (Leon [0088] “Activity tracker 190 is generally used to help generate physiological data for determining activity measurements associated with a user. Activity measurements include, for example, steps taken, distance traveled, steps climbed, duration of physical activity, type of physical activity, intensity of physical activity, time spent standing, respiratory rate, average respiratory rate, resting respiratory rate, Maximum respiratory rate, respiratory rate variability, heart rate, average heart rate, resting heart rate, maximum heart rate, heart rate variability, calorie consumption, blood oxygen saturation (SqO2), electrodermal activity (skin conductance or galvanic (also known as skin response), user position, user posture, or any combination thereof. Activity tracker 190 may include one or more sensors 130 described herein, such as, for example, motion sensor 138 (e.g., one or more accelerometers and/or gyroscopes), PPG sensor 154 and/or ECG sensor 156. can include.” and Nofzinger [000230] “Similar to device nights below, at 55 minutes prior to Good Night Time (GNT), the subject was asked to sit quietly in a comfortable chair in the lab bedroom and not to engage in potentially stimulating activities such as using a cell phone or computer or watching television. The subject had limited contact of study staff during this time.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to include screen-time metrics with thresholds to capture light-related sleep disruption, consistent with sleep hygiene guidance and routine device logging.
Regarding claim 8, Leon and Nofzinger teach the invention in claim 1, as discussed above, and further teach wherein: the insomnia-related signals further comprises an amount of ambient light while the user is asleep; and a corresponding threshold for the amount of ambient light while the user is asleep comprises 500 lux for at least one hour (Leon [0030] “In general, sleep hygiene refers to personal practices (e.g., diet, exercise, substance use, bedtime, activities before bed, activities in bed before bed, etc.) and/or environmental parameters (e.g., ambient light, noise, ambient temperature, etc.). At least in some cases, sleep hygiene can be improved by going to bed at a specific bedtime each night, sleeping for a specific period of time, waking up at a specific time, changing environmental parameters, or any combination thereof. can. Sleep hygiene education includes general guidelines regarding health habits (eg, diet, exercise, substance use) and environmental factors (eg, light, noise, excessive temperature) that can interfere with sleep.” and Leon [0088] “Activity tracker 190 is generally used to help generate physiological data for determining activity measurements associated with a user. Activity measurements include, for example, steps taken, distance traveled, steps climbed, duration of physical activity, type of physical activity, intensity of physical activity, time spent standing, respiratory rate, average respiratory rate, resting respiratory rate, Maximum respiratory rate, respiratory rate variability, heart rate, average heart rate, resting heart rate, maximum heart rate, heart rate variability, calorie consumption, blood oxygen saturation (SqO2), electrodermal activity (skin conductance or galvanic (also known as skin response), user position, user posture, or any combination thereof. Activity tracker 190 may include one or more sensors 130 described herein, such as, for example, motion sensor 138 (e.g., one or more accelerometers and/or gyroscopes), PPG sensor 154 and/or ECG sensor 156. can include.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to measure ambient light during sleep and apply a threshold to flag disruptive exposure, in view of established evening light effects and routine light sensing.
Regarding claim 12, Leon and Nofzinger teach the invention in claim 1, as discussed above, and further teach wherein the predetermined duration comprises two weeks (Nofzinger [000119] “A second study in insomnia patients was conducted to clarify the cerebral metabolic correlates of wakefulness after sleep onset (WASO) in primary insomnia patients testing the hypothesis that insomnia subjects with more WASO would demonstrate increased relative metabolism especially in the prefrontal cortex given the role of this region of the brain in restorative sleep and in cognitive arousal. Fifteen patients who met DSM-IV criteria for primary insomnia completed 1-week sleep diary (subjective) and polysomnographic (objective) assessments of WASO and regional cerebral glucose metabolic assessments during NREM sleep using [18F]fluoro-2-deoxy-D-glucose positron emission tomography (PET).” and Nofzinger [000127] “By setting a minimum duration criterion of at least one month and requiring the sleep complaints to be present on most days, we were also consistent with criteria for “Primary Insomnia” in the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to select a multi night predetermined duration (e.g., two weeks) to accumulate sufficient sleep data for reliable inference, as monitoring windows are routine design parameters in sleep tracking and sleep diaries.
Regarding claim 13, Leon and Nofzinger teach the invention in claim 1, as discussed above, and further teach further comprising determining whether the user is sleepwalking by: determining, based on a signal obtained by an accelerometer of the wearable device, whether the user is walking; and determining, by an EEG signal from a head-worn device of the user, whether the user is asleep (Leon [0120] “The wake time twake is the time associated with the time the user wakes up without going back to sleep (as opposed to, for example, the user waking up in the middle of the night and going back to sleep). After the user initially falls asleep, the user experiences more involuntary micro-arousals (e.g., micro-arousals MA1 and MA2) with short durations (e.g., 4 seconds, 10 seconds, 30 seconds, 1 minute, etc.). You may experience one. In contrast to the wake-up time twake, the user goes back to sleep after each of the micro-awakenings MA1 and MA2. Similarly, the user may experience one or more conscious awakenings (e.g., Awakening A) after initially falling asleep (e.g., getting up to go to the bathroom, taking care of a child or pet, sleepwalking, etc.) There is a possibility of doing this. However, the user goes back to sleep after awakening A. Thus, the wake-up time twake may be defined, for example, based on a wake-up threshold duration (eg, the user has been awake for 15 minutes or more, 20 minutes or more, 30 minutes or more, 1 hour or more, etc.)”, Leon [0064] “Motion sensor 138 outputs motion data that can be stored in memory device 114 and/or analyzed by processor 112 of control system 110. Motion sensor 138 may be used to detect movement of user 210 during a sleep session and/or movement of any of the components of respiratory therapy system 120, such as respiratory therapy device 122, user interface 124, or conduit 126. I can do it. Motion sensor 138 may include one or more inertial sensors, such as, for example, accelerometers, gyroscopes, and magnetometers. In some implementations, the motion sensor 138 alternatively or additionally generates one or more signals representative of the user's body movements, from which the user's movements can be determined, for example via the user's breathing movements. A signal representing a sleep state or stage may be obtained. I n some implementations, motion data from motion sensor 138 may be used in conjunction with additional data from another sensor 130 to determine a user's sleep state or stage. In some implementations, motion data may be used to determine a user's location, body position, and/or change in body position.”), and Leon [0077] “EEG sensor 158 outputs physiological data associated with the electrical activity of user's 210 brain. In some implementations, EEG sensor 158 includes one or more electrodes placed on or around the scalp of user 210 during a sleep session. Physiological data from EEG sensor 158 can be used, for example, to determine the sleep state or sleep stage of user 210 at any given time during a sleep session. In some implementations, EEG sensor 158 may be integrated into user interface 124 and/or headgear associated therewith (eg, a strap, etc.).”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to detect sleepwalking by using accelerometer-based walking detection and EEG based sleep state, using standard multi-sensors taught by Leon.
Claims 14-17 are analogous to claims 1-4, thus claims 14-17 are similarly analyzed and rejected in a manner consistent with the rejection of claims 1-4.
Claim 19 is analogous to claim 12, thus claim 19 is similarly analyzed and rejected in a manner consistent with the rejection of claim 12.
Claim 20 is analogous to claim 1, thus claim 20 is similarly analyzed and rejected in a manner consistent with the rejection of claim 1.
Claims 5, 7, 9-11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Leon et al. (JP Publication 2023544515 A), referred to hereinafter as Leon, in view of Nofzinger (AU Publication 2017268269 A1), referred to hereinafter as Nofzinger, and further in view of Irish et al. (Irish et al. The role of sleep hygiene in promoting public health: A review of empirical evidence. 2014. Sleep Medicine Reviews. 22. 23-36 (Year: 2014)) referred to hereinafter as Irish.
Regarding claim 5, Leon and Nofzinger teach the invention in claim 1, as discussed above, and further teach wherein: the insomnia-related signals further comprises a duration of exercise; and a corresponding threshold for the duration of exercise (Leon [0051] As described herein, system 100 generally performs data (e.g., physiological data, flow rate data, pressure data, motion data, acoustic data, etc.). The generated data may be analyzed to determine one or more physiological parameters (e.g., before, during, and/or after a sleep session) and/or which may include any parameters, measurements, etc. related to the user.” Leon [0029] Once diagnosed with insomnia, various techniques can be used and recommendations provided to the patient to manage or treat the insomnia. In general, patients should maintain healthy sleep habits (e.g., getting enough exercise and daytime activity.”).
Leon and Nofzinger fail to explicitly teach 30 minutes per day for a threshold number of days per week.
Irish teaches 30 minutes per day for a threshold number of days per week (Irish, page 29, “From earlier meta-analyses, duration of exercise moderated the acute effects of exercise on TST; the largest increases in TST were seen with exercise longer than 60 min.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to incorporate exercise duration (e.g., ~30 min/day on multiple days) as a threshold feature for insomnia modeling, given well understood exercise sleep benefits and routine activity tracking on wearables.
Regarding claim 7, Leon and Nofzinger teach the invention in claim 1, as discussed above, and further teach wherein one or more of: (1) the insomnia-related signals further comprises (Leon [0088] “Activity tracker 190 is generally used to help generate physiological data for determining activity measurements associated with a user. Activity measurements include, for example, steps taken, distance traveled, steps climbed, duration of physical activity, type of physical activity, intensity of physical activity, time spent standing, respiratory rate, average respiratory rate, resting respiratory rate, Maximum respiratory rate, respiratory rate variability, heart rate, average heart rate, resting heart rate, maximum heart rate, heart rate variability, calorie consumption, blood oxygen saturation (SqO2), electrodermal activity (skin conductance or galvanic (also known as skin response), user position, user posture, or any combination thereof. Activity tracker 190 may include one or more sensors 130 described herein, such as, for example, motion sensor 138 (e.g., one or more accelerometers and/or gyroscopes), PPG sensor 154 and/or ECG sensor 156. can include.”).
Leon and Nofzinger fail to explicitly teach an amount of deep sleep; and a corresponding threshold for the amount of deep sleep comprises 3 hours for at least a threshold number of nights each week; or (2) the insomnia-related signals further comprises a nap duration during the user’s waking hours; and a corresponding threshold for the nap duration during the user’s waking hours comprises 30 minutes per day for a least a threshold number of days per week.
Irish teaches an amount of deep sleep; and a corresponding threshold for the amount of deep sleep comprises 3 hours for at least a threshold number of nights each week; or (2) the insomnia-related signals further comprises a nap duration during the user’s waking hours; and a corresponding threshold for the nap duration during the user’s waking hours comprises 30 minutes per day for a least a threshold number of days per week (Irish, page 31, “Daytime napping has also been posited to disrupt the homeostatic sleep drive, and sleep hygiene recommendations often include the recommendation to avoid naps of greater than 30 min (see [3]).”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to use deep sleep amount and nap duration with thresholds as model inputs, as sleep stage proportions and napping behavior were recognized influencers and threshold levels are routine choices.
Regarding claim 9, Leon and Nofzinger teach the invention in claim 1, as discussed above, and further teach wherein one or more of: (1) one of the plurality of insomnia-related signals comprises a skin temperature while the user is asleep; the user while the user is asleep (Leon [0063] “Temperature sensor 136 outputs temperature data that can be stored in memory device 114 and/or analyzed by processor 112 of control system 110. In some implementations, temperature sensor 136 may detect core body temperature of user 210 (FIG. 2), skin temperature of user 210.” and Leon [0051] As described herein, system 100 generally performs data (e.g., physiological data, flow rate data, pressure data, motion data, acoustic data, etc.). The generated data may be analyzed to determine one or more physiological parameters (e.g., before, during, and/or after a sleep session) and/or which may include any parameters, measurements, etc. related to the user.”).
Leon and Nofzinger fail to explicitly teach the corresponding threshold comprises a .5º C increase relative to a baseline skin temperature of the user while the user is asleep; or (2) the insomnia-related signals further comprises an ambient air temperature while the user is asleep; and a corresponding threshold for the ambient air temperature while the user is asleep comprises 28º C.
Irish teaches the corresponding threshold comprises a .5º C increase relative to a baseline skin temperature of the user while the user is asleep; or (2) the insomnia-related signals further comprises an ambient air temperature while the user is asleep; and a corresponding threshold for the ambient air temperature while the user is asleep comprises 28º C (Irish, page 29, “The effects of exercise on core body temperature may be especially important during the afternoon or evening, as sleep onset typically coincides with the rapid decline in body temperature [67] and exercise increases the rate of decline in body temperature by initially raising core body temperature [68].”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to use skin temperature and ambient temperature deviations with numeric cutoffs as insomnia related indicators, given known thermal impacts on sleep and standard temperature sensing on wearables.
Regarding claim 10, Leon and Nofzinger teach the invention in claim 1, as discussed above, and further teach wherein: the insomnia-related signals further comprises an ambient noise level while the user is asleep (Leon [0051] As described herein, system 100 generally performs data (e.g., physiological data, flow rate data, pressure data, motion data, acoustic data, etc.). The generated data may be analyzed to determine one or more physiological parameters (e.g., before, during, and/or after a sleep session) and/or which may include any parameters, measurements, etc. related to the user.”).
Leon and Nofzinger fail to explicitly teach a corresponding threshold for the ambient noise level while the user is asleep comprises 50 db.
Irish teaches a corresponding threshold for the ambient noise level while the user is asleep comprises 50 db (Irish, page 30, “Noise is a relatively clear source of sleep disturbance, and sleep hygiene recommendations frequently advise individuals to minimize noise in their sleeping environment. However, nocturnal noises within one's normal surroundings (e.g., local traffic, music, plumbing) have the potential to impact sleep, even if they are not consciously observed.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to monitor bedroom noise and set a sound-level threshold (e.g., 50 dB) to capture disruptive noise events, consistent with environmental noise and routine acoustic sensing.
Regarding claim 11, Leon and Nofzinger teach the invention in claim 1, as discussed above, and further teach further comprising: further determining the insomnia condition of the user, by the trained machine-learning model, based on the description ((Leon, [0148] “In some implementations, step 602 includes determining an affective score based at least in part on demographic information associated with the user. Demographic information may include the user's age, the user's gender, the user's weight, the user's body mass index (BMI), the user's height, the user's race, the user's relationship or marital status, the user's family history of insomnia, and the user's body mass index (BMI). It may include information indicating employment status, the user's educational status, the user's socio-economic status, or any combination thereof. Demographic information may also include medical information associated with the user, such as, for example, indicating one or more medical conditions associated with the user, medication use by the user, or both. Demographic information may be received and stored by memory 114 (FIG. 1). (Figure 1). Demographic information may be provided manually by a user, for example, via user device 170 (eg, via a questionnaire or survey presented through display device 172). Alternatively, demographic information can be automatically collected from one or more data sources (eg, medical records) associated with the user. Because certain physiological parameters (such as heart rate) can change as a function of age, medical condition, etc., demographic information can be a useful input for determining affective scores.”, [0151] In some implementations, the predetermined condition is determined based at least in part on previously recorded physiological data associated with the user. In such implementations, machine learning algorithms may be used to determine the predetermined conditions. The machine learning algorithm can be trained (e.g., using supervised or unsupervised training techniques) to be configured to determine the predetermined condition using previously recorded physiological data associated with the user.”, Leon [0152] “In some implementations, method 600 includes communicating one or more indicators of the determined affective score to the user (eg, in the form of a report). For example, the determined affective score indicator may be communicated to the user via user device 170. The indicators can be communicated to the user before, during, or after the sleep session. Additionally or alternatively, the determined affective score indicator may be communicated to a third party (eg, a health care provider, physician, etc.). Additionally, method 600 can further include communicating to the user an indication of any one or more of the sleep-related parameters described herein. For example, indicators of sleep-related parameters can be communicated to the user via user device 170 before, during, or after a sleep session. Emotions and/or anxiety that are the subject of affective scores can cause insomnia (eg, anxiety-induced insomnia). Therefore, tracking emotions through scoring helps explain the user's insomnia status.”)”.
Leon and Nofzinger fail to explicitly teach receiving, from the user, a description of the user’s food intake, alcohol intake, or caffeine intake before the user’s bedtime.
Irish teaches receiving, from the user, a description of the user’s food intake, alcohol intake, or caffeine intake before the user’s bedtime (Irish, page 25, “Caffeine is the most widely used psychoactive substance in the world,21 and its stimulant properties make it a logical target for sleep promotion efforts in the general population. On a molecular level, caffeine's alerting and sleep-disruptive effects are driven by blockade of adenosine receptors in the basal forebrain and hypothalamus (see reviews of caffeine's pharmacology22,23). Plasma levels of caffeine peak approximately 30 minutes after oral administration, and the half-life of a single dose of caffeine is 3-7 hours, though this is influenced by individual differences in sensitivity, metabolism, and accumulation.22,23”and Irish, page 26, “In contrast, several studies have investigated afternoon and evening caffeine use. A recent study of 12 healthy young adults administered 400 mg of caffeine in the late afternoon and evening (i.e., within the half-life of caffeine), and found that even doses ingested up to 6 h before bedtime were associated with disturbances in both subjectively and objectively assessed sleep [27].).”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to ingest user reported diet, alcohol, or caffeine timing as model features for insomnia determination, because these factors are well known sleep modifiers and Leon teaches integrating user provided inputs into model pipelines.
Claim 18 is analogous to claim 7, thus claim 18 is similarly analyzed and rejected in a manner consistent with the rejection of claim 7.
Response to Arguments
Applicant’s arguments and amendments, see Remarks/Amendments submitted on 12/16/2025 with respect to the rejection of the claims have been carefully considered and is addressed below.
Claim Rejections - 35 USC § 101
Applicant’s arguments have been considered but are not persuasive, and the rejection of claims 1-20 under 35 U.S.C. § 101 is maintained. The Applicant’s statement that the claims cannot recite a mental process because they require detecting signals using sensors of a wearable device is not persuasive. Under Step 2A, Prong One, it is evaluated whether the claim is directed to a mental process (evaluating, comparing, or classifying information). Here, the claims compare insomnia-related signals to thresholds and determine an insomnia condition based on those comparisons, which constitutes data analysis and evaluation and falls within the mental process grouping (even when the data is obtained from sensors).
Applicant’s statement regarding the MPEP example involving an earring with a glucose sensor is not persuasive. The example is directed to a physical article defined by its structural components, whereas the present claims are directed to a method of analyzing sensor data to classify a medical condition. The claims do not merely recite a wearable device with sensors, but instead recite the abstract steps of comparing data to thresholds and making a diagnostic determination.
Applicant also states that the wearable device and sensors integrate the abstract idea into a practical application because the insomnia determination cannot occur without them. This argument confuses data acquisition with integration into a practical application. Under MPEP § 2106.05(g), data gathering, even when necessary to perform the abstract idea, is considered insignificant extra-solution activity. The claims do not recite any improvement to wearable device functionality, or technical action taken in response to the insomnia determination. Instead, the wearable device and sensors are used as tools to supply input data for an abstract evaluation.
Applicant also discusses the benefits described in the specification, such as avoiding clinical visits and passively detecting insomnia. However, eligibility is determined based on the claim limitations, not the advantages described in the specification. The claims do not recite any improvement to sensor technology, machine learning architecture, or computer functionality, but instead have generic computing components perform an abstract idea (a classification task).
Lastly, the amendments requiring detection while the user is asleep and the use of use specific thresholds narrows the context in which data is collected and evaluated and do not change the abstract nature of the claimed subject matter. Accordingly, the claims are not integrated into a practical application under Step 2A and do not include additional elements amounting to significantly more under Step 2B. The rejection under 35 U.S.C. § 101 is therefore maintained.
Claim Rejections - 35 USC § 103
Applicant’s arguments have been considered but are not persuasive. Applicant states that the amended limitation requiring “user-specific thresholds determined while the user is asleep” is not taught or suggested by the cited art. However, Leon teaches individualized physiological parameters and ranges for evaluating a user’s physiological data, monitoring physiological parameters during sleep, and comparing measured physiological values against ranges or tolerances to determine a user condition. Leon’s disclosure that physiological parameters can be individual specific teaches user specific thresholds, even if Leon does not use the same phrasing recited in the claims.
Applicant’s argument also suggests express disclosure of the claimed limitation rather than addressing whether the claimed subject matter would have been obvious. Because Leon teaches individualized baselines and sleep-time physiological monitoring, it would have been obvious to derive or apply those individualized baselines from data collected while the user is asleep, as sleep is a well-recognize baseline physiological state. This approach constitutes routine optimization.
Additionally, the rejection is based on Leon in view of Nofzinger, not Leon alone. Nofzinger teaches defining pre-sleep and in-sleep intervals, monitoring physiological states across sleep onset and sleep maintenance, and treating those intervals as the relevant monitoring duration. Nofzinger provides the teaching of collecting physiological data over predetermined sleep-related durations and reinforces that sleep-time windows are physiologically significant. Applying these teachings to Leon’s wearable, machine learning analysis would have been a predictable modification.
Accordingly, Leon teaches individualized physiological thresholds and sleep related monitoring, and Nofzinger teaches predetermined sleep time monitoring windows. Combining these teachings to apply user specific thresholds derived during sleep represents a predictable use of prior art elements according to their established functions and would have been obvious to a person having ordinary skill in the art. Therefore, the rejection under 35 U.S.C. § 103 is maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
Shouldice et al. (U.S. Patent Publication 2020/0297955) teaches systems and methods for managing a user’s chronic disease that include a physiological monitor carried by the user to sense physiological parameters and a management device that can analyze physiological and/or environmental parameters to detect a trigger pattern indicative of an event and to generate treatment instructions.
Eleftheriou et al. (U.S. Patent Publication 2023/0301586 A1) teaches the method involves deriving insomnia profiles from biosignal timeseries collected by a wearable device across two time periods, selecting a treatment pathway based on the first profile, and evaluating the treatment’s effectiveness by comparing the first and second profiles.
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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/K.R.L./Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685