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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/29/2025 has been entered.
Applicant' s arguments, filed 12/29/2025, have been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Applicants have amended their claims, filed 12/29/2025, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Claims 1-10 and 12-19 are the currently pending claims with claims 8-10, 12-14, and 16-17 previously withdrawn from further examination. Claims 11 and 20 have been previously canceled. The claims currently under consideration are 1-7, 15, and 18-19.
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 1-7 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Soulet et al. (US 20180236232 A1), hereto referred as Soulet, and further in view of Grossman et al. (US 20190143073 A1), hereto referred as Grossman, and further in view of Molnar et al. (US 20090264789 A1), hereto referred as Molnar.
Regarding claim 1, Soulet teaches that a system for inducing or changing a sleep state of a subject (Soulet, [0053]: "The subject of the invention is also a personalized acoustic brain wave stimulation system for a person", [0192]: "the invention can be practiced for example during a sleep phase of the person P (such as identified, for example, in the standards of the AASM, 'American Academy of Sleep Medicine'), for example a phase of deep sleep of the person P (commonly called stage 3 or stage 4) or during other sleep phases", [0134]: "stimulation might have an effect of lengthening the time of said deep sleep", explains that Soulet teaches a system directed to stimulation for sleep modification) comprises: a sensing module comprising (i) one or more sensors configured to detect at least one of a biological parameter of a subject and a biological signal of the subject upon contact with a portion of the subject's body (Soulet, [0096]-[0099]: "acquisition of the at least one measured signal S by means of the means of acquisition 3... comprise a plurality of electrodes 3 capable of being in contact with the person P... for acquiring at least one measured signal S representative of a physiological electrical signal E of the person P", describing sensors designed to detect physiological signals upon contact with the body), and (ii) an additional sensor configured to detect one or more ambient conditions associated with a surrounding environment of the subject (Soulet, [0112]-[0113]: "signal S can thus be representative of air quality of the air surrounding the person P, for example a carbon dioxide or oxygen level, or even a temperature or ambient noise level", describing an additional sensor capable of detecting ambient conditions), a signal processing module in communication with the sensing module, wherein the signal processing module is configured to aggregate and process data obtained using the one or more sensors (Soulet, [0115]-[0117] and [0131]: "The means of analysis 5 receives the measured signals S from the means of acquisition 3, which could be preprocessed as detailed above", explaining that the processing module communicates with the sensing module where signal processing and aggregation occurs), **an output device optimization module in communication with the signal processing module and the one or more output devices **(Soulet, [0090]: "the means of acquisition 3, means of emission 4, means of analysis 5 and the memory 6 are... close to each other that communication between these elements 3, 4, 5, 6 is particularly quick and at high data rate", demonstrating communication between modules, including the output device (means of emission) and the means of analysis (signal processing/optimization)),
Also regarding claim 1, Soulet does not fully teach that the signal processing module computes one or more markers of the subject comprising a ratio between two or more brainwave oscillation frequency bands. Rather, Soulet teaches that a signal processing module analyzes EEG signals and computes various metrics from multiple frequency bands, such as the energy within alpha, beta, delta, or theta bands, to assess the subject's brain state for sleep-related stimulation (Soulet, [0141]). However, Soulet does not disclose calculating a marker comprising a ratio between two or more brainwave oscillation frequency bands.
Grossman teaches that ratios between brainwave frequency bands, such as the theta/alpha ratio, are used as markers for sleep onset, sleep state, and related conditions, and that these ratios can be used to control phase-locked stimulation to accelerate or modulate sleep transitions (Grossman, [0015]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Soulet in view of Grossman to compute one or more markers of the subject comprising a ratio between two or more brainwave oscillation frequency bands. The combination would have been obvious and feasible because both Soulet and Grossman analyze EEG signals for the purpose of brain state characterization and control of sleep-related stimulation, and Soulet's signal processing module could be straightforwardly adapted to include the calculation of ratios between frequency bands as shown by Grossman. The benefit of combining the references is that frequency band ratios, as demonstrated in Grossman, are clinically established and sensitive markers that improve detection of sleep state changes and allow for more precise timing and control of neuromodulation or sleep-inducing stimulation.
Also regarding claim 1, Soulet does not fully teach that the output device optimization module is configured to determine an optimal stimulation output (wherein the optimal stimulation output is configured to induce or change a sleep state of the subject) for the one or more output devices and control an operation of the one or more output devices to provide the optimal stimulation output based on (i) the one or more computed markers and (ii) data obtained using the additional sensor. Rather, Soulet teaches that the output device optimization module (means of emission) is configured to provide stimulation outputs that are synchronized to the subject's brain wave temporal pattern and are triggered when the person is in a state susceptible to stimulation (Soulet, [0174], [0227]). Soulet further teaches that the system collects environmental data such as ambient air quality, temperature, or noise level through additional sensors (Soulet, [0113]: "The measured signal S can thus be representative of air quality of the air surrounding the person P, for example a carbon dioxide or oxygen level, or even a temperature or ambient noise level"). This environmental data forms part of the measured signal S, which is then received and processed by the means of analysis (Soulet, [0131]: "The means of analysis 5 receives the measured signals S from the means of acquisition 3, which could be preprocessed as detailed above"). Soulet explicitly explains that the operating parameters used to control the emission of the stimulation signal are determined based on the measured signals, and, as defined in Soulet ([0113]), those measured signals include both physiological and ambient/environmental sensor data (Soulet, [0227]). Therefore, in Soulet, environmental sensor data is not merely acquired but is actively processed and used as part of the parameter determination for stimulation output, enabling the system to adapt the output in response to both the subject's brain state and ambient conditions. The purpose of the system is to operate during different sleep phases to lengthen or maintain deep sleep or modulate other sleep states (Soulet, [0192]), However, Soulet does not expressly describe determining an "optimal" stimulation output in view of both computed markers and ambient data, nor does it expressly state the stimulation is always configured to induce or change a sleep state of the subject in every instance.
Grossman teaches that the output device is controlled in real time based on computed EEG markers, specifically including ratios between brainwave frequency bands, to provide phase-locked stimulation that is intended to induce or accelerate sleep onset and to change sleep stages (Grossman, [0006]: "falling asleep is characterized by a significant increase in absolute theta band power accompanied by an increase in the theta to alpha ratio (i.e., the ratio of theta power to alpha power)... a significant increase in the absolute power of theta is an indicator of sleep onset"; [0015]: "phase-locked stimulation may cause a person to fall asleep faster than the person otherwise would... may cause the ratio of theta band power to alpha band ratio to increase faster than it otherwise would"; [0456]: "the transition from wakefulness to sleep onset involves an increase in theta/alpha ratio of the endogenous signal... the apparatus is configured in such a way that... sleep onset is the first 30-second epoch... in which theta/alpha ratio... is greater than or equal to one"). Grossman further teaches that the stimulation output is adjusted and delivered to optimally induce a change in the sleep state based on these detected EEG markers (Grossman, [0006], [0015], [0456]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combined Soulet and Grossman in view of Grossman to configure the output device optimization module to determine an optimal stimulation output, configured to induce or change a sleep state of the subject, by controlling the output based on both computed EEG markers and environmental sensor data. The combination would have been obvious and feasible because both references teach neuromodulation systems that process EEG-derived information to control output in real time for sleep modification, and Soulet's approach of processing and using environmental data as part of the parameter set for stimulation could be directly integrated with Grossman's EEG marker-based optimization. The benefit of combining the references is enhanced precision and effectiveness in inducing and modulating sleep states, as the stimulation output is optimally adjusted using both physiological and environmental information, thereby improving comfort, safety, and therapeutic outcomes.
Also regarding claim 1, the modified Soulet does not fully teach that determining the optimal stimulation output comprises comparing the ratio between two or more brainwave oscillation frequency bands to a threshold, wherein controlling the operation of the one or more output devices comprises decreasing the optimal stimulation output when the ratio between two or more brainwave oscillation frequency bands exceeds the threshold. Rather, the modified Soulet teaches providing stimulation based on detected brain state conditions and triggering stimulation when the subject is in a state susceptible to stimulation (Soulet, [0174], [0227]). Soulet therefore teaches a stimulation framework in which sensed physiological signals are analyzed and used to determine when stimulation should occur, but it does not explicitly disclose comparing a ratio between EEG frequency bands to a threshold or decreasing stimulation output when such a ratio exceeds the threshold.
Grossman teaches evaluating a ratio between EEG frequency bands as a marker of sleep onset and sleep state. In particular, Grossman teaches that the transition from wakefulness to sleep onset involves an increase in the theta/alpha ratio of the endogenous EEG signal and that sleep onset may be defined as the first epoch in which the theta/alpha ratio is greater than or equal to one (Grossman, [0006]; [0450]; [0456]). Grossman therefore teaches evaluating a ratio between EEG frequency bands against a numeric criterion to determine a brain state condition associated with sleep onset (Grossman, [0007]; [0456]). However, Grossman does not expressly describe decreasing stimulation output when that ratio exceeds a threshold.
Molnar teaches analyzing biosignals using frequency-domain characteristics, including that a frequency characteristic may include “a ratio of power levels within two or more frequency bands”. (Molnar, [0208], “a frequency characteristic of a signal may include a ratio of power levels within two or more frequency bands…”; this directly teaches the claimed ratio-type marker from brainwave frequency-band information). Molnar further teaches that these frequency-band characteristics, including the ratio of power levels, may be compared to a stored value to determine whether a biosignal is detected (Molnar, [0208]: "the ratio of power levels or another frequency characteristic based on one or more frequency bands may be compared to a stored value in order to determine whether the biosignal is detected"). Thus, Molnar expressly teaches evaluating a ratio between EEG frequency bands and comparing the evaluated ratio or other frequency-domain characteristic to a stored value or threshold condition to determine the presence of a biosignal or patient state. Molnar further teaches that when the EEG-derived condition satisfies the threshold or comparison criterion, a processor implements control of a therapy device or therapy program (Molnar, [0227]-[0228]: “if the monitored EEG signal waveform comprises an amplitude that is greater than or equal to the threshold value (154)… processor 134 implements control of a therapy device”). Molnar additionally teaches that therapy parameters may be modified based on the detected patient state, including that stored therapy program indications may specify that the amplitude should be increased or decreased when a particular patient state is detected (Molnar, [0139]). Molnar also teaches that therapy may include external cues such as visual and auditory cues and that “Patient 12 or a clinician may modify the external cues”, for example they may “decrease the volume of an auditory cue”. (Molnar, [0096]). Accordingly, Molnar teaches both (i) evaluating EEG-derived markers including ratios between EEG frequency-band power levels, (ii) comparing those markers to a stored value or threshold to determine a detected condition, and (iii) modifying therapy parameters, including decreasing stimulation amplitude, based on the detected condition.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combined teachings of Soulet and Grossman in view of Molnar to determine stimulation output by comparing the EEG frequency-band ratio taught by Grossman to a threshold condition and controlling stimulation output accordingly. Further, Molnar expressly teaches comparing EEG frequency-domain characteristics including “a ratio of power levels within two or more frequency bands” to a “stored value” and implementing therapy control when the comparison criterion is satisfied, such that the stored value corresponds to the claimed threshold and the resulting control includes reducing a stimulation parameter when the ratio exceeds the threshold. (Molnar, [0208]; [0227]-[0228]). A person of ordinary skill in the art would have been motivated to apply Molnar's threshold-based therapy control to the EEG frequency-band ratio determination of Grossman within the stimulation framework of Soulet in order to automatically adjust stimulation intensity based on detected brain state. Such a modification would have been straightforward and technically feasible because all three references operate within the same field of EEG-based monitoring and therapy control and each teaches processing EEG-derived metrics to determine or modify stimulation delivery.
The benefit of combining the references is that threshold-based evaluation of EEG frequency-band ratios allows the stimulation system to automatically adjust therapy delivery in response to detected changes in brain state, thereby improving the accuracy and effectiveness of sleep-state modulation and providing adaptive stimulation control.
Regarding claim 2, the modified Soulet teaches that the one or more sensors comprise a sensor configured to detect the biological signal of the subject, wherein the sensor comprises an electrode, a surgically implanted electrode, a surface electrode, or an encephalogram (EEG) electrode (Soulet, ¶[0097]-[0101]: "The device 1 comprises at least two electrodes 3 including at least one reference electrode 3a and at least one EEG measurement electrode 3b... capable of being in contact with the person P... on the surface of the scalp", describing electrodes, including EEG and surface electrodes, used for detecting biological signals).
Regarding claim 3, the modified Soulet teaches that the biological signal comprises an electroencephalogram (EEG) signal, an electromyogram (EMG) signal, an electrocorticogram (ECoG) signal, or a field potential within a cerebral cortex region of the subject's brain (Soulet, ¶[0097]-[0098]: “The physiological electrical signal E can for example comprise an electroencephalogram (EEG), electromyogram (EMG), electrooculogram (EOG), electrocardiogram (ECG) or any other biosignal that can be measured on the person P”, demonstrating that EEG and EMG signals are explicitly disclosed as physiological electrical signals).
Regarding claim 4, the modified Soulet teaches that the one or more sensors comprise a sensor configured to detect the biological parameter of the subject (Soulet, ¶[0111]: “The means of acquisition 3 can comprise a heart rhythm detector, a body thermometer, an accelerometer, a respiration sensor, a bio-impedance sensor or even a microphone”, indicating the use of sensors to detect biological parameters such as heart rhythm and body temperature).
Regarding claim 5, the modified Soulet teaches that the additional sensor is configured to detect one or more environmental conditions of the surrounding environment (Soulet, ¶[0113]: “The measured signal S can thus be representative of air quality of the air surrounding the person P, for example a carbon dioxide or oxygen level, or even a temperature or ambient noise level”, indicating that the prior art includes sensors capable of measuring environmental conditions such as air quality, temperature, and noise).
Regarding claim 6, the modified Soulet teaches that the biological parameter comprises a physical or physiological condition, state, or property of the subject (Soulet, ¶[0109]-[0110]: “The measured signal S can in particular be representative of a non-electrical or not totally electrical physiological signal of the person P, for example a signal of heart activity, such as heart rhythm, body temperature of the person P or even movements of the person P”, highlighting that it includes physical and physiological conditions such as heart rhythm, body temperature, and movement as parameters detectable by the system).
Regarding claim 7, the modified Soulet teaches that the one or more ambient conditions correspond to at least one of a temperature of the surrounding environment, an amount or volume of sound or noise in the surrounding environment, a humidity of the surrounding environment, an air quality in the surrounding environment, and a lighting condition of the surrounding environment, wherein the lighting condition comprises at least one of an amount, an intensity, a directionality, a color, or a temperature of light in the surrounding environment (Soulet, ¶[0112]-¶[0113]: “The means of acquisition 3 can also comprise measured signal S acquisition devices representative of the environment... representative of air quality... carbon dioxide or oxygen level, or even a temperature or ambient noise level”, explicitly describing ambient conditions such as air quality, temperature, and noise).
Regarding claim 18, the modified Soulet teaches that the output device optimization module is configured to control the one or more output devices based on one or more threshold values associated with the one or more markers (Soulet, ¶[0154]: “A plurality of thresholds can be predefined and recorded in the memory … Said thresholds can vary over time, such that the determination of the index of susceptibility is variable over time”, explicitly indicating predefined threshold values, which can vary dynamically and are used to control device operation; ¶[0226]: “Said one and/or another among a brain wave phase of the person and a predefined brain wave temporal pattern M1 of the person P can vary over time such that synchronized emitting the acoustic signal A is adjusted over time”, highlighting that device control is based on dynamic thresholds related to brain wave patterns; ¶[0229]: “The control electronics 11 can in that way command the acoustic transducer 4 so that the predefined temporal pattern M2 of the acoustic signal A is synchronized in time with said instant I”, demonstrating control based on synchronization thresholds to ensure precise device operation).
Regarding claim 19, Soulet teaches that a method for inducing or changing a sleep state of a subject (Soulet, [0053]: "The subject of the invention is also a personalized acoustic brain wave stimulation system for a person", [0192]: "the invention can be practiced for example during a sleep phase of the person P (such as identified, for example, in the standards of the AASM, 'American Academy of Sleep Medicine'), for example a phase of deep sleep of the person P (commonly called stage 3 or stage 4) or during other sleep phases", [0134]: "stimulation might have an effect of lengthening the time of said deep sleep", explains that Soulet teaches a system directed to stimulation for sleep modification) comprises: using one or more sensors, detecting at least one biological signal of the subject (Soulet, [0096]-[0099]: "acquisition of the at least one measured signal S by means of the means of acquisition 3... comprise a plurality of electrodes 3 capable of being in contact with the person P... for acquiring at least one measured signal S representative of a physiological electrical signal E of the person P", describing sensors designed to detect biological/physiological signals upon contact with the body) using an additional sensor, detecting one or more ambient conditions associated with a surrounding environment of the subject (Soulet, [0112]-[0113]: "signal S can thus be representative of air quality of the air surrounding the person P, for example a carbon dioxide or oxygen level, or even a temperature or ambient noise level" describing an additional signal, and thus sensor, capable of detecting ambient conditions of the surroundings), wherein at least one of the one or more sensors is placed in contact with a portion of the subject's body (Soulet, [0097]-[0101]. "The EEG measurement electrodes 3c are arranged, for example, on the surface of the scalp of the person P", describing electrodes (i.e. sensors) in contact with the scalp of the subject).
Also regarding claim 19, Soulet does not fully teach that the processing data obtained using the one or more sensors to compute one or more markers of the subject comprises a ratio between two or more brainwave oscillation frequency bands. Rather, Soulet teaches that a signal processing module analyzes EEG signals and computes various metrics from multiple frequency bands, such as the energy within alpha, beta, delta, or theta bands, to assess the subject's brain state for sleep-related stimulation (Soulet, [0141]). However, Soulet does not disclose calculating a marker comprising a ratio between two or more brainwave oscillation frequency bands.
Grossman teaches that ratios between brainwave frequency bands, such as the theta/alpha ratio, are used as markers for sleep onset, sleep state, and related conditions, and that these ratios can be used to control phase-locked stimulation to accelerate or modulate sleep transitions (Grossman, [0015]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Soulet in view of Grossman to compute one or more markers of the subject comprising a ratio between two or more brainwave oscillation frequency bands. The combination would have been obvious and feasible because both Soulet and Grossman analyze EEG signals for the purpose of brain state characterization and control of sleep-related stimulation, and Soulet's signal processing module could be straightforwardly adapted to include the calculation of ratios between frequency bands as shown by Grossman. The benefit of combining the references is that frequency band ratios, as demonstrated in Grossman, are clinically established and sensitive markers that improve detection of sleep state changes and allow for more precise timing and control of neuromodulation or sleep-inducing stimulation.
Also regarding claim 19, the modified Soulet does not fully teach determining an optimal stimulation output (wherein the optimal stimulation output induces or changes a sleep state of the subject) for the one or more output devices based on the one or more computed markers and data obtained using the additional sensor and controlling an operation of the one or more output devices to provide the optimal stimulation output based on the one or more computed markers and the data obtained using the additional sensor. Rather, the modified Soulet teaches providing stimulation outputs that are synchronized to the subject's brain wave temporal pattern and are triggered when the person is in a state susceptible to stimulation (Soulet, [0174], [0227]). It further teaches that the system collects environmental data such as ambient air quality, temperature, or noise level through additional sensors (Soulet, [0113]: "The measured signal S can thus be representative of air quality of the air surrounding the person P, for example a carbon dioxide or oxygen level, or even a temperature or ambient noise level"). This environmental data forms part of the measured signal S, which is then received and processed by the means of analysis (Soulet, [0131]: "The means of analysis 5 receives the measured signals S from the means of acquisition 3, which could be preprocessed as detailed above"). Soulet explicitly explains that the operating parameters used to control the emission of the stimulation signal are determined based on the measured signals, and, as defined in Soulet ([0113]), those measured signals include both physiological and ambient/environmental sensor data (Soulet, [0227]). Therefore, in Soulet, environmental sensor data is not merely acquired but is actively processed and used as part of the parameter determination for stimulation output, enabling the system to adapt the output in response to both the subject's brain state and ambient conditions. The purpose of the system is to operate during different sleep phases to lengthen or maintain deep sleep or modulate other sleep states (Soulet, [0192]). However, the modified Soulet does not expressly describe determining an "optimal" stimulation output in view of both computed markers and ambient data, nor does it expressly state the stimulation is always configured to induce or change a sleep state of the subject in every instance.
Grossman teaches that the output device is controlled in real time based on computed EEG markers, specifically including ratios between brainwave frequency bands, to provide phase-locked stimulation that is intended to induce or accelerate sleep onset and to change sleep stages (Grossman, [0006]: "falling asleep is characterized by a significant increase in absolute theta band power accompanied by an increase in the theta to alpha ratio (i.e., the ratio of theta power to alpha power)... a significant increase in the absolute power of theta is an indicator of sleep onset"; [0015]: "phase-locked stimulation may cause a person to fall asleep faster than the person otherwise would... may cause the ratio of theta band power to alpha band ratio to increase faster than it otherwise would"; [0456]: "the transition from wakefulness to sleep onset involves an increase in theta/alpha ratio of the endogenous signal... the apparatus is configured in such a way that... sleep onset is the first 30-second epoch... in which theta/alpha ratio... is greater than or equal to one"). Grossman further teaches that the stimulation output is adjusted and delivered to optimally induce a change in the sleep state based on these detected EEG markers (Grossman, [0006], [0015], [0456]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combined Soulet and Grossman in view of Grossman to determine an optimal stimulation output (wherein the optimal stimulation output induces or changes a sleep state of the subject) for the one or more output devices based on the one or more computed markers and data obtained using the additional sensor, and to control an operation of the one or more output devices to provide the optimal stimulation output based on the one or more computed markers and the data obtained using the additional sensor. The combination would have been obvious and feasible because both references teach neuromodulation systems that process EEG-derived information to control output in real time for sleep modification, and Soulet's approach of processing and using environmental data as part of the parameter set for stimulation could be directly integrated with Grossman's EEG marker-based optimization. The benefit of combining the references is enhanced precision and effectiveness in inducing and modulating sleep states, as the stimulation output is optimally adjusted using both physiological and environmental information, thereby improving comfort, safety, and therapeutic outcomes.
Also regarding claim 19, the modified Soulet does not fully teach that determining the optimal stimulation output comprises comparing the ratio between two or more brainwave oscillation frequency bands to a threshold, wherein controlling the operation of the one or more output devices comprises decreasing the optimal stimulation output when the ratio between two or more brainwave oscillation frequency bands exceeds the threshold. Rather, the modified Soulet teaches providing stimulation based on detected brain state conditions and triggering stimulation when the subject is in a state susceptible to stimulation (Soulet, [0174], [0227]). Soulet therefore teaches a stimulation framework in which sensed physiological signals are analyzed and used to determine when stimulation should occur, but it does not explicitly disclose comparing a ratio between EEG frequency bands to a threshold or decreasing stimulation output when such a ratio exceeds the threshold.
Grossman teaches evaluating a ratio between EEG frequency bands as a marker of sleep onset and sleep state. In particular, Grossman teaches that the transition from wakefulness to sleep onset involves an increase in the theta/alpha ratio of the endogenous EEG signal and that sleep onset may be defined as the first epoch in which the theta/alpha ratio is greater than or equal to one (Grossman, [0006]; [0450]; [0456]). Grossman therefore teaches evaluating a ratio between EEG frequency bands against a numeric criterion to determine a brain state condition associated with sleep onset (Grossman, [0007]; [0456]). However, Grossman does not expressly describe decreasing stimulation output when that ratio exceeds a threshold.
Molnar teaches analyzing biosignals using frequency-domain characteristics, including that a frequency characteristic may include “a ratio of power levels within two or more frequency bands”. (Molnar, [0208], “a frequency characteristic of a signal may include a ratio of power levels within two or more frequency bands…”; this directly teaches the claimed ratio-type marker from brainwave frequency-band information). Molnar further teaches that these frequency-band characteristics, including the ratio of power levels, may be compared to a stored value to determine whether a biosignal is detected (Molnar, [0208]: "the ratio of power levels or another frequency characteristic based on one or more frequency bands may be compared to a stored value in order to determine whether the biosignal is detected"). Thus, Molnar expressly teaches evaluating a ratio between EEG frequency bands and comparing the evaluated ratio or other frequency-domain characteristic to a stored value or threshold condition to determine the presence of a biosignal or patient state. Molnar further teaches that when the EEG-derived condition satisfies the threshold or comparison criterion, a processor implements control of a therapy device or therapy program (Molnar, [0227]-[0228]: “if the monitored EEG signal waveform comprises an amplitude that is greater than or equal to the threshold value (154)… processor 134 implements control of a therapy device”). Molnar additionally teaches that therapy parameters may be modified based on the detected patient state, including that stored therapy program indications may specify that the amplitude should be increased or decreased when a particular patient state is detected (Molnar, [0139]). Molnar also teaches that therapy may include external cues such as visual and auditory cues and that “Patient 12 or a clinician may modify the external cues”, for example they may “decrease the volume of an auditory cue”. (Molnar, [0096]). Accordingly, Molnar teaches both (i) evaluating EEG-derived markers including ratios between EEG frequency-band power levels, (ii) comparing those markers to a stored value or threshold to determine a detected condition, and (iii) modifying therapy parameters, including decreasing stimulation amplitude, based on the detected condition.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combined teachings of Soulet and Grossman in view of Grossman and Molnar to determine stimulation output by comparing the EEG frequency-band ratio taught by Grossman to a threshold condition and controlling stimulation output accordingly. Further, Molnar expressly teaches comparing EEG frequency-domain characteristics including “a ratio of power levels within two or more frequency bands” to a “stored value” and implementing therapy control when the comparison criterion is satisfied, such that the stored value corresponds to the claimed threshold and the resulting control includes reducing a stimulation parameter when the ratio exceeds the threshold. (Molnar, [0208]; [0227]-[0228]). A person of ordinary skill in the art would have been motivated to apply Molnar's threshold-based therapy control to the EEG frequency-band ratio determination of Grossman within the stimulation framework of Soulet in order to automatically adjust stimulation intensity based on detected brain state. Such a modification would have been straightforward and technically feasible because all three references operate within the same field of EEG-based monitoring and therapy control and each teaches processing EEG-derived metrics to determine or modify stimulation delivery.
The benefit of combining the references is that threshold-based evaluation of EEG frequency-band ratios allows the stimulation system to automatically adjust therapy delivery in response to detected changes in brain state, thereby improving the accuracy and effectiveness of sleep-state modulation and providing adaptive stimulation control.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Soulet et al. (US 20180236232 A1), hereto referred as Soulet, and further in view of Grossman et al. (US 20190143073 A1), hereto referred as Grossman, and further in view of Molnar et al. (US 20090264789 A1), hereto referred as Molnar, and further in view of Coleman et al. (US 20190113973 A1), hereto referred as Coleman.
The combined Soulet, Grossman, and Molnar teaches claim 1 as described above.
Regarding claim 15, the modified Soulet teaches that the output device optimization module is configured to operate or control the one or more output devices in a graded proportional manner. The modified Soulet teaches the graded control of output devices by enabling dynamic adjustments of acoustic signal parameters, such as sound level, length, spectrum, and temporal pattern, which can vary over time (Soulet, ¶[0217]-[0220]). Additionally, the modified Soulet describes synchronized emitting of the acoustic signal based on a predefined brain wave temporal pattern, which adjusts dynamically in response to operating parameters, illustrating proportional adjustments based on synchronization (Soulet, ¶[0225]-[0227]). However, the modified Soulet does not specifically teach doing so in a in a graded proportional manner. Coleman demonstrates a proportional control mechanism through the use of feature extraction algorithms that transform raw time-series data into outputs optimized for various features, with tunable parameters selected based on algorithms like gradient methods or stochastic sampling (Coleman, ¶[0211]). Furthermore, Coleman explicitly describes the adjustment of outputs, such as gaming parameters, in direct proportion to computed brain states, reinforcing the concept of a graded and proportional control mechanism (Coleman, ¶[0176]). Together, these references provide a comprehensive system for real-time proportional control of output devices based on computed markers. Both the modified Soulet and Coleman describe systems capable of real-time data acquisition and processing for dynamic output control. The modified Soulet focuses on synchronization of outputs to brain waves, while Coleman’s description of feature extractors and brain signatures demonstrates how computed markers can influence proportional adjustments in outputs. These systems operate with compatible mechanisms, enabling seamless integration. A person skilled in the art would have recognized the obvious benefits of combining the modified Soulet’s graded adjustments based on brain wave synchronization with Coleman’s feature extraction and proportional feedback mechanisms. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combined Soulet, Grossman, and Molnar in view of Coleman to have the optimization module control the output devices in a graded proportional manner. This has a benefit of improved system adaptability, real-time responsiveness, and user-specific optimization, which are desirable in systems designed for personalized output control to improve system accuracy and efficacy.
Response to Arguments
Objections
Applicant's arguments filed 12/29/2025, page 6, regarding the previous Objections of claims 1 and 19 have been fully considered and are persuasive. The previous Objections have been withdrawn.
35 U.S.C. §103
Applicant's arguments filed 12/29/2025, pages 6-11, regarding the previous 103 Rejections of claims 1 and 19 (and their dependents) have been fully considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. That is, there are new grounds of rejection. Specific responses to Applicant’s arguments are found below.
Applicant’s Argument:Applicant argues that for claims 1 and 19 the applied prior art does not teach or suggest “wherein determining the optimal stimulation output comprises comparing the ratio between two or more brainwave oscillation frequency bands to a threshold” and “wherein controlling the operation of the one or more output devices comprises decreasing the optimal stimulation output when the ratio between two or more brainwave oscillation frequency bands exceeds the threshold.” Applicant asserts that the cited references do not disclose decreasing stimulation output based on such a ratio comparison.
Examiner’s Response:The argument is not persuasive. The rejection of claim 1 has been modified in view of the claim amendments and now further includes Molnar. Applicant’s argument is directed to the prior combination that did not include Molnar; however, as set forth in the modified rejection, Molnar expressly teaches the ratio-to-threshold comparison and threshold-based therapy control relied upon for the amended limitations. The combined teachings of Soulet, Grossman, and Molnar render the disputed limitations obvious.
Grossman teaches determining brain state using a ratio between EEG frequency bands. Specifically, Grossman teaches using a theta/alpha ratio of an EEG signal to identify sleep onset, where sleep onset is defined as the first epoch in which the theta/alpha ratio of the endogenous signal is greater than or equal to one (Grossman, ¶[0456]; see also ¶[0006]; ¶[0007]; ¶[0450]). Thus, Grossman teaches determining a condition based on evaluating a ratio between EEG frequency bands against a numeric criterion, which corresponds to comparing the ratio to a threshold.
Molnar teaches evaluating EEG frequency-domain characteristics including ratios of power levels between frequency bands and comparing those characteristics to a stored value to determine a detected condition. In particular, Molnar teaches that a frequency characteristic of a signal may include “a ratio of power levels within two or more frequency bands” and that such a ratio or other frequency characteristic may be compared to a stored value to determine whether a biosignal is detected (Molnar, ¶[0208]). Molnar further teaches that when the monitored signal satisfies a threshold condition, a processor implements control of a therapy device or therapy program (Molnar, ¶[0227]). Molnar also teaches that therapy parameters may be modified based on the detected patient state, including that therapy program indications may specify that stimulation amplitude should be increased or decreased when a particular patient state is detected (Molnar, ¶[0139]). Molnar further indicates that therapy delivery may include external cues such as auditory or visual cues whose parameters may be adjusted, including decreasing the volume of an auditory cue (Molnar, ¶[0096]).
Thus, Molnar teaches evaluating EEG frequency-band characteristics including ratios between frequency bands, comparing those characteristics to a stored value or threshold to determine a detected condition, and modifying therapy parameters including decreasing a stimulation parameter in response to the detected condition. A person of ordinary skill in the art would have found it obvious to apply Molnar’s threshold-based therapy control to the EEG frequency-band ratio determination of Grossman within the stimulation framework of Soulet in order to automatically adjust stimulation intensity based on detected brain state.
Accordingly, the combined teachings of Soulet, Grossman, and Molnar render obvious controlling the operation of the output devices by decreasing stimulation output when the ratio between brainwave oscillation frequency bands exceeds the threshold.
Applicant's Argument:Applicant argues claim 15 is not obvious because claim 1 is not obvious over Soulet in view of Grossman, and further argues Coleman does not cure the alleged deficiencies regarding comparing a ratio between two or more brainwave oscillation frequency bands to a threshold and decreasing the optimal stimulation output when the ratio exceeds the threshold.
Examiner's Response:The argument is not persuasive for at least the reasons provided above with respect to amended claim 1 and claim 19. Applicant's position regarding claim 15 relies on the same premise that the cited art fails to teach the ratio-to-threshold limitation and the stimulation control based on that comparison. Accordingly, because claim 15 depends from claim 1, and because the rejection of claim 1 is maintained, the rejection of claim 15 under 35 U.S.C. 103 is maintained.
Conclusion
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/AARON MERRIAM/Examiner, Art Unit 3791
/MATTHEW KREMER/Primary Examiner, Art Unit 3791
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