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 .
The amendment filed 01/13/2026 has been entered. Claims 1, 4-10, 12, 16, 28-30, 37-38, 40, 44-46, and 71-82 remain pending in the application.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 76, 79, 81, and 82 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 76 recites the limitation “wherein the therapy parameter comprises one or more of: a pressure setpoint, a ramp setting, a pressure range, a maximum pressure, a minimum pressure, a temperature, a flow rate”. While there is support for modification of temperature or flow rate in para [0101] of applicant’s specification, there is not support for the other therapy parameters listed in the claim. Para [0102] discloses “the adjustment of one or more settings of the respiratory system 120 (e.g., increasing the pressure) responsive to determining that the user 210 is experiencing or has experienced an event (step 703)”. An increase in pressure is not one of the parameters disclosed by the limitation.
Claim 79 recites the limitation “machine learning model”. Applicant’s disclosure does not provide support for the machine learning model. Para [0103] only states “Such monitoring can be to learn, for example, via a machine learning model”, but does not further describe a structure or algorithm for the model.
Claim 81 recites the limitation “machine learning model”. Applicant’s disclosure does not provide support for the machine learning model. Para [0103] only states “Such monitoring can be to learn, for example, via a machine learning model”, but does not further describe a structure or algorithm for the model.
Claim 82 recites the limitation “machine learning model”. Applicant’s disclosure does not provide support for the machine learning model. Para [0103] only states “Such monitoring can be to learn, for example, via a machine learning model”, but does not further describe a structure or algorithm for the model.
Claims 1, 4-10, 12, 16, 28-30, 37-38, 40, 44-46, and 71-82 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 and 38 recite the limitation “and activated by at least partially inflation thereof”. It is unclear if "activated by at least partially inflation" refers to the bladder being pre-inflated as a baseline or if the activation of the bladder is defined by inflating it to some degree.
Claims 1 and 38 further recite the limitation “determine, based at least in part on the generated data, that the user is sleeping; responsive to determining that the user is sleeping, activate the multi- compartment bladder”. It is unclear what “activating” the bladder pertains to. While applicant’s specification provides support for detecting that the user is sleeping in paragraph 92, stating “For example, step 702 can include analyzing the generated data from step 701 to determine that the user is sleeping (including, for example, a sleep state of the user) or that the user is awake.”, the specification does not further state how the bladder is then activated. Per paragraph [0093], the method includes “determining, based on the data obtained or generated during step 701 and/or the analysis during step 702, that the user is experiencing or has experienced an event” and “If the user is experiencing or has experienced an event, the method 700 proceeds to step 704.”. Per figure 7, the bladder is modified in step 705, which can be considered activation. Paragraph [0108] states “Alternatively, step 805 can be performed automatically at a predetermined time subsequent to determining that the user 210 is asleep”. Per this paragraph, the system may cause the bladder to be filled according a second fill scheme (Fig 8), which suggests that the bladder has already been activated and is thus not activated in response to the user being asleep. As the device would need to be activated in order to detect a sleep state, and inflation of the bladder is not activated until an event is detected, it is unclear in what manner the bladder is “activated” in response to a user sleeping. While the amendment to claims 1 and 38 stated “and activated by at least partially inflation thereof”, this language is unclear and does not further clarify the limitation.
Claims 4-10, 12, 16, 28-30, 37, 40, 44-46, and 71-82 are rejected due to dependency on claims 1 and 38.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1, 4-10, 12, 16, 28-30, 37-38, 40, 44-46, 75-77 and 80 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hariri (US 20190350747 A1) in view of Fu et al. (CN 108506198 A).
Regarding claim 1, Hariri discloses a system comprising: one or more sensors configured to generate data associated with a sleep session of a user ( [0165]: “sensors that sense biological or bio-signal data (pulse, brainwaves) may be used to determine that the user is sleeping”); a multi-compartment bladder configured to be positioned adjacent to the user during the sleep session and activated by at least partially inflation thereof ([0007]: “an inflatable bladder assembly configured to inflate and deflate to move a head of a user;”, Fig 4A elements 420a); a memory storing machine-readable instructions ([0174]: “a data storage device (including volatile memory or non-volatile memory or other data storage elements or a combination thereof)”); and a control system including one or more processors configured to execute the machine- readable instructions to ([0174]: “controller 130…at least one processor”): determine, based at least in part on the generated data, that the user is sleeping ([0165]: “sensors that sense biological or bio-signal data (pulse, brainwaves) may be used to determine that the user is sleeping (e.g. sleep state) or in a deep sleep state”); responsive to determining that the user is sleeping, activate the multi-compartment bladder ([0165]: “components may be in “sleep” mode until the apparatus determines the user is sleeping”); determine, based at least in part on the generated data, that the user is experiencing or has experienced an event (Fig 16 elements 701-705, wherein the event is snoring).
Hariri fails to disclose responsive to the multi-compartment bladder having been activated and the determination that the user is experiencing or has experienced the event, selectively cause the multi-compartment bladder to be modified to aid in causing the user to move if the user is in a first sleep state and likely to stay asleep during modification of the multi-compartment bladder or to inhibit modification of the multi-compartment bladder if the user is in a second sleep state and likely to awaken as a result of the modification.
Fu discloses responsive to the multi-compartment bladder having been activated and the determination that the user is experiencing or has experienced the event ([0046]: “The depth of sleep can be determined by detecting human electrical signals through human body coupling sensors installed on the surface of the adjustable mattress”), selectively cause the multi-compartment bladder to be modified to aid in causing the user to move if the user is in a first sleep state and likely to stay asleep during modification of the multi-compartment bladder ([0046]: “The controller 8 sets the inflation speed based on the user's sleep depth… if the human body is in a deep sleep state, the controller 8 can set a faster inflation speed… since the user is in a deep sleep, it will not disturb the user.”) or to inhibit modification of the multi-compartment bladder if the user is in a second sleep state and likely to awaken as a result of the modification ([0046]: “When the human body is in light sleep, the controller 8 can set a lower inflation speed… In this way, the noise generated by the vibration of the air pump chamber or the collision of the valve opening and closing during the inflation process will be smaller and will not disturb the user”).
It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to modify the system disclosed by Hariri to include selectively causing the multi-compartment bladder to be modified to aid in causing the user to move if the user is in a first sleep state and likely to stay asleep during modification of the multi-compartment bladder or inhibiting modification of the multi-compartment bladder if the user is in a second sleep state and likely to awaken as a result of the modification as disclosed by Fu in order prevent disruption of sleep (Fu [0047]).
Regarding claim 4, Hariri further discloses wherein the multi-compartment bladder has a U-shape and includes at least three separate and distinct compartments (Fig 4C, wherein the compartments are curved in an upwards U-shape).
Regarding claim 5, Hariri further discloses wherein the at least three separate and distinct compartments are fluidly coupled in series (Fig 4A).
Regarding claim 6, Hariri further discloses wherein the at least three separate and distinct compartments are independent and configured to be separately inflated.
Regarding claim 7, Hariri further discloses wherein the multi-compartment bladder is caused to be modified such that a head of the user, when positioned within the generally U-shaped multi-compartment bladder, is moved in a generally circular fashion ([0223]: “causing the snorer's head that is located on the pillow or the bladder assembly to gently move up or in any other direction depending on the position of the head during a predefined period of time.”).
Regarding claim 8, Fu further wherein the one or more sensors include a pressure sensor configured to measure a pressure within at least one of the compartments of the multi-compartment bladder. ([0045]: “the air pressure measurement unit can be an air pressure sensor”).
Regarding claim 9, Hariri further discloses wherein the control system is further configured to execute the machine-readable instructions to determine, based at least in part on data from the pressure sensor, a location of a head of the user relative to the multi-compartment bladder ([0028]: “identify a location of a snorer by analyzing the sound waves from the plurality of sources.”).
Regarding claim 10, Hariri further discloses wherein the one or more sensors include a location-based sensor coupled to each of the compartments of the multi-compartment bladder and the control system is further configured to execute the machine-readable instructions to determine, based at least in part on an output of the location-based sensors, a location of a head of the user relative to the multi-compartment bladder ([0255]: “For example, a location of a source of a snoring sound may be determined”).
Regarding claim 12, Hariri discloses wherein the location based-sensor includes one or more strain gauges, one or more potentiometers, a microphone, a speaker, an RF transmitter, an RF receiver, or any combination thereof ([0255]: “microphone”).
Regarding claim 16, Hariri discloses wherein at least one of the one or more sensors is coupled to a pillow and the multi-compartment bladder is coupled to or integrated within the pillow (Fig 1 element 160).
Regarding claim 28, Hariri further discloses wherein the modifying includes causing the multi-compartment bladder to be modified according to a first inflation scheme ([0137]: “one round of inflation and deflation may have a pre-determined pattern of inflation and deflation. A pattern of inflation and deflation may be, for example, 20 seconds of inflation followed by 10 seconds of deflation. Another pattern of inflation and deflation may be, for example, 10 seconds of inflation, 10 seconds of deflation, and 15 seconds of inflation.”).
Regarding claim 29, Hariri further discloses wherein the control system is further configured to execute the machine-readable instructions to, (i) subsequent to the multi-compartment bladder being modified according to the first inflation scheme ([1037]), continue to analyze the generated data to determine if the user is still experiencing the event and (ii) responsive to the continued analysis of the generated data resulting in a determination that the user is still experiencing the event (Fig 16 and 17, [0136]: “io processor 150 may transmit control commands to controller 130 to trigger cyclical actuation of air inflator 140 upon detection of the trigger event. An example trigger event may be detection of a snoring sound or another trigger event, such as a prediction of a snoring sound for a user, a predefined cycle or time based trigger event (e.g. every hour, at certain times).”, wherein the monitoring is updated periodically to activate again) cause the multi-compartment bladder to be modified according to a second inflation scheme ([0159]: “An inflation pattern may be linked to different trigger events such that different inflation patterns may be used depending on the detected trigger event.”).
Regarding claim 30, Hariri further discloses wherein the control system is further configured to execute the machine-readable instructions to, (i) subsequent to the multi-compartment bladder being modified according to the first inflation scheme, continue to analyze the generated data and (ii) based at least in part on the continued analysis, modify one or more aspects of the first inflation scheme event (Fig 16 and 17, [0136]: “io processor 150 may transmit control commands to controller 130 to trigger cyclical actuation of air inflator 140 upon detection of the trigger event. An example trigger event may be detection of a snoring sound or another trigger event, such as a prediction of a snoring sound for a user, a predefined cycle or time based trigger event (e.g. every hour, at certain times).”, wherein the monitoring is updated periodically to activate again).
Regarding claim 37, Hariri further discloses wherein the event includes an apnea, a hypopnea, a restless leg, a sleeping disorder, choking, an increased heart rate, labored breathing, an asthma attack, an epileptic episode, a seizure, or any combination thereof and does not include snoring (claim 4: “apnea”).
Regarding claim 38, Hariri discloses a method comprising: receiving, from one or more sensors, data associated with a sleep session of a user ([0165]: “, sensors that sense biological or bio-signal data (pulse, brainwaves) may be used to determine that the user is sleeping”); a multi-compartment bladder configured to be positioned adjacent to the user during the sleep session and activated by at least partially inflation thereof ([0007]: “: an inflatable bladder assembly configured to inflate and deflate to move a head of a user;”, Fig 4A); determining, based at least in part on the data, that the user is sleeping ([0165]: “user is sleeping”); responsive to determining that the user is sleeping, activate the multi-compartment bladder by at least partially inflation thereof, the multi-compartment bladder being positioned adjacent to the user ([0165]: “Some components may be in “sleep” mode until the apparatus determines the user is sleeping”, [0007]: “inflate and deflate to move a head of a user”); determine, based at least in part on the generated data, that the user is experiencing or has experienced an event (Fig 16 elements 701-705).
Hariri fails to disclose responsive to the multi-compartment bladder having been activated and the determination that the user is experiencing or has experienced the event, selectively cause the multi-compartment bladder to be modified to aid in causing the user to move if the user is in a first sleep state and likely to stay asleep during modification of the multi-compartment bladder or to inhibit modification of the multi-compartment bladder if the user is in a second sleep state and likely to awaken as a result of the modification.
Fu discloses responsive to the multi-compartment bladder having been activated and the determination that the user is experiencing or has experienced the event ([0046]: “The depth of sleep can be determined by detecting human electrical signals through human body coupling sensors installed on the surface of the adjustable mattress”), selectively cause the multi-compartment bladder to be modified to aid in causing the user to move if the user is in a first sleep state and likely to stay asleep during modification of the multi-compartment bladder ([0046]: “The controller 8 sets the inflation speed based on the user's sleep depth… if the human body is in a deep sleep state, the controller 8 can set a faster inflation speed… since the user is in a deep sleep, it will not disturb the user.”) or to inhibit modification of the multi-compartment bladder if the user is in a second sleep state and likely to awaken as a result of the modification ([0046]: “When the human body is in light sleep, the controller 8 can set a lower inflation speed… In this way, the noise generated by the vibration of the air pump chamber or the collision of the valve opening and closing during the inflation process will be smaller and will not disturb the user”).
It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to modify the system disclosed by Hariri to include selectively causing the multi-compartment bladder to be modified to aid in causing the user to move if the user is in a first sleep state and likely to stay asleep during modification of the multi-compartment bladder or inhibiting modification of the multi-compartment bladder if the user is in a second sleep state and likely to awaken as a result of the modification as disclosed by Fu in order prevent disruption of sleep (Fu [0047]).
Regarding claim 40, Hariri further discloses wherein the multi-compartment bladder is caused to be modified such that a head of the user, when positioned within the generally U-shaped multi-compartment bladder, is moved in a generally circular fashion ([0223]: “causing the snorer's head that is located on the pillow or the bladder assembly to gently move up or in any other direction depending on the position of the head during a predefined period of time.”).
Regarding claim 44, Hariri further discloses wherein the modifying includes causing the multi-compartment bladder to be modified according to a first inflation scheme ([0137]: “one round of inflation and deflation may have a pre-determined pattern of inflation and deflation. A pattern of inflation and deflation may be, for example, 20 seconds of inflation followed by 10 seconds of deflation. Another pattern of inflation and deflation may be, for example, 10 seconds of inflation, 10 seconds of deflation, and 15 seconds of inflation.”).
Regarding claim 29, Hariri further discloses wherein the control system is further configured to execute the machine-readable instructions to, (i) subsequent to the multi-compartment bladder being modified according to the first inflation scheme ([1037]), continue to analyze the generated data to determine if the user is still experiencing the event and (ii) responsive to the continued analysis of the generated data resulting in a determination that the user is still experiencing the event (Fig 16 and 17, [0136]: “io processor 150 may transmit control commands to controller 130 to trigger cyclical actuation of air inflator 140 upon detection of the trigger event. An example trigger event may be detection of a snoring sound or another trigger event, such as a prediction of a snoring sound for a user, a predefined cycle or time based trigger event (e.g. every hour, at certain times).”, wherein the monitoring is updated periodically to activate again) cause the multi-compartment bladder to be modified according to a second inflation scheme ([0159]: “An inflation pattern may be linked to different trigger events such that different inflation patterns may be used depending on the detected trigger event.”).
Regarding claim 30, Hariri further discloses wherein the control system is further configured to execute the machine-readable instructions to, (i) subsequent to the multi-compartment bladder being modified according to the first inflation scheme, continue to analyze the generated data and (ii) based at least in part on the continued analysis, modify one or more aspects of the first inflation scheme event (Fig 16 and 17, [0136]: “io processor 150 may transmit control commands to controller 130 to trigger cyclical actuation of air inflator 140 upon detection of the trigger event. An example trigger event may be detection of a snoring sound or another trigger event, such as a prediction of a snoring sound for a user, a predefined cycle or time based trigger event (e.g. every hour, at certain times).”, wherein the monitoring is updated periodically to activate again).
Regarding claim 37, Hariri further discloses wherein the event includes an apnea, a hypopnea, a restless leg, a sleeping disorder, choking, an increased heart rate, labored breathing, an asthma attack, an epileptic episode, a seizure, or any combination thereof and does not include snoring (claim 4: “apnea”).
Regarding claim 44, Hariri further discloses wherein the modifying includes causing the multi-compartment bladder to be modified according to a first inflation scheme ([0137]: “one round of inflation and deflation may have a pre-determined pattern of inflation and deflation. A pattern of inflation and deflation may be, for example, 20 seconds of inflation followed by 10 seconds of deflation. Another pattern of inflation and deflation may be, for example, 10 seconds of inflation, 10 seconds of deflation, and 15 seconds of inflation.”).
Regarding claim 45, Hariri further discloses analyzing the generated data subsequent to the causing the multi-compartment bladder to be modified ([0161]: “may be configured to set a pre-determined number of cycles to complete prior to detection of additional trigger events.”, wherein detection of trigger events occurs continuously) ; and modifying one or more aspects of the first inflation scheme based at least in part on the analyzing ([0159]: “The settings may be controlled or adjusted by the user or automatically by controller 130.”).
Regarding claim 46, Hariri further discloses wherein the control system is further configured to execute the machine-readable instructions to, (i) subsequent to the multi-compartment bladder being modified according to the first inflation scheme ([1037]), continue to analyze the generated data to determine if the user is still experiencing the event and (ii) responsive to the continued analysis of the generated data resulting in a determination that the user is still experiencing the event (Fig 16 and 17, [0136]: “io processor 150 may transmit control commands to controller 130 to trigger cyclical actuation of air inflator 140 upon detection of the trigger event. An example trigger event may be detection of a snoring sound or another trigger event, such as a prediction of a snoring sound for a user, a predefined cycle or time based trigger event (e.g. every hour, at certain times).”, wherein the monitoring is updated periodically to activate again) cause the multi-compartment bladder to be modified according to a second inflation scheme that is different than the initial fill scheme ([0159]: “An inflation pattern may be linked to different trigger events such that different inflation patterns may be used depending on the detected trigger event.”).
Regarding claim 71, Hariri discloses wherein the data associated with the sleep session of the user includes respiration data indicative of respiration of the user, and wherein the determination that the user is sleeping is based at least in part on the respiration data ([0267]: “a detection of the snoring sound from the sound waves;”, wherein snoring is respiration data”).
Regarding claim 75, Hariri discloses responsive to determining that the user is experiencing or has experienced the event and after causing the multi-compartment bladder to be modified according to a first inflation scheme, determine that the user is still experiencing the event (Fig 16 and 17, [0136]: “io processor 150 may transmit control commands to controller 130 to trigger cyclical actuation of air inflator 140 upon detection of the trigger event. An example trigger event may be detection of a snoring sound or another trigger event, such as a prediction of a snoring sound for a user, a predefined cycle or time based trigger event (e.g. every hour, at certain times).”, wherein the monitoring is updated periodically to activate again); and responsive thereto, adjust a therapy parameter of a respiratory device that is configured to supply pressurized air to the user (Fig 7 element 704, [0203]: “air pressure via conduit 518 a, 518 b”, wherein pressurized air is supplied to the user and wherein the system is a respiratory device as it modifies respiratory parameters of the patient (apnea, snoring, etc.))
Regarding claim 76, Hariri discloses wherein the therapy parameter comprises one or more of: a pressure setpoint, a ramp setting, a pressure range, a maximum pressure, a minimum pressure, a temperature, a flow rate ([0163]: “maximum size based on pressure control. The pressure control may be pre-determined by user setting or as a default setting”).
Regarding claim 77, Hariri discloses wherein the control system is further configured to: classify the event as one of a snore, an apnea, or a hypopnea based at least in part on the generated data (claim 4, note that classify is being interpreted as registering an event as an apnea, as the specification does not support the machine learning definition of classification); and select an inflation or deflation scheme of the multi-compartment bladder based at least in part on the classification ([0159]: “An inflation pattern may be linked to different trigger events such that different inflation patterns may be used depending on the detected trigger event”).
Regarding claim 80, Hariri further discloses further comprising determining a type of the event based at least in part on the generated data and selecting (Fig 7), based at least in part on the type, an inflation or deflation scheme of the multi-compartment bladder ([0159]: “An inflation pattern may be linked to different trigger events such that different inflation patterns may be used depending on the detected trigger event”).
Claim(s) 71-74 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hariri in view of Fu in further view of Kremer et al. (US 20190030278 A1), hereinafter Kremer.
Regarding claim 71, Hariri as modified by Fu discloses the system of claim 1, but fails to disclose the data associated with the sleep session of the user includes respiration data indicative of respiration of the user, and wherein the determination that the user is sleeping is based at least in part on the respiration data.
Kremer discloses a system wherein data associated with the sleep session of the user includes respiration data indicative of respiration of the user ([0005]: “monitor the user's respiration rate or breathing architecture”), and wherein the determination that the user is sleeping is based at least in part on the respiration data ([0017]: “the processor is configured to determine that the user's respiration rate meets a sleep criterion based upon a variance of the user's respiration rate deviating from the target respiration rate by a threshold.”).
It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to substitute the known method of sleep detection via pulse/brainwaves disclosed by Hong as modified by Hariri to the known method of sleep detection via respiration data as disclosed by Kremer for the predictable result of determining when a user has fallen asleep.
Regarding claim 72, Kremer further discloses wherein the one or more processors are further configured to analyze the respiration data to determine a respiration rate of the user, a respiration rate variability of the user, an inspiration amplitude of the user, an expiration amplitude of the user, an inspiration-expiration ratio of the user, or any combination thereof ([0005]: “monitor the user's respiration rate or breathing architecture”), and wherein the determination that the user is sleeping is based at least in part on anyone or more of the respiration rate of the user, the respiration rate variability of the user, the inspiration amplitude of the user, the expiration amplitude of the user, and the inspiration-expiration ratio of the user ([0017]: “the processor is configured to determine that the user's respiration rate meets a sleep criterion based upon a variance of the user's respiration rate deviating from the target respiration rate by a threshold.”).
Regarding claim 73, Hariri as modified by Fu discloses the method of claim 38, but fails to disclose the data associated with the sleep session of the user includes respiration data indicative of respiration of the user, and wherein the determination that the user is sleeping is based at least in part on the respiration data.
Kremer discloses a method wherein data associated with the sleep session of the user includes respiration data indicative of respiration of the user ([0005]: “monitor the user's respiration rate or breathing architecture”), and wherein the determination that the user is sleeping is based at least in part on the respiration data ([0017]: “the processor is configured to determine that the user's respiration rate meets a sleep criterion based upon a variance of the user's respiration rate deviating from the target respiration rate by a threshold.”).
It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to substitute the known method of sleep detection via pulse/brainwaves disclosed by Hong as modified by Hariri to the known method of sleep detection via respiration data as disclosed by Kremer for the predictable result of determining when a user has fallen asleep.
Regarding claim 74, Kremer further discloses wherein the one or more processors are further configured to analyze the respiration data to determine a respiration rate of the user, a respiration rate variability of the user, an inspiration amplitude of the user, an expiration amplitude of the user, an inspiration-expiration ratio of the user, or any combination thereof ([0005]: “monitor the user's respiration rate or breathing architecture”), and wherein the determination that the user is sleeping is based at least in part on anyone or more of the respiration rate of the user, the respiration rate variability of the user, the inspiration amplitude of the user, the expiration amplitude of the user, and the inspiration-expiration ratio of the user ([0017]: “the processor is configured to determine that the user's respiration rate meets a sleep criterion based upon a variance of the user's respiration rate deviating from the target respiration rate by a threshold.”).
Claim(s) 78-82 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hariri in view of Fu in further view of Main et al. (US 20130283530 A1).
Regarding claim 78, Hariri as modified by Fu discloses the system of claim 1, but fails to disclose wherein the selectively causing the multi-compartment bladder to be modified to aid in causing the user to move if the user is in the first sleep state and likely to stay asleep during modification of the multi-compartment bladder or to inhibit modification of the multi-compartment bladder if the user is in the second sleep state and likely to awaken as a result of the modification is based at least in part on a learned association between actuation of the multi-compartment bladder and effects for awakening in different sleep states caused by the multi-compartment bladder.
Main discloses selectively causing the multi-compartment bladder to be modified to aid in causing the user to move if the user is in the first sleep state and likely to stay asleep during modification of the multi-compartment bladder or to inhibit modification of the multi-compartment bladder if the user is in the second sleep state and likely to awaken ([0089-0090]: “The sleep state process (7) uses pressure data and data from the machine vision process (5) and the machine learning process (6) to assess the state of a person's sleep… In the "awaken" state, the support and comfort attributes can be adjusted such that sleep is inhibited”) as a result of the modification is based at least in part on a learned association between actuation of the multi-compartment bladder and effects for awakening in different sleep states caused by the multi-compartment bladder ([0086-0088]: “The machine learning process (6) uses the pressure data to detect changes in pressure that indicate movement or restlessness… When the machine learning process (6) detects a restless state then this information is passed to the comfort and support engine (9) where the bedding system comfort and support attributes are adjusted to help induce a deeper, more restful, sleep… The machine learning process (6) uses this feedback information to assess the success of support and comfort attribute adjustments that were made throughout the night” ).
As Hariri discloses a machine learning model ([0262]) it would have been obvious to a person of ordinary skill in the art prior to the effective filing date to modify the system disclosed by Hariri as modified by Fu to base the modification at least in part on a learned association between actuation of the multi-compartment bladder and effects for awakening in different sleep states caused by the multi-compartment bladder as disclosed by Main in order to improve the accuracy of sleep state detection (Main [0104]).
Regarding claim 79, Main further discloses wherein a machine learning model is configured to monitor the learned association between actuation of the multi-compartment bladder and effects for awakening in different sleep states caused by the multi-compartment bladder ([0086-0088]).
Regarding claim 81, Hariri as modified by Fu discloses the method of claim 38, but fails to disclose wherein selectively causing the multi-compartment bladder to be modified to aid in causing the user to move if the user is in the first sleep state and likely to stay asleep during modification of the multi-compartment bladder or to inhibit modification of the multi-compartment bladder if the user is in the second sleep state and likely to awaken as a result of the modification is based at least in part on a learned association between actuation of the multi-compartment bladder and effects for awakening in different sleep states caused by the multi-compartment bladder.
Main discloses selectively causing the multi-compartment bladder to be modified to aid in causing the user to move if the user is in the first sleep state and likely to stay asleep during modification of the multi-compartment bladder or to inhibit modification of the multi-compartment bladder if the user is in the second sleep state and likely to awaken ([0089-0090]: “The sleep state process (7) uses pressure data and data from the machine vision process (5) and the machine learning process (6) to assess the state of a person's sleep… In the "awaken" state, the support and comfort attributes can be adjusted such that sleep is inhibited, for example, the adjustable mattress can be made extra firm”) as a result of the modification is based at least in part on a learned association between actuation of the multi-compartment bladder and effects for awakening in different sleep states caused by the multi-compartment bladder([0086-0088]: “The machine learning process (6) uses the pressure data to detect changes in pressure that indicate movement or restlessness… When the machine learning process (6) detects a restless state then this information is passed to the comfort and support engine (9) where the bedding system comfort and support attributes are adjusted to help induce a deeper, more restful, sleep… The machine learning process (6) uses this feedback information to assess the success of support and comfort attribute adjustments that were made throughout the night” ).
As Hariri discloses a machine learning model ([0262]) it would have been obvious to a person of ordinary skill in the art prior to the effective filing date to modify the system disclosed by Hariri as modified by Fu to base the modification at least in part on a learned association between actuation of the multi-compartment bladder and effects for awakening in different sleep states caused by the multi-compartment bladder as disclosed by Main in order to improve the accuracy of sleep state detection (Main [0104]).
Regarding claim 82, Main further discloses wherein a machine learning model is configured to monitor the learned association between actuation of the multi-compartment bladder and effects for awakening in different sleep states caused by the multi-compartment bladder ([0086-0088]).
Response to Arguments
Applicant’s arguments, see Remarks, filed 01/13/2026, with respect to the rejection of claims 1, 4-10, 12, 16, 28-30, 37, 38, 40, and 44-46 under 35 U.S.C. 112 (b) have been fully considered and are not persuasive. The amendment to the claim is unclear (see rejection above) and therefore does not provide clarity for “activation of a bladder”.
Applicant’s arguments, see Applicant’s Remarks, filed 01/13/2026, with respect to the rejection of claims 1, 4-10, 12, 16, 28-30, 37, 38, 40, and 44-46 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of 35 U.S.C. 103 over Hariri in view of Fu (see above).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yu et al. (US 20200405526 A1) – smart mattress system, discloses multiple inflatable bladders
Halperin et al. (US 9681838 B2) – discloses adjusting a therapy parameter of a respiratory device on detection of an event
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/KAVYA SHOBANA BALAJI/Examiner, Art Unit 3791
/DANIEL L CERIONI/Primary Examiner, Art Unit 3791