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 .
Response to Amendment
This office action is responsive to the preliminary amendment filed on March 15, 2023. As directed by the amendment: claims 6, 8, 10, 15, 17, 22, 24, 27, 28, 33, 34, 39, 41-46, 51, and 56 have been amended, claims 3-5, 7, 9, 11-14, 18-21, 23, 25, 26, 29-32, 35-38, 40, 47-50, 53-55, 57-60, and 62-125 have been canceled, and no new claims have been added. Thus, claims 3-5, 7, 9, 11-14, 18-21, 23, 25, 26, 29-32, 35-38, 40, 47-50, 53-55, 57-60, and 62-125 are presently pending in the application.
Claim Rejections - 35 USC § 102
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 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 2, 6, 22, 27, 34, 39, 42-46, 56, and 61 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Schindhelm (US 2016/0135734).
As to claim 1, Schindhelm discloses a system (Fig. 1A) for monitoring a sleep session of a user (paragraph [0069]), the system comprising:
a respiratory therapy system (including patient interface 3000 and respiratory therapy device 4000) including:
a respiratory therapy device (RPT device 4000, Fig. 1A, Fig. 4A) configured to supply pressurized air (paragraph [0046]); and
a user interface (patient interface 3000, Fig. 1A, Fig. 3) coupled to the respiratory therapy device 4000 via a conduit (air circuit 4170), the user interface 3000 being configured to engage the user and aid in directing the supplied pressurized air to the airway of the user (see Fig. 1A, paragraph [0046]);
a memory device 4260 (Fig. 4C, Fig. 7) storing machine-readable instructions 726 (Fig. 7, paragraph [0157]: instruction sets 726; paragraph [0110]: one or more algorithms stored in memory 4260); and
a control system 4230 (central controller 4230, Fig. 4C) coupled to the memory device 4260, the control system 4230 including one or more processors configured to execute the machine-readable instructions (paragraph [0078]) to:
generate data 728, 738 (Fig. 7, paragraph [0156]), during a current sleep session, associated with the user (see step 802, Fig. 8, paragraph [0169]);
analyze the generated data 728, 738 to determine a value of a first metric that is associated with a sleep disordered breathing (SDB) condition (at step 804, Fig. 8, paragraph [0169]: SDB detected or absence confirmed and smooth hemodynamic baseline of one or more measured parameters is determined);
analyze the generated data 728, 738 to determine a value of a second metric that is associated with a health condition other than the SDB condition (at steps 806-810, paragraphs [0169]-[0170]: cardiac-related data is measured and analyzed to evaluate the patient's cardiac condition, such as whether one or more parameters has changed indicative of a response to treatment; paragraph [0162]:Such a report may show hemodynamic parameters (and/or their changes over time such as by calculating/determining trends in the data) using only data of such parameters that do not coincide in time with detected SDB events or untreated SDB events. Thus, the processor may disregard changes in hemodynamic parameters that are correlated (e.g., time or time period related) with detected SDB events and focus on an analysis of hemodynamic parameters that are isolated from (e.g., substantially out of time or time period synchronization with) SDB events); and
based at least in part on the determined value of the second metric, cause an action to be performed (step 812, 814, paragraph [0170]: a patient report is generated and cardiac treatment is adjusted).
As to claim 2, Schindhelm discloses the system of claim 1, wherein the value of the second metric is indicative of a presence of the health condition, a severity of the health condition, or both (paragraph [0170]: at step 810, may determine whether the patient has experienced a change or not in one or more measured parameters that may be indicative of a response to the cardiac treatment. For example, an analysis of measured parameters may detect changes in any acute or chronic cardiac conditions over some predetermined period of time).
As to claim 6, Schindhelm discloses the system of claim 1, wherein at least a first portion of the data is generated by one or more sensors 4270 (transducers 4270 within pneumatic block 4020, see Figs. 4B-4C) positioned within a housing 4010 of the respiratory therapy device 4000 (paragraph [0075]: transducers 4270 include pressure sensor(s) 4272, flow rate sensor(s) 4274; [0076]: pneumatic block 4020 is located within the external housing 4010; see also, [0088]-[0089]).
As to claim 22, Schindhelm discloses the system of claim 1, wherein the generated data includes data indicative of a pressure of the pressurized air, a flow of the pressurized air, or both, and wherein the action includes modifying a pressure of the pressurized air, modifying a flow rate of the pressurized air, modifying a ramp time of the pressurized air, modifying a humidity of the pressurized air, supplying a medicament into the airway of the user via the pressurized air, or any combination thereof (paragraph [0170]: titration of the cardiac treatment may involve an adjustment to a target ventilation of a respiratory pressure therapy, such as changing a tidal volume target or other volumetric target - this will inherently change the pressure/flow of the air delivered to the patient).
As to claim 27, Schindhelm discloses the system of claim 1, wherein the one or more processors of the control system are further configured to execute the machine-readable instructions to: track the second metric over a plurality of sleep sessions; determine the value of the second metric for each of the plurality of sleep sessions; determine (i) a duration of use of the respiratory therapy system for each of the plurality of sleep sessions, (ii) values of one or more settings of the respiratory therapy system for each of the plurality of sleep session, (iii) or both; and identify (i) an optimal duration of use of the respiratory therapy system, (ii) optimal values of the one or more settings of the respiratory therapy system, or (iii) both, to treat the health condition. (paragraph [0162]: changes to parameters over time are tracked, [0170]: an analysis of measured parameters may detect changes in any acute or chronic cardiac conditions over some predetermined period of time…Based on the patient's cardiac condition and/or the determined efficacy of the cardiac treatment, a determination may be made whether the patient's cardiac treatment is to be adjusted).
As to claim 34, Schindhelm discloses the system of claim 1, wherein the one or more processors of the control system are further configured to execute the machine-readable instructions to: determine a severity of the health condition based at least in part on the determined value of the second metric for the current sleep session; generate data during one or more subsequent sleep sessions; determine a value of the second metric for each of the one or more subsequent sleep sessions; determine an updated severity of the health condition based at least in part on the determined values of the second metric for each of the one or more subsequent sleep sessions; and based at least in part on the updated severity of the health condition, cause the action to be performed (paragraph [0162]: changes to parameters over time are tracked, [0170]: an analysis of measured parameters may detect changes in any acute or chronic cardiac conditions over some predetermined period of time).
As to claim 39, Schindhelm discloses the system of claim 1, wherein the one or more processors of the control system are further configured to execute the machine-readable instructions to: receive additional data associated with the user while awake (paragraph [0069]: to determine the efficacy of the prescribed cardiac treatment, the patient's cardiac condition may be monitored, such as by measuring the patient's heart rate, blood pressure, blood flow, ventricular contractions, or any other factors that may be affected by the cardiac condition and related treatment; the system may collect these cardiac-related measurements throughout the day, including while the patient is sleeping); analyze the additional data to determine a value of a third metric associated with the health condition (the cardiac-related measurements may then be analyzed by the system to determine if the prescribed cardiac treatment is performing effectively); and cause the action to be performed based at least in part on the determined value of the third metric associated with the health condition (paragraph [0170]: titration of the cardiac treatment is adjusted based on the determined efficacy of the treatment).
As to claim 42, The system of claim 39, wherein the value of the second metric is associated with a plurality of health conditions, and wherein the one or more processors of the control system are further configured to execute the machine-readable instructions to determine an identity of the health condition of the user from the plurality of health conditions based at least in part on the determined value of the third metric (paragraph: [0168] device may evaluate changes to predict cardiac events, such as acute decompensated heart failure).
As to claim 43, Schindhelm discloses the system of claim 39, wherein the value of the third metric is associated with a plurality of health conditions, and wherein the one or more processors of the control system are further configured to execute the machine-readable instructions to determine an identity of the health condition of the user from the plurality of health conditions based at least in part on the determined value of the second metric (paragraphs [0162],[0168]-[0170]: the second metric is the cardiac data not associated with SDB and the third metric is the determined efficacy of treatment (change in cardiac data over time), which is an indication of the progression or improvement of the condition ).
As to claim 44, Schindhelm discloses the system of claim 39, wherein the one or more processors of the control system are further configured to execute the machine- readable instructions to: determine an identity of the health condition of the user based at least in part on the determined value of the second metric; and determine a severity of the health condition of the user based at least in part on the determined value of the third metric (paragraphs [0162],[0168]-[0170]: the second metric is the cardiac data not associated with SDB and the third metric is the determined efficacy of treatment (change in cardiac data over time), which is an indication of the severity of the condition (whether the condition has improved or worsened with treatment)).
As to claim 45, Schindhelm discloses the system of claim 39, wherein the one or more processors of the control system are further configured to execute the machine- readable instructions to: determine an identity of the health condition of the user based at least in part on the determined value of the third metric; and determine a severity of the health condition of the user based at least in part on the determined value of the second metric (paragraphs [0162],[0168]-[0170]: the third metric is the cardiac data not associated with SDB and the second metric is the determined efficacy of treatment (change in cardiac data over time), which is an indication of the severity of the condition (whether the condition has improved or worsened with treatment)).
As to claim 46, Schindhelm discloses the system of claim 39, wherein the one or more processors of the control system are further configured to execute the machine-readable instructions to determine an identity of the health condition, a severity of the health condition, or both based at least in part on (i) the determined value of the second metric and (ii) additional data (paragraph [0168]: in acquiring or measuring the patient's pulmonary arterial pressure and/or any other of the sensor data available to it, implanted device 6000 or RPT device 4000 may evaluate changes to predict cardiac events, such as acute decompensated heart failure, or to evaluate cardiac condition; paragraph [0170]: for example, an analysis of measured parameters may detect changes in any acute or chronic cardiac conditions over some predetermined period of time, which is an indication of the severity of the condition (whether the condition has improved or worsened with treatment)).
As to claim 56, Schindhelm discloses the system of claim 1, wherein during operation of the respiratory therapy system, the respiratory therapy system is configured to increase a pressure of the pressurized air from an initial pressure to a working pressure over a first time period (the RPT device will inherently increase from 0 to a working pressure when treatment is initiated), and wherein the action includes (i) increasing a ramp-up time of the pressurized air supplied to the airway of the user, such that the pressure of the pressurized air increases from the initial pressure to the working pressure over a second time period that is greater than the first time period, (ii) modifying the working pressure to be a modified working pressure that is less than the working pressure, or (iii) both (i) and (ii). (paragraph [0170]: "In some cases, the titration of the cardiac treatment may involve an adjustment to a target ventilation of a respiratory pressure therapy, such as changing a tidal volume target or other volumetric target (e.g., minute ventilation target) where the ventilation target serves as a control in the delivery of Pressure Support (PS) ventilation therapy such as with an adaptive servo-ventilator of the system." By titrating the cardiac treatment via adjustment of a target ventilation, the working pressure will be decreased (or increased) depending on whether the cardiac condition has improved (or worsened)).
As to claim 61, Schindhelm discloses a method 800 (Fig. 8) of monitoring a sleep session of a user, the method comprising:
generating data 728, 738 (Fig. 7, paragraph [0156]), during a current sleep session, associated with the user (see step 802, Fig. 8, paragraph [0169]) of a respiratory therapy system (RPT device 4000, Fig. 1A, Fig. 4A, paragraph [0046]);
analyzing the generated data to determine a value of a first metric that is associated with a sleep disordered breathing (SDB) condition (at step 804, Fig. 8, paragraph [0169]: SDB detected or absence confirmed and smooth hemodynamic baseline of one or more measured parameters is determined);
analyzing the generated data to determine a value of a second metric that is associated with a health condition other than the SDB condition (at steps 806-810, paragraphs [0169]-[0170]: cardiac-related data is measured and analyzed to evaluate the patient's cardiac condition, such as whether one or more parameters has changed indicative of a response to treatment; paragraph [0162]:Such a report may show hemodynamic parameters (and/or their changes over time such as by calculating/determining trends in the data) using only data of such parameters that do not coincide in time with detected SDB events or untreated SDB events. Thus, the processor may disregard changes in hemodynamic parameters that are correlated (e.g., time or time period related) with detected SDB events and focus on an analysis of hemodynamic parameters that are isolated from (e.g., substantially out of time or time period synchronization with) SDB events); and
based at least in part on the determined value of the second metric, causing an action to be performed (step 812, 814, paragraph [0170]: a patient report is generated and cardiac treatment is adjusted).
Claims 1, 2, 6, 22, 27, 34, 39, 42-46, 56, and 61 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Lee et al. (US 2005/0076908).
As to claim 1, Lee discloses a system 1200 (Fig. 12) for monitoring a sleep session of a user, the system (Abstract) comprising:
a respiratory therapy system (including mask assembly 1254 and xPAP/external breathing therapy device 1220, Fig. 12) including:
a respiratory therapy device 1220 (xPAP/external breathing therapy device 1220, Fig. 12) configured to supply pressurized air (paragraph [0140]); and
a user interface (mask assembly 1254, Fig. 12) coupled to the respiratory therapy device 1220 via a conduit (tube system 1252, Fig. 12), the user interface 1254 being configured to engage the user and aid in directing the supplied pressurized air to the airway of the user (see Fig. 12, paragraph [0141]);
a memory device storing machine-readable instructions (paragraph [0044],[0061]); and
a control system (Fig. 1D) coupled to the memory device, the control system including one or more processors (processor of monitoring and diagnostic unit 167, Fig. 1D) configured to execute the machine-readable instructions (paragraphs [0061],[0070]) to:
generate data, during a current sleep session, associated with the user (data from sensors 170, 165, 166, paragraphs [0058],[0069],[0127]);
analyze the generated data to determine a value of a first metric that is associated with a sleep disordered breathing (SDB) condition (apnea/hypopnea index, paragraphs [0061],[0105]);
analyze the generated data to determine a value of a second metric that is associated with a health condition other than the SDB condition (e.g., limb movements measured via accelerometer or EMG used to detect sleep movement disorders such as PLMD, paragraph [0077],[0126],[0130]); and
based at least in part on the determined value of the second metric, cause an action to be performed (paragraph [0080]: the information is used to modify therapy for the sleep disorder via therapy control unit 175; paragraph [0129]-[0130]: therapy unit 175 may provide therapy, e.g., drug therapy for a movement disorder such as RLS, PLMD, paragraph [0132] lists examples of therapies that may be used to treat various types of sleep disorders, including non-SDB disorders).
As to claim 8, Lee discloses the system of claim 1, wherein the second metric is associated with breathing of the user during the current sleep session, and includes a cadence of the breathing, an amplitude of the breathing of the user, a time constant of expiration, a shape of expiration, a magnitude of expiration, a speed of expiration, a time constant of inspiration, a shape of inspiration, a magnitude of inspiration, a speed of inspiration, a tidal volume, a rate of the breathing, or any combination thereof (paragraph [0009]: Cheyne-Stokes respiration (CSR) is associated with rhythmic increases and decreases in tidal volume, when a patient's tidal volume falls below a threshold, a disordered breathing event is declared; paragraph [0090]: tidal volume is determined and monitored to detect disordered breathing events; paragraph [0113]: inspiration and expiration thresholds and/or tidal volume thresholds can be used to analyze the patient's respiration for disordered breathing episodes).
As to claim 10, Lee discloses the system of claim 8, wherein the second metric includes the cadence of the breathing and the health condition is obesity, chronic obstructive pulmonary disorder (COPD), pneumonia, asthma, Cheyne-Stokes respiration, heart failure, or any combination thereof (Cheyne-Stokes respiration determined by a rhythmic pattern of tidal volume changes, paragraph [0009], see also paragraph [0152]-[0153]: respiration waveform patterns detected for detecting changes in heart condition).
As to claim 15, Lee discloses the system of claim 1, wherein the health condition is insomnia, and wherein the second metric includes a total time in bed, a total sleep time, a total wake time, a sleep onset latency, a wake-after-sleep-onset parameter, a sleep efficiency, a fragmentation index, an amount of time to fall asleep, a consistency of breathing rate, a fall asleep time, a wake time, a rate of sleep disturbances, a number of movements, or any combination thereof (paragraph [0061]: the monitoring/diagnostic unit processor may determine an arousal index (arousals detected per unit time), which is equivalent to the claimed "rate of sleep disturbances" and is inherently associated with insomnia (see applicant's paragraph [0003]: "insomnia (characterized by, for example, difficult in initiating sleep, frequent or prolonged awakenings after initially falling asleep, and/or an early awakening with an inability to return to sleep); alternatively, Lee's system can detect limb movements associated with RLS or PLMD, which reads on "a number of movements", see Lee's paragraph [0077],[0126]-[0130]; since the claim only requires that the second metric is "associated with" insomnia and since movements during sleep are associated with insomnia, the claim limitation is met, even though Lee is specifically monitoring the limb movements for RLS/PMLD; it is noted that Lee also mentions that RLS/PMLD leads to insomnia or fragmented sleep at night in paragraph [0005]-[0006]).
As to claim 16, Lee discloses the system of claim 15, wherein in response to the second metric indicating a presence of insomnia, the action includes (i) adjusting one or more settings of the respiratory therapy system, (ii) transmitting a recommendation for future therapy to the user or to a third party, (iii) or both. (paragraph [0069]-[0070]: Automatic sleep detection facilitates calculation of various indices used to assess sleep quality such as number of arousals per sleep period, and/or other indices based on sleep period; arousal information may be used by a therapy control unit 175 within the implantable device 161 for initiating, terminating, or adjusting therapy).
As to claim 17, Lee discloses the system of claim 16, wherein the future therapy includes cognitive behavioral therapy, wherein adjusting one or more settings of the respiratory therapy system includes decreasing a pressure of the pressurized air supplied to the airway of the user, or both (paragraph [0070]: arousal feedback information may be used by an external respiration therapy device to provide closed-loop feedback control of the therapy using arousal information; paragraph [0087]: therapy may be terminated 360 following detection of arousal from sleep, which reads on decreasing a pressure of the pressurized air supplied to the airway of the user).
As to claim 28, Lee discloses the system of claim 1, wherein the one or more processors of the control system are further configured to execute the machine-readable instructions to: receive historical data associated with one or more prior sleep sessions of the user; analyze the historical data to determine a value of the second metric for each of the one or more prior sleep sessions (paragraph [0091]: a preliminary accelerometer signal sleep threshold is determined as step 312, Fig. 3b, from historical data taken from the patient over time); and compare the determined value of the second metric for each of the one or more prior sleep sessions to the determined value of the second metric for the current sleep session (at step 326, Fig. 3B, paragraph [0092]; see also paragraph [0077]: accelerometer signals are evaluated by event detector 164 to detect occurrences of sleep movement disorders) wherein the one or more processors of the control system are configured to execute the machine-readable instructions to cause the action to be performed in response to the comparison indicating that a severity of the health condition has increased (paragraph [0129]-[0130]: therapy unit 175 may provide therapy, e.g., drug therapy for a movement disorder such as RLS, PLMD, paragraph [0132]).
As to claim 51, Lee discloses the system of claim 1, wherein the second metric is indicative of a stress level of the user, and includes a heart rate, a heart rate variability, a skin conductance, an arterial pulse speed, an arterial pulse shape, an arterial pulse volume, an arterial pulse amplitude, or any combination thereof (paragraph [0055] - system detects changes in sympathetic and/or parasympathetic nervous system; paragraph [0073]: sympathetic and/or parasympathetic nervous system changes can be assessed by heart rate variability; these changes in the patient's nervous system would inherently be indicative of the stress level of the patient).
As to claim 52, Lee discloses the system of claim 51, wherein in response to the second metric indicating an elevated stress level of the user during an apnea event, the action includes increasing a pressure of the pressurized air (Lee's xPAP device can be an auto-titrating positive airway pressure device (paragraph [0140]), which inherently increases the pressure of air being delivered in response to an apnea event; thus, it would also be increased when an apnea event occurs simultaneously with elevated stress/heart rate variability).
As to claim 61, Lee discloses a method of monitoring a sleep session of a user, the method comprising:
generating data (data from sensors 170, 165, 166, paragraphs [0058],[0069],[0127]), during a current sleep session, associated with a user of a respiratory therapy system1220 (xPAP/external breathing therapy device 1220, Fig. 12, paragraph [0140]);
analyzing the generated data to determine a value of a first metric that is associated with a sleep disordered breathing (SDB) condition (apnea/hypopnea index, paragraphs [0061],[0105]);
analyzing the generated data to determine a value of a second metric that is associated with a health condition other than the SDB condition (e.g., limb movements measured via accelerometer or EMG used to detect sleep movement disorders such as PLMD, paragraph [0077],[0126],[0130]); and
based at least in part on the determined value of the second metric, causing an action to be performed (paragraph [0080]: the information is used to modify therapy for the sleep disorder via therapy control unit 175; paragraph [0129]-[0130]: therapy unit 175 may provide therapy, e.g., drug therapy for a movement disorder such as RLS, PLMD, paragraph [0132] lists examples of therapies that may be used to treat various types of sleep disorders, including non-SDB disorders).
Allowable Subject Matter
Claims 24, 33, and 41 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Stahmann et al. (US 2005/0113710) discloses detecting sleep-related disorders including at least an involuntary muscle movement disorder and sleep disordered breathing.
Gold (US 2004/0200472) discloses treating functional somatic syndromes and diagnosing sleep disorders based on functional somatic syndrome symptoms.
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/VALERIE L WOODWARD/Primary Examiner, Art Unit 3785
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