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 .
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.
Claim 7 is 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.
Claim 7 recites the limitation "the former sound source" and “the provision of the former sound source” in lines 3 and 5 respectively. There is insufficient antecedent basis for this limitation in the claim.
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 (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 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.
Claim(s) 1,2,6-8,10,11,15-16 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kahn et al.(20130234823).
Kahn et al.(20130234823) teaches a sleep sensing system comprising a sensor to obtain real-time information about a user, a sleep state logic to determine the user's current sleep state based on the real-time information. The system further comprising a sleep stage selector to select an optimal next sleep state for the user, and a sound output system to output sounds to guide the user from the current sleep state to the optimal next sleep state.
Regarding claims 1, 8 ,10,15, and 16 Kahn et al.(20130234823) teaches a communication interface configured to communicate with the sleep pad that acquires physiological index information of the user while the user lies down; and a controller configured to determine a sleep stage of the user based on the physiological index information, and to provide a sound source corresponding to the determined sleep stage. Note figures 1-5 and paragraphs 16-17 teach the sensor can be incorporated into a pillow which is considered a sleep pad. Also note paragraphs 43-49, 94 and 95.
Regarding claims 2 and 11, Kahn et al.(20130234823) teaches wherein the controller determines a sleep state of the user based on the physiological index information, and determines the sleep stage of the user by analyzing transition from one sleep state to another sleep state. Note paragraphs 43-49, 53, 94 and 95.
Regarding claim 6, Kahn et al.(20130234823) teaches wherein the controller triggers the determination of the sleep stage in response to detection of at least one of the user's input and execution of an installed application. Note paragraphs 34 in which the user may input preferences into a user interface, 43-49, 53, 94 and 95.
Regarding claim 7, Kahn et al.(20130234823) teaches wherein the controller provides the sound source, and provides a candidate sound source for the former sound source when a sleep state in the sleep stage is not maintained for a predicted period of time after the provision of the former sound source. Note paragraphs 31-32, 35, 48, 54 and 58 which sets forth the system generates sounds to transition the user from one sleep state to another and gently transitions from the current sounds to the appropriately selected sounds to optimize the sleep pattern.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 3, 4, 12, and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kahn et al. (20130234823) in view of Kang et al.( KR 20210121710).
Regarding claims 3,4,12, and 13, Kahn et al. (20130234823) teaches the claimed invention as set forth above including an automatic computer controlled sleep sensing system comprising a sensor to obtain real-time information about a user, a sleep state logic to determine the user's current sleep state based on the real-time information. The system further comprising a sleep stage selector to select an optimal next sleep state for the user, and a sound output system to output sounds to guide the user from the current sleep state to the optimal next sleep state.
Kahn et al. (20130234823) does not specifically teach the use of a trained deep learning model to determine sleep state or the calculation of a sleep score of the user based on the sensed physiologic information.
Kang et al.( KR 20210121710) teaches in the same field of endeavor a method and system for determining and taking care of the sleep status of a user by analyzing the posture and movement of the user, which are able to use a plurality of pieces of data collected by a sensor installed on a mat, determine whether the user is sleeping or not, and control the surrounding environment so that the user can sleep well. The method comprises: a step of collecting the data of posture and movement of the user from the sensor of the mat; a step of determining whether the user is lying on the mat or not; a step of determining the number of times of movement of the user; a step of counting the sleep continuity index of the user based on the number of times of movement; a step of determining the sleep status of the user in accordance with the sleep continuity index; a step of determining the sleep score of the user in accordance with the sleep status and the sleep environment information; and a step of controlling a surrounding device in accordance with the sleep score. The control unit 140 may include a sleep care unit 141 and an AI operation unit 142 . The sleep care unit 141 may obtain the user's sleep state and sleep environment information from the peripheral device, the sensor unit 120, and the like. The AI operation unit 142 may determine the sleep score of the user by learning the sleep state and sleep environment information obtained from the sleep care unit 141 . The AI calculator 142 may design a sleep score determination model or a sleep score prediction model by learning the sleep state and sleep environment information of a plurality of people. The AI calculator 142 may design the user's user sleep score judgment model or the user sleep score prediction model with reference to the sleep score judgment model or the sleep score prediction model. The sleep care unit 141 may control the peripheral device according to the user's sleep score prediction result of the AI operation unit 142 . The sleep care unit 141 may control the peripheral device to control the user's sleep environment. The sleep score prediction is a model capable of predicting a change in the user's sleep score according to a change in the sleep environment due to a change in the setting of the peripheral device.
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify the system of Kahn et al. (20130234823) to include an AI deep learning model for calculating a sleep score as taught by Kang et al.( KR 20210121710) to enable better sleep management for a user.
Claim(s) 5 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kahn et al. (20130234823) in view of Kang et al.( KR 20210121710) and further in view of Ware et al. (CN 109937010 ).
Regarding claims 5 and 14, Kahn et al. (20130234823) as modified by Kang et al.( KR 20210121710) teach the claimed the invention as set forth above including an automatic computer controlled sleep sensing system comprising a sensor to obtain real-time information about a user, a sleep state logic to determine the user's current sleep state based on the real-time information. The system further comprising a sleep stage selector to select an optimal next sleep state for the user, and a sound output system to output sounds to guide the user from the current sleep state to the optimal next sleep state. The system of Kahn et al. (20130234823) as modified by Kang et al.( KR 20210121710) further includes an AI deep learning model for calculating a sleep score.
Kahn et al. (20130234823) as modified by Kang et al.( KR 20210121710) does not specifically teach wherein the controller calculates the sleep score based on a physiological index score based on an analysis of the physiological index information of the user, a sleep pattern score based on an analysis of the sleep state, and a wake-up quality score based on an analysis of a sleep state for a predetermined period of time from the user's wake-up time.
Ware et al. (CN 109937010 ) teaches in the same field of endeavor of determining and promoting sleep quality, a sleep scoring device, which comprises a non-contact biometric sensor, a processor, a memory, and a microphone. The sleep scoring device can detect a user's sleep state by reading a signal from the non-contact biometric sensor based on at least one of the detected changes in heart rate, body movement or breathing, and log the biometric information. The sleep scoring device may also generate a sleep score for a sleep session based on the latency of the sleep session, the number of detected waking events, the amount of REM sleep, the amount of deep sleep, or the number of times the snooze button was pressed during the sleep session. The device further providing a sleep evaluation device, comprising a non-contact biometric sensor for determining at least one of heart rate, respiratory rate, presence of the user or in the movement of the user, a processor, a memory, and a microphone. the processor may be configured to: during a sleep session, at least one change based on heart rate, body motion or respiration is detected by reading of a signal from the non-contact biometric sensor to detect the sleep state of the user, and the information comprises biometric information and environmental factors recorded in the sleep recording log for the biometric information associated with the sleep quality of the user, the environmental factors may affect the quality of sleep of the user. sleep scoring device further may be based on log record for information with the corresponding information in the log record for consistency, to generate sleep score of the sleep session. The processor may be configured to: based on the sleep duration of the detected, or at least one of the sleep quality to generate the sleep score. the number of the sleep quality can be based on one or more of the following is determined: detected delayed sleep session, during a sleep session, REM sleep of wake event detected amount detected during the sleep session, deep sleep amount detected during the sleep session, or during a sleep session pressing times of snooze button. the processor may also be configured to analyze information in the sleep recording to identify potential inference in Sub-optimal sleep instance with the biometric information, the environmental factor or the external sleep factors.
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify the system of Kahn et al. (20130234823) as modified by Kang et al.( KR 20210121710) to determine the sleep score based on a number of sleep quality factors including the physiologic or biometric data, the sleep patterns of sleep states as well as the sleep state for a period of time from wake-up as taught by Ware et al. (CN 109937010 ) better track sleep and improve overall sleep quality.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kahn et al. (20130234823) in view of Gavish(US 20150367097).
Regarding claim 9, Kahn et al. (20130234823) teaches the claimed invention as set forth above including an automatic computer controlled sleep sensing system comprising a sensor to obtain real-time information about a user, a sleep state logic to determine the user's current sleep state based on the real-time information. The system further comprising a sleep stage selector to select an optimal next sleep state for the user, and a sound output system to output sounds to guide the user from the current sleep state to the optimal next sleep state. Paragraph [0026] The sleep sensing system 100 includes one or more sensors 205. The sensors 205 may include an accelerometer 210, a temperature sensor 212, a heart rate sensor 214. In one embodiment, temperature sensor 212 may include two sensors, one for the user's body temperature and the other for ambient temperature sensing. In one embodiment, sensors that detect brain waves 216 may also be used. In one embodiment, brainwave sensors, cameras to observe eye movement, or other sensors 218 may also monitor the user's state. Additional sensors to monitor the user's state 218, and/or the user's environment 219 may also be part of the sleep sensing system 100.
Kahn et al. (20130234823) does not specifically teach wherein the sleep pad comprises a piezoelectric film sensor as one of the many types of sensors set forth.
Gavish(US 20150367097) teaches in the same field of endeavor a system for sleep induction, detecting and tracking includes a monitor for analyzing single- and multiple-respiration patterns of a user and a stimulus generator for providing to the user multiple stimuli each bearing selectable relationships with selected temporal characteristics of the monitored respiration patterns. A sleep detector indicates onset of sleep and a driver continuously controls the operation of the stimulus generator, the sync detector and the sleep detector based on signals as they are received. Includes sleep score based on physiologic trends. Sensor includes a piezoelectric sensor. Paragraph [0090], It is appreciated that other types of contact respiration sensors are applicable. A partial list includes piezoelectric films, capacitive-, inductive-, sound-, and flow-based sensors.
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify the system of Kahn et al. (20130234823) to include as one of the additional types of sensors a piezoelectric films as one of the physiologic sensors as taught by Gavish(US 20150367097) to obtain real-time information about a user and provide better sleep management.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Doherty( US 20100049008) teaches Systems and/or methods for assessing the sleep quality of a patient in a sleep session are provided. Data is collected from the patient and/or physician including, for example, sleep session data in the form of one or more physiological parameters of the patient indicative of the patient's sleep quality during the sleep session, a subjective evaluation of sleep quality, etc.; patient profile data; etc. A sleep quality index algorithm, which optionally may be an adaptive algorithm, is applied, taking into account some or all of the collected data. Sleep quality data may be presented to at least the patient, and it may be displayed in any suitable format (e.g., a format useful for the patient to be appraised on the progress of the treatment, a format useful for a sleep clinician to monitor progress and/or assess the effectiveness of differing treatment regimens, etc).
Phillips et al.( US 20140088373) teaches Methods and apparatus monitor health by detection of sleep stage. For example, a sleep stage monitor may access sensor data signals related to bodily movement and respiration movements. At least a portion of the detected signals may be analyzed to calculate respiration variability. The respiration variability may include variability of respiration rate or variability of respiration amplitude. A processor may then determine a sleep stage based on a combination bodily movement and respiration variability. The determination of sleep stages may distinguish between deep sleep and other stages of sleep, or may differentiate between deep sleep, light sleep and REM sleep. The bodily movement and respiration movement signals may be derived from one or more sensors, such as non-invasive sensor (e.g., a non-contact radio-frequency motion sensor or a pressure sensitive mattress).
Shouldice et al.( US 20160151603) teaches processing system includes methods to promote sleep. The system may include a monitor such as a non-contact motion sensor from which sleep information may be determined. User sleep information, such as sleep stages, hypnograms, sleep scores, mind recharge scores and body scores, may be recorded, evaluated and/or displayed for a user. The system may further monitor ambient and/or environmental conditions corresponding to sleep sessions. Sleep advice may be generated based on the sleep information, user queries and/or environmental conditions from one or more sleep sessions. Communicated sleep advice may include content to promote good sleep habits and/or detect risky sleep conditions. In some versions of the system, any one or more of a bedside unit 3000 sensor module, a smart processing device, such as a smart phone or smart device 3002, and network servers may be implemented to perform the methodologies of the system.
Sayadi et al.( US 20190201269) teaches a bed having a sleep stage detecting feature including a deep learning model and classifier.
Hoskuldsson et al.( US 20210393211) teaches Methods and systems are provided for creating a personalized sleep classifier for a subject. Sleep data are obtained from biosignals from a subject in a High-Accuracy Sleep Study (HASS). Sleep data are also obtained from biosignals from the subject in a Simplified Sleep Study (SSS), the High-Accuracy Sleep Study being obtained simultaneously from the subject with the Simplified Sleep Study. A high-resolution HASS sleep profile is developed from the sleep data of the High-Accuracy Sleep Study. A personalized sleep classifier is created that outputs a SSS sleep profile of the subject based on the sleep data from the Simplified Sleep Study. And the personalized sleep classifier is calibrated such the SSS sleep profile output by the personalized sleep classifier based on the Simplified Sleep Study of the subject approaches or aligns with the high-resolution HASS sleep profile based on the High-Accuracy Sleep Study of the subject.
Rainere et al.( WO 2005055802) teaches A physiological characteristic of a sleeping person that indicates current sleep stage of the person is monitored. A sensory stimulus is generated to pace the person, and the characteristic is monitored to determine whether the stimulus was effective o lead the person to enter rapid eye movement (REM) sleep.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN L CASLER whose telephone number is (571)272-4956. The examiner can normally be reached M-Th 6:30 to 4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles Marmor can be reached at (571)272-4730. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRIAN L CASLER/Primary Examiner, Art Unit 3791