Information processing device and method

Abstract

The present invention provided an information processing device comprising a sensor arrangement configured to detect a physiological signal indicative of physiological information regarding a user, a sleep information determination unit configured to determine user sleep information based on the physiological signal, a sleep control unit configured to determine a wake-up time based on a predetermined sleep mode and the user sleep information and an alarm unit configured to output an alarm signal according to the wake-up time.

Description

[0001] Sony Group Corporation et al.

[0002] INFORMATION PROCESSING DEVICE AND METHOD

[0003] TECHNICAL FIELD

[0004] The present disclosure generally pertains to the field of sleep management, in particular to an information processing device and a method.

[0005] TECHNICAL BACKGROUND

[0006] Recent advancements in wearable technology have expanded the capabilities of personalized health monitoring and sleep management.

[0007] Sleep monitoring techniques utilize a variety of sensor systems to analyze physiological data and optimize sleep patterns. These systems incorporate sensors such as heart rate monitors, electroencephalography (EEG) sensors, and electromyography (EMG) sensors to track user sleep stages.

[0008] Although there exist techniques for monitoring sleep and providing wake-up recommendations, it is generally desirable to improve on existing techniques.

[0009] SUMMARY

[0010] According to a first aspect, the disclosure provides an information processing device comprising a sensor arrangement configured to detect a physiological signal indicative of physiological information regarding a user, a sleep information determination unit configured to determine user sleep information based on the physiological signal, a sleep control unit configured to determine a wake-up time based on a predetermined sleep mode and the user sleep information and an alarm unit configured to output an alarm signal according to the wake-up time.

[0011] According to a second aspect, the disclosure provides a method, comprising the steps of detecting a physiological signal indicative of physiological information regarding a user, determining user sleep information based on the physiological signal, determining a wake-up time based on a predetermined sleep mode and the user sleep information, and outputting an alarm signal according to the wake-up time.

[0012] Further aspects are set forth in the dependent claims, the drawings and the following description.

[0013] BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Embodiments are explained by way of example with respect to the accompanying drawings, in which:Sony Group Corporation et al.

[0015] Fig. 1 illustrates an information processing device wearable by a user;

[0016] Fig. 2 illustrates the components of the information processing device;

[0017] Fig. 3 a method for waking up the user at an optimal wake-up time;

[0018] Fig. 4 illustrates a method for determining a sleep mode; and

[0019] Fig. 5 illustrates a method for determining an optimal nap time and a recommended sleep mode

[0020] DETAILED DESCRIPTION OF EMBODIMENTS

[0021] Before a detailed description of the embodiments under reference of Fig. 1 is given, general explanations are made.

[0022] In the following, it is explained how wearable technology and artificial intelligence (Al) may by leveraged to significantly expand the capabilities of personalized health monitoring and sleep management.

[0023] In this way, it is possible to optimize napping for enhanced creativity and productivity by monitoring sleep stages and wakes the user at the optimal moment, ensuring they feel refreshed and alert.

[0024] Therefore, some embodiments are directed to an information processing device comprising a sensor arrangement configured to detect a physiological signal indicative of physiological information regarding a user, a sleep information determination unit configured to determine user sleep information based on the physiological signal, a sleep control unit configured to determine a wake-up time based on a predetermined sleep mode and the user sleep information and an alarm unit configured to output an alarm signal according to the wake-up time.

[0025] The sensor arrangement includes at least one of a plurality of sensors, each being configured to detect a physiological signal indicative of physiological information regarding a user. Such sensors include heart rate monitors, skin temperature sensors, electrodermal activity (EDA) sensors, electroencephalography (EEG) sensors, electrocardiography (ECG) sensors, etc.

[0026] The physiological signal is a measurable signal that is generated by a biological system, typically a human body, and reflects one or more physiological parameters. The physiological signal may be captured / acquired using bio-sensing devices such as the abovementioned sensors.

[0027] Physiological signals provide valuable information about the state and functioning of a user’s body, which can be used for monitoring health, fitness and activity.Sony Group Corporation et al.

[0028] The term “user” refers to any individual whose physiological measurements are utilized to generate the physiological signal, e.g., a user of a smartwatch, where the smartwatch is configured to acquire the physiological signal based on sensor information from physiological measurements, particularly when the user is sleeping.

[0029] The sleep information determination unit is configured to retrieve or receive the physiological signal from the sensor arrangement and to use the physiological signal to determine the user sleep information.

[0030] The user sleep information may include information such as a sleep onset, sleep stages, and transitions between sleep stages. Further, the user sleep information may include time information associated with the sleep information, such as the timings or point in times of the sleep onset, the sleep stages, and the transitions between sleep stages. Consequently, the user sleep information also includes timing or point in times when the user is awake.

[0031] Sleep stages refer to the different phases of sleep that the brain cycles through during a night's rest. Sleep is broadly divided into two types, i.e. Non-Rapid Eye Movement (NREM) sleep and Rapid Eye Movement (REM) sleep. When sleeping, a person follows a structured cycle, moving through the distinct stages multiple times throughout the night.

[0032] It begins with NREM stage 1, a light sleep phase lasting 1-5 minutes, where the body starts to relax, breathing slows, and brain activity decreases. In NREM stage 1, a transition between wakefulness and sleep occurs. It is easy to wake up from this stage. In this disclosure, this stage is also referred to as stage 1.

[0033] This brief transition quickly gives way to NREM stage 2, which lasts 10-25 minutes. Here, body temperature drops, heart rate slows, and the brain produces short bursts of activity called sleep spindles, which aid in memory processing. This stage accounts for about 50% of total sleep time. In this disclosure, this stage is also referred to as stage 2.

[0034] As sleep deepens, the body enters NREM stage 3, also known as deep sleep or slow-wave sleep, which lasts 20-40 minutes per cycle. This is the most restorative phase, crucial for physical recovery, immune function, and brain detoxification. Brain waves slow significantly, and it becomes much harder to wake up. In this disclosure, this stage is also referred to as stage 3. Following deep sleep, the body transitions into REM sleep, which typically starts about 90 minutes after falling asleep. The first REM phase lasts 10 minutes, but as the night progresses, later REM cycles extend up to 60 minutes. During REM sleep, the brain becomes highly active, similar to wakefulness, while the body remains temporarily paralyzed. This stage is vital forSony Group Corporation et al.

[0035] learning, emotional regulation, and memory consolidation, and it is when most vivid dreams occur. In this disclosure, this stage is also referred to as stage 4.

[0036] Incidentally, when the user is awake, this stage is referred to as the awake stage.

[0037] In some embodiments, for determining the sleep information, the sleep determination unit may include a machine learning model that is trained to output the sleep information based on the physiological signal as an input.

[0038] In case the sensor arrangement is configured to detect a plurality of physiological signals, the machine learning model is trained to output the sleep information based on the plurality of physiological signal as an input.

[0039] Generally, in view of this disclosure, a machine learning model is an artificial intelligence (Al) model, such as a neural network model. The neural network may include a convolutional neural network (CNN) or a multimodal Al model, e.g., transformer model, or the like. Alternatively, other machine learning models may also be used.

[0040] The sleep control unit is configured to determine a wake-up time based on a predetermined sleep mode and the user sleep information.

[0041] The predetermined sleep mode may be determined or selected from a plurality of sleep modes. The sleep modes may include at least a “short nap mode”, a “time-efficient rest mode”, a “creative boost mode”, and a “custom sleep mode”.

[0042] The short nap mode is designed for brief periods of rest, typically lasting around 20 minutes. This mode aims to wake the user as soon as they fall asleep, preventing entry into deeper sleep stages that can result in grogginess upon waking. The user U is awakened during light sleep (stages 1 or 2), before progressing to deeper sleep stages (stage 3 or stage 4). The benefits of this mode include providing a quick refresh and mental reset. By preventing the user from entering deeper sleep stages, sleep inertia and grogginess may be avoided.

[0043] The time-efficient rest mode is designed to maximize restorative rest within a predetermined timeframe. This mode ensures the user U is awakened at the optimal moment during the light sleep (stage 1), typically after a predetermined sleep duration, e.g., a 90-minute cycle. The benefits of this mode include enhancing creativity and productivity by providing the most restorative rest possible. Further, by waking up the user U in during light sleep, sleep inertia and grogginess may be avoided.Sony Group Corporation et al.

[0044] The creative boost mode enables the user U to capture creative ideas from the transitional phase between wakefulness and sleep. The user U is awakened just as they fall asleep, leveraging the unique state of consciousness for creativity. The user is awakened at the onset of sleep (stage 1), before progressing to deeper sleep stages. The benefits of this mode include helping achieve a creative state post-nap by harnessing the transitional phase between wakefulness and sleep.

[0045] The custom mode allows the user U to set a sleep duration and preferences for a personalized sleep experience. The user U may specify their desired wake-up time, sleep duration and preferred sleep stages to be awakened. The benefits of this mode include providing a tailored sleep experience to meet individual needs. The user preferences can be input via a user interface unit.

[0046] The “wake-up time” refers to the specific moment or time window at which the user is awakened from sleep. The wake-up time is determined to ensure that the user is awakened during the optimal sleep stage to minimize sleep inertia and maximize refreshment. The time between the sleep onset and the wake-up time depends on the predetermined sleep mode. That is, the wakeup time is determined such that the sleep duration corresponds to the predetermined sleep mode, and that the user is awakened in the sleep stage of the predetermined sleep mode. The sleep control unit is configured to generate a wake-up signal indicative of the wake-up time.

[0047] The alarm unit is configured to output the alarm signal according to the wake-up time. Therefore, the alarm unit is configured to receive the wake-up signal from the sleep control unit and to output the alarm signal based thereon. The alarm unit may be configured to output the alarm as an auditory signal, a visual signal, a tactile signal or a combination thereof.

[0048] The information processing device as described above is able to provide sleep information, to monitor sleep stages and to wake the user at the optimal moment, ensuring they feel refreshed and alert.

[0049] In some embodiments, the sensor arrangement may be further configured to detect an activity signal indicative of user activity information. The information processing device may further comprise a user exhaustion information determination unit configured to determine exhaustion information related to the user (user exhaustion information) based on the activity signal and a nap recommendation unit configured to determine, based on the user exhaustion information related to the user, a recommended sleep mode as the predetermined sleep mode.

[0050] The activity signal is a measurable signal that reflects the motions, actions, etc. the user.

[0051] Therefore, the activity signal may may be indicative of physical exhaustion of the user. TheSony Group Corporation et al.

[0052] activity signal may be captured / acquired using various motion-sensing devices such as accelerometers, gyroscopes, pedometers, GPS sensors, magnetometers, and environmental sensors like barometers. Activity signals provide information about the user’s activity levels (user activity information), including steps taken, exercise intensity, overall physical activity, and environmental context.

[0053] The user exhaustion information determination unit is configured to use the user activity signal to assess the user’s exhaustion. In other words, the user exhaustion information is determined based on the activity signal.

[0054] The user exhaustion information may include a user exhaustion score. That is, the user exhaustion information determination unit may be configured to determine, based on the activity signal, a score indicative of the user’s exhaustion.

[0055] The user exhaustion information determination unit may include a machine learning model that is trained to output the user exhaustion information including the score indicative of the user’s exhaustion based on the activity signal as an input.

[0056] The nap recommendation unit is configured to determine, based on the user exhaustion information related to the user, a recommended sleep mode as the predetermined sleep mode. That is, the nap recommendation unit is configured to select the recommended sleep mode from the plurality of sleep modes a sleep mode for the user to be fully rested in view of their exhaustion level.

[0057] By using the user activity information, the information processing device is able to proactively recommend a sleep mode in a context-aware manner.

[0058] In some embodiments, the user exhaustion information determination unit may be further configured to determine the exhaustion information related to the user based on the physiological signal. That is, stress information related to the user’s stress level (user stress information) can be determined based on the physiological signal. The physiological signal may reflect mental exhaustion of the user.

[0059] Therefore, the user exhaustion information may be indicative of the user’s physical exhaustion (as indicated by the activity signal) and mental exhaustion (as indicated by the physiological exhaustion).

[0060] The following sensors may be used to detect physiological signals indicative of stress.Sony Group Corporation et al.

[0061] The heart rate sensor or ECG sensor is configured to monitor the heart rate variability (HRV) of the user. HRV refers to the variations in the time interval between heartbeats. A high HRV is generally associated with low stress levels, while reduced HRV can indicate higher stress levels. The electrodermal activity (EDA) sensor measures changes in skin conductance. Electrodermal activity is a reliable indicator of physiological arousal and stress. Increased skin conductance levels are associated with higher stress levels.

[0062] The skin temperature sensor measures the variations in the user’s skin temperature. Stress can cause changes in peripheral skin temperature. By monitoring these variations, the device can provide insights into the user’s stress levels.

[0063] The accelerometer and gyroscope are used to track the levels of movement and activity of the user U. Increased movement or restlessness can be indicative of stress.

[0064] The user exhaustion information determination unit may include a machine learning model that is trained to output the user exhaustion information including the score indicative of the user’s exhaustion based on the activity signal and the physiological signal as an input.

[0065] In some embodiments, the user exhaustion information determination unit may be configured to determine the user exhaustion information based on the physiological signal and, optionally, the activity signal. That is, in some examples, the user exhaustion information is determined based on the physiological signal only and therefore reflects only the mental exhaustion of the user. In some embodiments, the nap recommendation unit may be further configured to retrieve user schedule information, and the recommended sleep mode is determined based on the user exhaustion information and the user schedule information.

[0066] The user schedule information may include details about the user’s planned activities, appointments, meetings, deadlines, and other time-bound commitments. It may be derived from calendars, reminders, or task management applications. The user schedule information can be retrieved from a user information database which may be stored in a storage of the information processing device, a cloud storage or a combination thereof.

[0067] The nap recommendation unit then uses the user schedule information to determine the recommended sleep mode.

[0068] In some embodiments, the nap recommendation unit may be further configured to determine free time slots from the user schedule information. The free time slots are potential time windows which the user may use for a nap.Sony Group Corporation et al.

[0069] By using the user schedule information and the user exhaustion information, the nap recommendation unit can provide personalized and context-aware sleep mode recommendations that help the user to manage their rest effectively while adhering to their schedules.

[0070] In some embodiments, the predetermined sleep mode may be selected by the user from a plurality of sleep modes. The user may input the selection via a user interface unit provided in the information processing device.

[0071] In some embodiments, the alarm unit may be configured to output the alarm signal based on the sleep stage of the user for minimizing sleep inertia. This means that characteristics of the alarm signal - such as its intensity, type, or pattern - may vary depending on the sleep stage the user is in when the determined wake-up time occurs. With this, it is possible to tailor the alarm to the user’s current sleep stage to ensure the most gentle and effective transition from sleep to wakefulness, thereby minimizing sleep inertia.

[0072] In some embodiments, the sleep information determination unit may be configured to continuously determine the user sleep information. That is, the sleep information determination unit is configured to the monitor the sleep of the user and provide feedback in real-time.

[0073] The term “continuously” means that the sleep information determination unit is configured to process data in real-time throughout the user’s sleep period. This continuous operation allows the information processing device to monitor and update the user’s sleep information without interruption, ensuring accurate and timely insights into the user’s sleep stages and transitions. For example, the sleep information determination unit may receive sensor signals in real-time. This means the sensor arrangement is constantly collecting physiological or activity signals and sending the signals to the sleep information determination unit without breaks.

[0074] The sleep information determination unit may process the incoming signals continuously, analyzing it to determine current sleep stages, transitions between sleep stages, and other relevant sleep metrics. This analysis may be performed on a rolling basis, ensuring that the sleep information determination unit provides always up-to-date information in line with the user’s current sleep state. The term “rolling basis” refers to a method of continuous, real-time analysis where incoming data is processed in overlapping segments or windows of time. As new data arrives, it is incorporated into the analysis while older data gradually moves out of the active analysis window.

[0075] As the user progresses through different sleep stages, the sleep information determination unit may be configured to update the sleep information in real-time. This includes detecting when theSony Group Corporation et al.

[0076] user falls asleep (sleep onset), identifying transitions between stages (e.g., from awaken to light sleep, and from light sleep to deep sleep), and recognizing wakefulness periods.

[0077] The continuous determination of sleep information allows the information processing device to adapt to changes in the user’s sleep patterns immediately. For example, if the user moves from deep sleep to light sleep, the information processing device may prepare to wake the user at the optimal wake-up time based on the updated sleep information.

[0078] In some embodiments, the information processing device may be a smartwatch which is wearable by the user.

[0079] Some embodiments pertain to a method comprising the step of detecting a physiological indicative of physiological information regarding a user, determining user sleep information based on the physiological signal, determining a wake-up time based on a predetermined sleep mode and the user sleep information, and outputting an alarm signal according to the wake-up time.

[0080] The method may include any feature of the information processing device discussed in this specification.

[0081] The methods as described herein are also implemented in some embodiments as a computer program causing a computer and / or a processor to perform the method, when being carried out on the computer and / or processor. In some embodiments, also a non-transitory computer-readable recording medium is provided that stores therein a computer program product, which, when executed by a processor, such as the processor described above, causes the methods described herein to be performed.

[0082] Referring to Fig. 1, an information processing device 1 is shown which is configured as a smartwatch worn on a wrist of a user U.

[0083] As shown in Fig. 2, the information processing device 1 comprises a sensor arrangement 10, a processor 30, an alarm unit 40, a user information database 50 and a user interface unit 60. The processor 30 includes a sleep information determination unit 31, a sleep control unit 32, a user exhaustion information determination unit 33, and a nap recommendation unit 34.

[0084] The sensor arrangement 10 is configured to detect a (i.e., at least one) physiological signal indicative of physiological information regarding the user U. The sensor arrangement 10 is further configured to detect an (i.e., at least one) activity signal indicative of user activity information. The sensor arrangement 10 is configured to provide the detected signals to both theSony Group Corporation et al.

[0085] sleep information determination unit 31 and the user exhaustion information determination unit 33.

[0086] The sensor arrangement 10 comprises multiple sensors. These sensors may include sensors configured to detect physiological signals and sensors configured to detect activity signals.

[0087] The physiological sensors include a heart rate sensor, a skin temperature sensor, an electrodermal activity (EDA) sensor, an electroencephalography (EEG) sensor, and an electrocardiography (ECG) sensor.

[0088] The activity sensors may include a motion sensor (e.g., an accelerometer, a gyroscope, a pedometer), a positioning sensor (e.g., a GPS sensor), an orientation sensor (e.g., a magnetometer) and / or an environmental sensor (e.g., a barometer).

[0089] The sleep information determination unit 31 is configured to receive or retrieve the signals from the sensor arrangement 10 and to determine the sleep information related to the user (user sleep information) based on the received signals. The sleep information determination unit 31 mainly uses the physiological signals. However, the determination of the sleep information determination unit 31 may be enhanced by using activity signals which are indicative of user’s motion during the sleep process. For example, reduced movement often correlates with deeper sleep stages.

[0090] In order to determine the sleep information, the sleep information determination unit 31 includes a machine learning model which is trained to determine the sleep information including sleep onset, current sleep stages and future sleep stages based on real-time measurements (i.e., currently acquired sensor signals). That is, the machine learning model is trained to output the user sleep information based on the physiological signal and / or the activity signal as the input. The sleep information determination unit 31 is configured to provide the sleep information to sleep control unit 32.

[0091] The sleep control unit 32 is configured to receive the sleep information determined by the sleep information determination unit 31. The sleep control unit 31 is configured to determine the optimal wake-up time based on the sleep information and a determined sleep mode. The determination of the sleep mode will be discussed later. The determination of the optimal wakeup time ensures that the user is awakened at the most appropriate moment based on their sleep information.Sony Group Corporation et al.

[0092] The sleep modes may comprise a “short nap mode”, a “time-efficient rest mode”, a “creative boost mode”, and a “custom sleep mode”.

[0093] Once the optimal wake-up time is determined, the sleep control unit 32 is configured generate and send a wake-up signal to the alarm unit 40.

[0094] The alarm unit 40 is configured to receive the wake-up signal from the sleep control unit 32 and to output an alarm signal according to wake-up signal to wake the user U at the determined optimal wake-up time. The alarm unit 40 is configured to output at least one of an auditory signal, a visual signal, and a tactile signal as the alarm signal.

[0095] The user exhaustion information determination unit 33 is configured to receive or retrieve the signals from the sensor arrangement 10 and to determine exhaustion information related to the user (user exhaustion information) based on these signals.

[0096] The user exhaustion information determination unit 33 includes a machine learning model which is trained to determine the user exhaustion information including a user exhaustion score based on real-time measurements (i.e., currently acquired sensor signals). That is, the machine learning model is trained to output the user exhaustion information based on the physiological signal and / or the activity signal as the input.

[0097] The user exhaustion information determination unit 33 is configured to integrate the activity signals, including metrics like steps taken, exercise intensity, and overall physical activity levels, providing additional context for assessing physical exhaustion. High activity levels combined with physiological indicators derived from the physiological signal can provide a more accurate assessment of the user’s exhaustion.

[0098] The user exhaustion information is used by the nap recommendation unit 34 to provide recommendations for sleep modes and, optionally, sleep times (i.e., point in time for sleeping). To this end, the nap recommendation unit 34 is configured to receive the user exhaustion information from the user exhaustion information determination unit 33. Based on the user exhaustion information, the nap recommendation unit 34 is configured to determine a recommended sleep mode tailored to the current state and needs of the user U.

[0099] The user information database 50 stores various types of user-related information for use in sleep management, such as user schedule information. The user information database 50 can be stored in a storage (not shown) of the information processing device 10 or in a cloud storage (not shown). Further, user information database 50 may store the signals detected by the sensor arrangement 10, which can be retrieved by the processor 20 for further processing.Sony Group Corporation et al.

[0100] The user interface unit 60 enables interaction between the user U and the information processing device. The user interface unit 60 is connected to the processor 30 and allows the user to input data, receive information, and control various functions of the device.

[0101] The user interface unit 60 may include as input means, for example, a (touch) display, button, keyboards, and a microphone. The display may use various forms of graphical user interfaces (GUIs) to present data clearly and intuitively. The user interface unit 60 may further include output means, for example, an audio output unit (e.g., speakers), a tactile feedback unit, and a visual feedback unit. In some examples, the alarm unit 40 may be part of the user interface unit 60.

[0102] Fig. 3 illustrates a flowchart of a method 300 for wake-up the user U at an optimal wake-up time. The method comprises the following steps:

[0103] Step 301 comprises detecting, by the sensor arrangement 10, a physiological signal and an activity signal. These signals are then provided to the processor 30.

[0104] Step 302 comprises determining, by the sleep information determination unit 31, the sleep information based on the physiological signal and the activity signal of the sensor arrangement 10. To this end, the sensor signal used may be indicative of at least one of EEG data, ECG data, heart rate data, respiratory rate data, EDA data, skin temperature data, and motion data.

[0105] Step 303 comprises determining the sleep mode for the user U. The sleep mode may be selected by the user U or determined by the nap recommendation unit 34.

[0106] Step 304 comprises determining, by the sleep control unit 32, the optimal wake-up time based on the determined sleep information and the determined sleep mode.

[0107] Step 305 comprises generating, by the sleep control unit 32, a wake-up signal based on the determined optimal wake-up time. Step 305 further comprises sending, by the sleep control unit 32, the wake-up signal to the alarm unit 40 which is configured to output an alarm signal in response to the wake-up signal for waking up the user U at the optimal wake-up time.

[0108] Fig. 4 illustrates a method for determining the sleep mode according to step 303.

[0109] Step 401 comprises checking, by the sleep control unit 32, whether a user input is present. The user input may be entered via the user interface unit 60 and includes a sleep mode desired by the user U. The sleep modes may be one of the short nap mode, the time-efficient rest mode, the creative boost mode, and the custom sleep mode. The custom sleep mode includes information regarding desired sleep duration and desired wake-up time as input by the user U.Sony Group Corporation et al.

[0110] If a user input is present, the method proceeds to step 402 which comprises determining the sleep mode according to the user input. In other words, the user U sets the sleep mode.

[0111] If a user input is not present, the method proceeds to step 403 which comprises determining, by the user exhaustion information determination unit 33, the user exhaustion information. In this step, the user exhaustion information determination unit 33 analyzes the physiological signal and the activity signal detected by the sensor arrangement 10 to assess the user’s physical and mental exhaustion.

[0112] Step 403 may be performed by applying (performing) the machine learning model that is trained to output the user exhaustion information based on the physiological signal and the activity signal as the input.

[0113] After step 403, the method proceeds to step 404 which comprises determining the sleep mode based on the user exhaustion information. To this end, the nap recommendation unit 34 is configured to analyze the user exhaustion information to recommend the most suitable sleep mode for the user.

[0114] Step 405 comprises sending the determined sleep mode to the sleep control unit 32. The sleep control unit 32 then uses the determined sleep mode to calculate the optimal wake-up time. The determined sleep mode is either step 402 (i.e., the user selected sleep mode) or step 404 (i.e., the recommended sleep mode), depending on whether a user input is present or not.

[0115] Fig. 5 illustrates a method 500 performed by the nap recommendation unit 34 for determining an optimal nap time (i.e., when to take a nap) based on the user exhaustion information and user schedule information.

[0116] Step 501 comprises retrieving the user exhaustion information determined in step 403 of method 400. For example, the user exhaustion information may be stored in the user information database 50 and the nap recommendation unit 34 retrieves the user exhaustion information from this database 50. In another example, the nap recommendation unit 34 receives the user exhaustion information from the user exhaustion information determination unit 33.

[0117] Step 502 comprises retrieving the user schedule information stored in the user information database 50. The user schedule information includes information about upcoming appointments, meetings, etc.

[0118] Step 503 comprises determining when the user U has time for a nap based on their schedule. The nap recommendation unit 34 is configured to determine free time slots available in the user’sSony Group Corporation et al.

[0119] schedule based on the user schedule information to identify periods when the user can potentially take a nap.

[0120] Step 504 comprises determining a recommended sleep mode based on the user exhaustion information and the determined free time slots. That is, the nap recommendation unit 34 recommends sleep mode that fits within the free time slot and helps the user U with relaxation and / or restoration.

[0121] Step 505 comprises determining an optimal nap time based on the recommended sleep mode and free time slots. The nap recommendation unit 34 identifies the best time for the user to take the nap, ensuring that it fits within the free time slots and aligns with the user’s current state of exhaustion. The recommended sleep mode may also be used as the determined sleep mode for determining the wake-up time according to step 304.

[0122] Step 506 comprises outputting the optimal nap time and the recommended sleep mode to the user via the user interface unit 60.

[0123] It should be recognized that the embodiments describe methods with an exemplary ordering of method steps. The specific ordering of method steps is however given for illustrative purposes only and should not be construed as binding.

[0124] Please note that the division of the processor 30 into a unit is only made for illustration purposes and that the present disclosure is not limited to any specific division of functions in specific units. For instance, the control of processor 30 could be implemented by a respective programmed processor, field programmable gate array (FPGA) and the like.

[0125] A method for controlling an electronic device, such as information processing device 10 discussed above, is described above and under reference of Fig. 2 to 5. The method can also be implemented as a computer program causing a computer and / or a processor, such as processor 30 discussed above, to perform the method, when being carried out on the computer and / or processor. In some embodiments, also a non-transitory computer-readable recording medium is provided that stores therein a computer program product, which, when executed by a processor, such as the processor described above, causes the method described to be performed.

[0126] All units and entities described in this specification and claimed in the appended claims can, if not stated otherwise, be implemented as integrated circuit logic, for example on a chip, and functionality provided by such units and entities can, if not stated otherwise, be implemented by software.Sony Group Corporation et al.

[0127] In so far as the embodiments of the disclosure described above are implemented, at least in part, using software-controlled data processing apparatus, it will be appreciated that a computer program providing such software control and a transmission, storage or other medium by which such a computer program is provided are envisaged as aspects of the present disclosure.

[0128] Note that the present technology can also be configured as described below.

[0129] (1) An information processing device comprising:

[0130] a sensor arrangement configured to detect a physiological signal indicative of physiological information regarding a user;

[0131] a sleep information determination unit configured to determine user sleep information based on the physiological signal;

[0132] a sleep control unit configured to determine a wake-up time based on a predetermined sleep mode and the user sleep information; and

[0133] an alarm unit configured to output an alarm signal according to the wake-up time.

[0134] (2) The information processing device according to (1), wherein the sensor arrangement is further configured to detect an activity signal indicative of user activity information, and the information processing device further comprises:

[0135] a user exhaustion information determination unit configured to determine the exhaustion information related to the user based on the activity signal; and

[0136] a nap recommendation unit configured to determine, based on the user exhaustion information related to the user, a recommended sleep mode as the predetermined sleep mode. (3) The information processing device according to (2), wherein the user exhaustion information determination unit is further configured to determine the exhaustion information related to the user based on the physiological signal.

[0137] (4) The information processing device according to (2) or (3), wherein the nap recommendation unit is further configured to retrieve user schedule information, and the recommended sleep mode is determined based on the user exhaustion information and the user schedule information.

[0138] (5) The information processing device according to (4), wherein the nap recommendation unit is further configured to determine free time slots based on the user schedule information.Sony Group Corporation et al.

[0139] (6) The information processing device according to anyone of (1) to (5), wherein the sleep information includes a sleep onset of the user, and sleep stages, and transitions between sleep stages.

[0140] (7) The information processing device according to anyone of (1) to (6), wherein the predetermined sleep mode is selected by the user from a plurality of sleep modes.

[0141] (8) The information processing device according to anyone of (1) to (7), wherein the alarm unit is configured to output the alarm signal based on the sleep stage of the user.

[0142] (9) The information processing device according to anyone of (1) to (8), wherein the sleep information determination unit includes a machine learning model that is trained to determine the user sleep information based on the physiological signal.

[0143] (10) The information processing device according to anyone of (1) to (9), wherein the sleep information determination unit is configured to continuously determine the user sleep information.

[0144] (11) A method, compri sing :

[0145] detecting a physiological indicative of physiological information regarding a user; determining user sleep information based on the physiological signal;

[0146] determining a wake-up time based on a predetermined sleep mode and the user sleep information; and

[0147] outputting an alarm signal according to the wake-up time.

[0148] (12) The method according to (11), further comprising

[0149] detecting an activity signal indicative of user activity information; and

[0150] determining exhaustion information related to the user based on the activity signal determining, based on the user exhaustion information related to the user, a recommended sleep mode as the predetermined sleep mode.

[0151] (13) The method according to (12), further comprising determining the exhaustion information related to the user based on the physiological signal.

[0152] (14) The method according to (12) or (13), further comprising:

[0153] retrieving user schedule information; and

[0154] determined based on the user exhaustion information and the user schedule information.Sony Group Corporation et al.

[0155] (15) The method according to (14), further comprising determining free time slots based on the user schedule information.

[0156] (16) The method according to anyone of (11) to (15), wherein the sleep information includes a sleep onset of the user, and sleep stages, and transitions between sleep stages.

[0157] (17) The method according to anyone of (11) to (16), wherein the predetermined sleep mode is selected by the user from a plurality of sleep modes.

[0158] (18) The method according to anyone of (11) to (17), further comprising outputting the alarm signal based on the sleep stage of the user.

[0159] (19) The method according to anyone of (11) to (18), further comprising applying machine learning model that is trained to determine the user sleep information based on the physiological signal.

[0160] (20) The method according to anyone of (11) to (19), wherein determining the user sleep information is performed continuously.

[0161] (21) A computer program comprising program code causing a computer to perform the method according to anyone of (11) to (20), when being carried out on a computer.

[0162] (22) A non-transitory computer-readable recording medium that stores therein a computer program product, which, when executed by a processor, causes the method according to anyone of (11) to (20) to be performed.

Claims

Sony Group Corporation et al.CLAIMS1. An information processing device comprising:a sensor arrangement configured to detect a physiological signal indicative of physiological information regarding a user;a sleep information determination unit configured to determine user sleep information based on the physiological signal;a sleep control unit configured to determine a wake-up time based on a predetermined sleep mode and the user sleep information; andan alarm unit configured to output an alarm signal according to the wake-up time.

2. The information processing device according to claim 1, wherein the sensor arrangement is further configured to detect an activity signal indicative of user activity information, and the information processing device further comprises:a user exhaustion information determination unit configured to determine the exhaustion information related to the user based on the activity signal; anda nap recommendation unit configured to determine, based on the user exhaustion information related to the user, a recommended sleep mode as the predetermined sleep mode.

3. The information processing device according to claim 2, wherein the user exhaustion information determination unit is further configured to determine the exhaustion information related to the user based on the physiological signal.

4. The information processing device according to claim 2, wherein the nap recommendation unit is further configured to retrieve user schedule information, and the recommended sleep mode is determined based on the user exhaustion information and the user schedule information.

5. The information processing device according to claim 4, wherein the nap recommendation unit is further configured to determine free time slots based on the user schedule information.

6. The information processing device according to claim 1, wherein the sleep information includes a sleep onset of the user, and sleep stages, and transitions between sleep stages.

7. The information processing device according to claim 1, wherein the predetermined sleep mode is selected by the user from a plurality of sleep modes.Sony Group Corporation et al.

8. The information processing device according to claim 1, wherein the alarm unit is configured to output the alarm signal based on the sleep stage of the user.

9. The information processing device according to claim 1, wherein the sleep information determination unit includes a machine learning model that is trained to determine the user sleep information based on the physiological signal.

10. The information processing device according to claim 1, wherein the sleep information determination unit is configured to continuously determine the user sleep information.

11. A method, comprising:detecting a physiological indicative of physiological information regarding a user; determining user sleep information based on the physiological signal;determining a wake-up time based on a predetermined sleep mode and the user sleep information; andoutputting an alarm signal according to the wake-up time.

12. The method according to claim 11, further comprisingdetecting an activity signal indicative of user activity information; anddetermining exhaustion information related to the user based on the activity signal determining, based on the user exhaustion information related to the user, a recommended sleep mode as the predetermined sleep mode.

13. The method according to claim 12, further comprising determining the exhaustion information related to the user based on the physiological signal.

14. The method according to claim 12, further comprising:retrieving user schedule information; anddetermined based on the user exhaustion information and the user schedule information.

15. The method according to claim 14, further comprising determining free time slots based on the user schedule information.

16. The method according to claim 11, wherein the sleep information includes a sleep onset of the user, and sleep stages, and transitions between sleep stages.

17. The method according to claim 11, wherein the predetermined sleep mode is selected by the user from a plurality of sleep modes.Sony Group Corporation et al.

18. The method according to claim 11, further comprising outputting the alarm signal based on the sleep stage of the user.

19. The method according to claim 11, further comprising applying machine learning model that is trained to determine the user sleep information based on the physiological signal.

20. The method according to claim 11, wherein determining the user sleep information is performed continuously.

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