system

Abstract

The system according to this embodiment aims to provide specific advice and environmental adjustments to improve the user's sleep quality. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, a provision unit, and an advice unit. The collection unit collects the user's sleep data. The analysis unit analyzes the data collected by the collection unit and evaluates the sleep patterns and quality. The proposal unit proposes recommended bedtimes and wake-up times based on the analysis results obtained by the analysis unit. The provision unit provides information on recommended external environments during sleep based on the analysis results obtained by the analysis unit. The advice unit provides advice on factors that affect sleep based on the analysis results obtained by the analysis unit.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, specific advice and environmental adjustments for improving the quality of a user's sleep have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to provide specific advice and environmental adjustments for improving the quality of a user's sleep.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a provision unit, and an advice unit. The data collection unit collects the user's sleep data. The analysis unit analyzes the data collected by the data collection unit and evaluates the sleep patterns and quality. The proposal unit proposes recommended bedtimes and wake-up times based on the analysis results obtained by the analysis unit. The provision unit provides information on recommended external environments during sleep based on the analysis results obtained by the analysis unit. The advice unit provides advice on factors that affect sleep based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide specific advice and environmental adjustments to improve the user's sleep quality. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9]This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The sleep support system according to an embodiment of the present invention is a system that analyzes a user's sleep patterns and quality and recommends optimal bedtime and wake-up times. This system collects and analyzes the user's sleep data and proposes optimal bedtime and wake-up times. It also provides relaxing ambient sounds and music to improve sleep quality. Furthermore, it provides advice on factors that affect sleep, such as the timing of caffeine intake and exercise. For example, if the user is wearing a smartwatch, its data can be collected. Next, the collected data is analyzed. AI is used for the analysis to evaluate sleep patterns and quality. For example, it can analyze the user's movements and heart rate fluctuations during sleep to identify periods of deep sleep and light sleep. Based on the analysis results, it proposes optimal bedtime and wake-up times. The proposal also includes estimating the user's emotions and suggesting optimal times for each emotion. For example, if the user is feeling stressed, it can suggest a time when they can relax. It also provides relaxing ambient sounds and music during sleep. For example, by providing relaxing sounds such as the sound of waves or birdsong, the quality of the user's sleep can be improved. Furthermore, it provides advice on factors that affect sleep, such as the timing of caffeine intake and exercise. For example, it can advise that caffeine intake should be avoided for several hours before bedtime, and that exercising before bedtime can improve sleep quality. In this way, the AI ​​assistant of the present invention not only collects and analyzes the user's sleep data and proposes optimal bedtime and wake-up times, but also provides relaxing ambient sounds and music, and offers advice on factors that affect sleep, thereby improving the user's sleep quality. As a result, the sleep support system can efficiently collect, analyze, propose, provide, and advise on the user's sleep data.

[0029] The sleep support system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a provision unit, and an advice unit. The data collection unit collects the user's sleep data. The collected data includes the time the user goes to bed, the time they wake up, and their movements and heart rate during sleep. For example, if the user is wearing a smartwatch, the data collection unit can collect that data. The data collection unit records, for example, the time the user goes to bed and the time they wake up. The data collection unit can also detect movements and heart rate during sleep using sensors and collect this data. The analysis unit analyzes the data collected by the data collection unit and evaluates the sleep patterns and quality. AI is used for the analysis, and for example, it can analyze the user's movements and heart rate fluctuations during sleep to identify periods of deep sleep and light sleep. The analysis unit uses AI to analyze the user's sleep data and identify sleep patterns. The analysis unit can also use AI to evaluate the quality of sleep. The proposal unit proposes recommended bedtimes and wake-up times based on the analysis results obtained by the analysis unit. The suggestions may include estimating the user's emotions and proposing the optimal time for each emotion. The suggestion unit, for example, uses AI to estimate the user's emotions and propose the optimal bedtime. The suggestion unit can also use AI to propose the optimal wake-up time. The provision unit provides information about the external environment recommended for sleep based on the analysis results obtained by the analysis unit. The provision unit provides, for example, relaxing ambient sounds or music. The provision unit can provide relaxing sounds such as the sound of waves or birdsong. The provision unit can also provide relaxing music. The advice unit provides advice on factors that affect sleep based on the analysis results obtained by the analysis unit. The advice unit provides, for example, advice on the timing of caffeine intake and exercise. The advice unit may advise that caffeine intake should be avoided for several hours before bedtime. The advice unit may also advise that exercising before bedtime improves sleep quality. As a result, the sleep support system according to this embodiment can efficiently collect, analyze, suggest, provide, and advise on the user's sleep data.

[0030] The data collection unit collects the user's sleep data. This data includes the user's bedtime, wake-up time, sleep movements, and heart rate. For example, if the user is wearing a smartwatch, the unit can collect data from it. Specifically, it uses the smartwatch's accelerometer and heart rate sensor to monitor the user's movements and heart rate in real time and collect data. The unit records, for instance, the user's bedtime and wake-up time. It can also detect and collect data on sleep movements and heart rate using sensors. Furthermore, the unit can detect the user's breathing patterns and body temperature fluctuations, comprehensively collecting this data. This allows the unit to gain a detailed understanding of the user's sleep state and collect highly accurate data for the analysis unit. The collected data is sent to a cloud server for access by the analysis unit. The unit can also adjust the data collection frequency and accuracy to provide flexible responses to specific situations and conditions. For example, if a user is experiencing stress or has a specific health condition, the frequency of data collection can be increased to collect more detailed data. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the data collection unit to evaluate sleep patterns and quality. AI is used in the analysis, for example, to analyze the user's movements and heart rate variability during sleep to identify periods of deep and light sleep. Specifically, the AI ​​classifies the user's sleep stages based on the collected data and analyzes the duration and frequency of each stage. For example, the AI ​​analyzes the user's heart rate variability patterns to identify the stages of deep sleep, light sleep, and REM sleep. The AI ​​can also analyze the user's movement data to evaluate the frequency and intensity of body movements during sleep. This allows the analysis unit to evaluate the user's sleep quality in detail and provide specific advice for improvement. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term sleep pattern variations and trends. For example, based on data from the past few months, it can evaluate variations in the user's sleep quality and identify the impact of specific factors on sleep. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term sleep management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The suggestion unit proposes recommended bedtimes and wake-up times based on the analysis results obtained by the analysis unit. The suggestions also include estimating the user's emotions and suggesting optimal times for each emotion. Specifically, it uses AI to estimate the user's emotions; for example, if stress levels are high, it suggests a time for relaxation, and if energy levels are low, it recommends going to bed earlier. The suggestion unit can also use AI to estimate the user's emotions and suggest the optimal bedtime. Furthermore, the suggestion unit can use AI to suggest the optimal wake-up time. In addition, the suggestion unit can provide individually customized suggestions considering the user's lifestyle and daily schedule. For example, if a user has a habit of exercising early in the morning, it will suggest an appropriate wake-up time, and if they work late into the night, it will suggest an appropriate bedtime. This allows the suggestion unit to provide an optimal sleep schedule tailored to the user's individual needs and improve sleep quality. Furthermore, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can provide feedback on the user's results following the suggested bedtimes and wake-up times, and adjust the suggestion algorithm based on that data. This allows the proposal department to provide users with more effective suggestions and improve their sleep quality.

[0033] The service provider provides information on recommended external environments for sleep based on the analysis results obtained by the analysis unit. Specifically, it provides relaxing ambient sounds and music. For example, the service provider can provide relaxing sounds such as the sound of waves or birdsong. It can also provide relaxing music. Furthermore, the service provider can provide individually customized ambient sounds and music based on the user's preferences and past data. For example, it can select and provide the optimal sounds based on music and ambient sounds that the user has found relaxing in the past. The service provider can also adjust the volume and playback time of the sounds to provide the environment in which the user can relax the most. In this way, the service provider can optimize the user's sleep environment and improve the quality of sleep. Furthermore, the service provider can also provide information on other external environmental factors such as light adjustment and temperature control. For example, it may recommend dimming the lights in the room before going to bed or maintaining an appropriate room temperature. In this way, the service provider can optimize the user's overall sleep environment and support better sleep.

[0034] The advice unit provides advice on factors affecting sleep based on the analysis results obtained by the analysis unit. Specifically, it provides advice on the timing of caffeine intake and exercise. For example, the advice unit may advise that caffeine intake should be avoided for several hours before bedtime. It may also advise that exercising before bedtime improves sleep quality. Furthermore, the advice unit can provide individually customized advice considering the user's lifestyle and health condition. For example, if the user is experiencing stress, it can provide advice on relaxation methods and stress management. If the user has a specific health problem, it can provide advice addressing that problem. In this way, the advice unit can provide specific advice tailored to the user's individual needs and improve sleep quality. In addition, the advice unit can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, it can provide feedback on the results of following the advice and adjust the advice algorithm based on that data. In this way, the advice unit can provide more effective advice to the user and improve sleep quality.

[0035] The service provider can provide information about ambient sounds and music for sleep. For example, it can provide relaxing ocean sounds. It can also provide nature sounds or birdsong. Furthermore, it can provide soothing melodies. By providing relaxing ambient sounds and music, the quality of the user's sleep can be improved. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can have a generating AI select relaxing music.

[0036] The data collection unit can collect data including the user's bedtime, wake-up time, sleep movements, and heart rate. For example, the data collection unit can record the user's bedtime and wake-up time. The data collection unit can also detect sleep movements and heart rate using sensors and collect this data. For example, the data collection unit can use a smartwatch to monitor the user's heart rate in real time and collect the data. This allows for more accurate analysis by collecting detailed sleep data of the user. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from a smartwatch into a generating AI and have the generating AI perform data analysis.

[0037] The advice unit can provide advice on the effects of caffeine intake timing and exercise timing on sleep. For example, the advice unit may advise that caffeine intake should be avoided for several hours before bedtime. The advice unit may also advise that exercising before bedtime improves sleep quality. In this way, the quality of the user's sleep can be improved by providing advice on caffeine intake and exercise timing. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input data on caffeine intake and exercise timing into a generating AI and have the generating AI generate advice.

[0038] The data collection unit can analyze the user's past sleep data and select the optimal data collection method. For example, the data collection unit can select the optimal data collection method based on the devices the user has used in the past (smartwatch, smartphone, etc.). For example, the data collection unit can select the device that provides the most accurate data from the user's past sleep data. The data collection unit can also analyze the user's past data collection history and propose the most efficient data collection method. This enables efficient data collection by selecting the optimal data collection method based on past data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past sleep data into a generating AI and have the generating AI select the optimal data collection method.

[0039] The data collection unit can filter sleep data based on the user's current lifestyle and health status. For example, if the user is ill, the data collection unit can temporarily suspend normal data collection and wait until their health improves. For example, if the user is traveling, the data collection unit can adjust normal data collection to adapt to the environment of the travel destination. Furthermore, if the user is going through a stressful period, the data collection unit can take stress levels into consideration when collecting data. This allows for more accurate data to be obtained by collecting data according to the user's lifestyle and health status. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and health status into a generating AI and have the generating AI perform the filtering of the collected data.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting sleep data. For example, if the user is at high altitude, the data collection unit can prioritize the collection of oxygen concentration and atmospheric pressure data. If the user is in an urban area, the data collection unit can prioritize the collection of data that takes into account noise levels and the effects of light. Furthermore, if the user is in a natural environment, the data collection unit can prioritize the collection of temperature and humidity data. In this way, by considering geographical location information, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI determine the priority of highly relevant data.

[0041] The data collection unit can analyze the user's social media activity and collect relevant data when collecting sleep data. For example, if the user is stressed on social media, the data collection unit can prioritize collecting heart rate and stress level data. For example, if the user is relaxed on social media, the data collection unit can prioritize collecting sleep movement and body temperature data. Furthermore, if the user is excited on social media, the data collection unit can prioritize collecting sleep depth and sleep cycle data. This allows for the efficient collection of relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the sleep data during the analysis. For example, the analysis unit performs detailed analysis on important data (heart rate, breathing patterns, etc.). For example, the analysis unit can perform simplified analysis on secondary data (body temperature, movement, etc.). Furthermore, the analysis unit can prioritize the analysis of data of high importance according to the user's health status. This enables efficient analysis by performing analysis according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the sleep data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of sleep data during analysis. For example, the analysis unit can apply a heart rate variability analysis algorithm to heart rate data. For example, the analysis unit can apply a motion detection algorithm to sleep movement data. The analysis unit can also apply a respiratory analysis algorithm to respiratory pattern data. By performing analysis according to the data category, more accurate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the sleep data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the timing of sleep data collection during the analysis process. For example, the analysis unit may prioritize the analysis of recently collected data. For example, the analysis unit may prioritize the analysis of data collected before and after a specific event (such as travel or illness). The analysis unit may also prioritize the analysis of data that affects the user's daily rhythm. This allows for the prioritization of important data by performing analysis based on the collection timing. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of sleep data collection into a generating AI and have the generating AI determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relationships between sleep data during the analysis. For example, the analysis unit may consider the relationship between heart rate data and respiratory pattern data during the analysis. For example, the analysis unit may consider the relationship between movement data during sleep and body temperature data during the analysis. The analysis unit may also consider the relationship between the user's health status and sleep data during the analysis. This enables efficient analysis by performing analysis based on the relationships between the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input the relationships between sleep data into a generating AI and have the generating AI adjust the order of analysis.

[0046] The suggestion unit can adjust the level of detail of suggestions based on the importance of bedtime and wake-up time. For example, the suggestion unit can provide detailed suggestions for important suggestions (such as bedtime and wake-up time). For example, it can provide simplified suggestions for secondary suggestions (such as relaxation methods and ambient sounds). The suggestion unit can also prioritize suggestions based on the user's health condition. This enables efficient suggestions by providing suggestions according to their importance. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the importance of bedtime and wake-up time into a generating AI and have the generating AI adjust the level of detail of the suggestions.

[0047] The suggestion unit can apply different suggestion algorithms depending on the user's lifestyle when making suggestions. For example, if the user is a night owl, the suggestion unit can suggest a late bedtime. If the user is an early riser, the suggestion unit can suggest an early wake-up time. The suggestion unit can also apply the most suitable suggestion algorithm according to the user's lifestyle. This allows for more appropriate suggestions by tailoring them to the user's lifestyle. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's lifestyle data into a generating AI and have the generating AI apply the suggestion algorithm.

[0048] The suggestion unit can determine the priority of suggestions based on the user's past sleep data when making suggestions. For example, the suggestion unit can prioritize the most effective suggestions based on the user's past sleep data. For example, the suggestion unit can analyze the user's past data and prioritize the most important suggestions. The suggestion unit can also prioritize the most relevant suggestions based on the user's past data. This makes it possible to make effective suggestions by making suggestions based on past data. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input past sleep data into a generating AI and have the generating AI determine the priority of suggestions.

[0049] The suggestion unit can adjust the order of suggestions based on the user's health condition. For example, if the user is tired, the suggestion unit may prioritize suggesting relaxation methods. If the user is healthy, the suggestion unit may prioritize suggesting the timing of exercise. Furthermore, if the user is unwell, the suggestion unit may prioritize advising on caffeine intake. This allows for more appropriate suggestions by tailoring them to the user's health condition. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's health condition data into a generating AI and have the generating AI adjust the order of suggestions.

[0050] The service provider can analyze the user's past relaxation data to select the most suitable ambient sounds and music at the time of service delivery. For example, the service provider can prioritize providing music that the user has previously found relaxing. For example, the service provider can select the most effective ambient sounds from the user's past data. The service provider can also analyze the user's past relaxation data to provide the most suitable music. By providing the most suitable ambient sounds and music based on past data, the relaxation effect can be enhanced. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input past relaxation data into a generating AI and have the generating AI select the most suitable ambient sounds and music.

[0051] The service provider can adjust the volume of ambient sounds and music based on the user's current stress level when providing the service. For example, if the user is at a high stress level, the service provider can provide calm music at a low volume. If the user is at a moderate stress level, the service provider can provide relaxing music at a moderate volume. The service provider can also provide ambient sounds at a normal volume if the user is at a low stress level. By adjusting the volume according to the stress level, the relaxation effect can be enhanced. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's stress level data into a generating AI and have the generating AI adjust the volume of ambient sounds and music.

[0052] The service provider can select the most suitable ambient sounds or music at the time of delivery, taking into account the user's geographical location information. For example, if the user is in a natural environment, the service provider can provide natural sounds. If the user is in an urban area, the service provider can provide music that blocks out urban noise. Furthermore, if the user is at high altitude, the service provider can provide music appropriate for the oxygen concentration. By providing the most suitable ambient sounds or music based on geographical location information, the relaxation effect can be enhanced. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the most suitable ambient sounds or music.

[0053] The service provider can analyze the user's social media activity and provide relevant ambient sounds or music at the time of delivery. For example, if the user is feeling stressed on social media, the service provider can provide relaxing music. For example, if the user is relaxing on social media, the service provider can provide nature sounds. The service provider can also provide a calming melody if the user is excited on social media. In this way, providing ambient sounds or music based on social media activity can enhance the relaxation effect. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the provision of relevant ambient sounds or music.

[0054] The advice unit can adjust the level of detail in its advice based on the importance of caffeine intake and exercise. For example, it can provide detailed advice for important matters (such as caffeine intake and exercise timing), and simplified advice for secondary matters (such as relaxation methods and ambient sounds). The advice unit can also prioritize advice based on the user's health condition. This allows for more efficient advice by providing advice according to its importance. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the importance of caffeine intake and exercise into a generating AI and have the generating AI adjust the level of detail in the advice.

[0055] The advice unit can apply different advice algorithms depending on the user's lifestyle when providing advice. For example, if the user is a night owl, the advice unit can advise avoiding caffeine intake late at night. If the user is an early riser, the advice unit can advise recommending early morning exercise. The advice unit can also apply the most appropriate advice algorithm according to the user's lifestyle. This allows for more appropriate advice by providing advice tailored to the user's lifestyle. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's lifestyle data into a generating AI and have the generating AI execute the application of the advice algorithm.

[0056] The advice unit can determine the priority of advice based on the user's past data when providing advice. For example, the advice unit can prioritize the most effective advice based on the user's past data. For example, the advice unit can analyze the user's past data and prioritize the most important advice. The advice unit can also prioritize the most relevant advice based on the user's past data. This makes it possible to provide effective advice by providing advice based on past data. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input past data into a generating AI and have the generating AI perform the determination of advice priorities.

[0057] The advice unit can adjust the order of advice based on the user's health condition. For example, if the user is tired, the advice unit may prioritize advice on relaxation methods. If the user is healthy, the advice unit may prioritize advice on the timing of exercise. Furthermore, if the user is unwell, the advice unit may prioritize advice on caffeine intake. This allows for more appropriate advice by providing advice tailored to the user's health condition. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's health condition data into a generating AI and have the generating AI adjust the order of advice.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The data collection unit can adjust the timing of data collection based on the user's lifestyle. For example, if the user is a night owl, the unit can prioritize nighttime data collection. Conversely, if the user is an early riser, the unit can prioritize early morning data collection. Furthermore, the frequency and method of data collection can be adjusted according to the user's lifestyle. This allows for more accurate data to be obtained by performing appropriate data collection according to the user's lifestyle.

[0060] The analysis unit can determine the priority of analysis based on the user's health status. For example, if the user is ill, the analysis unit can prioritize analyzing data related to their health status. Conversely, if the user is healthy, the analysis unit can prioritize analyzing data related to sleep quality. Furthermore, the level of detail and method of analysis can be adjusted according to the user's health status. This allows for more accurate analysis results by performing appropriate analysis according to the user's health status.

[0061] The system can adjust the type of ambient sounds and music provided based on the user's geographical location. For example, if the user is in a natural environment, the system can provide natural sounds. If the user is in an urban area, the system can provide music that blocks out urban noise. Furthermore, if the user is at high altitude, the system can provide music suitable for the oxygen concentration. By providing optimal ambient sounds and music based on the user's geographical location, the system can enhance relaxation.

[0062] The advice section can adjust the content of the advice based on the user's past data. For example, it can prioritize advice based on methods that the user has found relaxing in the past. It can also provide the most effective advice based on the user's past data. Furthermore, it can analyze the user's past data to provide optimal advice. By providing optimal advice based on past data, the relaxation effect can be enhanced.

[0063] The suggestion function can adjust its suggestions based on the user's lifestyle. For example, if the user is a night owl, the suggestion function can suggest a late bedtime. If the user is an early riser, the suggestion function can suggest an early wake-up time. Furthermore, it can provide optimal suggestions according to the user's lifestyle. This allows for more appropriate suggestions by providing optimal suggestions tailored to the user's lifestyle.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The data collection unit collects the user's sleep data. The collected data includes the time the user goes to bed, the time they wake up, and their movement and heart rate during sleep. For example, if the user is wearing a smartwatch, the data collection unit can collect that data. The data collection unit records, for example, the time the user goes to bed and the time they wake up. The data collection unit can also detect movement and heart rate during sleep using sensors and collect that data. Step 2: The analysis unit analyzes the data collected by the data collection unit to evaluate sleep patterns and quality. AI is used in the analysis, for example, to analyze the user's movements and heart rate fluctuations during sleep and identify the duration of deep and light sleep. The analysis unit uses AI to analyze the user's sleep data and identify sleep patterns. The analysis unit can also use AI to evaluate sleep quality. Step 3: The suggestion unit proposes recommended bedtimes and wake-up times based on the analysis results obtained by the analysis unit. The suggestions may also include estimating the user's emotions and proposing the optimal time for each emotion. For example, the suggestion unit can use AI to estimate the user's emotions and propose the optimal bedtime. The suggestion unit can also use AI to propose the optimal wake-up time. Step 4: The providing unit provides information about the external environment recommended for sleep based on the analysis results obtained by the analysis unit. The providing unit provides, for example, relaxing ambient sounds or music. The providing unit can provide relaxing sounds such as the sound of waves or birdsong. The providing unit can also provide relaxing music. Step 5: The advice unit provides advice on factors affecting sleep based on the analysis results obtained by the analysis unit. For example, the advice unit may advise on the timing of caffeine intake and exercise. For example, the advice unit may advise that caffeine intake should be avoided for several hours before bedtime. The advice unit may also advise that exercising before bedtime can improve sleep quality.

[0066] (Example of form 2) The sleep support system according to an embodiment of the present invention is a system that analyzes a user's sleep patterns and quality and recommends optimal bedtime and wake-up times. This system collects and analyzes the user's sleep data and proposes optimal bedtime and wake-up times. It also provides relaxing ambient sounds and music to improve sleep quality. Furthermore, it provides advice on factors that affect sleep, such as the timing of caffeine intake and exercise. For example, if the user is wearing a smartwatch, its data can be collected. Next, the collected data is analyzed. AI is used for the analysis to evaluate sleep patterns and quality. For example, it can analyze the user's movements and heart rate fluctuations during sleep to identify periods of deep sleep and light sleep. Based on the analysis results, it proposes optimal bedtime and wake-up times. The proposal also includes estimating the user's emotions and suggesting optimal times for each emotion. For example, if the user is feeling stressed, it can suggest a time when they can relax. It also provides relaxing ambient sounds and music during sleep. For example, by providing relaxing sounds such as the sound of waves or birdsong, the quality of the user's sleep can be improved. Furthermore, it provides advice on factors that affect sleep, such as the timing of caffeine intake and exercise. For example, it can advise that caffeine intake should be avoided for several hours before bedtime, and that exercising before bedtime can improve sleep quality. In this way, the AI ​​assistant of the present invention not only collects and analyzes the user's sleep data and proposes optimal bedtime and wake-up times, but also provides relaxing ambient sounds and music, and offers advice on factors that affect sleep, thereby improving the user's sleep quality. As a result, the sleep support system can efficiently collect, analyze, propose, provide, and advise on the user's sleep data.

[0067] The sleep support system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a provision unit, and an advice unit. The data collection unit collects the user's sleep data. The collected data includes the time the user goes to bed, the time they wake up, and their movements and heart rate during sleep. For example, if the user is wearing a smartwatch, the data collection unit can collect that data. The data collection unit records, for example, the time the user goes to bed and the time they wake up. The data collection unit can also detect movements and heart rate during sleep using sensors and collect this data. The analysis unit analyzes the data collected by the data collection unit and evaluates the sleep patterns and quality. AI is used for the analysis, and for example, it can analyze the user's movements and heart rate fluctuations during sleep to identify periods of deep sleep and light sleep. The analysis unit uses AI to analyze the user's sleep data and identify sleep patterns. The analysis unit can also use AI to evaluate the quality of sleep. The proposal unit proposes recommended bedtimes and wake-up times based on the analysis results obtained by the analysis unit. The suggestions may include estimating the user's emotions and proposing the optimal time for each emotion. The suggestion unit, for example, uses AI to estimate the user's emotions and propose the optimal bedtime. The suggestion unit can also use AI to propose the optimal wake-up time. The provision unit provides information about the external environment recommended for sleep based on the analysis results obtained by the analysis unit. The provision unit provides, for example, relaxing ambient sounds or music. The provision unit can provide relaxing sounds such as the sound of waves or birdsong. The provision unit can also provide relaxing music. The advice unit provides advice on factors that affect sleep based on the analysis results obtained by the analysis unit. The advice unit provides, for example, advice on the timing of caffeine intake and exercise. The advice unit may advise that caffeine intake should be avoided for several hours before bedtime. The advice unit may also advise that exercising before bedtime improves sleep quality. As a result, the sleep support system according to this embodiment can efficiently collect, analyze, suggest, provide, and advise on the user's sleep data.

[0068] The data collection unit collects the user's sleep data. This data includes the user's bedtime, wake-up time, sleep movements, and heart rate. For example, if the user is wearing a smartwatch, the unit can collect data from it. Specifically, it uses the smartwatch's accelerometer and heart rate sensor to monitor the user's movements and heart rate in real time and collect data. The unit records, for instance, the user's bedtime and wake-up time. It can also detect and collect data on sleep movements and heart rate using sensors. Furthermore, the unit can detect the user's breathing patterns and body temperature fluctuations, comprehensively collecting this data. This allows the unit to gain a detailed understanding of the user's sleep state and collect highly accurate data for the analysis unit. The collected data is sent to a cloud server for access by the analysis unit. The unit can also adjust the data collection frequency and accuracy to provide flexible responses to specific situations and conditions. For example, if a user is experiencing stress or has a specific health condition, the frequency of data collection can be increased to collect more detailed data. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0069] The analysis unit analyzes the data collected by the data collection unit to evaluate sleep patterns and quality. AI is used in the analysis, for example, to analyze the user's movements and heart rate variability during sleep to identify periods of deep and light sleep. Specifically, the AI ​​classifies the user's sleep stages based on the collected data and analyzes the duration and frequency of each stage. For example, the AI ​​analyzes the user's heart rate variability patterns to identify the stages of deep sleep, light sleep, and REM sleep. The AI ​​can also analyze the user's movement data to evaluate the frequency and intensity of body movements during sleep. This allows the analysis unit to evaluate the user's sleep quality in detail and provide specific advice for improvement. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term sleep pattern variations and trends. For example, based on data from the past few months, it can evaluate variations in the user's sleep quality and identify the impact of specific factors on sleep. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term sleep management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0070] The suggestion unit proposes recommended bedtimes and wake-up times based on the analysis results obtained by the analysis unit. The suggestions also include estimating the user's emotions and suggesting optimal times for each emotion. Specifically, it uses AI to estimate the user's emotions; for example, if stress levels are high, it suggests a time for relaxation, and if energy levels are low, it recommends going to bed earlier. The suggestion unit can also use AI to estimate the user's emotions and suggest the optimal bedtime. Furthermore, the suggestion unit can use AI to suggest the optimal wake-up time. In addition, the suggestion unit can provide individually customized suggestions considering the user's lifestyle and daily schedule. For example, if a user has a habit of exercising early in the morning, it will suggest an appropriate wake-up time, and if they work late into the night, it will suggest an appropriate bedtime. This allows the suggestion unit to provide an optimal sleep schedule tailored to the user's individual needs and improve sleep quality. Furthermore, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can provide feedback on the user's results following the suggested bedtimes and wake-up times, and adjust the suggestion algorithm based on that data. This allows the proposal department to provide users with more effective suggestions and improve their sleep quality.

[0071] The service provider provides information on recommended external environments for sleep based on the analysis results obtained by the analysis unit. Specifically, it provides relaxing ambient sounds and music. For example, the service provider can provide relaxing sounds such as the sound of waves or birdsong. It can also provide relaxing music. Furthermore, the service provider can provide individually customized ambient sounds and music based on the user's preferences and past data. For example, it can select and provide the optimal sounds based on music and ambient sounds that the user has found relaxing in the past. The service provider can also adjust the volume and playback time of the sounds to provide the environment in which the user can relax the most. In this way, the service provider can optimize the user's sleep environment and improve the quality of sleep. Furthermore, the service provider can also provide information on other external environmental factors such as light adjustment and temperature control. For example, it may recommend dimming the lights in the room before going to bed or maintaining an appropriate room temperature. In this way, the service provider can optimize the user's overall sleep environment and support better sleep.

[0072] The advice unit provides advice on factors affecting sleep based on the analysis results obtained by the analysis unit. Specifically, it provides advice on the timing of caffeine intake and exercise. For example, the advice unit may advise that caffeine intake should be avoided for several hours before bedtime. It may also advise that exercising before bedtime improves sleep quality. Furthermore, the advice unit can provide individually customized advice considering the user's lifestyle and health condition. For example, if the user is experiencing stress, it can provide advice on relaxation methods and stress management. If the user has a specific health problem, it can provide advice addressing that problem. In this way, the advice unit can provide specific advice tailored to the user's individual needs and improve sleep quality. In addition, the advice unit can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, it can provide feedback on the results of following the advice and adjust the advice algorithm based on that data. In this way, the advice unit can provide more effective advice to the user and improve sleep quality.

[0073] The service provider can provide information about ambient sounds and music for sleep. For example, it can provide relaxing ocean sounds. It can also provide nature sounds or birdsong. Furthermore, it can provide soothing melodies. By providing relaxing ambient sounds and music, the quality of the user's sleep can be improved. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can have a generating AI select relaxing music.

[0074] The data collection unit can collect data including the user's bedtime, wake-up time, sleep movements, and heart rate. For example, the data collection unit can record the user's bedtime and wake-up time. The data collection unit can also detect sleep movements and heart rate using sensors and collect this data. For example, the data collection unit can use a smartwatch to monitor the user's heart rate in real time and collect the data. This allows for more accurate analysis by collecting detailed sleep data of the user. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from a smartwatch into a generating AI and have the generating AI perform data analysis.

[0075] The advice unit can provide advice on the effects of caffeine intake timing and exercise timing on sleep. For example, the advice unit may advise that caffeine intake should be avoided for several hours before bedtime. The advice unit may also advise that exercising before bedtime improves sleep quality. In this way, the quality of the user's sleep can be improved by providing advice on caffeine intake and exercise timing. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input data on caffeine intake and exercise timing into a generating AI and have the generating AI generate advice.

[0076] The suggestion unit can estimate the user's emotions and suggest recommended bedtimes and wake-up times for each estimated emotion. For example, the suggestion unit can use AI to estimate the user's emotions and suggest the optimal bedtime. For example, if the user is feeling stressed, the suggestion unit can suggest a time when the user can relax. The suggestion unit can also use AI to suggest the optimal wake-up time. This allows for improved sleep quality by suggesting optimal bedtimes and wake-up times according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI suggest optimal bedtimes and wake-up times.

[0077] The data collection unit can estimate the user's emotions and adjust the timing of sleep data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect data during a time when the user is relaxed. For example, if the user is tired, the data collection unit can avoid collecting data immediately before bedtime and instead collect it after waking up. Furthermore, if the user is agitated, the data collection unit can delay data collection until their emotions have calmed down. This allows for more accurate data to be obtained by collecting data at the appropriate time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's emotion data into the generative AI and have the generative AI execute the timing of data collection.

[0078] The data collection unit can analyze the user's past sleep data and select the optimal data collection method. For example, the data collection unit can select the optimal data collection method based on the devices the user has used in the past (smartwatch, smartphone, etc.). For example, the data collection unit can select the device that provides the most accurate data from the user's past sleep data. The data collection unit can also analyze the user's past data collection history and propose the most efficient data collection method. This enables efficient data collection by selecting the optimal data collection method based on past data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past sleep data into a generating AI and have the generating AI select the optimal data collection method.

[0079] The data collection unit can filter sleep data based on the user's current lifestyle and health status. For example, if the user is ill, the data collection unit can temporarily suspend normal data collection and wait until their health improves. For example, if the user is traveling, the data collection unit can adjust normal data collection to adapt to the environment of the travel destination. Furthermore, if the user is going through a stressful period, the data collection unit can take stress levels into consideration when collecting data. This allows for more accurate data to be obtained by collecting data according to the user's lifestyle and health status. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and health status into a generating AI and have the generating AI perform the filtering of the collected data.

[0080] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting heart rate and breathing pattern data. If the user is relaxed, the data collection unit may prioritize collecting sleep movement and body temperature data. If the user is tired, the data collection unit may also prioritize collecting sleep depth and sleep cycle data. This allows for the priority collection of important data by determining the data priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the data priority.

[0081] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting sleep data. For example, if the user is at high altitude, the data collection unit can prioritize the collection of oxygen concentration and atmospheric pressure data. If the user is in an urban area, the data collection unit can prioritize the collection of data that takes into account noise levels and the effects of light. Furthermore, if the user is in a natural environment, the data collection unit can prioritize the collection of temperature and humidity data. In this way, by considering geographical location information, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI determine the priority of highly relevant data.

[0082] The data collection unit can analyze the user's social media activity and collect relevant data when collecting sleep data. For example, if the user is stressed on social media, the data collection unit can prioritize collecting heart rate and stress level data. For example, if the user is relaxed on social media, the data collection unit can prioritize collecting sleep movement and body temperature data. Furthermore, if the user is excited on social media, the data collection unit can prioritize collecting sleep depth and sleep cycle data. This allows for the efficient collection of relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0083] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide simple and visually easy-to-understand analysis results. If the user is relaxed, the analysis unit can provide detailed analysis results that offer deeper insights. The analysis unit can also provide visually stimulating analysis results if the user is excited. By providing analysis results that are tailored to the user's emotions, more easily understandable results can be obtained. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis results.

[0084] The analysis unit can adjust the level of detail of the analysis based on the importance of the sleep data during the analysis. For example, the analysis unit performs detailed analysis on important data (heart rate, breathing patterns, etc.). For example, the analysis unit can perform simplified analysis on secondary data (body temperature, movement, etc.). Furthermore, the analysis unit can prioritize the analysis of data of high importance according to the user's health status. This enables efficient analysis by performing analysis according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the sleep data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0085] The analysis unit can apply different analysis algorithms depending on the category of sleep data during analysis. For example, the analysis unit can apply a heart rate variability analysis algorithm to heart rate data. For example, the analysis unit can apply a motion detection algorithm to sleep movement data. The analysis unit can also apply a respiratory analysis algorithm to respiratory pattern data. By performing analysis according to the data category, more accurate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the sleep data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0086] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. In this way, appropriate analysis results can be provided by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0087] The analysis unit can determine the priority of analysis based on the timing of sleep data collection during the analysis process. For example, the analysis unit may prioritize the analysis of recently collected data. For example, the analysis unit may prioritize the analysis of data collected before and after a specific event (such as travel or illness). The analysis unit may also prioritize the analysis of data that affects the user's daily rhythm. This allows for the prioritization of important data by performing analysis based on the collection timing. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of sleep data collection into a generating AI and have the generating AI determine the analysis priority.

[0088] The analysis unit can adjust the order of analysis based on the relationships between sleep data during the analysis. For example, the analysis unit may consider the relationship between heart rate data and respiratory pattern data during the analysis. For example, the analysis unit may consider the relationship between movement data during sleep and body temperature data during the analysis. The analysis unit may also consider the relationship between the user's health status and sleep data during the analysis. This enables efficient analysis by performing analysis based on the relationships between the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input the relationships between sleep data into a generating AI and have the generating AI adjust the order of analysis.

[0089] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can provide simple and visually easy-to-understand suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions and deeper insights. If the user is excited, the suggestion unit can also provide visually stimulating suggestions. By providing suggestions tailored to the user's emotions, it is possible to offer suggestions that are easier to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way it presents suggestions.

[0090] The suggestion unit can adjust the level of detail of suggestions based on the importance of bedtime and wake-up time. For example, the suggestion unit can provide detailed suggestions for important suggestions (such as bedtime and wake-up time). For example, it can provide simplified suggestions for secondary suggestions (such as relaxation methods and ambient sounds). The suggestion unit can also prioritize suggestions based on the user's health condition. This enables efficient suggestions by providing suggestions according to their importance. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the importance of bedtime and wake-up time into a generating AI and have the generating AI adjust the level of detail of the suggestions.

[0091] The suggestion unit can apply different suggestion algorithms depending on the user's lifestyle when making suggestions. For example, if the user is a night owl, the suggestion unit can suggest a late bedtime. If the user is an early riser, the suggestion unit can suggest an early wake-up time. The suggestion unit can also apply the most suitable suggestion algorithm according to the user's lifestyle. This allows for more appropriate suggestions by tailoring them to the user's lifestyle. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's lifestyle data into a generating AI and have the generating AI apply the suggestion algorithm.

[0092] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, for example, the suggestion unit can provide detailed suggestions. Furthermore, if the user is excited, the suggestion unit can provide visually stimulating suggestions. In this way, appropriate suggestions can be provided by adjusting the length of the suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into the generative AI and have the generative AI adjust the length of the suggestions.

[0093] The suggestion unit can determine the priority of suggestions based on the user's past sleep data when making suggestions. For example, the suggestion unit can prioritize the most effective suggestions based on the user's past sleep data. For example, the suggestion unit can analyze the user's past data and prioritize the most important suggestions. The suggestion unit can also prioritize the most relevant suggestions based on the user's past data. This makes it possible to make effective suggestions by making suggestions based on past data. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input past sleep data into a generating AI and have the generating AI determine the priority of suggestions.

[0094] The suggestion unit can adjust the order of suggestions based on the user's health condition. For example, if the user is tired, the suggestion unit may prioritize suggesting relaxation methods. If the user is healthy, the suggestion unit may prioritize suggesting the timing of exercise. Furthermore, if the user is unwell, the suggestion unit may prioritize advising on caffeine intake. This allows for more appropriate suggestions by tailoring them to the user's health condition. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's health condition data into a generating AI and have the generating AI adjust the order of suggestions.

[0095] The service provider can estimate the user's emotions and adjust the types of ambient sounds and music provided based on the estimated emotions. For example, if the user is stressed, the service provider can provide relaxing ocean waves. If the user is relaxed, the service provider can provide nature sounds or birdsong. The service provider can also provide calming melodies if the user is excited. This enhances the relaxation effect by providing ambient sounds and music that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the types of ambient sounds and music provided.

[0096] The service provider can analyze the user's past relaxation data to select the most suitable ambient sounds and music at the time of service delivery. For example, the service provider can prioritize providing music that the user has previously found relaxing. For example, the service provider can select the most effective ambient sounds from the user's past data. The service provider can also analyze the user's past relaxation data to provide the most suitable music. By providing the most suitable ambient sounds and music based on past data, the relaxation effect can be enhanced. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input past relaxation data into a generating AI and have the generating AI select the most suitable ambient sounds and music.

[0097] The service provider can adjust the volume of ambient sounds and music based on the user's current stress level when providing the service. For example, if the user is at a high stress level, the service provider can provide calm music at a low volume. If the user is at a moderate stress level, the service provider can provide relaxing music at a moderate volume. The service provider can also provide ambient sounds at a normal volume if the user is at a low stress level. By adjusting the volume according to the stress level, the relaxation effect can be enhanced. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's stress level data into a generating AI and have the generating AI adjust the volume of ambient sounds and music.

[0098] The service provider can estimate the user's emotions and adjust the order of ambient sounds and music provided based on the estimated emotions. For example, if the user is stressed, the service provider can provide relaxing music first. If the user is relaxed, the service provider can provide nature sounds first. The service provider can also provide calming melodies first if the user is excited. This enhances the relaxation effect by providing ambient sounds and music in an order that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the order of ambient sounds and music provided.

[0099] The service provider can select the most suitable ambient sounds or music at the time of delivery, taking into account the user's geographical location information. For example, if the user is in a natural environment, the service provider can provide natural sounds. If the user is in an urban area, the service provider can provide music that blocks out urban noise. Furthermore, if the user is at high altitude, the service provider can provide music appropriate for the oxygen concentration. By providing the most suitable ambient sounds or music based on geographical location information, the relaxation effect can be enhanced. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the most suitable ambient sounds or music.

[0100] The service provider can analyze the user's social media activity and provide relevant ambient sounds or music at the time of delivery. For example, if the user is feeling stressed on social media, the service provider can provide relaxing music. For example, if the user is relaxing on social media, the service provider can provide nature sounds. The service provider can also provide a calming melody if the user is excited on social media. In this way, providing ambient sounds or music based on social media activity can enhance the relaxation effect. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the provision of relevant ambient sounds or music.

[0101] The advice unit can estimate the user's emotions and adjust the way it presents advice based on those emotions. For example, if the user is stressed, the advice unit can provide simple, visually easy-to-understand advice. If the user is relaxed, the advice unit can provide detailed advice and deeper insights. If the user is excited, the advice unit can also provide visually stimulating advice. By providing advice tailored to the user's emotions, it is possible to provide more easily understandable advice. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input user emotion data into the generative AI and have the generative AI adjust the way it presents the advice.

[0102] The advice unit can adjust the level of detail in its advice based on the importance of caffeine intake and exercise. For example, it can provide detailed advice for important matters (such as caffeine intake and exercise timing), and simplified advice for secondary matters (such as relaxation methods and ambient sounds). The advice unit can also prioritize advice based on the user's health condition. This allows for more efficient advice by providing advice according to its importance. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the importance of caffeine intake and exercise into a generating AI and have the generating AI adjust the level of detail in the advice.

[0103] The advice unit can apply different advice algorithms depending on the user's lifestyle when providing advice. For example, if the user is a night owl, the advice unit can advise avoiding caffeine intake late at night. If the user is an early riser, the advice unit can advise recommending early morning exercise. The advice unit can also apply the most appropriate advice algorithm according to the user's lifestyle. This allows for more appropriate advice by providing advice tailored to the user's lifestyle. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's lifestyle data into a generating AI and have the generating AI execute the application of the advice algorithm.

[0104] The advice unit can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is in a hurry, the advice unit can provide short, concise advice. If the user is relaxed, the advice unit can provide detailed advice. Furthermore, if the user is excited, the advice unit can provide visually stimulating advice. By adjusting the length of the advice according to the user's emotions, appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input user emotion data into the generative AI and have the generative AI adjust the length of the advice.

[0105] The advice unit can determine the priority of advice based on the user's past data when providing advice. For example, the advice unit can prioritize the most effective advice based on the user's past data. For example, the advice unit can analyze the user's past data and prioritize the most important advice. The advice unit can also prioritize the most relevant advice based on the user's past data. This makes it possible to provide effective advice by providing advice based on past data. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input past data into a generating AI and have the generating AI perform the determination of advice priorities.

[0106] The advice unit can adjust the order of advice based on the user's health condition. For example, if the user is tired, the advice unit may prioritize advice on relaxation methods. If the user is healthy, the advice unit may prioritize advice on the timing of exercise. Furthermore, if the user is unwell, the advice unit may prioritize advice on caffeine intake. This allows for more appropriate advice by providing advice tailored to the user's health condition. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's health condition data into a generating AI and have the generating AI adjust the order of advice.

[0107] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0108] The suggestion unit can estimate the user's emotions and customize the user's sleep environment based on those emotions. For example, if the user is stressed, the suggestion unit can suggest relaxing lighting and scents. If the user is relaxed, the suggestion unit can also suggest comfortable temperature and humidity levels. Furthermore, if the user is agitated, the suggestion unit can suggest ways to create a calming environment. This allows for improved sleep quality by providing an optimal sleep environment tailored to the user's emotions.

[0109] The data collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can increase the frequency of data collection to collect more detailed data. Conversely, if the user is relaxed, the data collection unit can decrease the frequency of data collection to reduce the user's burden. Furthermore, if the user is agitated, the data collection unit can adjust the timing of data collection and wait until the user's emotions stabilize. This allows for more accurate data to be obtained by collecting data appropriately according to the user's emotions.

[0110] The analysis unit can estimate the user's emotions and adjust the feedback method of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide simple and easy-to-understand feedback. If the user is relaxed, the analysis unit can provide detailed feedback, giving the user deeper insights. Furthermore, if the user is excited, the analysis unit can provide visually stimulating feedback. In this way, by providing appropriate feedback according to the user's emotions, a deeper understanding of the user can be achieved.

[0111] The system can estimate the user's emotions and adjust the type of ambient sounds and music provided based on those estimates. For example, if the user is stressed, the system can provide relaxing ocean waves. If the user is relaxed, it can provide nature sounds or birdsong. Furthermore, if the user is agitated, it can provide calming melodies. By providing ambient sounds and music that match the user's emotions, the system can enhance the relaxing effect.

[0112] The advice unit can estimate the user's emotions and adjust the advice based on those emotions. For example, if the user is stressed, the advice unit can provide advice on relaxation methods and stress relief techniques. If the user is relaxed, the advice unit can also provide advice on further improving sleep quality. Furthermore, if the user is agitated, the advice unit can provide advice on how to calm down. In this way, by providing appropriate advice tailored to the user's emotions, the quality of the user's sleep can be improved.

[0113] The data collection unit can adjust the timing of data collection based on the user's lifestyle. For example, if the user is a night owl, the unit can prioritize nighttime data collection. Conversely, if the user is an early riser, the unit can prioritize early morning data collection. Furthermore, the frequency and method of data collection can be adjusted according to the user's lifestyle. This allows for more accurate data to be obtained by performing appropriate data collection according to the user's lifestyle.

[0114] The analysis unit can determine the priority of analysis based on the user's health status. For example, if the user is ill, the analysis unit can prioritize analyzing data related to their health status. Conversely, if the user is healthy, the analysis unit can prioritize analyzing data related to sleep quality. Furthermore, the level of detail and method of analysis can be adjusted according to the user's health status. This allows for more accurate analysis results by performing appropriate analysis according to the user's health status.

[0115] The system can adjust the type of ambient sounds and music provided based on the user's geographical location. For example, if the user is in a natural environment, the system can provide natural sounds. If the user is in an urban area, the system can provide music that blocks out urban noise. Furthermore, if the user is at high altitude, the system can provide music suitable for the oxygen concentration. By providing optimal ambient sounds and music based on the user's geographical location, the system can enhance relaxation.

[0116] The advice section can adjust the content of the advice based on the user's past data. For example, it can prioritize advice based on methods that the user has found relaxing in the past. It can also provide the most effective advice based on the user's past data. Furthermore, it can analyze the user's past data to provide optimal advice. By providing optimal advice based on past data, the relaxation effect can be enhanced.

[0117] The suggestion function can adjust its suggestions based on the user's lifestyle. For example, if the user is a night owl, the suggestion function can suggest a late bedtime. If the user is an early riser, the suggestion function can suggest an early wake-up time. Furthermore, it can provide optimal suggestions according to the user's lifestyle. This allows for more appropriate suggestions by providing optimal suggestions tailored to the user's lifestyle.

[0118] The following briefly describes the processing flow for example form 2.

[0119] Step 1: The data collection unit collects the user's sleep data. The collected data includes the time the user goes to bed, the time they wake up, and their movement and heart rate during sleep. For example, if the user is wearing a smartwatch, the data collection unit can collect that data. The data collection unit records, for example, the time the user goes to bed and the time they wake up. The data collection unit can also detect movement and heart rate during sleep using sensors and collect that data. Step 2: The analysis unit analyzes the data collected by the data collection unit to evaluate sleep patterns and quality. AI is used in the analysis, for example, to analyze the user's movements and heart rate fluctuations during sleep and identify the duration of deep and light sleep. The analysis unit uses AI to analyze the user's sleep data and identify sleep patterns. The analysis unit can also use AI to evaluate sleep quality. Step 3: The suggestion unit proposes recommended bedtimes and wake-up times based on the analysis results obtained by the analysis unit. The suggestions may also include estimating the user's emotions and proposing the optimal time for each emotion. For example, the suggestion unit can use AI to estimate the user's emotions and propose the optimal bedtime. The suggestion unit can also use AI to propose the optimal wake-up time. Step 4: The providing unit provides information about the external environment recommended for sleep based on the analysis results obtained by the analysis unit. The providing unit provides, for example, relaxing ambient sounds or music. The providing unit can provide relaxing sounds such as the sound of waves or birdsong. The providing unit can also provide relaxing music. Step 5: The advice unit provides advice on factors affecting sleep based on the analysis results obtained by the analysis unit. For example, the advice unit may advise on the timing of caffeine intake and exercise. For example, the advice unit may advise that caffeine intake should be avoided for several hours before bedtime. The advice unit may also advise that exercising before bedtime can improve sleep quality.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0122] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] For example, the data collection unit can collect the user's sleep data using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12, which analyzes the collected data and evaluates the sleep patterns and quality. The suggestion unit is implemented by the specific processing unit 290 of the data processing device 12, which suggests the optimal bedtime and wake-up time based on the analysis results. The provision unit is implemented by the control unit 46A of the smart device 14, which provides relaxing ambient sounds or music. The advice unit is implemented by the specific processing unit 290 of the data processing device 12, which provides advice on the timing of caffeine intake and exercise. The correspondence between each unit and the device and control unit is not limited to the example described above, and various modifications are possible.

[0124] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0125] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0127] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0128] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0130] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0131] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0132] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0133] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0134] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0138] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0139] For example, the data collection unit can collect the user's sleep data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12, which analyzes the collected data and evaluates the sleep pattern and quality. The suggestion unit is implemented by the specific processing unit 290 of the data processing device 12, which suggests the optimal bedtime and wake-up time based on the analysis results. The provision unit is implemented by the control unit 46A of the smart glasses 214, which provides relaxing ambient sounds or music. The advice unit is implemented by the specific processing unit 290 of the data processing device 12, which provides advice on the timing of caffeine intake and exercise. The correspondence between each unit and the device and control unit is not limited to the example described above, and various modifications are possible.

[0140] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0141] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0143] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0144] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0146] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0147] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0148] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] For example, the data collection unit can collect the user's sleep data using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12, which analyzes the collected data and evaluates the sleep pattern and quality. The suggestion unit is implemented by the specific processing unit 290 of the data processing device 12, which suggests the optimal bedtime and wake-up time based on the analysis results. The provision unit is implemented by the control unit 46A of the headset terminal 314, which provides relaxing ambient sounds or music. The advice unit is implemented by the specific processing unit 290 of the data processing device 12, which provides advice on the timing of caffeine intake and exercise. The correspondence between each unit and the device and control unit is not limited to the example described above, and various modifications are possible.

[0156] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0157] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0159] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0160] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0162] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0163] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0164] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0165] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0166] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0167] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0168] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0169] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0170] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0171] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0172] For example, the data collection unit can collect the user's sleep data using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12, which analyzes the collected data and evaluates the sleep pattern and quality. The suggestion unit is implemented by the specific processing unit 290 of the data processing device 12, which suggests the optimal bedtime and wake-up time based on the analysis results. The provision unit is implemented by the control unit 46A of the robot 414, which provides relaxing ambient sounds or music. The advice unit is implemented by the specific processing unit 290 of the data processing device 12, which provides advice on the timing of caffeine intake and exercise. The correspondence between each unit and the device and control unit is not limited to the example described above, and various modifications are possible.

[0173] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0175] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0176] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0177] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0180] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0181] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0182] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0183] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0184] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0185] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0186] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0188] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0189] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0190] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0191] (Note 1) A data collection unit that collects user sleep data, An analysis unit analyzes the data collected by the aforementioned collection unit and evaluates sleep patterns and quality. A proposal unit proposes recommended bedtime and wake-up time based on the analysis results obtained by the aforementioned analysis unit, A providing unit that provides information on the external environment recommended during sleep based on the analysis results obtained by the aforementioned analysis unit, The system includes an advice unit that provides advice on factors affecting sleep based on the analysis results obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned supply unit is, Provides information on ambient sounds and music during sleep. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is The system collects data including the user's bedtime, wake-up time, sleep movements, and heart rate. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned advice section, This provides advice on the impact of caffeine intake timing and exercise on sleep. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, It estimates the user's emotions and suggests recommended bedtimes and wake-up times based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of sleep data collection based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past sleep data and select the data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting sleep data, filtering is performed based on the user's current lifestyle and health status. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting sleep data, the system prioritizes data collection by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting sleep data, the system analyzes and collects data on the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the sleep data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of sleep data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of analysis is determined based on when the sleep data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the sleep data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of bedtime and wake-up time. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the user's daily routine. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making suggestions, the priority of suggestions is determined based on the user's past sleep data. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the user's health status. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts the types of ambient sounds and music provided based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing the service, the system analyzes the user's past data on relaxation effects to select ambient sounds and music. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, the volume of ambient sounds and music will be adjusted based on the user's current stress level. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and adjusts the order of ambient sounds and music provided based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, ambient sounds and music are selected considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, the system analyzes the user's social media activity to deliver ambient sounds and music. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned advice section, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned advice section, When providing advice, adjust the level of detail based on the importance of caffeine intake and exercise. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned advice section, When providing advice, different advice algorithms are applied depending on the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned advice section, It estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned advice section, When providing advice, the system prioritizes the advice based on the user's past data. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned advice section, When providing advice, the order of advice is adjusted based on the user's health status. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects user sleep data, An analysis unit analyzes the data collected by the aforementioned collection unit and evaluates sleep patterns and quality. A proposal unit proposes recommended bedtime and wake-up time based on the analysis results obtained by the aforementioned analysis unit, A providing unit that provides information on the external environment recommended during sleep based on the analysis results obtained by the aforementioned analysis unit, The system includes an advice unit that provides advice on factors affecting sleep based on the analysis results obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned supply unit is, Provides information on ambient sounds and music during sleep. The system according to feature 1.

3. The aforementioned collection unit is The system collects data including the user's bedtime, wake-up time, sleep movements, and heart rate. The system according to feature 1.

4. The aforementioned advice section, This provides advice on the impact of caffeine intake timing and exercise on sleep. The system according to feature 1.

5. The aforementioned proposal section is, It estimates the user's emotions and suggests recommended bedtimes and wake-up times based on the estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of sleep data collection based on those emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the user's past sleep data and select the data collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting sleep data, filtering is performed based on the user's current lifestyle and health status. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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