System

Abstract

An object of a system according to an embodiment is to analyze sleep data of a parent and a child and provide an individual sleep improvement plan.SOLUTION: A system includes a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects sleep data of the parent and the child. The analysis unit analyzes the sleep data collected by the data collection unit. The proposal unit provides an individual sleep improvement plan on the basis of a result analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem of not being able to effectively utilize parent-child sleep data to provide individualized sleep improvement plans.

[0005] The system according to the embodiment aims to analyze sleep data of parents and children and provide an individual sleep improvement plan. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects sleep data of the parent and child. The analysis unit analyzes the sleep data collected by the data collection unit. The proposal unit provides an individualized sleep improvement plan based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze sleep data of parents and children and provide an individualized sleep improvement plan. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple 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), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The sleep support system according to an embodiment of the present invention uses AI to provide effective sleep support so that parents and children can sleep healthily and comfortably. This helps promote healthy mental and physical growth for parents and children and reduces stress for parents.

[0029] A sleep support system according to an embodiment includes a data collection unit, an analysis unit, and a suggestion unit. The data collection unit collects sleep data of both parents and children. For example, the data collection unit may collect data such as sleep duration, sleep quality, and the number of times the parent turns over in sleep via a smartwatch or smartphone app. The data collection unit may also use sensors to collect the sleep data of both parents and children. For example, a pressure sensor installed on the bed or a heart rate sensor built into a pillow may be used. The analysis unit analyzes the sleep data collected by the data collection unit. For example, an AI may analyze the sleep patterns of both parents and children based on the collected data. The analysis unit may also analyze the sleep data using a machine learning algorithm. For example, an AI may learn from past data and predict future sleep patterns. The suggestion unit provides an individualized sleep improvement plan based on the analysis results of the analysis unit. For example, the suggestion unit may suggest appropriate bedtimes, wake-up times, and relaxation methods before bed to the child, and suggest relaxation methods to reduce stress and ways to improve the sleep environment to the parent. The suggestion unit may also provide an optimal sleep improvement plan for each parent and child based on the plan generated by the AI. For example, the AI ​​can provide specific advice based on a prompt such as "It's good to go to bed at this time." This allows the sleep support system according to the embodiment to support parents and children in achieving healthy sleep with peace of mind. For example, when parents and children get high-quality sleep, they become more active during the day and their overall quality of life improves. Furthermore, reducing parental stress improves the atmosphere in the home and increases positive parenting experiences.

[0030] The data collection unit can collect daytime activity data in addition to sleep data. The analysis unit can analyze overall health status based on the sleep data and daytime activity data. The data collection unit, for example, uses a smartwatch or smartphone app to collect daytime exercise volume in addition to sleep data. For example, it records the number of steps and exercise intensity and analyzes the correlation with sleep quality. The data collection unit also uses an app that records meal content to collect meal timing and content as data. This allows for analyzing the relationship between meals and sleep and suggesting optimal meal times. The data collection unit also uses a device that records heart rate and electrodermal activity to measure daytime stress levels. This allows for analyzing the correlation between stress and sleep quality and suggesting stress management. In this way, by collecting daytime activity data in addition to sleep data and analyzing overall health status, a more accurate sleep improvement plan can be provided.

[0031] The data collection unit can take into account the influence of seasons and weather when analyzing sleep data. The analysis unit can suggest optimal sleep patterns for each season. The data collection unit, for example, collects temperature and humidity data for each season and analyzes it in combination with sleep data. For example, it can suggest optimal sleep environments that differ between summer and winter. The data collection unit also obtains weather data in real time and links it with sleep data. For example, it can analyze different sleep patterns on rainy days and sunny days and provide appropriate advice. The data collection unit also takes into account seasonal changes in daylight hours and suggests adjusting sleep times. For example, since daylight hours are shorter in winter, it can recommend going to bed earlier. In this way, by taking the influence of seasons and weather into account in the analysis, it is possible to suggest optimal sleep patterns for each season.

[0032] The data collection unit can add a function that allows a user to report their sleep status by voice using a voice assistant. The analysis unit can analyze the sleep data reported by voice. For example, the data collection unit can add a function that allows a user to report their sleep quality and dream content by voice every morning when they wake up using a voice assistant. This makes it possible to collect detailed sleep data. The data collection unit also provides a function that allows a user to report their stress level and mood for the day before going to bed through the voice assistant. This makes it possible to analyze the relationship between emotional state and sleep. The data collection unit also adds a function that allows a user to report discomfort felt during sleep and the number of times they woke up during sleep using the voice assistant. This makes it possible to analyze the quality of sleep in detail. By adding a function that allows a user to report their sleep status by voice using a voice assistant, detailed sleep data can be collected and analyzed.

[0033] The data collection unit can provide customizable data collection modules for different age groups and lifestyles. The analysis unit can analyze the data collected by the customizable data collection modules. The data collection unit can provide, for example, a data collection module for children to collect sleep data according to developmental stages. For example, it can provide data collection methods suitable for infants, toddlers, and school-age children. The data collection unit can also provide a data collection module for elderly people to analyze sleep patterns according to age. For example, it can take into account the effects of frequent awakenings at night and daytime naps. The data collection unit can also provide a data collection module for night shift workers and shift workers to analyze irregular sleep patterns. For example, it can evaluate the effects of daytime sleep and short naps. As a result, by providing customizable data collection modules for different age groups and lifestyles, it is possible to provide a more accurate sleep improvement plan.

[0034] The suggestion unit can incorporate dietary and exercise advice into the sleep improvement plan, aiming for overall health improvement. The suggestion unit, for example, incorporates advice on the timing and content of meals into the sleep improvement plan. For example, eating dinner earlier can promote digestion and result in higher quality sleep. The suggestion unit can also provide advice on the timing and type of exercise, aiming for overall health improvement. For example, it can suggest that light exercise in the morning improves the quality of sleep at night. The suggestion unit can also provide advice that combines both diet and exercise, aiming for overall health improvement. For example, it can suggest that a balanced diet and moderate exercise improves sleep quality. In this way, by incorporating dietary and exercise advice into the sleep improvement plan, it is possible to aim for overall health improvement.

[0035] The suggestion unit can propose a customizable schedule that matches the lifestyle rhythm of each parent and child. For example, the suggestion unit can propose a customizable schedule that matches the lifestyle rhythm of each parent and child. For example, it can propose an optimal sleep time that matches the child's school hours and the parent's work hours. The suggestion unit also analyzes the lifestyle rhythm of parents and children and provides a customizable schedule that takes into account weekend and holiday schedules. For example, it can evaluate the impact that going to bed late on weekends and getting up late on weekday sleep. The suggestion unit also updates the customizable schedule in real time according to the lifestyle rhythm of parents and children. For example, it can propose schedule adjustments to accommodate sudden changes in plans or events. This makes it possible to propose a customizable schedule that matches the lifestyle rhythm of each parent and child, enabling more effective sleep improvement.

[0036] The suggestion unit can add a linking function with music or a meditation app to the sleep improvement plan to enhance the relaxation effect. For example, the suggestion unit can add a linking function with an app that provides relaxing music to the sleep improvement plan. For example, it can automatically play relaxing music before going to bed. The suggestion unit can also add a linking function with a meditation app to enhance the relaxation effect by having parents and children meditate together. For example, it can suggest a meditation session before going to bed. The suggestion unit can also enhance the relaxation effect between parents and children by using the linking function with music or a meditation app. For example, it can suggest using a meditation app to relax during times of high stress. In this way, adding a linking function with music or a meditation app to the sleep improvement plan can enhance the relaxation effect.

[0037] The suggestion unit can provide a customizable sleep improvement plan tailored to different cultural spheres and regions. For example, the suggestion unit provides a customizable sleep improvement plan that takes into account the sleeping habits of different cultural spheres. For example, in cultural spheres where naps are common, the suggestion unit can suggest nap times. The suggestion unit also provides a customizable sleep improvement plan that takes into account the climate and lifestyle habits of each region. For example, the suggestion unit can recommend the use of warm bedding in cold regions. The suggestion unit also updates the customizable sleep improvement plan in real time, taking into account the characteristics of different cultural spheres and regions. For example, it can provide a plan that corresponds to seasonal temperature changes. This allows for more effective sleep improvement by providing a customizable sleep improvement plan tailored to different cultural spheres and regions.

[0038] The suggestion unit can analyze the bedding material and arrangement in addition to room environmental data to suggest an optimal sleeping environment. The suggestion unit analyzes, for example, the room temperature, humidity, and lighting brightness as well as the bedding material and arrangement. For example, it can suggest bedding with good breathability and an appropriate pillow height. The suggestion unit also analyzes the bedding arrangement to suggest an optimal sleeping environment. For example, adjusting the position and orientation of the bed can promote comfortable sleep. The suggestion unit also analyzes the bedding material and suggests an optimal sleeping environment. For example, it can suggest how to select bedding according to the season or materials to prevent allergies. In this way, by analyzing the bedding material and arrangement in addition to room environmental data, it is possible to suggest an optimal sleeping environment.

[0039] The suggestion unit can take into account the presence of pets and the noise level in the home when analyzing the parent-child sleep environment. For example, the suggestion unit can take into account the presence of pets when analyzing the parent-child sleep environment. For example, the suggestion unit can evaluate the impact of having a pet in the bedroom and suggest optimal measures. The suggestion unit can also measure the noise level in the home and analyze the impact on the sleep environment. For example, the suggestion unit can suggest soundproofing measures to reduce noise at night. The suggestion unit can also collect pet activity data and analyze the impact on the sleep of the parent-child. For example, the suggestion unit can suggest measures to reduce the pet's movement at night. In this way, by taking into account the presence of pets and the noise level in the home when analyzing the parent-child sleep environment, a more accurate sleep improvement plan can be provided.

[0040] The suggestion unit can work with smart home devices to add a function to automatically adjust lighting and temperature. For example, the suggestion unit can work with smart home devices to add a function to automatically adjust lighting brightness. For example, gradually dimming the lights before going to bed can induce natural drowsiness. The suggestion unit can also work with a smart thermostat to provide a function to automatically adjust room temperature. For example, maintaining an appropriate temperature at night can create a comfortable sleeping environment. The suggestion unit can also work with a smart speaker to add a function to automatically play relaxing music. For example, playing relaxing music before going to bed can promote high-quality sleep. In this way, by working with smart home devices to add a function to automatically adjust lighting and temperature, a more comfortable sleeping environment can be provided.

[0041] The suggestion unit can provide customizable environmental settings according to different seasons and climates. The suggestion unit provides customizable environmental settings according to, for example, seasonal changes in temperature and humidity. For example, the suggestion unit can appropriately adjust air conditioning in summer and heating in winter. The suggestion unit also acquires climate data in real time and reflects it in the sleeping environment. For example, the suggestion unit can suggest using a dehumidifier during periods of high humidity. The suggestion unit also adjusts lighting settings taking into account seasonal changes in daylight hours. For example, since daylight hours are shorter in winter, the suggestion unit can suggest lighting to promote waking up in the morning. In this way, by providing customizable environmental settings according to different seasons and climates, a more comfortable sleeping environment can be provided.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The data collection unit collects parent-child sleep data. For example, it collects data such as sleep duration, sleep quality, and the number of times the child turns over in sleep through a smartwatch or smartphone app. The data collection unit can also use sensors to collect parent-child sleep data. For example, it can use a pressure sensor installed on the bed or a heart rate sensor built into the pillow. The analysis unit analyzes the sleep data collected by the data collection unit. For example, AI can analyze parent-child sleep patterns based on the collected data. The analysis unit can also analyze the sleep data using machine learning algorithms. For example, AI can learn from past data and predict future sleep patterns. The suggestion unit provides an individualized sleep improvement plan based on the results of the analysis by the analysis unit. For example, it can suggest appropriate bedtimes, wake-up times, and relaxation methods before bed to children, and suggest relaxation methods to reduce stress and ways to improve the sleep environment to parents. The suggestion unit also provides an optimal sleep improvement plan for each parent and child based on the plan generated by the AI. For example, the AI ​​can provide specific advice based on a prompt such as, "It's good to go to bed at this time." As a result, the sleep support system according to the embodiment can support parents and children in achieving healthy sleep with peace of mind. For example, when parents and children achieve high-quality sleep, they become more active during the day and their overall quality of life improves. Furthermore, reducing parental stress improves the atmosphere in the home and increases positive child-rearing experiences.

[0044] The data collection unit uses, for example, a smartwatch or smartphone app to collect daytime exercise volume in addition to sleep data. For example, the number of steps and exercise intensity are recorded and the correlation with sleep quality is analyzed. The data collection unit also uses an app that records meal content to collect meal timing and content as data. This allows the relationship between meals and sleep to be analyzed and optimal meal times to be suggested. The data collection unit also uses a device that records heart rate and electrodermal activity to measure daytime stress levels. This allows the correlation between stress and sleep quality to be analyzed and stress management suggestions to be made. In this way, by collecting daytime activity data in addition to sleep data and analyzing overall health status, a more accurate sleep improvement plan can be provided.

[0045] The data collection unit, for example, collects seasonal temperature and humidity data and analyzes it in combination with sleep data. For example, it can propose different optimal sleeping environments for summer and winter. The data collection unit also obtains weather data in real time and links it to sleep data. For example, it can analyze different sleep patterns on rainy and sunny days and provide appropriate advice. The data collection unit also takes into account seasonal changes in daylight hours and suggests adjusting sleep times. For example, since daylight hours are shorter in winter, it can recommend going to bed earlier. This allows the system to propose optimal sleep patterns for each season by taking into account the effects of seasons and weather in the analysis.

[0046] The data collection unit adds a function that allows the user to use the voice assistant to vocally report the quality of their sleep and the content of their dreams when they wake up each morning. This makes it possible to collect detailed sleep data. The data collection unit also provides a function that allows the user to use the voice assistant to vocally report their stress level and mood for the day before going to bed. This makes it possible to analyze the relationship between emotional state and sleep. The data collection unit also adds a function that allows the user to use the voice assistant to report the discomfort they felt during sleep and the number of times they woke up during sleep. This makes it possible to analyze sleep quality in detail. By adding a function that allows the user to vocally report their sleep status using the voice assistant, detailed sleep data can be collected and analyzed.

[0047] The data collection unit may provide, for example, a data collection module for children to collect sleep data according to the developmental stage. For example, it may provide data collection methods suitable for infants, toddlers, and school-age children. The data collection unit may also provide a data collection module for elderly people to analyze sleep patterns according to age. For example, it may take into account the effects of frequent awakenings at night and daytime naps. The data collection unit may also provide a data collection module for night shift workers and shift workers to analyze irregular sleep patterns. For example, it may evaluate the effects of daytime sleep and short naps. This allows for the provision of customizable data collection modules for different age groups and lifestyles, thereby providing more accurate sleep improvement plans.

[0048] The suggestion unit, for example, incorporates advice on the timing and content of meals into a sleep improvement plan. For example, having dinner earlier can promote digestion and result in higher quality sleep. The suggestion unit also provides advice on the timing and type of exercise, aiming to improve overall health. For example, it can suggest that light exercise in the morning will improve the quality of sleep at night. The suggestion unit also provides advice that combines both diet and exercise, aiming to improve overall health. For example, it can suggest that a balanced diet and moderate exercise will improve the quality of sleep. In this way, by incorporating diet and exercise advice into a sleep improvement plan, it is possible to aim to improve overall health.

[0049] The suggestion unit proposes, for example, a customizable schedule that matches the lifestyle rhythms of each parent and child. For example, it can propose optimal sleep times that match the child's school hours and the parent's work hours. The suggestion unit also analyzes the lifestyle rhythms of parents and children and provides a customizable schedule that takes into account weekend and holiday schedules. For example, it can evaluate the impact that going to bed late on weekends and getting up late on weekdays has on sleep. The suggestion unit also updates the customizable schedule in real time according to the lifestyle rhythms of parents and children. For example, it can propose schedule adjustments to accommodate sudden changes in plans or events. This allows for more effective sleep improvement by proposing a customizable schedule that matches the lifestyle rhythms of each parent and child.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The data collection unit collects sleep data for both parent and child. For example, data such as sleep duration, sleep quality, and number of turns in sleep can be collected through a smartwatch or smartphone app. Pressure sensors installed on the bed or heart rate sensors built into the pillow can also be used. Step 2: The analysis unit analyzes the sleep data collected by the data collection unit. For example, AI can analyze the parent-child sleep patterns based on the collected data. It can also use machine learning algorithms to learn from past data and predict future sleep patterns. Step 3: The suggestion unit provides an individualized sleep improvement plan based on the results of the analysis by the analysis unit. For example, it suggests appropriate bedtimes, wake-up times, and ways to relax before bed for children, and suggests relaxation methods to reduce stress and ways to improve the sleep environment for parents. Based on the plan generated by AI, the unit also provides an optimal sleep improvement plan for each parent and child.

[0052] (Example 2) The sleep support system according to an embodiment of the present invention uses AI to provide effective sleep support so that parents and children can sleep healthily and comfortably. This helps promote healthy mental and physical growth for parents and children and reduces stress for parents.

[0053] A sleep support system according to an embodiment includes a data collection unit, an analysis unit, and a suggestion unit. The data collection unit collects sleep data of both parents and children. For example, the data collection unit may collect data such as sleep duration, sleep quality, and the number of times the parent turns over in sleep via a smartwatch or smartphone app. The data collection unit may also use sensors to collect the sleep data of both parents and children. For example, a pressure sensor installed on the bed or a heart rate sensor built into a pillow may be used. The analysis unit analyzes the sleep data collected by the data collection unit. For example, an AI may analyze the sleep patterns of both parents and children based on the collected data. The analysis unit may also analyze the sleep data using a machine learning algorithm. For example, an AI may learn from past data and predict future sleep patterns. The suggestion unit provides an individualized sleep improvement plan based on the analysis results of the analysis unit. For example, the suggestion unit may suggest appropriate bedtimes, wake-up times, and relaxation methods before bed to the child, and suggest relaxation methods to reduce stress and ways to improve the sleep environment to the parent. The suggestion unit may also provide an optimal sleep improvement plan for each parent and child based on the plan generated by the AI. For example, the AI ​​can provide specific advice based on a prompt such as "It's good to go to bed at this time." This allows the sleep support system according to the embodiment to support parents and children in achieving healthy sleep with peace of mind. For example, when parents and children get high-quality sleep, they become more active during the day and their overall quality of life improves. Furthermore, reducing parental stress improves the atmosphere in the home and increases positive parenting experiences.

[0054] The data collection unit can collect daytime activity data in addition to sleep data. The analysis unit can analyze overall health status based on the sleep data and daytime activity data. The data collection unit, for example, uses a smartwatch or smartphone app to collect daytime exercise volume in addition to sleep data. For example, it records the number of steps and exercise intensity and analyzes the correlation with sleep quality. The data collection unit also uses an app that records meal content to collect meal timing and content as data. This allows for analyzing the relationship between meals and sleep and suggesting optimal meal times. The data collection unit also uses a device that records heart rate and electrodermal activity to measure daytime stress levels. This allows for analyzing the correlation between stress and sleep quality and suggesting stress management. In this way, by collecting daytime activity data in addition to sleep data and analyzing overall health status, a more accurate sleep improvement plan can be provided.

[0055] The data collection unit can take into account the influence of seasons and weather when analyzing sleep data. The analysis unit can suggest optimal sleep patterns for each season. The data collection unit, for example, collects temperature and humidity data for each season and analyzes it in combination with sleep data. For example, it can suggest optimal sleep environments that differ between summer and winter. The data collection unit also obtains weather data in real time and links it with sleep data. For example, it can analyze different sleep patterns on rainy days and sunny days and provide appropriate advice. The data collection unit also takes into account seasonal changes in daylight hours and suggests adjusting sleep times. For example, since daylight hours are shorter in winter, it can recommend going to bed earlier. In this way, by taking the influence of seasons and weather into account in the analysis, it is possible to suggest optimal sleep patterns for each season.

[0056] The data collection unit can analyze the emotional states of parents and children using the emotion estimation function. The analysis unit can evaluate the impact of emotional fluctuations on sleep. For example, the data collection unit collects facial expressions and voice using a smartphone camera and microphone to analyze the emotional states of parents and children. This makes it possible to evaluate the relationship between emotional fluctuations and sleep quality. The data collection unit also uses an emotion estimation algorithm to calculate an emotion score for parents and children and analyzes it in combination with sleep data. For example, it can evaluate sleep patterns during periods of high stress or anxiety. The data collection unit also uses a parent-child diary app to record emotional states. This makes it possible to analyze the relationship between emotional fluctuations and sleep quality over a long period of time. This makes it possible to provide a more accurate sleep improvement plan by analyzing emotional states using the emotion estimation function and evaluating the impact of emotional fluctuations on sleep.

[0057] The data collection unit can add a function that allows a user to report their sleep status by voice using a voice assistant. The analysis unit can analyze the sleep data reported by voice. For example, the data collection unit can add a function that allows a user to report their sleep quality and dream content by voice every morning when they wake up using a voice assistant. This makes it possible to collect detailed sleep data. The data collection unit also provides a function that allows a user to report their stress level and mood for the day before going to bed through the voice assistant. This makes it possible to analyze the relationship between emotional state and sleep. The data collection unit also adds a function that allows a user to report discomfort felt during sleep and the number of times they woke up during sleep using the voice assistant. This makes it possible to analyze the quality of sleep in detail. By adding a function that allows a user to report their sleep status by voice using a voice assistant, detailed sleep data can be collected and analyzed.

[0058] The data collection unit can provide customizable data collection modules for different age groups and lifestyles. The analysis unit can analyze the data collected by the customizable data collection modules. The data collection unit can provide, for example, a data collection module for children to collect sleep data according to developmental stages. For example, it can provide data collection methods suitable for infants, toddlers, and school-age children. The data collection unit can also provide a data collection module for elderly people to analyze sleep patterns according to age. For example, it can take into account the effects of frequent awakenings at night and daytime naps. The data collection unit can also provide a data collection module for night shift workers and shift workers to analyze irregular sleep patterns. For example, it can evaluate the effects of daytime sleep and short naps. As a result, by providing customizable data collection modules for different age groups and lifestyles, it is possible to provide a more accurate sleep improvement plan.

[0059] The data collection unit can monitor the emotional states of parents and children in real time using an emotion estimation function. The analysis unit can make sleep improvement suggestions based on the emotional states. For example, the data collection unit can analyze facial expressions and voice using a smartphone camera or microphone to monitor the emotional states of parents and children in real time. This allows for sleep improvement suggestions based on emotions. The data collection unit also uses an emotion estimation algorithm to calculate emotional scores for parents and children in real time and analyzes them in combination with sleep data. For example, it can suggest relaxation methods during periods of high stress. The data collection unit also uses a parent-child diary app to record emotional states in real time. This allows for sleep improvement suggestions based on emotional fluctuations. This allows for more effective sleep improvement by monitoring emotional states in real time using the emotion estimation function and making sleep improvement suggestions based on emotions.

[0060] The suggestion unit can incorporate dietary and exercise advice into the sleep improvement plan, aiming for overall health improvement. The suggestion unit, for example, incorporates advice on the timing and content of meals into the sleep improvement plan. For example, eating dinner earlier can promote digestion and result in higher quality sleep. The suggestion unit can also provide advice on the timing and type of exercise, aiming for overall health improvement. For example, it can suggest that light exercise in the morning improves the quality of sleep at night. The suggestion unit can also provide advice that combines both diet and exercise, aiming for overall health improvement. For example, it can suggest that a balanced diet and moderate exercise improves sleep quality. In this way, by incorporating dietary and exercise advice into the sleep improvement plan, it is possible to aim for overall health improvement.

[0061] The suggestion unit can propose a customizable schedule that matches the lifestyle rhythm of each parent and child. For example, the suggestion unit can propose a customizable schedule that matches the lifestyle rhythm of each parent and child. For example, it can propose an optimal sleep time that matches the child's school hours and the parent's work hours. The suggestion unit also analyzes the lifestyle rhythm of parents and children and provides a customizable schedule that takes into account weekend and holiday schedules. For example, it can evaluate the impact that going to bed late on weekends and getting up late on weekday sleep. The suggestion unit also updates the customizable schedule in real time according to the lifestyle rhythm of parents and children. For example, it can propose schedule adjustments to accommodate sudden changes in plans or events. This makes it possible to propose a customizable schedule that matches the lifestyle rhythm of each parent and child, enabling more effective sleep improvement.

[0062] The suggestion unit can use the emotion estimation function to suggest relaxation methods according to the emotional state of the parent and child. The suggestion unit, for example, uses the emotion estimation function to suggest relaxation methods according to the emotional state of the parent and child. For example, deep breathing or meditation can be recommended during times of high stress. The suggestion unit also analyzes the emotion scores of the parent and child in real time and provides relaxation methods according to the emotional state. For example, it can suggest music with a relaxing effect during times of high anxiety. The suggestion unit also suggests relaxation methods according to emotional fluctuations by using a parent-child diary app to record the emotional state. For example, it can recommend aromatherapy during times of large emotional fluctuations. As a result, more effective sleep improvement is possible by using the emotion estimation function to suggest relaxation methods according to the emotional state.

[0063] The suggestion unit can add a linking function with music or a meditation app to the sleep improvement plan to enhance the relaxation effect. For example, the suggestion unit can add a linking function with an app that provides relaxing music to the sleep improvement plan. For example, it can automatically play relaxing music before going to bed. The suggestion unit can also add a linking function with a meditation app to enhance the relaxation effect by having parents and children meditate together. For example, it can suggest a meditation session before going to bed. The suggestion unit can also enhance the relaxation effect between parents and children by using the linking function with music or a meditation app. For example, it can suggest using a meditation app to relax during times of high stress. In this way, adding a linking function with music or a meditation app to the sleep improvement plan can enhance the relaxation effect.

[0064] The suggestion unit can provide a customizable sleep improvement plan tailored to different cultural spheres and regions. For example, the suggestion unit provides a customizable sleep improvement plan that takes into account the sleeping habits of different cultural spheres. For example, in cultural spheres where naps are common, the suggestion unit can suggest nap times. The suggestion unit also provides a customizable sleep improvement plan that takes into account the climate and lifestyle habits of each region. For example, the suggestion unit can recommend the use of warm bedding in cold regions. The suggestion unit also updates the customizable sleep improvement plan in real time, taking into account the characteristics of different cultural spheres and regions. For example, it can provide a plan that corresponds to seasonal temperature changes. This allows for more effective sleep improvement by providing a customizable sleep improvement plan tailored to different cultural spheres and regions.

[0065] The suggestion unit can use the emotion estimation function to update an individual sleep improvement plan based on the emotional states of the parent and child in real time. The suggestion unit, for example, uses the emotion estimation function to update an individual sleep improvement plan based on the emotional states of the parent and child in real time. For example, relaxation methods can be strengthened during times of high stress. The suggestion unit also analyzes the emotion scores of the parent and child in real time and provides a sleep improvement plan based on the emotional states. For example, music with a relaxing effect can be suggested during times of high anxiety. The suggestion unit also uses a parent-child diary app to record the emotional states in real time and provides a sleep improvement plan based on emotional fluctuations. For example, aromatherapy can be recommended during times of large emotional fluctuations. This enables more effective sleep improvement by using the emotion estimation function to update an individual sleep improvement plan based on the emotional states in real time.

[0066] The suggestion unit can analyze the bedding material and arrangement in addition to room environmental data to suggest an optimal sleeping environment. The suggestion unit analyzes, for example, the room temperature, humidity, and lighting brightness as well as the bedding material and arrangement. For example, it can suggest bedding with good breathability and an appropriate pillow height. The suggestion unit also analyzes the bedding arrangement to suggest an optimal sleeping environment. For example, adjusting the position and orientation of the bed can promote comfortable sleep. The suggestion unit also analyzes the bedding material and suggests an optimal sleeping environment. For example, it can suggest how to select bedding according to the season or materials to prevent allergies. In this way, by analyzing the bedding material and arrangement in addition to room environmental data, it is possible to suggest an optimal sleeping environment.

[0067] The suggestion unit can take into account the presence of pets and the noise level in the home when analyzing the parent-child sleep environment. For example, the suggestion unit can take into account the presence of pets when analyzing the parent-child sleep environment. For example, the suggestion unit can evaluate the impact of having a pet in the bedroom and suggest optimal measures. The suggestion unit can also measure the noise level in the home and analyze the impact on the sleep environment. For example, the suggestion unit can suggest soundproofing measures to reduce noise at night. The suggestion unit can also collect pet activity data and analyze the impact on the sleep of the parent-child. For example, the suggestion unit can suggest measures to reduce the pet's movement at night. In this way, by taking into account the presence of pets and the noise level in the home when analyzing the parent-child sleep environment, a more accurate sleep improvement plan can be provided.

[0068] The suggestion unit can use the emotion estimation function to suggest optimal environmental settings according to the emotional state of the parent and child. For example, the suggestion unit can suggest lighting with a relaxing effect during times of high stress. The suggestion unit also analyzes the emotion scores of the parent and child in real time and provides environmental settings according to the emotional state. For example, it can suggest a quiet environment during times of high anxiety. The suggestion unit also uses a parent-child diary app to record the emotional state and suggests environmental settings according to emotional fluctuations. For example, it can suggest using an aroma diffuser during times of large emotional fluctuations. This makes it possible to more effectively improve sleep by using the emotion estimation function to suggest optimal environmental settings according to the emotional state.

[0069] The suggestion unit can work with smart home devices to add a function to automatically adjust lighting and temperature. For example, the suggestion unit can work with smart home devices to add a function to automatically adjust lighting brightness. For example, gradually dimming the lights before going to bed can induce natural drowsiness. The suggestion unit can also work with a smart thermostat to provide a function to automatically adjust room temperature. For example, maintaining an appropriate temperature at night can create a comfortable sleeping environment. The suggestion unit can also work with a smart speaker to add a function to automatically play relaxing music. For example, playing relaxing music before going to bed can promote high-quality sleep. In this way, by working with smart home devices to add a function to automatically adjust lighting and temperature, a more comfortable sleeping environment can be provided.

[0070] The suggestion unit can provide customizable environmental settings according to different seasons and climates. The suggestion unit provides customizable environmental settings according to, for example, seasonal changes in temperature and humidity. For example, the suggestion unit can appropriately adjust air conditioning in summer and heating in winter. The suggestion unit also acquires climate data in real time and reflects it in the sleeping environment. For example, the suggestion unit can suggest using a dehumidifier during periods of high humidity. The suggestion unit also adjusts lighting settings taking into account seasonal changes in daylight hours. For example, since daylight hours are shorter in winter, the suggestion unit can suggest lighting to promote waking up in the morning. In this way, by providing customizable environmental settings according to different seasons and climates, a more comfortable sleeping environment can be provided.

[0071] The suggestion unit can use the emotion estimation function to adjust the optimal sleeping environment in real time based on the emotional states of the parent and child. The suggestion unit, for example, uses the emotion estimation function to adjust the optimal sleeping environment in real time based on the emotional states of the parent and child. For example, it can suggest lighting that has a relaxing effect during times of high stress. The suggestion unit also analyzes the emotion scores of the parent and child in real time and provides environmental settings according to the emotional states. For example, it can suggest a quiet environment during times of high anxiety. The suggestion unit also uses a parent-child diary app to record the emotional states and suggests environmental settings according to emotional fluctuations. For example, it can suggest using an aroma diffuser during times of large emotional fluctuations. In this way, a more comfortable sleeping environment can be provided by using the emotion estimation function to adjust the optimal sleeping environment in real time based on the emotional states.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The data collection unit collects parent-child sleep data. For example, it collects data such as sleep duration, sleep quality, and the number of times the child turns over in sleep through a smartwatch or smartphone app. The data collection unit can also use sensors to collect parent-child sleep data. For example, it can use a pressure sensor installed on the bed or a heart rate sensor built into the pillow. The analysis unit analyzes the sleep data collected by the data collection unit. For example, AI can analyze parent-child sleep patterns based on the collected data. The analysis unit can also analyze the sleep data using machine learning algorithms. For example, AI can learn from past data and predict future sleep patterns. The suggestion unit provides an individualized sleep improvement plan based on the results of the analysis by the analysis unit. For example, it can suggest appropriate bedtimes, wake-up times, and relaxation methods before bed to children, and suggest relaxation methods to reduce stress and ways to improve the sleep environment to parents. The suggestion unit also provides an optimal sleep improvement plan for each parent and child based on the plan generated by the AI. For example, the AI ​​can provide specific advice based on a prompt such as, "It's good to go to bed at this time." As a result, the sleep support system according to the embodiment can support parents and children in achieving healthy sleep with peace of mind. For example, when parents and children achieve high-quality sleep, they become more active during the day and their overall quality of life improves. Furthermore, reducing parental stress improves the atmosphere in the home and increases positive child-rearing experiences.

[0074] The data collection unit uses, for example, a smartwatch or smartphone app to collect daytime exercise volume in addition to sleep data. For example, the number of steps and exercise intensity are recorded and the correlation with sleep quality is analyzed. The data collection unit also uses an app that records meal content to collect meal timing and content as data. This allows the relationship between meals and sleep to be analyzed and optimal meal times to be suggested. The data collection unit also uses a device that records heart rate and electrodermal activity to measure daytime stress levels. This allows the correlation between stress and sleep quality to be analyzed and stress management suggestions to be made. In this way, by collecting daytime activity data in addition to sleep data and analyzing overall health status, a more accurate sleep improvement plan can be provided.

[0075] The data collection unit, for example, collects seasonal temperature and humidity data and analyzes it in combination with sleep data. For example, it can propose different optimal sleeping environments for summer and winter. The data collection unit also obtains weather data in real time and links it to sleep data. For example, it can analyze different sleep patterns on rainy and sunny days and provide appropriate advice. The data collection unit also takes into account seasonal changes in daylight hours and suggests adjusting sleep times. For example, since daylight hours are shorter in winter, it can recommend going to bed earlier. This allows the system to propose optimal sleep patterns for each season by taking into account the effects of seasons and weather in the analysis.

[0076] For example, the data collection unit may use a smartphone's camera and microphone to collect facial expressions and voices to analyze the emotional states of parents and children. This allows the relationship between emotional fluctuations and sleep quality to be evaluated. The data collection unit may also use an emotion estimation algorithm to calculate an emotion score for parents and children, which is then combined with sleep data for analysis. For example, it may be possible to evaluate sleep patterns during periods of high stress or anxiety. The data collection unit may also use a parent-child diary app to record emotional states. This allows the relationship between emotional fluctuations and sleep quality to be analyzed over a long period of time. This allows the emotion estimation function to analyze emotional states and evaluate the impact of emotional fluctuations on sleep, thereby providing a more accurate sleep improvement plan.

[0077] The data collection unit adds a function that allows the user to use the voice assistant to vocally report the quality of their sleep and the content of their dreams when they wake up each morning. This makes it possible to collect detailed sleep data. The data collection unit also provides a function that allows the user to use the voice assistant to vocally report their stress level and mood for the day before going to bed. This makes it possible to analyze the relationship between emotional state and sleep. The data collection unit also adds a function that allows the user to use the voice assistant to report the discomfort they felt during sleep and the number of times they woke up during sleep. This makes it possible to analyze sleep quality in detail. By adding a function that allows the user to vocally report their sleep status using the voice assistant, detailed sleep data can be collected and analyzed.

[0078] The data collection unit may provide, for example, a data collection module for children to collect sleep data according to the developmental stage. For example, it may provide data collection methods suitable for infants, toddlers, and school-age children. The data collection unit may also provide a data collection module for elderly people to analyze sleep patterns according to age. For example, it may take into account the effects of frequent awakenings at night and daytime naps. The data collection unit may also provide a data collection module for night shift workers and shift workers to analyze irregular sleep patterns. For example, it may evaluate the effects of daytime sleep and short naps. This allows for the provision of customizable data collection modules for different age groups and lifestyles, thereby providing more accurate sleep improvement plans.

[0079] For example, the data collection unit uses a smartphone's camera and microphone to analyze facial expressions and voice to monitor the emotional states of parents and children in real time. This makes it possible to make sleep improvement suggestions based on emotions. The data collection unit also uses an emotion estimation algorithm to calculate an emotional score for parents and children in real time and analyzes it in combination with sleep data. For example, it can suggest relaxation methods during times of high stress. The data collection unit also uses a parent-child diary app to record the emotional state in real time. This makes it possible to make sleep improvement suggestions based on emotional fluctuations. This makes it possible to monitor emotional states in real time using the emotion estimation function and make sleep improvement suggestions based on emotions, thereby enabling more effective sleep improvement.

[0080] The suggestion unit, for example, incorporates advice on the timing and content of meals into a sleep improvement plan. For example, having dinner earlier can promote digestion and result in higher quality sleep. The suggestion unit also provides advice on the timing and type of exercise, aiming to improve overall health. For example, it can suggest that light exercise in the morning will improve the quality of sleep at night. The suggestion unit also provides advice that combines both diet and exercise, aiming to improve overall health. For example, it can suggest that a balanced diet and moderate exercise will improve the quality of sleep. In this way, by incorporating diet and exercise advice into a sleep improvement plan, it is possible to aim to improve overall health.

[0081] The suggestion unit proposes, for example, a customizable schedule that matches the lifestyle rhythms of each parent and child. For example, it can propose optimal sleep times that match the child's school hours and the parent's work hours. The suggestion unit also analyzes the lifestyle rhythms of parents and children and provides a customizable schedule that takes into account weekend and holiday schedules. For example, it can evaluate the impact that going to bed late on weekends and getting up late on weekdays has on sleep. The suggestion unit also updates the customizable schedule in real time according to the lifestyle rhythms of parents and children. For example, it can propose schedule adjustments to accommodate sudden changes in plans or events. This allows for more effective sleep improvement by proposing a customizable schedule that matches the lifestyle rhythms of each parent and child.

[0082] The suggestion unit uses, for example, an emotion estimation function to suggest relaxation methods according to the emotional state of the parent and child. For example, deep breathing or meditation can be recommended during times of high stress. The suggestion unit also analyzes the emotion scores of the parent and child in real time and provides relaxation methods according to the emotional state. For example, it can suggest music with a relaxing effect during times of high anxiety. The suggestion unit also uses a parent-child diary app to record the emotional state and suggests relaxation methods according to emotional fluctuations. For example, it can recommend aromatherapy during times of large emotional fluctuations. This makes it possible to more effectively improve sleep by using the emotion estimation function to suggest relaxation methods according to the emotional state.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The data collection unit collects sleep data for both parent and child. For example, data such as sleep duration, sleep quality, and number of turns in sleep can be collected through a smartwatch or smartphone app. Pressure sensors installed on the bed or heart rate sensors built into the pillow can also be used. Step 2: The analysis unit analyzes the sleep data collected by the data collection unit. For example, AI can analyze the parent-child sleep patterns based on the collected data. It can also use machine learning algorithms to learn from past data and predict future sleep patterns. Step 3: The suggestion unit provides an individualized sleep improvement plan based on the results of the analysis by the analysis unit. For example, it suggests appropriate bedtimes, wake-up times, and ways to relax before bed for children, and suggests relaxation methods to reduce stress and ways to improve the sleep environment for parents. Based on the plan generated by AI, the unit also provides an optimal sleep improvement plan for each parent and child.

[0085] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0091] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0092] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0093] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0094] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0095] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0096] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0097] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0099] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0100] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0105] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0126] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0132] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0135] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0136] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0137] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0138] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0139] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0140] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0141] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0142] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0143] 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.

[0144] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0145] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0146] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0147] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0148] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0149] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0150] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0151] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0152] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection unit that collects parent-child sleep data; an analysis unit that analyzes the sleep data collected by the data collection unit; a proposal unit that provides an individual sleep improvement plan based on the results of the analysis by the analysis unit. A system characterized by:

2. The data collection unit collecting daytime activity data in addition to said sleep data; The analysis unit Analyzing overall health status based on the sleep data and daytime activity data 2. The system of claim 1.

3. The data collection unit When analyzing the sleep data, the influence of seasons and weather is taken into consideration. The analysis unit Suggesting optimal sleep patterns for each season 2. The system of claim 1.

4. The data collection unit Analyzing the emotional state of parents and children, The analysis unit Assessing the impact of emotional fluctuations on sleep 2. The system of claim 1.

5. The data collection unit Add a feature that allows users to report their sleep status by voice using a voice assistant. The analysis unit Analyzing the audio-reported sleep data 2. The system of claim 1.

6. The data collection unit We provide customizable data collection modules for different age groups and lifestyles, The analysis unit Analyzing the data collected by the customizable data collection module 2. The system of claim 1.

7. The data collection unit Real-time monitoring of parent-child emotional states, The analysis unit Proposing sleep improvement suggestions according to the emotional state 2. The system of claim 1.

8. The proposal unit Incorporating dietary and exercise advice into the sleep improvement plan, Aiming for overall health improvement 2. The system of claim 1.

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