System

Abstract

An object of a system according to an embodiment is to analyze sleep data of a user and improve the quality of sleep.SOLUTION: A system includes a data collection unit, an analysis unit, and an advice generation unit. The data collection unit collects sleep data of a user. The analysis unit analyzes the sleep data collected by the data collection unit. The advice generation unit proposes a 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 not effectively utilized a user's sleep data to improve the quality of their sleep, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the sleep data of a user and improve the quality of sleep. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, and an advice generation unit. The data collection unit collects sleep data of a user. The analysis unit analyzes the sleep data collected by the data collection unit. The advice generation unit proposes sleep improvement measures based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the sleep data of a user and improve the quality of sleep. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 improvement system according to an embodiment of the present invention uses AI to collect and analyze a user's sleep data and propose personalized sleep improvement measures, thereby improving the quality of the user's sleep and daily life.

[0029] A sleep improvement system according to an embodiment includes a data collection unit, an analysis unit, and an advice generation unit. The data collection unit collects a user's sleep data. For example, a smart watch can be used to collect a user's heart rate and sleep patterns in real time. A smart bed can also be used to collect the number of turns and breathing rate. The data collection unit can also record the user's sleep time using a smartphone app. The analysis unit analyzes the sleep data collected by the data collection unit. For example, the generation AI can analyze the sleep patterns using a text generation AI (e.g., LLM). The generation AI can also identify sleep depth and problem areas using a multimodal generation AI. The generation AI can also analyze the sleep data using an algorithm to evaluate the user's sleep quality. The advice generation unit proposes sleep improvement measures based on the results of the analysis by the analysis unit. For example, the generation AI can provide the user with specific advice such as "Try to go to bed at the same time every night." The generation AI can also provide customized advice tailored to the user's lifestyle and environment. The generation AI can also make suggestions to optimize the user's sleep environment. As a result, the sleep improvement system according to the embodiment can improve the quality of a user's sleep and the quality of daily life. For example, the output unit displays the sleep improvement measures to the user via a web application or a mobile application. If the user desires feedback in paper form, the results can be printed using a printer. Sending the results via email provides quick feedback by sending the results directly to the user.

[0030] The data collection unit can collect sleep data in real time using at least one device, a smartwatch or a smart bed. The data collection unit, for example, uses a smartwatch to collect heart rate and sleep patterns in real time. For example, the smartwatch measures heart rate with high accuracy and transmits the data wirelessly to the generation AI. The data collection unit can also collect the number of times a user turns over in bed and the breathing rate using a smart bed. For example, the smart bed detects the number of times a user turns over in bed using a sensor and transmits the data to the generation AI. The data collection unit can also record the user's sleep time using a smartphone app. For example, the app records the time the user goes to bed and wakes up and transmits the data to the generation AI. This allows for more accurate analysis by collecting sleep data in real time.

[0031] If the depth of sleep is low, the analysis unit can identify the cause and suggest remedial measures. For example, the analysis unit closely monitors changes in the user's body temperature and estimates the depth of sleep from the pattern of body temperature fluctuations. For example, a smartwatch can be used to measure skin temperature with high accuracy and send the data to the generation AI. The analysis unit can also analyze the user's breathing sounds and snoring patterns. For example, a microphone built into the smart bed can be used to record breathing sounds and snoring with high accuracy and send the data to the generation AI. The analysis unit can also analyze fluctuations in the user's heart rate and estimate the depth of sleep. For example, a smartwatch can be used to measure heart rate in real time and send the data to the generation AI. This allows the analysis unit to identify the cause of shallow sleep and suggest appropriate remedial measures to improve sleep quality.

[0032] The advice generation unit can provide the user with specific advice such as "Try to go to bed at the same time every night" or "Try not to use your smartphone before going to bed." For example, the advice generation unit can provide the user with specific advice such as "Try to go to bed at the same time every night." For example, the generation AI can analyze the user's sleep data and show that going to bed at the same time every night improves sleep quality. The advice generation unit can also provide the user with specific advice such as "Try not to use your smartphone before going to bed." For example, the generation AI can analyze the user's sleep data and show that not using your smartphone before going to bed improves sleep quality. The advice generation unit can also provide customized advice tailored to the user's lifestyle and environment. For example, the generation AI can provide specific advice based on the user's lifestyle and environment. By providing specific advice, the user's sleep habits and sleep quality can be improved.

[0033] The advice generation unit can provide customized advice tailored to the user's lifestyle and environment. The advice generation unit provides, for example, customized advice tailored to the user's lifestyle and environment. For example, the generation AI provides specific advice based on the user's lifestyle and environment. For example, if the user works the night shift, the generation AI can suggest a sleep schedule tailored to the night shift. Also, if the user has a pet, the generation AI can suggest ways to improve the sleep environment that take the pet's movements and sounds into consideration. It can also provide specific advice tailored to the environment, such as adjusting the temperature, humidity, and lighting in the user's bedroom. This allows for more effective sleep improvement by providing customized advice tailored to the user's lifestyle and environment.

[0034] The data collection unit may have a function for analyzing the user's breathing sounds and snoring patterns. For example, the data collection unit may use a microphone built into the smart bed to analyze the user's breathing sounds and snoring patterns. For example, the microphone may record the breathing sounds and snoring with high accuracy and send the data to the generation AI. The data collection unit may also use a smartwatch to analyze the user's breathing sounds and snoring patterns. For example, the smartwatch may detect the breathing sounds and snoring with a sensor and send the data to the generation AI. The data collection unit may also use a smartphone app to analyze the user's breathing sounds and snoring patterns. For example, the app may record the user's breathing sounds and snoring and send the data to the generation AI. This allows for a more detailed evaluation of sleep quality by analyzing the breathing sounds and snoring patterns.

[0035] The data collection unit can monitor changes in the user's body temperature in detail and estimate the depth of sleep from the pattern of body temperature fluctuations. The data collection unit, for example, uses a smartwatch to monitor changes in the user's body temperature in detail. For example, the smartwatch measures skin temperature with high accuracy and transmits the data to the generation AI. The data collection unit can also use a smart bed to monitor changes in the user's body temperature in detail. For example, the smart bed has a built-in body temperature sensor that measures changes in the user's body temperature in real time and transmits the data to the generation AI. The data collection unit can also use a smartphone app to monitor changes in the user's body temperature in detail. For example, the app records changes in the user's body temperature and transmits the data to the generation AI. This allows for more accurate estimation of the depth of sleep by monitoring changes in body temperature in detail.

[0036] The data collection unit can simultaneously collect pet movements or sounds and perform sleep analysis that takes the pet's influence into account. The data collection unit, for example, uses a camera and microphone installed in the user's bedroom to collect pet movements and sounds. For example, the camera records the pet's movements in real time and transmits the data to the generation AI. The microphone also records the pet's sounds with high accuracy and transmits the data to the generation AI. The data collection unit can also use a smartwatch to collect pet movements and sounds. For example, the smartwatch detects the pet's movements with a sensor and transmits the data to the generation AI. The data collection unit can also use a smart bed to collect pet movements and sounds. For example, the smart bed detects the pet's movements with a sensor and transmits the data to the generation AI. This enables more accurate sleep analysis by taking the pet's influence into account.

[0037] The data collection unit may have a function for analyzing the user's breathing sounds and snoring patterns. For example, the data collection unit may use a microphone built into the smart bed to analyze the user's breathing sounds and snoring patterns. For example, the microphone may record the breathing sounds and snoring with high accuracy and send the data to the generation AI. The data collection unit may also use a smartwatch to analyze the user's breathing sounds and snoring patterns. For example, the smartwatch may detect the breathing sounds and snoring with a sensor and send the data to the generation AI. The data collection unit may also use a smartphone app to analyze the user's breathing sounds and snoring patterns. For example, the app may record the user's breathing sounds and snoring and send the data to the generation AI. This allows for a more detailed evaluation of sleep quality by analyzing the breathing sounds and snoring patterns.

[0038] When analyzing a user's sleep data, the analysis unit can compare it with past data to identify long-term changes in sleep patterns. The analysis unit, for example, uses a database so that the generation AI can analyze the user's past sleep data and identify long-term changes in sleep patterns. For example, the database stores the user's past sleep data, and the generation AI performs analysis based on this data. The analysis unit can also compare the user's sleep data on a weekly or monthly basis to identify long-term changes. For example, the generation AI compares sleep data from week to week or month to identify changes in sleep quality. The analysis unit can also compare the user's sleep data with that of other users to provide benchmarks. For example, the generation AI compares the user's sleep data with that of other users to evaluate the position of the user's sleep data. This allows the generation AI to identify long-term changes in sleep patterns by comparing it with past data.

[0039] The analysis unit can also integrate the user's dietary and exercise data into the sleep data analysis to evaluate the user's overall health status. For example, the analysis unit uses a food recording app to collect the user's dietary data. For example, the app records the contents of the meals the user eats and sends the data to the generation AI. The analysis unit can also use a fitness tracker to collect the user's exercise data. For example, the fitness tracker measures the user's exercise volume and calories burned and sends the data to the generation AI. The analysis unit can also integrate the user's sleep data, dietary data, and exercise data to evaluate the user's overall health status. For example, the generation AI analyzes the user's sleep data, dietary data, and exercise data to evaluate the user's overall health status. This allows the user's overall health status to be evaluated by integrating the dietary and exercise data.

[0040] The analysis unit can compare the sleep data analyzed by the generation AI with the data of other users and provide a benchmark. The analysis unit, for example, uses a database to compare the sleep data analyzed by the generation AI with the data of other users. For example, the database stores the sleep data of other users, and the generation AI performs analysis based on this data. The analysis unit can also compare the user's sleep data with the data of other users and provide a benchmark. For example, the generation AI compares the user's sleep data with the data of other users to evaluate the position of the user's sleep data. The analysis unit can also compare the user's sleep data with the data of other users and set goals to improve sleep quality. For example, the generation AI compares the user's sleep data with the data of other users to identify areas for improvement in the user's sleep data and set specific goals. This allows the user's sleep data to be compared with the data of other users to provide a benchmark and evaluate the user's sleep data.

[0041] The analysis unit can customize the sleep data analysis results according to the user's occupation and lifestyle and provide more specific advice. The analysis unit, for example, uses profile information to collect the user's occupation data. For example, the generation AI provides customized advice based on the user's occupation. The analysis unit can also use a questionnaire to collect the user's lifestyle data. For example, the generation AI provides customized advice based on the user's lifestyle. The analysis unit can also customize the sleep data analysis results according to the user's occupation and lifestyle and provide specific advice. For example, a user who works a desk job can be provided with advice that takes into account the effects of sitting for long periods of time. A user who works shifts can be suggested a sleep schedule that matches their shifts. This allows for more effective sleep improvement by providing customized advice based on the user's occupation and lifestyle.

[0042] The advice generation unit can automatically generate and periodically update an individualized sleep coaching program based on the user's sleep data. The advice generation unit, for example, uses a database so that the generation AI can automatically generate an individualized sleep coaching program based on the user's sleep data. For example, the database stores the user's past sleep data, and the generation AI generates a program based on this data. The advice generation unit can also periodically update the individualized sleep coaching program based on the user's sleep data. For example, the generation AI analyzes the user's latest sleep data and updates the program. The advice generation unit can also automatically generate an individualized sleep coaching program based on the user's sleep data and provide it through a smartphone app. For example, the app tracks the user's progress and provides feedback. This allows the individualized sleep coaching program to be automatically generated and periodically updated, thereby continuously supporting the user's sleep improvement.

[0043] The advice generation unit can propose customized sleep improvement measures based on the user's lifestyle habits and environment. The advice generation unit, for example, uses a questionnaire to collect lifestyle habit data of the user. For example, the generation AI proposes customized sleep improvement measures based on the user's lifestyle habits. The advice generation unit can also use smart home devices to collect environmental data of the user. For example, the smart home devices collect data on the temperature, humidity, and lighting in the user's bedroom and send it to the generation AI. The advice generation unit can also propose customized sleep improvement measures based on the user's lifestyle habits and environment. For example, the generation AI proposes specific improvement measures based on the user's lifestyle habits and environment. This enables more effective sleep improvement by proposing customized sleep improvement measures based on the lifestyle habits and environment.

[0044] The advice generation unit can suggest improvements to co-sleeping with family or partners based on the user's sleep data. The advice generation unit, for example, uses a database so that the generation AI can suggest improvements to co-sleeping based on the sleep data of the user and their family or partners. For example, the database stores everyone's sleep data, and the generation AI performs analysis based on this data. The advice generation unit can also suggest improvements to co-sleeping based on the sleep data of the user and their family or partners. For example, the generation AI analyzes everyone's sleep data and suggests specific improvements. The advice generation unit can also suggest joint relaxation methods based on the sleep data of the user and their family or partners. For example, the generation AI analyzes everyone's emotional data and suggests joint relaxation methods. In this way, by suggesting improvements to co-sleeping with family or partners, the quality of sleep for everyone is improved.

[0045] The advice generation unit can provide the user with meditation or breathing exercise training as part of sleep coaching. For example, the advice generation unit uses a generation AI to automatically generate a meditation or breathing exercise training program based on the user's sleep data and provide it through a smartphone app. For example, the app tracks the user's progress and provides feedback. The advice generation unit can also use a database to provide the meditation or breathing exercise training program based on the user's sleep data. For example, the database stores the user's past sleep data, and the generation AI generates the program based on this data. The advice generation unit can also use a smartwatch to provide the meditation or breathing exercise training program based on the user's sleep data. For example, the smartwatch measures the user's heart rate and breathing rate and sends the data to the generation AI. This allows the generation AI to provide meditation or breathing exercise training, promoting relaxation and improving the quality of sleep for the user.

[0046] The advice generation unit can analyze the user's sleep environment data and suggest optimal bedding selection. The advice generation unit, for example, uses a database so that the generation AI can analyze the user's sleep environment data and suggest optimal bedding selection. For example, the database stores the user's sleep environment data, and the generation AI performs analysis based on this data. The advice generation unit can also analyze the user's sleep environment data and suggest optimal bedding selection. For example, the generation AI can analyze the user's sleep environment data and suggest specific selection methods, such as mattress firmness and pillow height. The advice generation unit can also use smart home devices so that the generation AI can analyze the user's sleep environment data and suggest optimal bedding selection. For example, the smart home devices can collect temperature, humidity, and lighting data from the user's bedroom and send it to the generation AI. This can improve the user's sleep environment and enhance sleep quality by suggesting optimal bedding selection.

[0047] The advice generation unit can perform a detailed analysis of the effects of sound and light in the user's sleep environment and propose optimal environment settings. The advice generation unit, for example, uses a smartphone microphone to perform a detailed analysis of the effects of sound in the user's sleep environment. For example, the microphone records environmental sounds and transmits the data to the generation AI. The advice generation unit can also use a smart home device to perform a detailed analysis of the effects of light in the user's sleep environment. For example, the smart home device collects bedroom lighting data and transmits it to the generation AI. The advice generation unit can also perform a detailed analysis of the effects of sound and light in the user's sleep environment and propose optimal environment settings. For example, the generation AI analyzes the user's sleep environment data and evaluates specific effects, such as sound frequency and light intensity. The advice generation unit can also use a database to enable the generation AI to analyze the user's sleep environment data and propose optimal environment settings. For example, the database stores the user's sleep environment data, and the generation AI performs analysis based on this data. This allows the generation AI to perform a detailed analysis of the effects of sound and light and propose optimal environment settings, thereby improving the user's sleep environment and improving sleep quality.

[0048] The advice generation unit can suggest environmental settings according to the season and weather based on the user's sleep environment data. The advice generation unit, for example, uses a database so that the generation AI can suggest environmental settings according to the season and weather based on the user's sleep environment data. For example, the database stores the user's sleep environment data, and the generation AI performs analysis based on this data. The advice generation unit can also suggest environmental settings according to the season and weather based on the user's sleep environment data. For example, the generation AI analyzes the user's sleep environment data and suggests specific environmental settings such as temperature adjustment and humidity control. The advice generation unit can also use smart home devices so that the generation AI can suggest environmental settings according to the season and weather based on the user's sleep environment data. For example, the smart home devices collect data on the temperature, humidity, and lighting in the bedroom and send it to the generation AI. This allows the generation AI to suggest environmental settings according to the season and weather, optimizing the user's sleep environment and improving sleep quality.

[0049] The advice generation unit can suggest to the user the placement of plants and interior items as part of optimizing the sleep environment. For example, the advice generation unit uses a database so that the generation AI can suggest optimal plant placement based on the user's sleep environment data. For example, the database stores the user's sleep environment data, and the generation AI performs analysis based on this data. The advice generation unit can also suggest optimal interior placement based on the user's sleep environment data. For example, the generation AI analyzes the user's sleep environment data and makes specific suggestions, such as the type of plants and the location of interior items. The advice generation unit can also use smart home devices so that the generation AI can suggest optimal plant and interior placement based on the user's sleep environment data. For example, the smart home devices collect data on the temperature, humidity, and lighting in the bedroom and send it to the generation AI. This improves the user's sleep environment and enhances sleep quality by suggesting plant and interior placement.

[0050] The data collection unit can continuously monitor the user's sleep data and provide feedback in real time. For example, the data collection unit uses a smartwatch so that the generation AI can continuously monitor the user's sleep data. For example, the smartwatch measures heart rate and sleep patterns in real time and sends the data to the generation AI. The data collection unit can also use a smart bed so that the generation AI can continuously monitor the user's sleep data. For example, the smart bed measures the number of times the user turns over in bed and the number of breaths in real time and sends the data to the generation AI. The data collection unit can also use a smartphone app so that the generation AI can continuously monitor the user's sleep data. For example, the app records the user's sleep time and sends the data to the generation AI. This allows for continuous monitoring and real-time feedback to help improve the user's sleep.

[0051] The data collection unit can automatically generate detailed weekly or monthly reports based on the user's sleep data. For example, the data collection unit uses a database so that the generation AI can automatically generate detailed weekly or monthly reports based on the user's sleep data. For example, the database stores the user's sleep data, and the generation AI generates reports based on the user's sleep data. The data collection unit can also automatically generate detailed weekly or monthly reports based on the user's sleep data. For example, the generation AI analyzes the user's sleep data and identifies weekly or monthly sleep patterns and areas for improvement. The data collection unit can also automatically generate detailed weekly or monthly reports based on the user's sleep data and provide them through a smartphone app. For example, the app displays the reports to the user and provides feedback. This makes it easier for the user to understand their sleep patterns and areas for improvement by automatically generating detailed reports.

[0052] The data collection unit can provide a comprehensive health report based on the user's sleep data and other health data. The data collection unit, for example, uses a database so that the generation AI can provide a comprehensive health report based on the user's sleep data and other health data (e.g., diet, exercise, etc.). For example, the database stores the user's health data, and the generation AI generates a report based on this data. The data collection unit can also provide a comprehensive health report based on the user's sleep data and other health data. For example, the generation AI can analyze the user's sleep data, diet data, and exercise data to evaluate the user's overall health status. The data collection unit can also provide a comprehensive health report based on the user's sleep data and other health data through a smartphone app. For example, the app can display the report to the user and provide feedback. This allows the user's health status to be comprehensively evaluated by providing a comprehensive health report based on the user's sleep data and other health data.

[0053] As part of the continuous monitoring, the data collection unit can conduct periodic surveys of users and reflect their feedback. For example, the data collection unit uses a smartphone app so that the generation AI can conduct periodic surveys of users and reflect their feedback. For example, the app sends surveys to users and collects responses. The data collection unit can also use a web application so that the generation AI can conduct periodic surveys of users and reflect their feedback. For example, the web application sends surveys to users and collects responses. The data collection unit can also use email so that the generation AI can conduct periodic surveys of users and reflect their feedback. For example, the email sends surveys to users and collects responses. In this way, by conducting periodic surveys and reflecting their feedback, it is possible to continuously support the user's sleep improvement.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The data collection unit can monitor the sound environment during sleep and analyze the noise level and type of sound. For example, it can use a microphone built into the smart bed to record ambient sounds with high accuracy and send the data to the generation AI. The data collection unit can also monitor the sound environment in the user's bedroom in real time using a smartphone app. For example, the app can record ambient sounds and analyze the noise level and type of sound. The data collection unit can also monitor the sound environment during sleep using a smartwatch. For example, the smartwatch can detect ambient sounds with a sensor and send the data to the generation AI. This allows for a detailed analysis of the sound environment and can suggest specific improvements to improve sleep quality.

[0056] The analysis unit can identify seasonal changes in sleep patterns based on the user's sleep data and suggest seasonal sleep improvement measures. For example, the generation AI uses a database to analyze the user's past sleep data and identify seasonal changes in sleep patterns. For example, the database stores the user's past sleep data, and the generation AI performs analysis based on this. The analysis unit can also compare the user's sleep data on a monthly basis to identify seasonal changes. For example, the generation AI compares monthly sleep data to identify seasonal changes in sleep quality. This allows the user's sleep quality to be improved by suggesting specific sleep improvement measures according to the season.

[0057] The advice generation unit can set individual sleep goals based on the user's sleep data and periodically evaluate the degree of achievement of those goals. For example, the generation AI can analyze the user's sleep data and set specific sleep goals. For example, a goal such as "getting at least seven hours of sleep every night" can be set. The advice generation unit can also periodically evaluate the degree of achievement of those goals based on the user's sleep data. For example, the generation AI can analyze the user's latest sleep data and evaluate the degree of achievement of the goal. The advice generation unit can also evaluate the degree of achievement of the goal based on the user's sleep data and provide feedback. For example, the generation AI can provide feedback to the user via a smartphone app. This allows the user to set individual sleep goals and evaluate the degree of achievement, thereby supporting the user's sleep improvement.

[0058] The data collection unit can monitor the user's body movements in detail while they sleep and analyze their patterns. For example, it can use sensors built into the smart bed to record the user's body movements with high accuracy and send the data to the generation AI. The data collection unit can also use a smartwatch to monitor the user's body movements in real time. For example, the smartwatch detects the user's body movements with a sensor and sends the data to the generation AI. The data collection unit can also monitor the user's body movements using a smartphone app. For example, the app can record the user's body movements and analyze their patterns. This allows for a detailed analysis of the body movement patterns to suggest specific improvements to improve sleep quality.

[0059] The analysis unit can evaluate the stress level based on the user's sleep data and analyze the impact of stress on sleep. For example, the generation AI analyzes the user's heart rate and breathing rate data to evaluate the stress level. For example, a smartwatch can be used to measure the heart rate in real time and send the data to the generation AI. The analysis unit can also analyze the impact of stress on sleep based on the user's sleep data. For example, the generation AI can analyze the user's sleep data and stress level data to identify the impact of stress on sleep quality. This can help improve the user's sleep by evaluating the stress level and analyzing its impact.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The data collection unit collects the user's sleep data. For example, a smart watch can be used to collect heart rate and sleep patterns in real time. A smart bed can also be used to collect the number of times the user turns over in bed and the breathing rate. Furthermore, the data collection unit can record the user's sleep time using a smartphone app. Step 2: The analysis unit analyzes the sleep data collected by the data collection unit. For example, the generation AI may use a text generation AI (e.g., LLM) to analyze sleep patterns. The generation AI may also use a multimodal generation AI to identify sleep depth and problem areas. The generation AI may also use an algorithm to analyze the sleep data and evaluate the user's sleep quality. Step 3: The advice generation unit proposes sleep improvement measures based on the results of the analysis by the analysis unit. For example, the generation AI may provide the user with specific advice such as "Try to go to bed at the same time every night." The generation AI may also provide customized advice tailored to the user's lifestyle and environment. The generation AI may also make suggestions to optimize the user's sleep environment.

[0062] (Example 2) The sleep improvement system according to an embodiment of the present invention uses AI to collect and analyze a user's sleep data and propose personalized sleep improvement measures, thereby improving the quality of the user's sleep and daily life.

[0063] A sleep improvement system according to an embodiment includes a data collection unit, an analysis unit, and an advice generation unit. The data collection unit collects a user's sleep data. For example, a smart watch can be used to collect a user's heart rate and sleep patterns in real time. A smart bed can also be used to collect the number of turns and breathing rate. The data collection unit can also record the user's sleep time using a smartphone app. The analysis unit analyzes the sleep data collected by the data collection unit. For example, the generation AI can analyze the sleep patterns using a text generation AI (e.g., LLM). The generation AI can also identify sleep depth and problem areas using a multimodal generation AI. The generation AI can also analyze the sleep data using an algorithm to evaluate the user's sleep quality. The advice generation unit proposes sleep improvement measures based on the results of the analysis by the analysis unit. For example, the generation AI can provide the user with specific advice such as "Try to go to bed at the same time every night." The generation AI can also provide customized advice tailored to the user's lifestyle and environment. The generation AI can also make suggestions to optimize the user's sleep environment. As a result, the sleep improvement system according to the embodiment can improve the quality of a user's sleep and the quality of daily life. For example, the output unit displays the sleep improvement measures to the user via a web application or a mobile application. If the user desires feedback in paper form, the results can be printed using a printer. Sending the results via email provides quick feedback by sending the results directly to the user.

[0064] The data collection unit can collect sleep data in real time using at least one device, a smartwatch or a smart bed. The data collection unit, for example, uses a smartwatch to collect heart rate and sleep patterns in real time. For example, the smartwatch measures heart rate with high accuracy and transmits the data wirelessly to the generation AI. The data collection unit can also collect the number of times a user turns over in bed and the breathing rate using a smart bed. For example, the smart bed detects the number of times a user turns over in bed using a sensor and transmits the data to the generation AI. The data collection unit can also record the user's sleep time using a smartphone app. For example, the app records the time the user goes to bed and wakes up and transmits the data to the generation AI. This allows for more accurate analysis by collecting sleep data in real time.

[0065] If the depth of sleep is low, the analysis unit can identify the cause and suggest remedial measures. For example, the analysis unit closely monitors changes in the user's body temperature and estimates the depth of sleep from the pattern of body temperature fluctuations. For example, a smartwatch can be used to measure skin temperature with high accuracy and send the data to the generation AI. The analysis unit can also analyze the user's breathing sounds and snoring patterns. For example, a microphone built into the smart bed can be used to record breathing sounds and snoring with high accuracy and send the data to the generation AI. The analysis unit can also analyze fluctuations in the user's heart rate and estimate the depth of sleep. For example, a smartwatch can be used to measure heart rate in real time and send the data to the generation AI. This allows the analysis unit to identify the cause of shallow sleep and suggest appropriate remedial measures to improve sleep quality.

[0066] The advice generation unit can provide the user with specific advice such as "Try to go to bed at the same time every night" or "Try not to use your smartphone before going to bed." For example, the advice generation unit can provide the user with specific advice such as "Try to go to bed at the same time every night." For example, the generation AI can analyze the user's sleep data and show that going to bed at the same time every night improves sleep quality. The advice generation unit can also provide the user with specific advice such as "Try not to use your smartphone before going to bed." For example, the generation AI can analyze the user's sleep data and show that not using your smartphone before going to bed improves sleep quality. The advice generation unit can also provide customized advice tailored to the user's lifestyle and environment. For example, the generation AI can provide specific advice based on the user's lifestyle and environment. By providing specific advice, the user's sleep habits and sleep quality can be improved.

[0067] The advice generation unit can provide customized advice tailored to the user's lifestyle and environment. The advice generation unit provides, for example, customized advice tailored to the user's lifestyle and environment. For example, the generation AI provides specific advice based on the user's lifestyle and environment. For example, if the user works the night shift, the generation AI can suggest a sleep schedule tailored to the night shift. Also, if the user has a pet, the generation AI can suggest ways to improve the sleep environment that take the pet's movements and sounds into consideration. It can also provide specific advice tailored to the environment, such as adjusting the temperature, humidity, and lighting in the user's bedroom. This allows for more effective sleep improvement by providing customized advice tailored to the user's lifestyle and environment.

[0068] The data collection unit may have a function for analyzing the user's breathing sounds and snoring patterns. For example, the data collection unit may use a microphone built into the smart bed to analyze the user's breathing sounds and snoring patterns. For example, the microphone may record the breathing sounds and snoring with high accuracy and send the data to the generation AI. The data collection unit may also use a smartwatch to analyze the user's breathing sounds and snoring patterns. For example, the smartwatch may detect the breathing sounds and snoring with a sensor and send the data to the generation AI. The data collection unit may also use a smartphone app to analyze the user's breathing sounds and snoring patterns. For example, the app may record the user's breathing sounds and snoring and send the data to the generation AI. This allows for a more detailed evaluation of sleep quality by analyzing the breathing sounds and snoring patterns.

[0069] The data collection unit can monitor changes in the user's body temperature in detail and estimate the depth of sleep from the pattern of body temperature fluctuations. The data collection unit, for example, uses a smartwatch to monitor changes in the user's body temperature in detail. For example, the smartwatch measures skin temperature with high accuracy and transmits the data to the generation AI. The data collection unit can also use a smart bed to monitor changes in the user's body temperature in detail. For example, the smart bed has a built-in body temperature sensor that measures changes in the user's body temperature in real time and transmits the data to the generation AI. The data collection unit can also use a smartphone app to monitor changes in the user's body temperature in detail. For example, the app records changes in the user's body temperature and transmits the data to the generation AI. This allows for more accurate estimation of the depth of sleep by monitoring changes in body temperature in detail.

[0070] The data collection unit can use the emotion estimation function to analyze the user's emotional state before going to bed and evaluate the impact of the emotion on sleep. The data collection unit can, for example, use a smartphone camera to analyze the user's emotional state before going to bed using the emotion estimation function. For example, the camera analyzes the user's facial expressions and transmits the emotional data to the generation AI. The data collection unit can also use a smartwatch to analyze the user's emotional state before going to bed using the emotion estimation function. For example, the smartwatch measures heart rate and electrodermal activity and transmits the emotional data to the generation AI. The data collection unit can also use a smart bed to analyze the user's emotional state before going to bed using the emotion estimation function. For example, the smart bed measures respiratory rate and heart rate and transmits the emotional data to the generation AI. This allows the system to analyze the user's emotional state before going to bed and evaluate its impact, thereby proposing more appropriate measures to improve sleep.

[0071] The data collection unit can simultaneously collect pet movements or sounds and perform sleep analysis that takes the pet's influence into account. The data collection unit, for example, uses a camera and microphone installed in the user's bedroom to collect pet movements and sounds. For example, the camera records the pet's movements in real time and transmits the data to the generation AI. The microphone also records the pet's sounds with high accuracy and transmits the data to the generation AI. The data collection unit can also use a smartwatch to collect pet movements and sounds. For example, the smartwatch detects the pet's movements with a sensor and transmits the data to the generation AI. The data collection unit can also use a smart bed to collect pet movements and sounds. For example, the smart bed detects the pet's movements with a sensor and transmits the data to the generation AI. This enables more accurate sleep analysis by taking the pet's influence into account.

[0072] The data collection unit may have a function for analyzing the user's breathing sounds and snoring patterns. For example, the data collection unit may use a microphone built into the smart bed to analyze the user's breathing sounds and snoring patterns. For example, the microphone may record the breathing sounds and snoring with high accuracy and send the data to the generation AI. The data collection unit may also use a smartwatch to analyze the user's breathing sounds and snoring patterns. For example, the smartwatch may detect the breathing sounds and snoring with a sensor and send the data to the generation AI. The data collection unit may also use a smartphone app to analyze the user's breathing sounds and snoring patterns. For example, the app may record the user's breathing sounds and snoring and send the data to the generation AI. This allows for a more detailed evaluation of sleep quality by analyzing the breathing sounds and snoring patterns.

[0073] The data collection unit can use the emotion estimation function to analyze the emotional impact of content viewed by a user before going to bed and evaluate the impact on sleep quality. For example, the data collection unit uses a smartphone camera to analyze the emotional impact of a movie viewed by a user before going to bed using the emotion estimation function. For example, the camera analyzes the user's facial expressions and transmits the emotional data to the generation AI. The data collection unit can also use a smartwatch to analyze the emotional impact of music viewed by a user before going to bed using the emotion estimation function. For example, the smartwatch measures heart rate and electrodermal activity and transmits the emotional data to the generation AI. The data collection unit can also use a smart bed to analyze the emotional impact of content viewed by a user before going to bed using the emotion estimation function. For example, the smart bed measures respiratory rate and heart rate and transmits the emotional data to the generation AI. In this way, the emotional impact of the viewed content can be analyzed to evaluate its impact on sleep quality.

[0074] When analyzing a user's sleep data, the analysis unit can compare it with past data to identify long-term changes in sleep patterns. The analysis unit, for example, uses a database so that the generation AI can analyze the user's past sleep data and identify long-term changes in sleep patterns. For example, the database stores the user's past sleep data, and the generation AI performs analysis based on this data. The analysis unit can also compare the user's sleep data on a weekly or monthly basis to identify long-term changes. For example, the generation AI compares sleep data from week to week or month to identify changes in sleep quality. The analysis unit can also compare the user's sleep data with that of other users to provide benchmarks. For example, the generation AI compares the user's sleep data with that of other users to evaluate the position of the user's sleep data. This allows the generation AI to identify long-term changes in sleep patterns by comparing it with past data.

[0075] The analysis unit can also integrate the user's dietary and exercise data into the sleep data analysis to evaluate the user's overall health status. For example, the analysis unit uses a food recording app to collect the user's dietary data. For example, the app records the contents of the meals the user eats and sends the data to the generation AI. The analysis unit can also use a fitness tracker to collect the user's exercise data. For example, the fitness tracker measures the user's exercise volume and calories burned and sends the data to the generation AI. The analysis unit can also integrate the user's sleep data, dietary data, and exercise data to evaluate the user's overall health status. For example, the generation AI analyzes the user's sleep data, dietary data, and exercise data to evaluate the user's overall health status. This allows the user's overall health status to be evaluated by integrating the dietary and exercise data.

[0076] The analysis unit can use the emotion estimation function to estimate the content of the user's dreams during sleep and analyze the impact of the dreams on the quality of sleep. The analysis unit, for example, uses the emotion estimation function to analyze electroencephalogram data to estimate the content of the user's dreams during sleep. For example, the analysis unit estimates the content of the dreams from the electroencephalogram data and sends the data to the generation AI. The analysis unit can also analyze heart rate and breathing rate data to estimate the content of the user's dreams during sleep. For example, a smartwatch or smart bed can be used to measure the heart rate and breathing rate and send the data to the generation AI. The analysis unit can also analyze audio data to estimate the content of the user's dreams during sleep. For example, if the user talks in their sleep, the analysis unit analyzes the audio data and estimates the content of the dream. This allows the quality of sleep to be evaluated in more detail by estimating the content of the dreams and analyzing their impact.

[0077] The analysis unit can compare the sleep data analyzed by the generation AI with the data of other users and provide a benchmark. The analysis unit, for example, uses a database to compare the sleep data analyzed by the generation AI with the data of other users. For example, the database stores the sleep data of other users, and the generation AI performs analysis based on this data. The analysis unit can also compare the user's sleep data with the data of other users and provide a benchmark. For example, the generation AI compares the user's sleep data with the data of other users to evaluate the position of the user's sleep data. The analysis unit can also compare the user's sleep data with the data of other users and set goals to improve sleep quality. For example, the generation AI compares the user's sleep data with the data of other users to identify areas for improvement in the user's sleep data and set specific goals. This allows the user's sleep data to be compared with the data of other users to provide a benchmark and evaluate the user's sleep data.

[0078] The analysis unit can customize the sleep data analysis results according to the user's occupation and lifestyle and provide more specific advice. The analysis unit, for example, uses profile information to collect the user's occupation data. For example, the generation AI provides customized advice based on the user's occupation. The analysis unit can also use a questionnaire to collect the user's lifestyle data. For example, the generation AI provides customized advice based on the user's lifestyle. The analysis unit can also customize the sleep data analysis results according to the user's occupation and lifestyle and provide specific advice. For example, a user who works a desk job can be provided with advice that takes into account the effects of sitting for long periods of time. A user who works shifts can be suggested a sleep schedule that matches their shifts. This allows for more effective sleep improvement by providing customized advice based on the user's occupation and lifestyle.

[0079] The analysis unit can use the emotion estimation function to monitor the user's emotional fluctuations during sleep in real time and reflect the data in the analysis. The analysis unit can, for example, use a heart rate sensor to monitor the user's emotional fluctuations during sleep in real time using the emotion estimation function. For example, the heart rate sensor measures heart rate fluctuations and transmits the data to the generation AI. The analysis unit can also use an electrodermal activity sensor to monitor the user's emotional fluctuations during sleep in real time using the emotion estimation function. For example, the electrodermal activity sensor measures skin electrical activity and transmits the data to the generation AI. The analysis unit can also use an electroencephalogram (EEG) sensor to monitor the user's emotional fluctuations during sleep in real time using the emotion estimation function. For example, the EEG sensor measures brain waves and transmits the data to the generation AI. This allows for real-time monitoring of emotional fluctuations during sleep and reflects the data in the analysis, enabling more accurate sleep analysis.

[0080] The advice generation unit can automatically generate and periodically update an individualized sleep coaching program based on the user's sleep data. The advice generation unit, for example, uses a database so that the generation AI can automatically generate an individualized sleep coaching program based on the user's sleep data. For example, the database stores the user's past sleep data, and the generation AI generates a program based on this data. The advice generation unit can also periodically update the individualized sleep coaching program based on the user's sleep data. For example, the generation AI analyzes the user's latest sleep data and updates the program. The advice generation unit can also automatically generate an individualized sleep coaching program based on the user's sleep data and provide it through a smartphone app. For example, the app tracks the user's progress and provides feedback. This allows the individualized sleep coaching program to be automatically generated and periodically updated, thereby continuously supporting the user's sleep improvement.

[0081] The advice generation unit can propose customized sleep improvement measures based on the user's lifestyle habits and environment. The advice generation unit, for example, uses a questionnaire to collect lifestyle habit data of the user. For example, the generation AI proposes customized sleep improvement measures based on the user's lifestyle habits. The advice generation unit can also use smart home devices to collect environmental data of the user. For example, the smart home devices collect data on the temperature, humidity, and lighting in the user's bedroom and send it to the generation AI. The advice generation unit can also propose customized sleep improvement measures based on the user's lifestyle habits and environment. For example, the generation AI proposes specific improvement measures based on the user's lifestyle habits and environment. This enables more effective sleep improvement by proposing customized sleep improvement measures based on the lifestyle habits and environment.

[0082] The advice generation unit can use the emotion estimation function to suggest relaxation methods according to the user's emotional state, thereby improving sleep quality. The advice generation unit can use, for example, a smartphone camera to suggest relaxation methods according to the user's emotional state using the emotion estimation function. For example, the camera analyzes the user's facial expressions and transmits the emotion data to the generation AI. The advice generation unit can also use a smartwatch to suggest relaxation methods according to the user's emotional state using the emotion estimation function. For example, the smartwatch measures heart rate and electrodermal activity and transmits the emotion data to the generation AI. The advice generation unit can also use a smart bed to suggest relaxation methods according to the user's emotional state using the emotion estimation function. For example, the smart bed measures respiratory rate and heart rate and transmits the emotion data to the generation AI. This improves sleep quality by suggesting relaxation methods according to the user's emotional state.

[0083] The advice generation unit can suggest improvements to co-sleeping with family or partners based on the user's sleep data. The advice generation unit, for example, uses a database so that the generation AI can suggest improvements to co-sleeping based on the sleep data of the user and their family or partners. For example, the database stores everyone's sleep data, and the generation AI performs analysis based on this data. The advice generation unit can also suggest improvements to co-sleeping based on the sleep data of the user and their family or partners. For example, the generation AI analyzes everyone's sleep data and suggests specific improvements. The advice generation unit can also suggest joint relaxation methods based on the sleep data of the user and their family or partners. For example, the generation AI analyzes everyone's emotional data and suggests joint relaxation methods. In this way, by suggesting improvements to co-sleeping with family or partners, the quality of sleep for everyone is improved.

[0084] The advice generation unit can provide the user with meditation or breathing exercise training as part of sleep coaching. For example, the advice generation unit uses a generation AI to automatically generate a meditation or breathing exercise training program based on the user's sleep data and provide it through a smartphone app. For example, the app tracks the user's progress and provides feedback. The advice generation unit can also use a database to provide the meditation or breathing exercise training program based on the user's sleep data. For example, the database stores the user's past sleep data, and the generation AI generates the program based on this data. The advice generation unit can also use a smartwatch to provide the meditation or breathing exercise training program based on the user's sleep data. For example, the smartwatch measures the user's heart rate and breathing rate and sends the data to the generation AI. This allows the generation AI to provide meditation or breathing exercise training, promoting relaxation and improving the quality of sleep for the user.

[0085] The advice generation unit can use the emotion estimation function to suggest relaxing activities that the user should do before sleeping. The advice generation unit can use, for example, a smartphone camera to suggest relaxing activities that the user should do before sleeping using the emotion estimation function. For example, the camera analyzes the user's facial expressions and sends the emotional data to the generation AI. The advice generation unit can also use a smartwatch to suggest relaxing activities that the user should do before sleeping using the emotion estimation function. For example, the smartwatch measures heart rate and electrodermal activity and sends the emotional data to the generation AI. The advice generation unit can also use a smart bed to suggest relaxing activities that the user should do before sleeping using the emotion estimation function. For example, the smart bed measures respiratory rate and heart rate and sends the emotional data to the generation AI. In this way, the suggestion of relaxing activities promotes the user's relaxation and improves the quality of sleep.

[0086] The advice generation unit can analyze the user's sleep environment data and suggest optimal bedding selection. The advice generation unit, for example, uses a database so that the generation AI can analyze the user's sleep environment data and suggest optimal bedding selection. For example, the database stores the user's sleep environment data, and the generation AI performs analysis based on this data. The advice generation unit can also analyze the user's sleep environment data and suggest optimal bedding selection. For example, the generation AI can analyze the user's sleep environment data and suggest specific selection methods, such as mattress firmness and pillow height. The advice generation unit can also use smart home devices so that the generation AI can analyze the user's sleep environment data and suggest optimal bedding selection. For example, the smart home devices can collect temperature, humidity, and lighting data from the user's bedroom and send it to the generation AI. This can improve the user's sleep environment and enhance sleep quality by suggesting optimal bedding selection.

[0087] The advice generation unit can perform a detailed analysis of the effects of sound and light in the user's sleep environment and propose optimal environment settings. The advice generation unit, for example, uses a smartphone microphone to perform a detailed analysis of the effects of sound in the user's sleep environment. For example, the microphone records environmental sounds and transmits the data to the generation AI. The advice generation unit can also use a smart home device to perform a detailed analysis of the effects of light in the user's sleep environment. For example, the smart home device collects bedroom lighting data and transmits it to the generation AI. The advice generation unit can also perform a detailed analysis of the effects of sound and light in the user's sleep environment and propose optimal environment settings. For example, the generation AI analyzes the user's sleep environment data and evaluates specific effects, such as sound frequency and light intensity. The advice generation unit can also use a database to enable the generation AI to analyze the user's sleep environment data and propose optimal environment settings. For example, the database stores the user's sleep environment data, and the generation AI performs analysis based on this data. This allows the generation AI to perform a detailed analysis of the effects of sound and light and propose optimal environment settings, thereby improving the user's sleep environment and improving sleep quality.

[0088] The advice generation unit can use the emotion estimation function to suggest bedroom scents and music that correspond to the user's emotional state. The advice generation unit can, for example, use a smartphone camera to suggest bedroom scents that correspond to the user's emotional state using the emotion estimation function. For example, the camera analyzes the user's facial expressions and sends the emotion data to the generation AI. The advice generation unit can also use a smartwatch to suggest bedroom music that corresponds to the user's emotional state using the emotion estimation function. For example, the smartwatch measures heart rate and electrodermal activity and sends the emotion data to the generation AI. The advice generation unit can also use a smart bed to suggest bedroom scents and music that correspond to the user's emotional state using the emotion estimation function. For example, the smart bed measures breathing rate and heart rate and sends the emotion data to the generation AI. In this way, the advice generation unit can promote relaxation and improve sleep quality by suggesting bedroom scents and music that correspond to the user's emotional state.

[0089] The advice generation unit can suggest environmental settings according to the season and weather based on the user's sleep environment data. The advice generation unit, for example, uses a database so that the generation AI can suggest environmental settings according to the season and weather based on the user's sleep environment data. For example, the database stores the user's sleep environment data, and the generation AI performs analysis based on this data. The advice generation unit can also suggest environmental settings according to the season and weather based on the user's sleep environment data. For example, the generation AI analyzes the user's sleep environment data and suggests specific environmental settings such as temperature adjustment and humidity control. The advice generation unit can also use smart home devices so that the generation AI can suggest environmental settings according to the season and weather based on the user's sleep environment data. For example, the smart home devices collect data on the temperature, humidity, and lighting in the bedroom and send it to the generation AI. This allows the generation AI to suggest environmental settings according to the season and weather, optimizing the user's sleep environment and improving sleep quality.

[0090] The advice generation unit can suggest to the user the placement of plants and interior items as part of optimizing the sleep environment. For example, the advice generation unit uses a database so that the generation AI can suggest optimal plant placement based on the user's sleep environment data. For example, the database stores the user's sleep environment data, and the generation AI performs analysis based on this data. The advice generation unit can also suggest optimal interior placement based on the user's sleep environment data. For example, the generation AI analyzes the user's sleep environment data and makes specific suggestions, such as the type of plants and the location of interior items. The advice generation unit can also use smart home devices so that the generation AI can suggest optimal plant and interior placement based on the user's sleep environment data. For example, the smart home devices collect data on the temperature, humidity, and lighting in the bedroom and send it to the generation AI. This improves the user's sleep environment and enhances sleep quality by suggesting plant and interior placement.

[0091] The advice generation unit can use the emotion estimation function to suggest the color and design of the bedroom that will make the user most relaxed. For example, the advice generation unit uses the smartphone camera to suggest the color of the bedroom that will make the user most relaxed using the emotion estimation function. For example, the camera analyzes the user's facial expressions and sends the emotion data to the generation AI. The advice generation unit can also use a smartwatch to suggest the design of the bedroom that will make the user most relaxed using the emotion estimation function. For example, the smartwatch measures the heart rate and electrodermal activity and sends the emotion data to the generation AI. The advice generation unit can also use a smart bed to suggest the color and design of the bedroom that will make the user most relaxed using the emotion estimation function. For example, the smart bed measures the breathing rate and heart rate and sends the emotion data to the generation AI. In this way, the suggested color and design of the bedroom promotes relaxation for the user and improves the quality of sleep.

[0092] The data collection unit can continuously monitor the user's sleep data and provide feedback in real time. For example, the data collection unit uses a smartwatch so that the generation AI can continuously monitor the user's sleep data. For example, the smartwatch measures heart rate and sleep patterns in real time and sends the data to the generation AI. The data collection unit can also use a smart bed so that the generation AI can continuously monitor the user's sleep data. For example, the smart bed measures the number of times the user turns over in bed and the number of breaths in real time and sends the data to the generation AI. The data collection unit can also use a smartphone app so that the generation AI can continuously monitor the user's sleep data. For example, the app records the user's sleep time and sends the data to the generation AI. This allows for continuous monitoring and real-time feedback to help improve the user's sleep.

[0093] The data collection unit can automatically generate detailed weekly or monthly reports based on the user's sleep data. For example, the data collection unit uses a database so that the generation AI can automatically generate detailed weekly or monthly reports based on the user's sleep data. For example, the database stores the user's sleep data, and the generation AI generates reports based on the user's sleep data. The data collection unit can also automatically generate detailed weekly or monthly reports based on the user's sleep data. For example, the generation AI analyzes the user's sleep data and identifies weekly or monthly sleep patterns and areas for improvement. The data collection unit can also automatically generate detailed weekly or monthly reports based on the user's sleep data and provide them through a smartphone app. For example, the app displays the reports to the user and provides feedback. This makes it easier for the user to understand their sleep patterns and areas for improvement by automatically generating detailed reports.

[0094] The advice generation unit can use the emotion estimation function to provide feedback according to the user's emotional state and maintain motivation. The advice generation unit can use, for example, a smartphone camera to provide feedback according to the user's emotional state using the emotion estimation function. For example, the camera analyzes the user's facial expressions and transmits the emotion data to the generation AI. The advice generation unit can also use a smartwatch to provide feedback according to the user's emotional state using the emotion estimation function. For example, the smartwatch measures heart rate and electrodermal activity and transmits the emotion data to the generation AI. The advice generation unit can also use a smart bed to provide feedback according to the user's emotional state using the emotion estimation function. For example, the smart bed measures respiratory rate and heart rate and transmits the emotion data to the generation AI. In this way, by providing feedback according to the emotional state, the user's motivation can be maintained and continuous support can be provided for improving sleep.

[0095] The data collection unit can provide a comprehensive health report based on the user's sleep data and other health data. The data collection unit, for example, uses a database so that the generation AI can provide a comprehensive health report based on the user's sleep data and other health data (e.g., diet, exercise, etc.). For example, the database stores the user's health data, and the generation AI generates a report based on this data. The data collection unit can also provide a comprehensive health report based on the user's sleep data and other health data. For example, the generation AI can analyze the user's sleep data, diet data, and exercise data to evaluate the user's overall health status. The data collection unit can also provide a comprehensive health report based on the user's sleep data and other health data through a smartphone app. For example, the app can display the report to the user and provide feedback. This allows the user's health status to be comprehensively evaluated by providing a comprehensive health report based on the user's sleep data and other health data.

[0096] As part of the continuous monitoring, the data collection unit can conduct periodic surveys of users and reflect their feedback. For example, the data collection unit uses a smartphone app so that the generation AI can conduct periodic surveys of users and reflect their feedback. For example, the app sends surveys to users and collects responses. The data collection unit can also use a web application so that the generation AI can conduct periodic surveys of users and reflect their feedback. For example, the web application sends surveys to users and collects responses. The data collection unit can also use email so that the generation AI can conduct periodic surveys of users and reflect their feedback. For example, the email sends surveys to users and collects responses. In this way, by conducting periodic surveys and reflecting their feedback, it is possible to continuously support the user's sleep improvement.

[0097] The advice generation unit can use the emotion estimation function to suggest a feedback format that will evoke the most positive emotion in the user. For example, the advice generation unit uses a smartphone camera to suggest a feedback format that will evoke the most positive emotion in the user using the emotion estimation function. For example, the camera analyzes the user's facial expressions and transmits the emotion data to the generation AI. The advice generation unit can also use a smartwatch to suggest a feedback format that will evoke the most positive emotion in the user using the emotion estimation function. For example, the smartwatch measures the user's heart rate and electrodermal activity and transmits the emotion data to the generation AI. The advice generation unit can also use a smart bed to suggest a feedback format that will evoke the most positive emotion in the user using the emotion estimation function. For example, the smart bed measures the user's respiratory rate and heart rate and transmits the emotion data to the generation AI. This can maintain the user's motivation and continuously support sleep improvement by suggesting a feedback format that will evoke positive emotion.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The data collection unit can monitor the sound environment during sleep and analyze the noise level and type of sound. For example, it can use a microphone built into the smart bed to record ambient sounds with high accuracy and send the data to the generation AI. The data collection unit can also monitor the sound environment in the user's bedroom in real time using a smartphone app. For example, the app can record ambient sounds and analyze the noise level and type of sound. The data collection unit can also monitor the sound environment during sleep using a smartwatch. For example, the smartwatch can detect ambient sounds with a sensor and send the data to the generation AI. This allows for a detailed analysis of the sound environment and can suggest specific improvements to improve sleep quality.

[0100] The analysis unit can identify seasonal changes in sleep patterns based on the user's sleep data and suggest seasonal sleep improvement measures. For example, the generation AI uses a database to analyze the user's past sleep data and identify seasonal changes in sleep patterns. For example, the database stores the user's past sleep data, and the generation AI performs analysis based on this. The analysis unit can also compare the user's sleep data on a monthly basis to identify seasonal changes. For example, the generation AI compares monthly sleep data to identify seasonal changes in sleep quality. This allows the user's sleep quality to be improved by suggesting specific sleep improvement measures according to the season.

[0101] The advice generation unit can set individual sleep goals based on the user's sleep data and periodically evaluate the degree of achievement of those goals. For example, the generation AI can analyze the user's sleep data and set specific sleep goals. For example, a goal such as "getting at least seven hours of sleep every night" can be set. The advice generation unit can also periodically evaluate the degree of achievement of those goals based on the user's sleep data. For example, the generation AI can analyze the user's latest sleep data and evaluate the degree of achievement of the goal. The advice generation unit can also evaluate the degree of achievement of the goal based on the user's sleep data and provide feedback. For example, the generation AI can provide feedback to the user via a smartphone app. This allows the user to set individual sleep goals and evaluate the degree of achievement, thereby supporting the user's sleep improvement.

[0102] The data collection unit can monitor the user's body movements in detail while they sleep and analyze their patterns. For example, it can use sensors built into the smart bed to record the user's body movements with high accuracy and send the data to the generation AI. The data collection unit can also use a smartwatch to monitor the user's body movements in real time. For example, the smartwatch detects the user's body movements with a sensor and sends the data to the generation AI. The data collection unit can also monitor the user's body movements using a smartphone app. For example, the app can record the user's body movements and analyze their patterns. This allows for a detailed analysis of the body movement patterns to suggest specific improvements to improve sleep quality.

[0103] The analysis unit can evaluate the stress level based on the user's sleep data and analyze the impact of stress on sleep. For example, the generation AI analyzes the user's heart rate and breathing rate data to evaluate the stress level. For example, a smartwatch can be used to measure the heart rate in real time and send the data to the generation AI. The analysis unit can also analyze the impact of stress on sleep based on the user's sleep data. For example, the generation AI can analyze the user's sleep data and stress level data to identify the impact of stress on sleep quality. This can help improve the user's sleep by evaluating the stress level and analyzing its impact.

[0104] The advice generation unit can use the emotion estimation function to suggest a relaxation method before sleep that corresponds to the user's emotional state. For example, the emotion estimation function can be used to use a smartphone camera to analyze the user's emotional state. For example, the camera can analyze the user's facial expressions and send the emotional data to the generation AI. The advice generation unit can also use a smartwatch to suggest a relaxation method that corresponds to the user's emotional state using the emotion estimation function. For example, the smartwatch can measure heart rate and electrodermal activity and send the emotional data to the generation AI. The advice generation unit can also use a smart bed to suggest a relaxation method that corresponds to the user's emotional state using the emotion estimation function. For example, the smart bed can measure respiratory rate and heart rate and send the emotional data to the generation AI. In this way, suggesting a relaxation method that corresponds to the user's emotional state promotes relaxation and improves the quality of sleep.

[0105] The analysis unit can use the emotion estimation function to monitor the user's emotional fluctuations during sleep in real time and reflect the data in the analysis. For example, a heart rate sensor can be used to monitor the user's emotional fluctuations during sleep in real time using the emotion estimation function. For example, the heart rate sensor measures heart rate fluctuations and transmits the data to the generation AI. The analysis unit can also use an electrodermal activity sensor to monitor the user's emotional fluctuations during sleep in real time using the emotion estimation function. For example, the electrodermal activity sensor measures skin electrical activity and transmits the data to the generation AI. The analysis unit can also use an electroencephalogram (EEG) sensor to monitor the user's emotional fluctuations during sleep in real time using the emotion estimation function. For example, the EEG sensor measures brain waves and transmits the data to the generation AI. This allows for real-time monitoring of emotional fluctuations during sleep and reflects the data in the analysis, enabling more accurate sleep analysis.

[0106] The advice generation unit can use the emotion estimation function to suggest relaxing music that matches the user's emotional state. For example, the emotion estimation function can be used to use a smartphone camera to analyze the user's emotional state. For example, the camera can analyze the user's facial expressions and send the emotional data to the generation AI. The advice generation unit can also use a smartwatch to suggest relaxing music that matches the user's emotional state using the emotion estimation function. For example, the smartwatch can measure heart rate and electrodermal activity and send the emotional data to the generation AI. The advice generation unit can also use a smart bed to suggest relaxing music that matches the user's emotional state using the emotion estimation function. For example, the smart bed can measure respiratory rate and heart rate and send the emotional data to the generation AI. In this way, suggesting relaxing music that matches the user's emotional state promotes relaxation and improves sleep quality.

[0107] The advice generation unit can use the emotion estimation function to suggest a meditation guide that matches the user's emotional state. For example, the emotion estimation function can be used to use a smartphone camera to analyze the user's emotional state. For example, the camera can analyze the user's facial expressions and send the emotional data to the generation AI. The advice generation unit can also use a smartwatch to suggest a meditation guide that matches the user's emotional state using the emotion estimation function. For example, the smartwatch can measure heart rate and electrodermal activity and send the emotional data to the generation AI. The advice generation unit can also use a smart bed to suggest a meditation guide that matches the user's emotional state using the emotion estimation function. For example, the smart bed can measure breathing rate and heart rate and send the emotional data to the generation AI. In this way, suggesting a meditation guide that matches the user's emotional state promotes relaxation and improves the quality of sleep.

[0108] The advice generation unit can use the emotion estimation function to provide feedback according to the user's emotional state and maintain motivation. For example, the emotion estimation function can be used to use a smartphone camera to analyze the user's emotional state. For example, the camera analyzes the user's facial expressions and transmits the emotional data to the generation AI. The advice generation unit can also use a smartwatch to provide feedback according to the user's emotional state using the emotion estimation function. For example, the smartwatch measures heart rate and electrodermal activity and transmits the emotional data to the generation AI. The advice generation unit can also use a smart bed to provide feedback according to the user's emotional state using the emotion estimation function. For example, the smart bed measures respiratory rate and heart rate and transmits the emotional data to the generation AI. This can maintain the user's motivation and continuously support sleep improvement by providing feedback according to the emotional state.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The data collection unit collects the user's sleep data. For example, a smart watch can be used to collect heart rate and sleep patterns in real time. A smart bed can also be used to collect the number of times the user turns over in bed and the breathing rate. Furthermore, the data collection unit can record the user's sleep time using a smartphone app. Step 2: The analysis unit analyzes the sleep data collected by the data collection unit. For example, the generation AI may use a text generation AI (e.g., LLM) to analyze sleep patterns. The generation AI may also use a multimodal generation AI to identify sleep depth and problem areas. The generation AI may also use an algorithm to analyze the sleep data and evaluate the user's sleep quality. Step 3: The advice generation unit proposes sleep improvement measures based on the results of the analysis by the analysis unit. For example, the generation AI may provide the user with specific advice such as "Try to go to bed at the same time every night." The generation AI may also provide customized advice tailored to the user's lifestyle and environment. The generation AI may also make suggestions to optimize the user's sleep environment.

[0111] 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.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0113] 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.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0124] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0137] 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.

[0138] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0139] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0155] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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]

[0178] 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 sleep data of a user; an analysis unit that analyzes the sleep data collected by the data collection unit; an advice generation unit that proposes sleep improvement measures based on the results of the analysis by the analysis unit; A system characterized by:

2. The data collection unit The sleep data is collected in real time using at least one device selected from the group consisting of a smart watch and a smart bed.

2. The system of claim 1.

3. The advice generation unit Providing customized advice tailored to the user's lifestyle and environment 2. The system of claim 1.

4. The analysis unit When analyzing the sleep data of the user, comparing it with past data to identify changes in long-term sleep patterns.

2. The system of claim 1.

5. The advice generation unit Automatically generate and periodically update an individualized sleep coaching program based on the user's sleep data.

2. The system of claim 1.

6. The advice generation unit Analyzing the user's sleep environment data and suggesting how to select the optimal bedding 2. The system of claim 1.

7. The data collection unit Continuously monitoring the user's sleep data and providing real-time feedback 2. The system of claim 1.

8. The data collection unit Using an emotion estimation function, the emotional state of the user before going to bed is analyzed and the effect of the emotion on sleep is evaluated.

2. The system of claim 1.

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