System

Abstract

An object of a system according to an embodiment is to provide a sleep improvement plan optimized for an individual user.SOLUTION: A system includes a biological data collection part, a life habit data collection part, an environmental factor data collection part, an analysis part, and a plan creation part. The biological data collection unit collects biological data. The lifestyle habit data collection unit collects lifestyle habit data. The environmental factor data collection unit collects environmental factor data. The analysis unit analyzes the biological data, the lifestyle data, and the environmental factor data. The plan creation unit creates a personalized sleep improvement plan based on the data 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 techniques have had the problem of making it difficult to provide an optimized sleep improvement plan for each individual user.

[0005] The system according to the embodiment aims to provide an optimized sleep improvement plan for each individual user. [Means for solving the problem]

[0006] The system according to the embodiment includes a biological data collection unit, a lifestyle habit data collection unit, an environmental factor data collection unit, an analysis unit, and a plan creation unit. The biological data collection unit collects biological data. The lifestyle habit data collection unit collects lifestyle data. The environmental factor data collection unit collects environmental factor data. The analysis unit analyzes the biological data, lifestyle habit data, and environmental factor data. The plan creation unit creates a personalized sleep improvement plan based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an optimized sleep improvement plan for each individual user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The Sleep Master AI system according to an embodiment of the present invention collects and analyzes a user's biometric data, lifestyle data, and environmental factor data to provide a personalized sleep improvement plan, thereby providing specific advice to optimize the user's energy, focus, and longevity.

[0029] The sleep master AI system according to the embodiment includes a biometric data collection unit, a lifestyle data collection unit, an environmental factor data collection unit, an analysis unit, and a plan creation unit. The biometric data collection unit collects biometric data, such as heart rate, body temperature, respiratory rate, and sleep depth. The biometric data collection unit collects data through sensors in a wearable device or smartphone. For example, the biometric data collection unit measures a user's heart rate and body temperature while they sleep and collects the data. The lifestyle data collection unit collects lifestyle data, such as meal timing and content, exercise frequency, and stress level. The lifestyle data collection unit collects information such as the user's daily meal times and exercise intensity. The environmental factor data collection unit collects environmental factor data, such as room temperature, humidity, noise level, and light intensity. The environmental factor data collection unit collects information such as the temperature in the user's room. The analysis unit analyzes the biometric data, lifestyle data, and environmental factor data. For example, the generation AI analyzes this data to understand the user's sleep patterns and health status. The generation AI analyzes the data using a text generation AI (e.g., LLM) or a multimodal generation AI. The plan creation unit creates a personalized sleep improvement plan based on the data analyzed by the analysis unit. For example, the generation AI provides specific advice to optimize the user's energy, concentration, and longevity. The generation AI provides specific advice to the user, such as "Go to bed at 10 p.m. every night," "Avoid using your smartphone before bed," and "Keep the room temperature at 20 degrees." As a result, the sleep master AI system according to the embodiment can comprehensively analyze the user's biometric data, lifestyle habit data, and environmental factor data to provide a personalized sleep improvement plan. For example, the user can receive specific advice to optimize their energy, concentration, and longevity.

[0030] The biometric data collection unit can collect genetic information and analyze the impact of genetic factors on sleep patterns. The biometric data collection unit, for example, collects a user's genetic information and analyzes the impact of genetic factors on sleep patterns. For example, it investigates how specific genes affect sleep quality. The biometric data collection unit also builds a system that predicts a user's sleep patterns based on the genetic information and proposes individual improvement measures. For example, it proposes the optimal sleep duration for a user who is genetically suited to short sleep durations. The biometric data collection unit also integrates the user's genetic information and biometric data to perform a detailed analysis of the impact of genetic factors on sleep. For example, it identifies the sleep pattern of a user who is genetically susceptible to stress and provides advice on stress management. This allows the user's genetic information to be analyzed and the impact of genetic factors on sleep to be evaluated.

[0031] The biometric data collection unit can record dietary details in detail and analyze the effects of nutrients on sleep. For example, the biometric data collection unit can record a user's dietary details in detail and analyze the effects of specific nutrients on sleep. For example, the biometric data collection unit can evaluate the effects of caffeine and alcohol intake on sleep. The biometric data collection unit can also integrate dietary details and biometric data to build a system that analyzes the effects of specific nutrients on sleep patterns. For example, the biometric data collection unit can investigate how vitamin and mineral intake affects sleep quality. The biometric data collection unit can also record a user's dietary details in real time and analyze the effects of specific nutrients on sleep. For example, the system can evaluate the effects of meal timing and content on sleep and suggest improvements. This allows the user's dietary details to be recorded in detail and the effects of specific nutrients on sleep to be evaluated.

[0032] The biometric data collection unit can collect data from users of different age groups and genders and perform analysis based on their characteristics. For example, the biometric data collection unit collects data from users of different age groups and genders and performs analysis based on their individual characteristics. For example, it evaluates the differences in sleep patterns between young people and the elderly. The biometric data collection unit also collects biometric data according to age and gender, and builds a system that performs analysis based on their individual characteristics. For example, it identifies the differences in sleep patterns between men and women. The biometric data collection unit also collects data from users of different age groups and genders and performs analysis based on their individual characteristics. For example, it evaluates the differences in sleep patterns between adolescent users and adult users. This makes it possible to collect and analyze data from users of different age groups and genders.

[0033] The lifestyle data collection unit can analyze the impact of minute changes in lifestyle on sleep. The lifestyle data collection unit, for example, analyzes the impact of minute changes in the user's lifestyle on sleep. For example, it evaluates how a sleep pattern changes when meal times are changed by a few minutes. The lifestyle data collection unit also records minute changes in lifestyle in detail and builds a system that analyzes the impact on sleep based on that data. For example, it evaluates the impact of minute changes in exercise duration and intensity on sleep. The lifestyle data collection unit also records minute changes in the user's lifestyle in real time and analyzes the impact on sleep. For example, it evaluates the impact of minute changes in meal content and timing on sleep. This makes it possible to evaluate the impact of minute changes in the user's lifestyle on sleep.

[0034] The lifestyle data collection unit can analyze the impact of seasonal variations in environmental factors on sleep. The lifestyle data collection unit, for example, analyzes the impact of seasonal variations in the user's environmental factors on sleep. For example, it evaluates how seasonal changes in temperature and humidity affect sleep patterns. The lifestyle data collection unit also records detailed seasonal variations in environmental factors and builds a system that analyzes the impact on sleep based on that data. For example, it evaluates the impact of differences in temperature and humidity between winter and summer on sleep. The lifestyle data collection unit also records seasonal variations in the user's environmental factors in real time and analyzes the impact on sleep. For example, it evaluates the impact of temperature and humidity variations between spring and autumn on sleep. This makes it possible to evaluate the impact of seasonal variations in the user's environmental factors on sleep.

[0035] The lifestyle data collection unit can add lifestyle data of family members and housemates to the lifestyle data and analyze the impact of communal living on sleep. For example, the lifestyle data collection unit adds lifestyle data of family members and housemates to the user's lifestyle data and analyzes the impact of communal living on sleep. For example, it evaluates how the timing of meals and exercise for all family members affects sleep. The lifestyle data collection unit also collects lifestyle data of family members and housemates and builds a system that analyzes the impact of communal living on sleep based on that data. For example, it evaluates the impact of the stress levels of all family members on sleep. The lifestyle data collection unit also records lifestyle data of the user and family members and housemates in real time and analyzes the impact of communal living on sleep. For example, it evaluates how the temperature and humidity of all family members' rooms affect sleep. In this way, it is possible to add lifestyle data of family members and housemates to the user's lifestyle data and evaluate the impact of communal living on sleep.

[0036] The lifestyle data collection unit can compare the lifestyle data with data from different cultural spheres and regions to analyze the impact of cultural factors on sleep. For example, the lifestyle data collection unit compares the user's lifestyle data with data from different cultural spheres and regions to analyze the impact of cultural factors on sleep. For example, it evaluates sleep patterns in cultural spheres with different meal timings and contents. The lifestyle data collection unit also collects lifestyle data from different cultural spheres and regions, and builds a system that analyzes the impact of cultural factors on sleep based on the collected data. For example, it evaluates sleep patterns in regions with different exercise frequencies and stress levels. The lifestyle data collection unit also compares the user's lifestyle data with data from different cultural spheres and regions in real time to analyze the impact of cultural factors on sleep. For example, it evaluates sleep patterns in regions with different room temperatures and humidity. This allows the user's lifestyle data to be compared with data from different cultural spheres and regions to evaluate the impact of cultural factors on sleep.

[0037] The plan creation unit can take genetic information into account and create a personalized sleep improvement plan based on genetic factors. The plan creation unit, for example, collects the user's genetic information and creates a personalized sleep improvement plan based on genetic factors. For example, the plan creation unit suggests an optimal sleep duration for a user who is genetically suited to short sleep durations. The plan creation unit also builds a system that predicts the user's sleep pattern based on the genetic information and suggests individual improvement measures. For example, the plan creation unit provides stress management advice to a user who is genetically susceptible to stress. The plan creation unit also integrates the user's genetic information and biometric data to create a personalized sleep improvement plan based on genetic factors. For example, the plan creation unit suggests limiting caffeine intake for a user who is genetically sensitive to caffeine. This makes it possible to create a personalized sleep improvement plan that takes the user's genetic information into account.

[0038] The plan creation unit can analyze dietary content in detail and create a sleep improvement plan that includes the timing of nutrient intake. For example, the plan creation unit can analyze the user's dietary content in detail and create a sleep improvement plan that includes the timing of ingesting specific nutrients. For example, the timing of caffeine and alcohol intake can be adjusted. The plan creation unit can also integrate dietary content with biological data to build a system that creates a sleep improvement plan that includes the timing of ingesting specific nutrients. For example, the timing of vitamin and mineral intake can be adjusted. The plan creation unit can also record the user's dietary content in real time and create a sleep improvement plan that includes the timing of ingesting specific nutrients. For example, the timing and content of meals can be adjusted. This allows the user's dietary content to be analyzed in detail and a sleep improvement plan that includes the timing of ingesting specific nutrients.

[0039] The plan creation unit can provide variations of the sleep improvement plan according to different age groups and genders. For example, the plan creation unit provides variations of the user's sleep improvement plan according to different age groups and genders. For example, the plan creation unit creates a plan that takes into account differences in sleep patterns between young people and the elderly. The plan creation unit also builds a system that provides sleep improvement plans according to age and gender. For example, the plan creation unit creates a plan that takes into account differences in sleep patterns between men and women. The plan creation unit also provides sleep improvement plans targeted at users of different age groups and genders. For example, the plan creation unit creates a plan that takes into account differences in sleep patterns between adolescent users and adult users. This makes it possible to provide variations of the user's sleep improvement plan according to different age groups and genders.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The Sleep Master AI system can also collect a user's social activity data and analyze the impact of social factors on sleep. For example, it can record the frequency and quality of a user's interactions with friends and family and evaluate how this affects sleep patterns. It can also use social activity data to identify sleep patterns during periods when the user feels lonely and suggest ways to improve them. For example, during periods of intense loneliness, it can provide advice on relaxing activities and increasing opportunities for social interaction. This allows the system to evaluate the impact of a user's social activities on sleep and provide personalized improvements.

[0042] The Sleep Master AI system can also collect the user's exercise data and analyze the impact of exercise on sleep. For example, it can record the type, frequency, and intensity of the user's exercise and evaluate how this affects sleep patterns. It can also use the exercise data to identify the user's sleep pattern on the day they exercise and suggest optimal exercise duration and amount. For example, if exercise improves sleep quality, it can provide an appropriate exercise plan. This allows the system to evaluate the impact of the user's exercise on sleep and provide a personalized exercise plan.

[0043] The Sleep Master AI system can also collect data on the user's workplace environment and analyze its impact on sleep. For example, it can record the noise level, lighting intensity, and temperature in the user's workplace and evaluate how this affects sleep patterns. It can also use this data to identify sleep patterns during periods when the user is feeling stressed and suggest improvements. For example, if stress is increasing due to the workplace environment, it can provide advice on relaxation and suggestions for improving the workplace. This allows the system to evaluate the impact of the user's workplace environment on sleep and provide personalized improvements.

[0044] The Sleep Master AI system can also collect a user's travel data and analyze the impact of travel on sleep. For example, it can evaluate how the time difference and environmental changes at the user's destination affect their sleep patterns. It can also use the travel data to identify the user's sleep patterns while traveling and suggest an optimal sleep schedule. For example, it can provide advice on reducing jet lag and ways to relax while traveling. This allows it to evaluate the impact of travel on the user's sleep and provide personalized sleep improvement measures while traveling.

[0045] The Sleep Master AI system can also collect data on a user's hobbies and interests to analyze their impact on sleep. For example, it can record the user's hobbies and interests and evaluate how they affect sleep patterns. It can also suggest activities that help the user relax based on the data on hobbies and interests. For example, if reading or listening to music is relaxing, it will recommend these activities. This allows the system to evaluate the impact of a user's hobbies and interests on sleep and provide personalized relaxation activities.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The biometric data collection unit collects biometric data. For example, it collects data such as heart rate, body temperature, respiratory rate, and sleep depth. The biometric data collection unit collects data through sensors in wearable devices and smartphones. For example, it measures the heart rate and body temperature while the user is sleeping and collects that data. Step 2: The lifestyle data collection unit collects lifestyle data. For example, it collects data such as the timing and content of meals, the frequency of exercise, and stress levels. The lifestyle data collection unit collects information such as what time the user eats each day and how much exercise they do. Step 3: The environmental factor data collection unit collects environmental factor data. For example, it collects data such as room temperature, humidity, noise level, and light intensity. The environmental factor data collection unit collects information such as the temperature in the user's room. Step 4: The analysis unit analyzes the biometric data, lifestyle data, and environmental factor data. For example, the generation AI analyzes this data to understand the user's sleep patterns and health status. The generation AI analyzes the data using text generation AI (e.g., LLM) and multimodal generation AI. Step 5: The plan creation unit creates a personalized sleep improvement plan based on the data analyzed by the analysis unit. For example, the generation AI provides specific advice to optimize the user's energy, concentration, and longevity. The generation AI provides specific advice to the user, such as "Go to bed at 10 o'clock every night," "Avoid using your smartphone before bed," and "Keep the room temperature at 20 degrees."

[0048] (Example 2) The Sleep Master AI system according to an embodiment of the present invention collects and analyzes a user's biometric data, lifestyle data, and environmental factor data to provide a personalized sleep improvement plan, thereby providing specific advice to optimize the user's energy, focus, and longevity.

[0049] The sleep master AI system according to the embodiment includes a biometric data collection unit, a lifestyle data collection unit, an environmental factor data collection unit, an analysis unit, and a plan creation unit. The biometric data collection unit collects biometric data, such as heart rate, body temperature, respiratory rate, and sleep depth. The biometric data collection unit collects data through sensors in a wearable device or smartphone. For example, the biometric data collection unit measures a user's heart rate and body temperature while they sleep and collects the data. The lifestyle data collection unit collects lifestyle data, such as meal timing and content, exercise frequency, and stress level. The lifestyle data collection unit collects information such as the user's daily meal times and exercise intensity. The environmental factor data collection unit collects environmental factor data, such as room temperature, humidity, noise level, and light intensity. The environmental factor data collection unit collects information such as the temperature in the user's room. The analysis unit analyzes the biometric data, lifestyle data, and environmental factor data. For example, the generation AI analyzes this data to understand the user's sleep patterns and health status. The generation AI analyzes the data using a text generation AI (e.g., LLM) or a multimodal generation AI. The plan creation unit creates a personalized sleep improvement plan based on the data analyzed by the analysis unit. For example, the generation AI provides specific advice to optimize the user's energy, concentration, and longevity. The generation AI provides specific advice to the user, such as "Go to bed at 10 p.m. every night," "Avoid using your smartphone before bed," and "Keep the room temperature at 20 degrees." As a result, the sleep master AI system according to the embodiment can comprehensively analyze the user's biometric data, lifestyle habit data, and environmental factor data to provide a personalized sleep improvement plan. For example, the user can receive specific advice to optimize their energy, concentration, and longevity.

[0050] The biometric data collection unit can estimate the user's emotional state in real time and analyze the impact of emotional fluctuations on sleep. The biometric data collection unit, for example, collects the user's heart rate, body temperature, facial expression, and voice data to estimate the user's emotional state in real time. For example, the unit analyzes the user's emotions using a camera or microphone and evaluates the impact of emotional fluctuations on sleep. The biometric data collection unit also uses an emotion estimation algorithm to quantify the user's emotional state and integrates and analyzes the data with biometric data. For example, the unit identifies sleep patterns during periods of high stress or anxiety and proposes improvements. The biometric data collection unit also monitors the user's emotional state in real time and builds a system that analyzes the impact of emotional fluctuations on sleep. For example, the unit compares sleep data from days with large emotional fluctuations with sleep data from days with stable emotional fluctuations. This allows the user's emotional state to be analyzed in real time and the impact of emotional fluctuations on sleep to be evaluated.

[0051] The biometric data collection unit can collect genetic information and analyze the impact of genetic factors on sleep patterns. The biometric data collection unit, for example, collects a user's genetic information and analyzes the impact of genetic factors on sleep patterns. For example, it investigates how specific genes affect sleep quality. The biometric data collection unit also builds a system that predicts a user's sleep patterns based on the genetic information and proposes individual improvement measures. For example, it proposes the optimal sleep duration for a user who is genetically suited to short sleep durations. The biometric data collection unit also integrates the user's genetic information and biometric data to perform a detailed analysis of the impact of genetic factors on sleep. For example, it identifies the sleep pattern of a user who is genetically susceptible to stress and provides advice on stress management. This allows the user's genetic information to be analyzed and the impact of genetic factors on sleep to be evaluated.

[0052] The biometric data collection unit can record dietary details in detail and analyze the effects of nutrients on sleep. For example, the biometric data collection unit can record a user's dietary details in detail and analyze the effects of specific nutrients on sleep. For example, the biometric data collection unit can evaluate the effects of caffeine and alcohol intake on sleep. The biometric data collection unit can also integrate dietary details and biometric data to build a system that analyzes the effects of specific nutrients on sleep patterns. For example, the biometric data collection unit can investigate how vitamin and mineral intake affects sleep quality. The biometric data collection unit can also record a user's dietary details in real time and analyze the effects of specific nutrients on sleep. For example, the system can evaluate the effects of meal timing and content on sleep and suggest improvements. This allows the user's dietary details to be recorded in detail and the effects of specific nutrients on sleep to be evaluated.

[0053] The biometric data collection unit can add voice data and facial expression data to analyze the emotional state from multiple angles. The biometric data collection unit, for example, adds voice data and facial expression data to the collected biometric data to analyze the user's emotional state from multiple angles. For example, it analyzes voice tone and facial expressions to evaluate emotional fluctuations. The biometric data collection unit also uses the voice data and facial expression data to estimate the user's emotional state in real time and integrates the data with the biometric data for analysis. For example, it identifies sleep patterns during periods of high stress or anxiety. The biometric data collection unit also collects voice data and facial expression data to build a system that analyzes the emotional state from multiple angles. For example, it compares sleep data from days with large emotional fluctuations with sleep data from days with stable emotions. This allows the user's emotional state to be analyzed from multiple angles by adding voice data and facial expression data.

[0054] The biometric data collection unit can collect data from users of different age groups and genders and perform analysis based on their characteristics. For example, the biometric data collection unit collects data from users of different age groups and genders and performs analysis based on their individual characteristics. For example, it evaluates the differences in sleep patterns between young people and the elderly. The biometric data collection unit also collects biometric data according to age and gender, and builds a system that performs analysis based on their individual characteristics. For example, it identifies the differences in sleep patterns between men and women. The biometric data collection unit also collects data from users of different age groups and genders and performs analysis based on their individual characteristics. For example, it evaluates the differences in sleep patterns between adolescent users and adult users. This makes it possible to collect and analyze data from users of different age groups and genders.

[0055] The biometric data collection unit can use the emotion estimation function to determine in real time whether the user is relaxed and provide advice to promote the relaxed state. The biometric data collection unit, for example, uses the emotion estimation function to determine in real time whether the user is relaxed and provide advice to promote the relaxed state. For example, it suggests breathing techniques or meditation to help with relaxation. The biometric data collection unit also monitors the user's emotional state in real time and builds a system that provides advice to promote the relaxed state. For example, it suggests music or aromas to help with relaxation. The biometric data collection unit also uses the emotion estimation function to determine whether the user is relaxed and provide advice to promote the relaxed state. For example, it suggests stretching or yoga to help with relaxation. In this way, it is possible to determine in real time whether the user is relaxed and provide advice to promote the relaxed state.

[0056] The lifestyle data collection unit can estimate a user's emotional state and analyze the effects of stress and anxiety on sleep. For example, the lifestyle data collection unit estimates the user's emotional state and analyzes the effects of stress and anxiety on sleep. For example, it identifies sleep patterns during periods of high stress and proposes improvements. The lifestyle data collection unit also uses an emotion estimation algorithm to quantify the user's emotional state and integrates the data with lifestyle habits for analysis. For example, it evaluates the effects of diet and exercise during periods of high stress or anxiety. The lifestyle data collection unit also monitors the user's emotional state in real time and builds a system that analyzes the effects of stress and anxiety on sleep. For example, it compares lifestyle data from days with significant emotional fluctuations with those from days with stable emotions. This allows the user's emotional state to be estimated and the effects of stress and anxiety on sleep to be evaluated.

[0057] The lifestyle data collection unit can analyze the impact of minute changes in lifestyle on sleep. The lifestyle data collection unit, for example, analyzes the impact of minute changes in the user's lifestyle on sleep. For example, it evaluates how a sleep pattern changes when meal times are changed by a few minutes. The lifestyle data collection unit also records minute changes in lifestyle in detail and builds a system that analyzes the impact on sleep based on that data. For example, it evaluates the impact of minute changes in exercise duration and intensity on sleep. The lifestyle data collection unit also records minute changes in the user's lifestyle in real time and analyzes the impact on sleep. For example, it evaluates the impact of minute changes in meal content and timing on sleep. This makes it possible to evaluate the impact of minute changes in the user's lifestyle on sleep.

[0058] The lifestyle data collection unit can analyze the impact of seasonal variations in environmental factors on sleep. The lifestyle data collection unit, for example, analyzes the impact of seasonal variations in the user's environmental factors on sleep. For example, it evaluates how seasonal changes in temperature and humidity affect sleep patterns. The lifestyle data collection unit also records detailed seasonal variations in environmental factors and builds a system that analyzes the impact on sleep based on that data. For example, it evaluates the impact of differences in temperature and humidity between winter and summer on sleep. The lifestyle data collection unit also records seasonal variations in the user's environmental factors in real time and analyzes the impact on sleep. For example, it evaluates the impact of temperature and humidity variations between spring and autumn on sleep. This makes it possible to evaluate the impact of seasonal variations in the user's environmental factors on sleep.

[0059] The lifestyle data collection unit can add lifestyle data of family members and housemates to the lifestyle data and analyze the impact of communal living on sleep. For example, the lifestyle data collection unit adds lifestyle data of family members and housemates to the user's lifestyle data and analyzes the impact of communal living on sleep. For example, it evaluates how the timing of meals and exercise for all family members affects sleep. The lifestyle data collection unit also collects lifestyle data of family members and housemates and builds a system that analyzes the impact of communal living on sleep based on that data. For example, it evaluates the impact of the stress levels of all family members on sleep. The lifestyle data collection unit also records lifestyle data of the user and family members and housemates in real time and analyzes the impact of communal living on sleep. For example, it evaluates how the temperature and humidity of all family members' rooms affect sleep. In this way, it is possible to add lifestyle data of family members and housemates to the user's lifestyle data and evaluate the impact of communal living on sleep.

[0060] The lifestyle data collection unit can compare the lifestyle data with data from different cultural spheres and regions to analyze the impact of cultural factors on sleep. For example, the lifestyle data collection unit compares the user's lifestyle data with data from different cultural spheres and regions to analyze the impact of cultural factors on sleep. For example, it evaluates sleep patterns in cultural spheres with different meal timings and contents. The lifestyle data collection unit also collects lifestyle data from different cultural spheres and regions, and builds a system that analyzes the impact of cultural factors on sleep based on the collected data. For example, it evaluates sleep patterns in regions with different exercise frequencies and stress levels. The lifestyle data collection unit also compares the user's lifestyle data with data from different cultural spheres and regions in real time to analyze the impact of cultural factors on sleep. For example, it evaluates sleep patterns in regions with different room temperatures and humidity. This allows the user's lifestyle data to be compared with data from different cultural spheres and regions to evaluate the impact of cultural factors on sleep.

[0061] The lifestyle data collection unit can use the emotion estimation function to provide advice for eliciting positive emotions in the user's lifestyle. The lifestyle data collection unit, for example, uses the emotion estimation function to provide advice for eliciting positive emotions in the user's lifestyle. For example, it suggests breathing techniques or meditation for relaxation. The lifestyle data collection unit also monitors the user's emotional state in real time and builds a system for providing advice for eliciting positive emotions. For example, it suggests music or aromas for relaxation. The lifestyle data collection unit also uses the emotion estimation function to provide advice for eliciting positive emotions in the user's lifestyle. For example, it suggests stretching or yoga for relaxation. This makes it possible to provide advice for eliciting positive emotions in the user's lifestyle.

[0062] The plan creation unit can estimate the emotional state and create a personalized sleep improvement plan based on the emotions. The plan creation unit, for example, uses an emotion estimation function to analyze the user's emotional state in real time and create a personalized sleep improvement plan based on the emotions. For example, during times of high stress, advice on how to relax is provided. The plan creation unit also quantifies the user's emotional state and builds a system that creates a personalized sleep improvement plan based on the data. For example, during times of strong positive emotions, advice on how to increase energy is provided. The plan creation unit also uses the emotion estimation function to monitor the user's emotional state in real time and create a personalized sleep improvement plan based on the emotions. For example, on days when emotions fluctuate greatly, advice on how to relax is provided. In this way, a personalized sleep improvement plan based on the user's emotional state can be created.

[0063] The plan creation unit can take genetic information into account and create a personalized sleep improvement plan based on genetic factors. The plan creation unit, for example, collects the user's genetic information and creates a personalized sleep improvement plan based on genetic factors. For example, the plan creation unit suggests an optimal sleep duration for a user who is genetically suited to short sleep durations. The plan creation unit also builds a system that predicts the user's sleep pattern based on the genetic information and suggests individual improvement measures. For example, the plan creation unit provides stress management advice to a user who is genetically susceptible to stress. The plan creation unit also integrates the user's genetic information and biometric data to create a personalized sleep improvement plan based on genetic factors. For example, the plan creation unit suggests limiting caffeine intake for a user who is genetically sensitive to caffeine. This makes it possible to create a personalized sleep improvement plan that takes the user's genetic information into account.

[0064] The plan creation unit can analyze dietary content in detail and create a sleep improvement plan that includes the timing of nutrient intake. For example, the plan creation unit can analyze the user's dietary content in detail and create a sleep improvement plan that includes the timing of ingesting specific nutrients. For example, the timing of caffeine and alcohol intake can be adjusted. The plan creation unit can also integrate dietary content with biological data to build a system that creates a sleep improvement plan that includes the timing of ingesting specific nutrients. For example, the timing of vitamin and mineral intake can be adjusted. The plan creation unit can also record the user's dietary content in real time and create a sleep improvement plan that includes the timing of ingesting specific nutrients. For example, the timing and content of meals can be adjusted. This allows the user's dietary content to be analyzed in detail and a sleep improvement plan that includes the timing of ingesting specific nutrients.

[0065] The plan creation unit can incorporate relaxation techniques such as music and aromatherapy into the sleep improvement plan. For example, the plan creation unit incorporates relaxation techniques such as music and aromatherapy into the user's sleep improvement plan. For example, it suggests music or aromas for relaxation. The plan creation unit also uses relaxation techniques to build a system that personalizes the user's sleep improvement plan. For example, it suggests breathing techniques or meditation for relaxation. The plan creation unit also monitors the user's emotional state in real time and creates a sleep improvement plan that incorporates relaxation techniques. For example, it suggests stretching or yoga for relaxation. This makes it possible to incorporate relaxation techniques into the user's sleep improvement plan.

[0066] The plan creation unit can provide variations of the sleep improvement plan according to different age groups and genders. For example, the plan creation unit provides variations of the user's sleep improvement plan according to different age groups and genders. For example, the plan creation unit creates a plan that takes into account differences in sleep patterns between young people and the elderly. The plan creation unit also builds a system that provides sleep improvement plans according to age and gender. For example, the plan creation unit creates a plan that takes into account differences in sleep patterns between men and women. The plan creation unit also provides sleep improvement plans targeted at users of different age groups and genders. For example, the plan creation unit creates a plan that takes into account differences in sleep patterns between adolescent users and adult users. This makes it possible to provide variations of the user's sleep improvement plan according to different age groups and genders.

[0067] The plan creation unit can use the emotion estimation function to suggest a sleeping environment that will cause the user to feel positive emotions and provide advice for maintaining that environment. For example, the plan creation unit can use the emotion estimation function to suggest a sleeping environment that will cause the user to feel positive emotions and provide advice for maintaining that environment. For example, the plan creation unit can suggest music or aromas that will help the user relax. The plan creation unit can also monitor the user's emotional state in real time and build a system that suggests a sleeping environment that will cause the user to feel positive emotions. For example, the plan creation unit can suggest breathing techniques or meditation that will help the user relax. The plan creation unit can also use the emotion estimation function to suggest a sleeping environment that will cause the user to feel positive emotions and provide advice for maintaining that environment. For example, the plan creation unit can suggest stretching or yoga that will help the user to feel relaxed. This makes it possible to suggest a sleeping environment that will cause the user to feel positive emotions and provide advice for maintaining that environment.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The Sleep Master AI system can also collect a user's social activity data and analyze the impact of social factors on sleep. For example, it can record the frequency and quality of a user's interactions with friends and family and evaluate how this affects sleep patterns. It can also use social activity data to identify sleep patterns during periods when the user feels lonely and suggest ways to improve them. For example, during periods of intense loneliness, it can provide advice on relaxing activities and increasing opportunities for social interaction. This allows the system to evaluate the impact of a user's social activities on sleep and provide personalized improvements.

[0070] The Sleep Master AI system can also estimate the user's emotional state and provide relaxing music and environmental sounds based on the estimated emotion. For example, if the user is feeling stressed, it can play relaxing music. If the user is feeling anxious, it can provide natural sounds or white noise. Furthermore, it can monitor the user's emotional state in real time and automatically adjust the appropriate music and environmental sounds according to emotional fluctuations. This allows the system to provide relaxing music and environmental sounds according to the user's emotional state, improving the quality of sleep.

[0071] The Sleep Master AI system can also collect the user's exercise data and analyze the impact of exercise on sleep. For example, it can record the type, frequency, and intensity of the user's exercise and evaluate how this affects sleep patterns. It can also use the exercise data to identify the user's sleep pattern on the day they exercise and suggest optimal exercise duration and amount. For example, if exercise improves sleep quality, it can provide an appropriate exercise plan. This allows the system to evaluate the impact of the user's exercise on sleep and provide a personalized exercise plan.

[0072] The Sleep Master AI system can also estimate the user's emotional state and suggest aromatherapy to help the user relax based on the estimated emotion. For example, if the user is feeling stressed, it can suggest relaxing aromas such as lavender or chamomile. If the user is feeling anxious, it can suggest aromas such as bergamot or ylang-ylang. Furthermore, it can monitor the user's emotional state in real time and automatically suggest appropriate aromas according to emotional fluctuations. This allows it to provide relaxing aromas that suit the user's emotional state and improve the quality of their sleep.

[0073] The Sleep Master AI system can also collect data on the user's workplace environment and analyze its impact on sleep. For example, it can record the noise level, lighting intensity, and temperature in the user's workplace and evaluate how this affects sleep patterns. It can also use this data to identify sleep patterns during periods when the user is feeling stressed and suggest improvements. For example, if stress is increasing due to the workplace environment, it can provide advice on relaxation and suggestions for improving the workplace. This allows the system to evaluate the impact of the user's workplace environment on sleep and provide personalized improvements.

[0074] The Sleep Master AI system can also estimate the user's emotional state and provide a meditation guide that helps the user relax based on the estimated emotion. For example, if the user is feeling stressed, it can provide a meditation guide with a relaxing effect. Alternatively, if the user is feeling anxious, it can provide a meditation guide that gives a sense of security. Furthermore, it can monitor the user's emotional state in real time and automatically adjust the appropriate meditation guide according to emotional fluctuations. This allows the system to provide a relaxing meditation guide that matches the user's emotional state and improve the quality of their sleep.

[0075] The Sleep Master AI system can also collect a user's travel data and analyze the impact of travel on sleep. For example, it can evaluate how the time difference and environmental changes at the user's destination affect their sleep patterns. It can also use the travel data to identify the user's sleep patterns while traveling and suggest an optimal sleep schedule. For example, it can provide advice on reducing jet lag and ways to relax while traveling. This allows it to evaluate the impact of travel on the user's sleep and provide personalized sleep improvement measures while traveling.

[0076] The Sleep Master AI system can also estimate the user's emotional state and, based on the estimated emotion, suggest stretches and yoga poses that will help the user relax. For example, if the user is feeling stressed, it can suggest stretches and yoga poses that have a relaxing effect. Or, if the user is feeling anxious, it can suggest stretches and yoga poses that will give a sense of relief. Furthermore, it can monitor the user's emotional state in real time and automatically suggest appropriate stretches and yoga poses according to emotional fluctuations. This allows the system to provide relaxing stretches and yoga poses that are tailored to the user's emotional state, improving the quality of their sleep.

[0077] The Sleep Master AI system can also collect data on a user's hobbies and interests to analyze their impact on sleep. For example, it can record the user's hobbies and interests and evaluate how they affect sleep patterns. It can also suggest activities that help the user relax based on the data on hobbies and interests. For example, if reading or listening to music is relaxing, it will recommend these activities. This allows the system to evaluate the impact of a user's hobbies and interests on sleep and provide personalized relaxation activities.

[0078] The Sleep Master AI system can also estimate the user's emotional state and suggest breathing techniques to help the user relax based on the estimated emotions. For example, if the user is feeling stressed, it can suggest deep breathing techniques that have a relaxing effect. If the user is feeling anxious, it can suggest breathing techniques that give a sense of security. Furthermore, it can monitor the user's emotional state in real time and automatically suggest appropriate breathing techniques according to emotional fluctuations. This allows the system to provide breathing techniques with a relaxing effect that are tailored to the user's emotional state, improving the quality of their sleep.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The biometric data collection unit collects biometric data. For example, it collects data such as heart rate, body temperature, respiratory rate, and sleep depth. The biometric data collection unit collects data through sensors in wearable devices and smartphones. For example, it measures the heart rate and body temperature while the user is sleeping and collects that data. Step 2: The lifestyle data collection unit collects lifestyle data. For example, it collects data such as the timing and content of meals, the frequency of exercise, and stress levels. The lifestyle data collection unit collects information such as what time the user eats each day and how much exercise they do. Step 3: The environmental factor data collection unit collects environmental factor data. For example, it collects data such as room temperature, humidity, noise level, and light intensity. The environmental factor data collection unit collects information such as the temperature in the user's room. Step 4: The analysis unit analyzes the biometric data, lifestyle data, and environmental factor data. For example, the generation AI analyzes this data to understand the user's sleep patterns and health status. The generation AI analyzes the data using text generation AI (e.g., LLM) and multimodal generation AI. Step 5: The plan creation unit creates a personalized sleep improvement plan based on the data analyzed by the analysis unit. For example, the generation AI provides specific advice to optimize the user's energy, concentration, and longevity. The generation AI provides specific advice to the user, such as "Go to bed at 10 o'clock every night," "Avoid using your smartphone before bed," and "Keep the room temperature at 20 degrees."

[0081] 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.

[0082] 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.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0094] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0107] 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.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0109] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 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.

[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 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.

[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 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).

[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] 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.

[0122] 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.

[0123] 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.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0125] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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]

[0148] 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 biometric data collection unit that collects biometric data; a lifestyle data collection unit that collects lifestyle data; an environmental factor data collection unit that collects environmental factor data; an analysis unit that analyzes the biological data, the lifestyle habit data, and the environmental factor data; a plan creation unit that creates a personalized sleep improvement plan based on the data analyzed by the analysis unit. A system characterized by:

2. The biological data collection unit Estimating emotional states in real time and analyzing the impact of emotional fluctuations on sleep 2. The system of claim 1.

3. The biological data collection unit Collect genetic information and analyze the impact of genetic factors on sleep patterns 2. The system of claim 1.

4. The biological data collection unit Record your diet in detail and analyze the effects of nutrients on sleep 2. The system of claim 1.

5. The biological data collection unit Add voice and facial expression data to analyze emotional states from multiple angles 2. The system of claim 1.

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