System

Abstract

An object of a system according to an embodiment is to propose a life rhythm that prevents falling asleep on the basis of a sleep rhythm of a user.SOLUTION: A system according to an embodiment includes a sleeping rhythm learning unit, a life rhythm proposing unit, and an alarm notifying unit. The sleeping rhythm learning unit learns a sleeping rhythm of the user. The life rhythm proposing unit proposes a life rhythm that prevents falling asleep based on the sleeping rhythm learned by the sleeping rhythm learning unit. The alarm notification unit notifies the user of a time when the user is likely to fall asleep using an alarm on the basis of the life rhythm proposed by the life rhythm proposing 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 a problem in that they have not adequately proposed appropriate lifestyle rhythms to prevent users from falling asleep based on their sleep rhythms.

[0005] The system according to the embodiment aims to propose a lifestyle rhythm that prevents a user from falling asleep based on the user's sleep rhythm. [Means for solving the problem]

[0006] The system according to the embodiment includes a sleep rhythm learning unit, a lifestyle rhythm proposing unit, and an alarm notification unit. The sleep rhythm learning unit learns the user's sleep rhythm. The lifestyle rhythm proposing unit proposes a lifestyle rhythm that prevents the user from falling asleep based on the sleep rhythm learned by the sleep rhythm learning unit. The alarm notification unit issues an alarm to notify the user at a time when they are likely to fall asleep based on the lifestyle rhythm proposed by the lifestyle rhythm proposing unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest a lifestyle rhythm that will prevent the user from falling asleep based on the user's sleep rhythm. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) In a sleep support system according to an embodiment of the present invention, a generation AI learns a user's sleep rhythm, suggests a lifestyle rhythm that will prevent them from falling asleep, and issues an alarm to alert them when they are about to fall asleep. This allows the sleep support system to support a healthy lifestyle for the user and prevent them from unintentionally falling asleep.

[0029] A sleep support system according to an embodiment includes a sleep rhythm learning unit, a lifestyle rhythm suggestion unit, and an alarm notification unit. The sleep rhythm learning unit learns the user's sleep rhythm. For example, the generation AI collects data on the user's daily life and analyzes the sleep rhythm. The generation AI collects data such as when the user goes to bed, when they wake up, and what activities they engage in. The generation AI can understand the user's sleep patterns and suggest an optimal lifestyle rhythm. The lifestyle rhythm suggestion unit suggests a lifestyle rhythm that prevents the user from falling asleep based on the sleep rhythm learned by the sleep rhythm learning unit. For example, the generation AI suggests appropriate bedtimes, wake-up times, and times for relaxation activities. The generation AI makes optimal suggestions based on the user's data. The alarm notification unit issues an alarm to alert the user when they are likely to fall asleep based on the lifestyle rhythm suggested by the lifestyle rhythm suggestion unit. For example, the generation AI monitors the user's lifestyle rhythm and predicts the time when they are likely to fall asleep. The generation AI identifies the time when the user is relaxing on the sofa and the time when their fatigue is at its peak. The generation AI sounds an alarm to notify the user when they are likely to fall asleep. As a result, the sleep support system according to the embodiment can support a user's healthy lifestyle and prevent the user from unintentionally falling asleep. For example, the system can prevent the user from unintentionally falling asleep on the sofa, allowing the user to get high-quality sleep. This is expected to effectively relieve daily fatigue and help maintain a healthy lifestyle.

[0030] The sleep rhythm learning unit can collect dietary data and exercise data and analyze the impact these factors have on sleep rhythms. In the sleep rhythm learning unit, for example, the generation AI collects the user's dietary data and analyzes the impact of the content and time of meals on sleep rhythms. For example, the generation AI learns that eating dinner late reduces sleep quality. The generation AI also collects the user's exercise data and analyzes the impact of the type and time of exercise on sleep rhythms. For example, the generation AI learns the positive impact exercise has on sleep quality. This makes it possible to learn sleep rhythms more accurately by taking dietary and exercise data into account.

[0031] The sleep rhythm learning unit can monitor the sleep environment in real time and learn sleep rhythms based on temperature, humidity, and sound data. For example, the generation AI in the sleep rhythm learning unit collects the user's sleep environment data in real time and analyzes the effects of temperature and humidity on sleep rhythms. For example, the generation AI learns that high bedroom temperatures reduce sleep quality. The generation AI also collects sound data and analyzes the effects of noise on sleep rhythms. For example, the generation AI learns that a quiet environment improves sleep quality. This allows for more accurate sleep rhythm learning by taking sleep environment data into consideration.

[0032] The lifestyle rhythm suggestion unit can suggest an optimal lifestyle rhythm by taking into account work and academic schedules. For example, the generation AI collects the user's work and academic schedules and suggests an optimal lifestyle rhythm based on them. For example, the generation AI suggests a bedtime that matches the start time of work. The generation AI suggests an optimal lifestyle rhythm based on the user's schedule. This makes it possible to optimize the user's lifestyle rhythm by taking into account work and academic schedules.

[0033] The lifestyle rhythm suggestion unit can suggest a lifestyle rhythm that incorporates hobbies and relaxation methods. For example, the generation AI collects the user's hobbies and relaxation methods and suggests an optimal lifestyle rhythm based on the information. For example, the generation AI reduces stress by setting aside time for hobbies. The generation AI suggests an optimal lifestyle rhythm based on the user's hobbies and relaxation methods. This allows the user's lifestyle rhythm to be optimized by incorporating hobbies and relaxation methods.

[0034] The lifestyle rhythm suggestion unit can suggest lifestyle rhythms according to the season and weather. For example, the generation AI collects seasonal and weather data and suggests an optimal lifestyle rhythm based on that data. For example, the generation AI suggests going to bed earlier in the winter. The generation AI suggests an optimal lifestyle rhythm based on seasonal and weather data. This makes it possible to optimize the user's lifestyle rhythm by suggesting a lifestyle rhythm according to the season and weather.

[0035] The alarm notification unit can monitor activity data in real time and optimize the timing of the alarm according to the level of fatigue. For example, the generation AI in the alarm notification unit monitors the user's activity data in real time and optimizes the timing of the alarm according to the level of fatigue. For example, the generation AI sounds the alarm after a long period of work. The generation AI adjusts the optimal alarm timing based on the user's activity data. This prevents the user from falling asleep by optimizing the alarm timing according to the level of fatigue.

[0036] The alarm notification unit can customize the alarm sound and vibration pattern to suit the user's preferences. In the alarm notification unit, for example, the generation AI customizes the alarm sound and vibration pattern to suit the user's preferences. For example, the generation AI sets the user's favorite music as the alarm sound. The generation AI suggests the optimal alarm sound and vibration pattern based on the user's preferences. In this way, customizing the alarm sound and vibration pattern prevents the user from falling asleep.

[0037] The alarm notification unit works in conjunction with smart home devices and can adjust the lighting and temperature in the room at the same time as the alarm goes off. For example, the generation AI in the alarm notification unit works in conjunction with the user's smart home devices and adjusts the lighting in the room at the same time as the alarm goes off. For example, the generation AI brightens the lights at the same time as the alarm goes off. The generation AI adjusts the lighting and temperature to the optimum level based on the user's smart home devices. This prevents the user from falling asleep by working in conjunction with smart home devices.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The sleep rhythm learning unit can collect the user's heart rate data and analyze heart rate fluctuations. For example, the generation AI can identify times when the user's heart rate is high and suggest activities for relaxation during those times. It can also suggest activities that require concentration during times when the heart rate is low. By taking heart rate data into consideration, this makes it possible to suggest more accurate lifestyle rhythms.

[0040] The alarm notification unit can analyze the user's activity data and adjust the timing of the alarm depending on the type of activity. For example, the generation AI can prevent the alarm from sounding when the user is exercising. It can also adjust the timing of the alarm when the user is relaxing. This allows for more effective alarm notifications by taking activity data into consideration.

[0041] The sleep rhythm learning unit can collect the user's body temperature data and analyze fluctuations in body temperature. For example, the generation AI can identify times when the user's body temperature is high and suggest activities to help them relax during those times. It can also suggest activities that require concentration during times when the user's body temperature is low. By taking body temperature data into consideration, this makes it possible to suggest more accurate lifestyle rhythms.

[0042] The alarm notification unit can analyze the user's activity data and adjust the alarm vibration pattern according to the intensity of the activity. For example, the generation AI can sound the alarm with strong vibrations when the user is exercising vigorously. It can also sound the alarm with gentle vibrations when the user is relaxing. This prevents the user from falling asleep by adjusting the alarm vibration pattern according to the intensity of the activity.

[0043] The alarm notification unit can analyze the user's activity data and adjust the timing of the alarm according to the frequency of the activity. For example, the generation AI can prevent the alarm from sounding during times when the user is frequently active. It can also adjust the timing of the alarm to sound during times when the user is less active. This allows for more effective alarm notifications by taking the frequency of activity into account.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The sleep rhythm learning unit learns the user's sleep rhythm. For example, the generation AI collects data on the user's daily life and analyzes their sleep rhythm. The generation AI collects data such as when the user goes to sleep, when they wake up, and what activities they are engaged in, and understands the user's sleep patterns. Step 2: The lifestyle rhythm suggestion unit proposes lifestyle rhythms that will help you avoid falling asleep based on the sleep rhythms learned by the sleep rhythm learning unit. For example, the generation AI would suggest appropriate bedtimes, wake-up times, and times for relaxation activities. Step 3: The alarm notification unit issues an alarm to notify the user when they are likely to fall asleep based on the lifestyle rhythm suggested by the lifestyle rhythm suggestion unit. For example, the generation AI monitors the user's lifestyle, predicts the time when they are likely to fall asleep, and sounds an alarm to notify the user.

[0046] (Example 2) In a sleep support system according to an embodiment of the present invention, a generation AI learns a user's sleep rhythm, suggests a lifestyle rhythm that will prevent them from falling asleep, and issues an alarm to alert them when they are about to fall asleep. This allows the sleep support system to support a healthy lifestyle for the user and prevent them from unintentionally falling asleep.

[0047] A sleep support system according to an embodiment includes a sleep rhythm learning unit, a lifestyle rhythm suggestion unit, and an alarm notification unit. The sleep rhythm learning unit learns the user's sleep rhythm. For example, the generation AI collects data on the user's daily life and analyzes the sleep rhythm. The generation AI collects data such as when the user goes to bed, when they wake up, and what activities they engage in. The generation AI can understand the user's sleep patterns and suggest an optimal lifestyle rhythm. The lifestyle rhythm suggestion unit suggests a lifestyle rhythm that prevents the user from falling asleep based on the sleep rhythm learned by the sleep rhythm learning unit. For example, the generation AI suggests appropriate bedtimes, wake-up times, and times for relaxation activities. The generation AI makes optimal suggestions based on the user's data. The alarm notification unit issues an alarm to alert the user when they are likely to fall asleep based on the lifestyle rhythm suggested by the lifestyle rhythm suggestion unit. For example, the generation AI monitors the user's lifestyle rhythm and predicts the time when they are likely to fall asleep. The generation AI identifies the time when the user is relaxing on the sofa and the time when their fatigue is at its peak. The generation AI sounds an alarm to notify the user when they are likely to fall asleep. As a result, the sleep support system according to the embodiment can support a user's healthy lifestyle and prevent the user from unintentionally falling asleep. For example, the system can prevent the user from unintentionally falling asleep on the sofa, allowing the user to get high-quality sleep. This is expected to effectively relieve daily fatigue and help maintain a healthy lifestyle.

[0048] The sleep rhythm learning unit can analyze emotional data and learn sleep rhythms taking into account stress levels and mood fluctuations. For example, the generation AI analyzes emotional data collected from the user's daily life to understand stress levels and mood fluctuations. For example, the generation AI analyzes emotions entered by the user in a diary app or posts on social media to quantify emotional fluctuations. Based on the emotional data, the generation AI learns sleep rhythms taking into account the user's stress levels and mood fluctuations. This allows for more accurate sleep rhythm learning by taking emotional data into account.

[0049] The sleep rhythm learning unit can collect dietary data and exercise data and analyze the impact these factors have on sleep rhythms. In the sleep rhythm learning unit, for example, the generation AI collects the user's dietary data and analyzes the impact of the content and time of meals on sleep rhythms. For example, the generation AI learns that eating dinner late reduces sleep quality. The generation AI also collects the user's exercise data and analyzes the impact of the type and time of exercise on sleep rhythms. For example, the generation AI learns the positive impact exercise has on sleep quality. This makes it possible to learn sleep rhythms more accurately by taking dietary and exercise data into account.

[0050] The sleep rhythm learning unit can monitor the sleep environment in real time and learn sleep rhythms based on temperature, humidity, and sound data. For example, the generation AI in the sleep rhythm learning unit collects the user's sleep environment data in real time and analyzes the effects of temperature and humidity on sleep rhythms. For example, the generation AI learns that high bedroom temperatures reduce sleep quality. The generation AI also collects sound data and analyzes the effects of noise on sleep rhythms. For example, the generation AI learns that a quiet environment improves sleep quality. This allows for more accurate sleep rhythm learning by taking sleep environment data into consideration.

[0051] The lifestyle rhythm suggestion unit can analyze emotional data and suggest lifestyle rhythms that will bring out positive emotions. In the lifestyle rhythm suggestion unit, for example, the generation AI analyzes the user's emotional data and suggests lifestyle rhythms that will bring out positive emotions. For example, the generation AI suggests activities for relaxing during times of low stress. Based on the user's emotional data, the generation AI suggests an optimal lifestyle rhythm that will bring out positive emotions. In this way, by suggesting lifestyle rhythms that will bring out positive emotions, the quality of the user's life is improved.

[0052] The lifestyle rhythm suggestion unit can suggest an optimal lifestyle rhythm by taking into account work and academic schedules. For example, the generation AI collects the user's work and academic schedules and suggests an optimal lifestyle rhythm based on them. For example, the generation AI suggests a bedtime that matches the start time of work. The generation AI suggests an optimal lifestyle rhythm based on the user's schedule. This makes it possible to optimize the user's lifestyle rhythm by taking into account work and academic schedules.

[0053] The lifestyle rhythm suggestion unit can suggest a lifestyle rhythm that incorporates hobbies and relaxation methods. For example, the generation AI collects the user's hobbies and relaxation methods and suggests an optimal lifestyle rhythm based on the information. For example, the generation AI reduces stress by setting aside time for hobbies. The generation AI suggests an optimal lifestyle rhythm based on the user's hobbies and relaxation methods. This allows the user's lifestyle rhythm to be optimized by incorporating hobbies and relaxation methods.

[0054] The lifestyle rhythm suggestion unit can use the emotion estimation function to suggest relaxation methods according to the user's emotional state. For example, the generation AI in the lifestyle rhythm suggestion unit uses the emotion estimation function to analyze the user's emotional state in real time and suggest relaxation methods according to that state. For example, the generation AI suggests deep breathing or meditation when stress is high. The generation AI suggests the optimal relaxation method based on the user's emotional state. This makes it possible to optimize the user's lifestyle rhythm by suggesting relaxation methods according to the user's emotional state.

[0055] The lifestyle rhythm suggestion unit can suggest lifestyle rhythms according to the season and weather. For example, the generation AI collects seasonal and weather data and suggests an optimal lifestyle rhythm based on that data. For example, the generation AI suggests going to bed earlier in the winter. The generation AI suggests an optimal lifestyle rhythm based on seasonal and weather data. This makes it possible to optimize the user's lifestyle rhythm by suggesting a lifestyle rhythm according to the season and weather.

[0056] The alarm notification unit can analyze emotional data and adjust the timing of the alarm according to emotional fluctuations. In the alarm notification unit, for example, the generation AI analyzes the user's emotional data and adjusts the timing of the alarm according to emotional fluctuations. For example, the generation AI sounds the alarm earlier when stress is high. The generation AI adjusts the optimal alarm timing based on the user's emotional data. In this way, adjusting the alarm timing according to emotional fluctuations prevents the user from falling asleep.

[0057] The alarm notification unit can monitor activity data in real time and optimize the timing of the alarm according to the level of fatigue. For example, the generation AI in the alarm notification unit monitors the user's activity data in real time and optimizes the timing of the alarm according to the level of fatigue. For example, the generation AI sounds the alarm after a long period of work. The generation AI adjusts the optimal alarm timing based on the user's activity data. This prevents the user from falling asleep by optimizing the alarm timing according to the level of fatigue.

[0058] The alarm notification unit can customize the alarm sound and vibration pattern to suit the user's preferences. In the alarm notification unit, for example, the generation AI customizes the alarm sound and vibration pattern to suit the user's preferences. For example, the generation AI sets the user's favorite music as the alarm sound. The generation AI suggests the optimal alarm sound and vibration pattern based on the user's preferences. In this way, customizing the alarm sound and vibration pattern prevents the user from falling asleep.

[0059] The alarm notification unit can use the emotion estimation function to suggest the most relaxing alarm sound for the user. For example, the generation AI in the alarm notification unit uses the emotion estimation function to analyze the user's emotional state and suggest the most relaxing alarm sound. For example, the generation AI sets music that the user finds relaxing as the alarm sound. The generation AI suggests the optimal alarm sound based on the user's emotional state. This prevents the user from falling asleep by suggesting a relaxing alarm sound.

[0060] The alarm notification unit works in conjunction with smart home devices and can adjust the lighting and temperature in the room at the same time as the alarm goes off. For example, the generation AI in the alarm notification unit works in conjunction with the user's smart home devices and adjusts the lighting in the room at the same time as the alarm goes off. For example, the generation AI brightens the lights at the same time as the alarm goes off. The generation AI adjusts the lighting and temperature to the optimum level based on the user's smart home devices. This prevents the user from falling asleep by working in conjunction with smart home devices.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The sleep rhythm learning unit can collect the user's heart rate data and analyze heart rate fluctuations. For example, the generation AI can identify times when the user's heart rate is high and suggest activities for relaxation during those times. It can also suggest activities that require concentration during times when the heart rate is low. By taking heart rate data into consideration, this makes it possible to suggest more accurate lifestyle rhythms.

[0063] The lifestyle rhythm suggestion unit can analyze the user's emotional data and suggest lifestyle rhythms to reduce negative emotions. For example, the generation AI can suggest activities to help the user relax during times when they are prone to feeling stressed. It can also suggest activities to lift the user's spirits during times when they are prone to feeling depressed. This improves the user's quality of life by suggesting lifestyle rhythms to reduce negative emotions.

[0064] The alarm notification unit can analyze the user's activity data and adjust the timing of the alarm depending on the type of activity. For example, the generation AI can prevent the alarm from sounding when the user is exercising. It can also adjust the timing of the alarm when the user is relaxing. This allows for more effective alarm notifications by taking activity data into consideration.

[0065] The lifestyle rhythm suggestion unit can analyze the user's emotional data and suggest a meal plan that corresponds to their emotional fluctuations. For example, when the user is feeling stressed, the generation AI can suggest a meal that uses ingredients that have a relaxing effect. Also, when the user is tired, it can suggest a meal that will replenish energy. In this way, suggesting a meal plan that corresponds to their emotional fluctuations improves the user's quality of life.

[0066] The alarm notification unit can analyze the user's emotional data and adjust the alarm volume according to emotional fluctuations. For example, the generation AI can set the alarm volume low when the user is feeling stressed, or high when the user is relaxed. This prevents the user from falling asleep by adjusting the alarm volume according to emotional fluctuations.

[0067] The sleep rhythm learning unit can collect the user's body temperature data and analyze fluctuations in body temperature. For example, the generation AI can identify times when the user's body temperature is high and suggest activities to help them relax during those times. It can also suggest activities that require concentration during times when the user's body temperature is low. By taking body temperature data into consideration, this makes it possible to suggest more accurate lifestyle rhythms.

[0068] The lifestyle rhythm suggestion unit can analyze the user's emotional data and suggest an exercise plan that corresponds to the user's emotional fluctuations. For example, when the user is feeling stressed, the generation AI can suggest relaxing yoga or stretching. It can also suggest running or strength training when the user has too much energy. This improves the user's quality of life by suggesting an exercise plan that corresponds to the user's emotional fluctuations.

[0069] The alarm notification unit can analyze the user's activity data and adjust the alarm vibration pattern according to the intensity of the activity. For example, the generation AI can sound the alarm with strong vibrations when the user is exercising vigorously. It can also sound the alarm with gentle vibrations when the user is relaxing. This prevents the user from falling asleep by adjusting the alarm vibration pattern according to the intensity of the activity.

[0070] The lifestyle rhythm suggestion unit can analyze the user's emotional data and suggest adjustments to the sleep environment in response to emotional fluctuations. For example, when the user is feeling stressed, the generation AI can suggest relaxing lighting and music. It can also suggest comfortable temperature and humidity settings when the user is tired. This improves the user's quality of life by suggesting adjustments to the sleep environment in response to emotional fluctuations.

[0071] The alarm notification unit can analyze the user's activity data and adjust the timing of the alarm according to the frequency of the activity. For example, the generation AI can prevent the alarm from sounding during times when the user is frequently active. It can also adjust the timing of the alarm to sound during times when the user is less active. This allows for more effective alarm notifications by taking the frequency of activity into account.

[0072] The processing flow of the second embodiment will be briefly explained below.

[0073] Step 1: The sleep rhythm learning unit learns the user's sleep rhythm. For example, the generation AI collects data on the user's daily life and analyzes their sleep rhythm. The generation AI collects data such as when the user goes to sleep, when they wake up, and what activities they are engaged in, and understands the user's sleep patterns. Step 2: The lifestyle rhythm suggestion unit proposes lifestyle rhythms that will help you avoid falling asleep based on the sleep rhythms learned by the sleep rhythm learning unit. For example, the generation AI would suggest appropriate bedtimes, wake-up times, and times for relaxation activities. Step 3: The alarm notification unit issues an alarm to notify the user when they are likely to fall asleep based on the lifestyle rhythm suggested by the lifestyle rhythm suggestion unit. For example, the generation AI monitors the user's lifestyle, predicts the time when they are likely to fall asleep, and sounds an alarm to notify the user.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0078] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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).

[0083] 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.

[0084] 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.

[0085] 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.

[0086] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0087] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0102] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0118] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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."

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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]

[0141] 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 sleep rhythm learning unit that learns the sleep rhythm of a user; a life rhythm suggestion unit that suggests a life rhythm that prevents the user from falling asleep based on the sleep rhythm learned by the sleep rhythm learning unit; and an alarm notification unit that issues an alarm to notify the user at a time when the user is likely to fall asleep based on the life rhythm proposed by the life rhythm proposal unit.

2. The sleep rhythm learning unit Analyzes emotional data and learns sleep rhythms that take stress levels and mood fluctuations into account 2. The system of claim 1.

3. The lifestyle rhythm suggestion unit 2. The system according to claim 1, further comprising: analyzing emotional data and suggesting a lifestyle rhythm that will elicit positive emotions.

4. The alarm notification unit 2. The system of claim 1, wherein emotional data is analyzed and the timing of the alarm is adjusted according to emotional fluctuations.

5. The sleep rhythm learning unit 2. The system according to claim 1, wherein dietary data and exercise data are collected and the effects of these factors on sleep rhythms are analyzed.

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