System

Abstract

An object of a system according to an embodiment is to propose an optimal refresh method or stress release method based on user data.SOLUTION: A system includes a data input unit, an analysis unit, and a proposal unit. The data input unit allows the user to input data on sleep, meals, emotions, and events for the last week. The analysis unit analyzes the data input by the data input unit. A proposal part proposes an optimum refresh method, stress divergence method and self-development book matched with the schedule of the user on the basis of the result analyzed by the analysis part.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 made it difficult for users to find ways to effectively manage their emotions and stress.

[0005] The system according to the embodiment aims to propose optimal methods for refreshing and relieving stress based on user data. [Means for solving the problem]

[0006] The system according to the embodiment includes a data input unit, an analysis unit, and a suggestion unit. The data input unit allows a user to input data on sleep, meals, emotions, and events from the past week. The analysis unit analyzes the data input by the data input unit. The suggestion unit suggests optimal refreshment methods, stress relief methods, and self-help books tailored to the user's schedule based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest optimal ways to refresh or relieve stress based on the user's data. [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) In the stress relief suggestion system according to an embodiment of the present invention, a user inputs data about sleep, meals, emotions, and events from the past week, and a generation AI analyzes the data to suggest optimal ways to refresh oneself, relieve stress, and self-help books that fit the user's schedule. This allows the stress relief suggestion system to support the user's physical and mental health.

[0029] A stress relief suggestion system according to an embodiment includes a data input unit, an analysis unit, and a suggestion unit. The data input unit allows a user to input data about sleep, meals, emotions, and events from the past week. For example, the user can input data about sleep time, meal contents, emotional fluctuations, and daily events using a smartphone or PC. The data input unit saves the data input by the user and transmits it to the analysis unit. The analysis unit analyzes the data input by the data input unit. For example, the generation AI analyzes the user's sleep data and evaluates the quality and duration of sleep. The generation AI also analyzes the dietary data and evaluates nutritional balance and meal regularity. The generation AI also analyzes the emotion data and evaluates the user's stress level and emotional fluctuations. The suggestion unit suggests optimal refreshment methods, stress relief methods, and self-help books tailored to the user's schedule based on the results of the analysis by the analysis unit. For example, the suggestion unit takes the user's schedule into consideration and suggests ways to relax in nature on the weekend or ways to incorporate daily exercise. The suggestion unit can also suggest movies, music, and hobbies based on the user's preferences. As a result, the stress relief suggestion system according to the embodiment can support the user's physical and mental health. For example, the user can find a way to refresh themselves or relieve stress that suits them, thereby improving the quality of their daily life.

[0030] The analysis unit can analyze the user's past data history, grasp long-term trends, and build a predictive model. The analysis unit, for example, collects data on the user's past sleep, meals, emotions, and events, and analyzes long-term trends. For example, based on data from the past few months, it identifies fluctuation patterns in the user's stress level. The analysis unit also builds a model that predicts fluctuations in the user's health condition and emotions based on the past data history. For example, if stress tends to increase at a specific time, it can suggest countermeasures at that time. The analysis unit also analyzes the user's past data and develops a system that predicts long-term fluctuations in the user's health condition and emotions. For example, it can predict future stress levels based on past data and take countermeasures in advance. This enables more accurate predictions by analyzing the user's past data history and grasping long-term trends.

[0031] The suggestion unit allows the generation AI to automatically suggest complementary information for the user's input data, reducing the effort required for input. For example, the suggestion unit allows the generation AI to automatically suggest complementary information based on the data entered by the user. For example, if the user enters "I haven't been able to sleep lately," the generation AI automatically generates detailed questions about "sleep time" and "sleep quality." The suggestion unit also analyzes the user's input data, and the generation AI automatically completes related information. For example, if the user enters "I have irregular meals," the generation AI suggests detailed questions about "meal contents" and "meal times." The suggestion unit also builds a system in which the generation AI automatically provides complementary information for the user's input data, reducing the effort required for input. For example, if the user enters "I failed at work," the generation AI automatically generates questions about "specific details of the failure" and "subsequent response." This allows the generation AI to automatically suggest complementary information, reducing the effort required for input by the user.

[0032] The data input unit can use voice input or image recognition to enable users to input data in a natural way. The data input unit, for example, is equipped with a voice input function and develops a system that allows users to input data simply by speaking. For example, when a user says, "I can't sleep lately," the content is automatically entered as text data. The data input unit also uses image recognition technology to build a system that automatically extracts data from photos taken by the user. For example, when a user takes a photo of a meal, the content is automatically analyzed and entered as meal data. The data input unit also combines voice input and image recognition to develop a system that allows users to input data in a natural way. For example, when a user takes a photo while talking, the content is automatically analyzed and entered as data. This allows users to input data in a natural way by using voice input or image recognition.

[0033] The data entry department seamlessly synchronizes data between different devices, enabling data entry anywhere. The data entry department builds a system that seamlessly synchronizes data between different devices, such as smartphones, tablets, and PCs. For example, data entered on a smartphone is instantly reflected on a PC or tablet. The data entry department also develops a cloud-based data synchronization system that allows users to enter and view data from any device. For example, data stored in the cloud is automatically synchronized to each device. The data entry department also builds a system that synchronizes data between different devices in real time, allowing users to enter data anywhere. For example, data entered on a smartphone is instantly reflected on a tablet or PC. This seamless synchronization of data between different devices enables data entry anywhere.

[0034] The suggestion unit can evaluate the effectiveness of the user's past refreshment methods and prioritize suggest the most effective method. The suggestion unit, for example, builds a system that collects data on the user's past refreshment methods and evaluates their effectiveness. For example, it prioritizes suggesting refreshment methods that have been effective in the past. The suggestion unit also analyzes the effectiveness of past refreshment methods and identifies the most effective method. For example, it prioritizes suggesting methods that have helped the user relax in the past. The suggestion unit also develops a system that prioritizes suggesting the most effective method based on data on the user's past refreshment methods. For example, it identifies and suggests an effective refreshment method based on past data. In this way, the most effective method can be suggested by evaluating the effectiveness of past refreshment methods.

[0035] The suggestion unit can suggest a refreshment method that suits the user's living environment (weather, location, time of day). The suggestion unit, for example, collects the user's living environment data (weather, location, time of day) and builds a system that suggests a refreshment method based on that data. For example, on a nice day, it suggests outdoor activities. The suggestion unit also suggests a refreshment method that suits the user's living environment. For example, when the user is at home, it suggests a refreshment method that can be done indoors. The suggestion unit also develops a system that suggests a refreshment method that suits the user's situation based on the living environment data. For example, it suggests a method for relaxing at night and suggests active activities during the day. This allows for more effective refreshment by suggesting a refreshment method that suits the user's living environment.

[0036] The suggestion unit can link with data on the user's friends and family to suggest ways to refresh that can be done together. The suggestion unit, for example, collects data on the user's friends and family and builds a system that suggests ways to refresh that can be done together. For example, it suggests playing sports together with friends. The suggestion unit also links with data on friends and family to suggest ways to refresh that can be done together. For example, it suggests spending more time with family. The suggestion unit also develops a system that suggests ways to refresh that can be done together based on data on the user's friends and family. For example, it suggests going on a trip with friends. In this way, by linking with friends and family, it is possible to suggest ways to refresh that can be done together.

[0037] The suggestion unit can add a survey function to reflect the user's hobbies and interests in the suggested refreshment methods. The suggestion unit, for example, adds a survey function to understand the user's hobbies and interests, and builds a system that suggests refreshment methods based on the results. For example, it suggests listening to music that the user likes. The suggestion unit also uses the survey function to collect the user's hobbies and interests, and suggests refreshment methods based on that data. For example, it suggests activities that the user is interested in. The suggestion unit also adds a survey function to reflect the user's hobbies and interests, and develops a system that suggests refreshment methods based on the results. For example, it suggests watching a movie that the user likes. This makes it possible to suggest more personalized refreshment methods by reflecting the user's hobbies and interests.

[0038] The suggestion unit can evaluate the effectiveness of the user's past stress relief methods and prioritize suggest the most effective method. The suggestion unit, for example, collects data on the user's past stress relief methods and builds a system to evaluate their effectiveness. For example, it prioritizes suggesting stress relief methods that have been effective in the past. The suggestion unit also analyzes the effectiveness of the past stress relief methods and identifies the most effective method. For example, it prioritizes suggesting methods that have helped the user relax in the past. The suggestion unit also develops a system that prioritizes suggesting the most effective method based on data on the user's past stress relief methods. For example, it identifies and suggests effective stress relief methods based on past data. In this way, the most effective method can be suggested by evaluating the effectiveness of past stress relief methods.

[0039] The suggestion unit can suggest stress relief methods that suit the user's living environment (weather, location, time of day). The suggestion unit, for example, collects the user's living environment data (weather, location, time of day) and builds a system that suggests stress relief methods based on that data. For example, on days with good weather, it suggests outdoor activities. The suggestion unit also suggests stress relief methods that suit the user's living environment. For example, when the user is at home, it suggests stress relief methods that can be done indoors. The suggestion unit also develops a system that suggests stress relief methods that suit the user's situation based on the living environment data. For example, it suggests ways to relax at night and suggests active activities during the day. In this way, more effective stress relief is possible by suggesting stress relief methods that suit the user's living environment.

[0040] The suggestion unit can work in conjunction with data on the user's friends and family to suggest joint stress relief methods. The suggestion unit, for example, collects data on the user's friends and family and builds a system that suggests joint stress relief methods. For example, it suggests playing sports together with friends. The suggestion unit also works in conjunction with data on friends and family to suggest joint stress relief methods. For example, it suggests spending more time with family. The suggestion unit also develops a system that suggests joint stress relief methods based on data on the user's friends and family. For example, it suggests going on a trip with friends. In this way, by working in conjunction with friends and family, it is possible to suggest joint stress relief methods.

[0041] The suggestion unit can add a questionnaire function to reflect the user's hobbies and interests when proposing stress relief methods. The suggestion unit, for example, adds a questionnaire function to understand the user's hobbies and interests, and builds a system that suggests stress relief methods based on the results. For example, it suggests listening to music that the user likes. The suggestion unit also uses the questionnaire function to collect the user's hobbies and interests, and suggests stress relief methods based on that data. For example, it suggests activities that the user is interested in. The suggestion unit also adds a questionnaire function to reflect the user's hobbies and interests, and develops a system that suggests stress relief methods based on the results. For example, it suggests watching a movie that the user likes. This makes it possible to suggest more personalized stress relief methods by reflecting the user's hobbies and interests.

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

[0043] The data input unit can automatically acquire data from the user's health device (such as a smartwatch or fitness tracker). For example, it automatically collects data such as the user's heart rate, number of steps, and amount of exercise, and transmits it to the analysis unit. The data input unit can also link with the user's health device to acquire data in real time. For example, when the user starts exercising, the data is immediately reflected in the system. Furthermore, the data input unit can evaluate the user's health condition based on the data acquired from the user's health device. For example, it can analyze heart rate fluctuations and estimate stress levels. This allows for more accurate data collection by automatically acquiring data from the user's health device.

[0044] The analysis unit can analyze a user's past data history, grasp long-term trends, and build a predictive model. For example, it collects data on the user's past sleep, meals, emotions, and events, and analyzes long-term trends. For example, it identifies fluctuation patterns in the user's stress level based on data from the past few months. The analysis unit also builds a model that predicts fluctuations in the user's health condition and emotions based on past data history. For example, if stress tends to increase at a certain time, it can suggest countermeasures for that time. The analysis unit also analyzes a user's past data and develops a system that predicts long-term fluctuations in health condition and emotions. For example, it predicts future stress levels based on past data and takes countermeasures in advance. This enables more accurate predictions by analyzing a user's past data history and grasping long-term trends.

[0045] The suggestion unit allows the generation AI to automatically suggest complementary information for the user's input data, reducing the effort required for input. For example, the generation AI automatically suggests complementary information based on the data entered by the user. For example, if a user enters "I haven't been able to sleep lately," the generation AI automatically generates detailed questions about "sleep time" and "sleep quality." The suggestion unit also analyzes the user's input data, and the generation AI automatically completes related information. For example, if a user enters "I have irregular meals," the generation AI suggests detailed questions about "meal contents" and "meal times." The suggestion unit also builds a system in which the generation AI automatically provides complementary information for the user's input data, reducing the effort required for input. For example, if a user enters "I failed at work," the generation AI automatically generates questions about "specific details of the failure" and "subsequent response." This allows the generation AI to automatically suggest complementary information, reducing the effort required for input by the user.

[0046] The data input unit can use voice input and image recognition to enable users to input data in a natural way. For example, we will develop a system equipped with a voice input function that allows users to input data simply by speaking. For example, if a user says, "I can't sleep lately," the content of that conversation will be automatically entered as text data. The data input unit will also build a system that uses image recognition technology to automatically extract data from photos taken by the user. For example, if a user takes a photo of a meal, the content will be automatically analyzed and entered as meal data. The data input unit will also combine voice input and image recognition to develop a system that allows users to input data in a natural way. For example, if a user takes a photo while talking, the content will be automatically analyzed and entered as data. This allows users to input data in a natural way by using voice input and image recognition.

[0047] The Data Entry Department will seamlessly synchronize data between different devices, enabling data entry anywhere. For example, we will build a system that seamlessly synchronizes data between different devices, such as smartphones, tablets, and PCs. For example, data entered on a smartphone will be instantly reflected on the PC or tablet. The Data Entry Department will also develop a cloud-based data synchronization system that allows users to enter and view data from any device. For example, data stored in the cloud will be automatically synchronized to each device. The Data Entry Department will also build a system that synchronizes data between different devices in real time, allowing users to enter data anywhere. For example, data entered on a smartphone will be instantly reflected on the tablet or PC. This seamless synchronization of data between different devices will enable data entry anywhere.

[0048] The suggestion unit can evaluate the effectiveness of the user's past refreshment methods and prioritize suggest the most effective method. For example, a system is constructed that collects data on the user's past refreshment methods and evaluates their effectiveness. For example, refreshment methods that have been effective in the past are prioritized for suggestion. The suggestion unit also analyzes the effectiveness of past refreshment methods and identifies the most effective method. For example, methods that have helped the user relax in the past are prioritized for suggestion. The suggestion unit also develops a system that prioritizes suggesting the most effective method based on data on the user's past refreshment methods. For example, an effective refreshment method is identified and suggested based on past data. In this way, the most effective method can be suggested by evaluating the effectiveness of past refreshment methods.

[0049] The suggestion unit can suggest a refreshment method that suits the user's living environment (weather, location, time of day). For example, a system is constructed that collects the user's living environment data (weather, location, time of day) and suggests a refreshment method based on that data. For example, outdoor activities are suggested on days with good weather. The suggestion unit also suggests a refreshment method that suits the user's living environment. For example, when the user is at home, it suggests a refreshment method that can be done indoors. The suggestion unit also develops a system that suggests a refreshment method that suits the user's situation based on the living environment data. For example, it suggests relaxation methods at night and suggests active activities during the day. This allows for more effective refreshment by suggesting a refreshment method that suits the user's living environment.

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

[0051] Step 1: The data input unit allows the user to input data on sleep, diet, emotions, and events from the past week. For example, the user can use a smartphone or PC to input data on sleep time, diet, emotional fluctuations, and daily events. The data input unit also saves the data entered by the user and sends it to the analysis unit. Step 2: The analysis unit analyzes the data input by the data input unit. For example, the generation AI analyzes the user's sleep data and evaluates the quality and duration of sleep. The generation AI also analyzes dietary data and evaluates nutritional balance and meal regularity. Furthermore, the generation AI analyzes emotional data and evaluates the user's stress level and emotional fluctuations. Step 3: Based on the results of the analysis by the analysis unit, the suggestion unit suggests optimal ways to refresh yourself, relieve stress, and self-help books that fit the user's schedule. For example, the suggestion unit may take the user's schedule into consideration and suggest ways to relax in nature on the weekend or incorporate daily exercise. The suggestion unit may also suggest movies, music, and hobby activities based on the user's preferences.

[0052] (Example 2) In the stress relief suggestion system according to an embodiment of the present invention, a user inputs data about sleep, meals, emotions, and events from the past week, and a generation AI analyzes the data to suggest optimal ways to refresh oneself, relieve stress, and self-help books that fit the user's schedule. This allows the stress relief suggestion system to support the user's physical and mental health.

[0053] A stress relief suggestion system according to an embodiment includes a data input unit, an analysis unit, and a suggestion unit. The data input unit allows a user to input data about sleep, meals, emotions, and events from the past week. For example, the user can input data about sleep time, meal contents, emotional fluctuations, and daily events using a smartphone or PC. The data input unit saves the data input by the user and transmits it to the analysis unit. The analysis unit analyzes the data input by the data input unit. For example, the generation AI analyzes the user's sleep data and evaluates the quality and duration of sleep. The generation AI also analyzes the dietary data and evaluates nutritional balance and meal regularity. The generation AI also analyzes the emotion data and evaluates the user's stress level and emotional fluctuations. The suggestion unit suggests optimal refreshment methods, stress relief methods, and self-help books tailored to the user's schedule based on the results of the analysis by the analysis unit. For example, the suggestion unit takes the user's schedule into consideration and suggests ways to relax in nature on the weekend or ways to incorporate daily exercise. The suggestion unit can also suggest movies, music, and hobbies based on the user's preferences. As a result, the stress relief suggestion system according to the embodiment can support the user's physical and mental health. For example, the user can find a way to refresh themselves or relieve stress that suits them, thereby improving the quality of their daily life.

[0054] The data input unit can estimate the user's emotions in real time and adjust the timing and content of data input according to emotional fluctuations. For example, when the user inputs data, the data input unit estimates the user's emotions in real time using a camera or microphone and adjusts the timing of input according to emotional fluctuations. For example, if the user is feeling stressed, the data input unit temporarily suspends input and allows the user time to relax. The data input unit also uses the emotion estimation function to adjust the content of the data input by the user. For example, if the user is feeling positive, the data input unit prompts the user to enter detailed data, and if the user is feeling negative, the data input unit suggests simplified input. The data input unit also monitors the user's emotional fluctuations in real time and prompts the user to enter data when their emotions are stable. For example, the system suggests that the user enter data during a time period when they are relaxed. This allows for more accurate data collection by adjusting the timing and content of data input according to the user's emotions.

[0055] The analysis unit can analyze the user's past data history, grasp long-term trends, and build a predictive model. The analysis unit, for example, collects data on the user's past sleep, meals, emotions, and events, and analyzes long-term trends. For example, based on data from the past few months, it identifies fluctuation patterns in the user's stress level. The analysis unit also builds a model that predicts fluctuations in the user's health condition and emotions based on the past data history. For example, if stress tends to increase at a specific time, it can suggest countermeasures at that time. The analysis unit also analyzes the user's past data and develops a system that predicts long-term fluctuations in the user's health condition and emotions. For example, it can predict future stress levels based on past data and take countermeasures in advance. This enables more accurate predictions by analyzing the user's past data history and grasping long-term trends.

[0056] The suggestion unit allows the generation AI to automatically suggest complementary information for the user's input data, reducing the effort required for input. For example, the suggestion unit allows the generation AI to automatically suggest complementary information based on the data entered by the user. For example, if the user enters "I haven't been able to sleep lately," the generation AI automatically generates detailed questions about "sleep time" and "sleep quality." The suggestion unit also analyzes the user's input data, and the generation AI automatically completes related information. For example, if the user enters "I have irregular meals," the generation AI suggests detailed questions about "meal contents" and "meal times." The suggestion unit also builds a system in which the generation AI automatically provides complementary information for the user's input data, reducing the effort required for input. For example, if the user enters "I failed at work," the generation AI automatically generates questions about "specific details of the failure" and "subsequent response." This allows the generation AI to automatically suggest complementary information, reducing the effort required for input by the user.

[0057] The data input unit can use voice input or image recognition to enable users to input data in a natural way. The data input unit, for example, is equipped with a voice input function and develops a system that allows users to input data simply by speaking. For example, when a user says, "I can't sleep lately," the content is automatically entered as text data. The data input unit also uses image recognition technology to build a system that automatically extracts data from photos taken by the user. For example, when a user takes a photo of a meal, the content is automatically analyzed and entered as meal data. The data input unit also combines voice input and image recognition to develop a system that allows users to input data in a natural way. For example, when a user takes a photo while talking, the content is automatically analyzed and entered as data. This allows users to input data in a natural way by using voice input or image recognition.

[0058] The data entry department seamlessly synchronizes data between different devices, enabling data entry anywhere. The data entry department builds a system that seamlessly synchronizes data between different devices, such as smartphones, tablets, and PCs. For example, data entered on a smartphone is instantly reflected on a PC or tablet. The data entry department also develops a cloud-based data synchronization system that allows users to enter and view data from any device. For example, data stored in the cloud is automatically synchronized to each device. The data entry department also builds a system that synchronizes data between different devices in real time, allowing users to enter data anywhere. For example, data entered on a smartphone is instantly reflected on a tablet or PC. This seamless synchronization of data between different devices enables data entry anywhere.

[0059] The data input unit can use the emotion estimation function to analyze the emotion of the user when entering data in real time and provide feedback according to the entered content. The data input unit, for example, uses the emotion estimation function to analyze the emotion of the user when entering data in real time and provide feedback based on the results. For example, if the user is feeling stressed, advice to relax is displayed. The data input unit also builds a system that analyzes the user's emotion in real time and provides feedback according to the entered content. For example, if the user has positive emotions, an encouraging message is displayed. The data input unit also uses the emotion estimation function to analyze the emotion of the user when entering data and provide appropriate feedback according to the entered content. For example, if the user has negative emotions, a positive suggestion is made. In this way, by using the emotion estimation function, feedback according to the user's emotions can be provided.

[0060] The suggestion unit can analyze the user's emotional state in real time and dynamically suggest a refreshment method according to the emotion. The suggestion unit, for example, builds a system that analyzes the user's emotional state in real time and dynamically suggests a refreshment method based on the results. For example, if the user is feeling stressed, it suggests a method for relaxation. The suggestion unit also dynamically suggests a refreshment method according to the user's emotional state based on the emotion analysis data. For example, if the user is feeling positive, it suggests active activities. The suggestion unit also develops a system that monitors the user's emotional state in real time and suggests a refreshment method according to the emotion. For example, if the user is tired, it suggests a method for relaxation. This allows for more effective refreshment by dynamically suggesting a refreshment method according to the user's emotional state.

[0061] The suggestion unit can evaluate the effectiveness of the user's past refreshment methods and prioritize suggest the most effective method. The suggestion unit, for example, builds a system that collects data on the user's past refreshment methods and evaluates their effectiveness. For example, it prioritizes suggesting refreshment methods that have been effective in the past. The suggestion unit also analyzes the effectiveness of past refreshment methods and identifies the most effective method. For example, it prioritizes suggesting methods that have helped the user relax in the past. The suggestion unit also develops a system that prioritizes suggesting the most effective method based on data on the user's past refreshment methods. For example, it identifies and suggests an effective refreshment method based on past data. In this way, the most effective method can be suggested by evaluating the effectiveness of past refreshment methods.

[0062] The suggestion unit can suggest a refreshment method that suits the user's living environment (weather, location, time of day). The suggestion unit, for example, collects the user's living environment data (weather, location, time of day) and builds a system that suggests a refreshment method based on that data. For example, on a nice day, it suggests outdoor activities. The suggestion unit also suggests a refreshment method that suits the user's living environment. For example, when the user is at home, it suggests a refreshment method that can be done indoors. The suggestion unit also develops a system that suggests a refreshment method that suits the user's situation based on the living environment data. For example, it suggests a method for relaxing at night and suggests active activities during the day. This allows for more effective refreshment by suggesting a refreshment method that suits the user's living environment.

[0063] The suggestion unit can link with data on the user's friends and family to suggest ways to refresh that can be done together. The suggestion unit, for example, collects data on the user's friends and family and builds a system that suggests ways to refresh that can be done together. For example, it suggests playing sports together with friends. The suggestion unit also links with data on friends and family to suggest ways to refresh that can be done together. For example, it suggests spending more time with family. The suggestion unit also develops a system that suggests ways to refresh that can be done together based on data on the user's friends and family. For example, it suggests going on a trip with friends. In this way, by linking with friends and family, it is possible to suggest ways to refresh that can be done together.

[0064] The suggestion unit can add a survey function to reflect the user's hobbies and interests in the suggested refreshment methods. The suggestion unit, for example, adds a survey function to understand the user's hobbies and interests, and builds a system that suggests refreshment methods based on the results. For example, it suggests listening to music that the user likes. The suggestion unit also uses the survey function to collect the user's hobbies and interests, and suggests refreshment methods based on that data. For example, it suggests activities that the user is interested in. The suggestion unit also adds a survey function to reflect the user's hobbies and interests, and develops a system that suggests refreshment methods based on the results. For example, it suggests watching a movie that the user likes. This makes it possible to suggest more personalized refreshment methods by reflecting the user's hobbies and interests.

[0065] The suggestion unit uses the emotion estimation function to predict how the user will feel about the proposed refreshment method and select the optimal method. For example, the suggestion unit uses the emotion estimation function to build a system that predicts how the user will feel about the proposed refreshment method. For example, it prioritizes suggesting methods that allow the user to relax. The suggestion unit also predicts the user's emotional response and selects the optimal refreshment method based on the result. For example, it suggests methods that will make the user feel positive. The suggestion unit also uses the emotion estimation function to develop a system that predicts how the user will feel about the proposed refreshment method and selects the optimal method. For example, it suggests methods that will not cause the user stress. In this way, the optimal refreshment method can be selected by predicting the user's emotions.

[0066] The suggestion unit can analyze the user's emotional state in real time and dynamically suggest stress relief methods according to the emotion. The suggestion unit, for example, builds a system that analyzes the user's emotional state in real time and dynamically suggests stress relief methods based on the results. For example, if the user is feeling stressed, it suggests methods for relaxation. The suggestion unit also dynamically suggests stress relief methods according to the user's emotional state based on the emotion analysis data. For example, if the user is feeling positive, it suggests active activities. The suggestion unit also develops a system that monitors the user's emotional state in real time and suggests stress relief methods according to the emotion. For example, if the user is tired, it suggests methods for relaxation. This allows for more effective stress relief by dynamically suggesting stress relief methods according to the user's emotional state.

[0067] The suggestion unit can evaluate the effectiveness of the user's past stress relief methods and prioritize suggest the most effective method. The suggestion unit, for example, collects data on the user's past stress relief methods and builds a system to evaluate their effectiveness. For example, it prioritizes suggesting stress relief methods that have been effective in the past. The suggestion unit also analyzes the effectiveness of the past stress relief methods and identifies the most effective method. For example, it prioritizes suggesting methods that have helped the user relax in the past. The suggestion unit also develops a system that prioritizes suggesting the most effective method based on data on the user's past stress relief methods. For example, it identifies and suggests effective stress relief methods based on past data. In this way, the most effective method can be suggested by evaluating the effectiveness of past stress relief methods.

[0068] The suggestion unit can suggest stress relief methods that suit the user's living environment (weather, location, time of day). The suggestion unit, for example, collects the user's living environment data (weather, location, time of day) and builds a system that suggests stress relief methods based on that data. For example, on days with good weather, it suggests outdoor activities. The suggestion unit also suggests stress relief methods that suit the user's living environment. For example, when the user is at home, it suggests stress relief methods that can be done indoors. The suggestion unit also develops a system that suggests stress relief methods that suit the user's situation based on the living environment data. For example, it suggests ways to relax at night and suggests active activities during the day. In this way, more effective stress relief is possible by suggesting stress relief methods that suit the user's living environment.

[0069] The suggestion unit can work in conjunction with data on the user's friends and family to suggest joint stress relief methods. The suggestion unit, for example, collects data on the user's friends and family and builds a system that suggests joint stress relief methods. For example, it suggests playing sports together with friends. The suggestion unit also works in conjunction with data on friends and family to suggest joint stress relief methods. For example, it suggests spending more time with family. The suggestion unit also develops a system that suggests joint stress relief methods based on data on the user's friends and family. For example, it suggests going on a trip with friends. In this way, by working in conjunction with friends and family, it is possible to suggest joint stress relief methods.

[0070] The suggestion unit can add a questionnaire function to reflect the user's hobbies and interests when proposing stress relief methods. The suggestion unit, for example, adds a questionnaire function to understand the user's hobbies and interests, and builds a system that suggests stress relief methods based on the results. For example, it suggests listening to music that the user likes. The suggestion unit also uses the questionnaire function to collect the user's hobbies and interests, and suggests stress relief methods based on that data. For example, it suggests activities that the user is interested in. The suggestion unit also adds a questionnaire function to reflect the user's hobbies and interests, and develops a system that suggests stress relief methods based on the results. For example, it suggests watching a movie that the user likes. This makes it possible to suggest more personalized stress relief methods by reflecting the user's hobbies and interests.

[0071] The suggestion unit uses the emotion estimation function to predict how the user will feel in response to the proposed stress relief method and select the optimal method. For example, the suggestion unit uses the emotion estimation function to build a system that predicts how the user will feel in response to the proposed stress relief method. For example, it prioritizes suggesting methods that allow the user to relax. The suggestion unit also predicts the user's emotional response and selects the optimal stress relief method based on the result. For example, it suggests methods that make the user feel positive. The suggestion unit also uses the emotion estimation function to develop a system that predicts how the user will feel in response to the proposed stress relief method and selects the optimal method. For example, it suggests methods that do not make the user feel stressed. In this way, the optimal stress relief method can be selected by predicting the user's emotions.

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

[0073] The data input unit can automatically acquire data from the user's health device (such as a smartwatch or fitness tracker). For example, it automatically collects data such as the user's heart rate, number of steps, and amount of exercise, and transmits it to the analysis unit. The data input unit can also link with the user's health device to acquire data in real time. For example, when the user starts exercising, the data is immediately reflected in the system. Furthermore, the data input unit can evaluate the user's health condition based on the data acquired from the user's health device. For example, it can analyze heart rate fluctuations and estimate stress levels. This allows for more accurate data collection by automatically acquiring data from the user's health device.

[0074] The data input unit can estimate the user's emotions in real time and adjust the timing and content of data input according to emotional fluctuations. For example, when a user inputs data, the system estimates the user's emotions in real time using a camera or microphone and adjusts the timing of input according to emotional fluctuations. For example, if the user is feeling stressed, the system can temporarily suspend input and allow the user time to relax. The data input unit also uses the emotion estimation function to adjust the content of the data input by the user. For example, if the user is feeling positive, the system can prompt the user to enter detailed data, and if the user is feeling negative, the system can suggest simplified input. The data input unit also monitors the user's emotional fluctuations in real time and prompts the user to enter data when their emotions are stable. For example, the system can suggest that the user enter data during a time when they are relaxed. This allows for more accurate data collection by adjusting the timing and content of data input according to the user's emotions.

[0075] The analysis unit can analyze a user's past data history, grasp long-term trends, and build a predictive model. For example, it collects data on the user's past sleep, meals, emotions, and events, and analyzes long-term trends. For example, it identifies fluctuation patterns in the user's stress level based on data from the past few months. The analysis unit also builds a model that predicts fluctuations in the user's health condition and emotions based on past data history. For example, if stress tends to increase at a certain time, it can suggest countermeasures for that time. The analysis unit also analyzes a user's past data and develops a system that predicts long-term fluctuations in health condition and emotions. For example, it predicts future stress levels based on past data and takes countermeasures in advance. This enables more accurate predictions by analyzing a user's past data history and grasping long-term trends.

[0076] The suggestion unit allows the generation AI to automatically suggest complementary information for the user's input data, reducing the effort required for input. For example, the generation AI automatically suggests complementary information based on the data entered by the user. For example, if a user enters "I haven't been able to sleep lately," the generation AI automatically generates detailed questions about "sleep time" and "sleep quality." The suggestion unit also analyzes the user's input data, and the generation AI automatically completes related information. For example, if a user enters "I have irregular meals," the generation AI suggests detailed questions about "meal contents" and "meal times." The suggestion unit also builds a system in which the generation AI automatically provides complementary information for the user's input data, reducing the effort required for input. For example, if a user enters "I failed at work," the generation AI automatically generates questions about "specific details of the failure" and "subsequent response." This allows the generation AI to automatically suggest complementary information, reducing the effort required for input by the user.

[0077] The data input unit can use voice input and image recognition to enable users to input data in a natural way. For example, we will develop a system equipped with a voice input function that allows users to input data simply by speaking. For example, if a user says, "I can't sleep lately," the content of that conversation will be automatically entered as text data. The data input unit will also build a system that uses image recognition technology to automatically extract data from photos taken by the user. For example, if a user takes a photo of a meal, the content will be automatically analyzed and entered as meal data. The data input unit will also combine voice input and image recognition to develop a system that allows users to input data in a natural way. For example, if a user takes a photo while talking, the content will be automatically analyzed and entered as data. This allows users to input data in a natural way by using voice input and image recognition.

[0078] The Data Entry Department will seamlessly synchronize data between different devices, enabling data entry anywhere. For example, we will build a system that seamlessly synchronizes data between different devices, such as smartphones, tablets, and PCs. For example, data entered on a smartphone will be instantly reflected on the PC or tablet. The Data Entry Department will also develop a cloud-based data synchronization system that allows users to enter and view data from any device. For example, data stored in the cloud will be automatically synchronized to each device. The Data Entry Department will also build a system that synchronizes data between different devices in real time, allowing users to enter data anywhere. For example, data entered on a smartphone will be instantly reflected on the tablet or PC. This seamless synchronization of data between different devices will enable data entry anywhere.

[0079] The data input unit can use the emotion estimation function to analyze the emotion of the user when entering data in real time and provide feedback according to the entered content. For example, the emotion estimation function can be used to analyze the emotion of the user when entering data in real time and provide feedback based on the results. For example, if the user is feeling stressed, advice to relax can be displayed. The data input unit also builds a system that analyzes the user's emotion in real time and provides feedback according to the entered content. For example, if the user has positive emotions, an encouraging message can be displayed. The data input unit also uses the emotion estimation function to analyze the emotion of the user when entering data and provide appropriate feedback according to the entered content. For example, if the user has negative emotions, a positive suggestion can be made. In this way, by using the emotion estimation function, feedback according to the user's emotions can be provided.

[0080] The suggestion unit can analyze the user's emotional state in real time and dynamically suggest a refreshment method according to the emotion. For example, a system is constructed that analyzes the user's emotional state in real time and dynamically suggests a refreshment method based on the results. For example, if the user is feeling stressed, a method for relaxation is suggested. The suggestion unit also dynamically suggests a refreshment method according to the user's emotional state based on the emotion analysis data. For example, if the user is feeling positive, an active activity is suggested. The suggestion unit also monitors the user's emotional state in real time and develops a system that suggests a refreshment method according to the emotion. For example, if the user is tired, a method for relaxation is suggested. This allows for more effective refreshment by dynamically suggesting a refreshment method according to the user's emotional state.

[0081] The suggestion unit can evaluate the effectiveness of the user's past refreshment methods and prioritize suggest the most effective method. For example, a system is constructed that collects data on the user's past refreshment methods and evaluates their effectiveness. For example, refreshment methods that have been effective in the past are prioritized for suggestion. The suggestion unit also analyzes the effectiveness of past refreshment methods and identifies the most effective method. For example, methods that have helped the user relax in the past are prioritized for suggestion. The suggestion unit also develops a system that prioritizes suggesting the most effective method based on data on the user's past refreshment methods. For example, an effective refreshment method is identified and suggested based on past data. In this way, the most effective method can be suggested by evaluating the effectiveness of past refreshment methods.

[0082] The suggestion unit can suggest a refreshment method that suits the user's living environment (weather, location, time of day). For example, a system is constructed that collects the user's living environment data (weather, location, time of day) and suggests a refreshment method based on that data. For example, outdoor activities are suggested on days with good weather. The suggestion unit also suggests a refreshment method that suits the user's living environment. For example, when the user is at home, it suggests a refreshment method that can be done indoors. The suggestion unit also develops a system that suggests a refreshment method that suits the user's situation based on the living environment data. For example, it suggests relaxation methods at night and suggests active activities during the day. This allows for more effective refreshment by suggesting a refreshment method that suits the user's living environment.

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

[0084] Step 1: The data input unit allows the user to input data on sleep, diet, emotions, and events from the past week. For example, the user can use a smartphone or PC to input data on sleep time, diet, emotional fluctuations, and daily events. The data input unit also saves the data entered by the user and sends it to the analysis unit. Step 2: The analysis unit analyzes the data input by the data input unit. For example, the generation AI analyzes the user's sleep data and evaluates the quality and duration of sleep. The generation AI also analyzes dietary data and evaluates nutritional balance and meal regularity. Furthermore, the generation AI analyzes emotional data and evaluates the user's stress level and emotional fluctuations. Step 3: Based on the results of the analysis by the analysis unit, the suggestion unit suggests optimal ways to refresh yourself, relieve stress, and self-help books that fit the user's schedule. For example, the suggestion unit may take the user's schedule into consideration and suggest ways to relax in nature on the weekend or incorporate daily exercise. The suggestion unit may also suggest movies, music, and hobby activities based on the user's preferences.

[0085] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

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

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

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

[0090] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

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

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

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

[0096] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0102] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

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

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

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

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

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

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

[0117] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0119] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

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

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

[0125] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

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

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also 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 perform processing similar to that of the specific processing unit 290 using these models.

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

[0131] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0133] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

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

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

[0139] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

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

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

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

[0143] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0144] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

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

[0146] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

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

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

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

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

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

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

Claims

1. a data input section where the user inputs data on sleep, meals, emotions, and events from the past week; an analysis unit that analyzes the data input by the data input unit; a suggestion unit that suggests an optimal refreshment method, stress relief method, or self-improvement book that matches the user's schedule based on the results of the analysis by the analysis unit. A system characterized by:

2. The data input unit The user's emotions are estimated in real time, and the timing and content of data input are adjusted according to fluctuations in the emotions.

2. The system of claim 1.

3. The analysis unit Analyze the user's past data history, understand long-term trends, and build a predictive model 2. The system of claim 1.

4. The proposal unit The generation AI automatically suggests complementary information for the data entered by the user, reducing the effort required for input.

2. The system of claim 1.

5. The data input unit Using voice input and image recognition, the user can input data in a natural way.

2. The system of claim 1.

6. The data input unit Seamlessly sync said data across different devices, allowing data entry anywhere 2. The system of claim 1.

7. The data input unit Analyzing the user's emotions in real time when inputting information and providing feedback according to the input content 2. The system of claim 1.

8. The proposal unit The emotional state of the user is analyzed in real time, and the refreshing method is dynamically suggested according to the emotional state.

2. The system of claim 1.

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