System

Abstract

An object of the system according to the embodiment is to support life in general by utilizing a life log of an individual and create a funeral on a Metaverse or a tomb on a virtual space.SOLUTION: A system according to an embodiment includes a life log collection unit, a learning unit, a support unit, and a virtual space generation unit. The life log collection unit collects a life log. The learning unit learns the life log collected by the life log collecting unit. The support unit supports the overall life of the individual based on the life log learned by the learning unit. The virtual space generation unit creates a funeral on the Metaverse or a tomb on the virtual space by utilizing the life log.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 technology does not adequately utilize personal life logs to support daily life in general, or to hold funerals in the metaverse or create virtual graves, so there is room for improvement.

[0005] The system of the embodiment aims to support all aspects of life by utilizing an individual's life log, and to create funerals in the metaverse and graves in a virtual space. [Means for solving the problem]

[0006] The system according to the embodiment includes a life log collection unit, a learning unit, a support unit, and a virtual space generation unit. The life log collection unit collects life logs. The learning unit learns the life logs collected by the life log collection unit. The support unit supports an individual's overall life based on the life logs learned by the learning unit. The virtual space generation unit uses the life logs to create funerals and virtual graves on the metaverse. [Effects of the Invention]

[0007] The system according to the embodiment utilizes an individual's life log to support all aspects of life, and can hold funerals in the metaverse and create graves in virtual space. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The life support system according to an embodiment of the present invention automatically collects an individual's life log, uses a generation AI to learn, supports all aspects of life, and creates funerals in the metaverse and graves in a virtual space. This allows the life support system to utilize an individual's life log to support all aspects of life and enable continuous memories and memorial services in the metaverse.

[0029] A life support system according to an embodiment includes a life log collection unit, a learning unit, a support unit, and a virtual space generation unit. The life log collection unit collects an individual's life log. For example, it collects data from a smartphone or a wearable device. The life log collection unit can also collect health data, behavioral history, location information, and the like. The learning unit learns the life log collected by the life log collection unit. For example, it uses a machine learning algorithm to learn an individual's lifestyle patterns and preferences. The learning unit can also perform more advanced analysis using deep learning. The support unit supports the individual's overall life based on the life log learned by the learning unit. For example, it provides health management, household assistance, and schedule management. The support unit can also analyze an individual's health data and propose optimal insurance plans. The virtual space generation unit uses the life log to create funerals and virtual graves in the metaverse. For example, it creates virtual spaces based on an individual's hobbies and preferences. The virtual space generation unit can also automatically generate memorable videos and messages using the deceased's life log. As a result, the life support system according to the embodiment can utilize an individual's life log to support their overall life and enable continuous remembrance and memorial services in the metaverse. For example, the system can analyze an individual's life log and propose an optimal insurance plan. The system can also create funerals and virtual graves in the metaverse, allowing family and friends to continuously reminisce and memorialize.

[0030] The life log collection unit can collect data from a smartphone or a wearable device. For example, the life log collection unit collects location information and app usage history from a smartphone. The life log collection unit can also collect data such as heart rate and step count from a wearable device. For example, a smartwatch can be used to collect heart rate and sleep patterns. A fitness tracker can also be used to record daily activity levels. In this way, collecting data from a smartphone or a wearable device can provide a more accurate life log.

[0031] The support unit can analyze an individual's health data and propose the most suitable insurance plan. For example, the support unit analyzes an individual's health data and predicts future illness risks. For example, it calculates health risks based on dietary habits, exercise habits, sleep patterns, etc. The support unit also proposes an insurance plan suitable for the individual based on the predicted health risks. For example, it can propose the most suitable insurance plan using a method for calculating insurance premiums based on health status. The support unit can also build an early warning system for specific health risks. For example, if it detects abnormalities in blood pressure or blood sugar levels, it can send a notification recommending a doctor's consultation. In this way, the most suitable insurance plan can be proposed by analyzing an individual's health data.

[0032] The virtual space generation unit can analyze an individual's life log and create a virtual space based on the individual's hobbies and preferences. For example, the virtual space generation unit can analyze an individual's life log and create a virtual space based on the individual's hobbies and preferences. For example, the virtual space generation unit can provide a virtual space incorporating the deceased's favorite scenery and music. The virtual space generation unit can also use an emotion estimation function to monitor the user's emotional fluctuations in real time and suggest relaxation methods based on the emotions. For example, the emotion estimation function can monitor the user's emotional fluctuations in real time. For example, the emotion estimation function can analyze facial expressions and vocal tone to calculate an emotion score. The virtual space generation unit can also suggest relaxation methods appropriate for the user based on the emotional fluctuations. For example, if stress increases, the virtual space generation unit can recommend meditation or relaxation music. The virtual space generation unit can also provide specific actions to improve the user's emotional state based on the emotion estimation data. For example, the virtual space generation unit can suggest hobbies or activities that will elicit positive emotions. This allows for the creation of a virtual space based on an individual's hobbies and preferences, enabling more familiar and continuous memories and memorial services.

[0033] The support unit can suggest products that match the user's preferences based on the user's personal shopping history. For example, the support unit analyzes the user's shopping history to suggest products that match the user's preferences. For example, the support unit recommends products that the user may be interested in based on past purchase history. The support unit can also use life log data to suggest new hobbies or activities based on the user's preferences. For example, the support unit analyzes life log data to identify the user's hobbies and interests. For example, the support unit understands the user's interests based on past activity history and search history. The support unit can also suggest new hobbies and activities based on the hobbies and interests. For example, the support unit can introduce new hiking trails to a user who loves the outdoors. The support unit can also use life log data to suggest activities that the user has not tried yet but may be interested in. For example, the support unit can introduce new recipes or cooking classes to a user who loves cooking. In this way, the support unit can suggest products that match the user's preferences based on the user's personal shopping history.

[0034] The support unit can support the elderly's daily life and reduce the burden of caregiving. For example, the support unit performs health management, housework assistance, and transportation assistance to support the elderly's daily life. For example, the support unit analyzes health data and performs regular health checks and recommends exercise. Housework assistance can also provide support for shopping, cleaning, and cooking. Transportation assistance can also provide transportation arrangements and transportation support. The support unit can also compare life log data with other users and introduce communities with common interests and activities. For example, the support unit can analyze life log data to find commonalities with other users. For example, the support unit can identify users with the same hobbies and interests. The support unit can also introduce communities with common interests and activities. For example, the support unit can suggest online forums and events where users with the same hobbies gather. The support unit can also suggest community activities that the user can participate in based on the life log data. For example, the support unit can introduce local sports clubs and hobby circles. This can support the elderly's daily life and reduce the burden of caregiving.

[0035] The support unit can compensate for financial shortfalls in retirement by proposing optimal insurance plans and asset management. For example, the support unit can analyze personal health data and life log data to propose optimal insurance plans. For example, the support unit can propose optimal insurance plans using a method for calculating insurance premiums based on health status. The support unit can also propose asset management. For example, the support unit can propose asset management methods such as investment trusts, stock investments, and real estate investments. The support unit can also use an emotion estimation function to identify the time of day and environment in which the user is most relaxed and suggest relaxing activities during that time of day. For example, the support unit can use the emotion estimation function to identify the time of day in which the user is most relaxed. For example, the support unit can analyze past emotion data to find the time of day when the user is most relaxed. The support unit can also identify a relaxing environment and suggest activities suitable for that environment. For example, the support unit can recommend reading or listening to music in a quiet place. The support unit can also suggest relaxation activities during times when the user is most relaxed based on the emotion estimation data. For example, the support unit can recommend meditation or yoga for evening relaxation. In this way, the support unit can compensate for financial shortfalls in retirement by proposing optimal insurance plans and asset management.

[0036] The support unit can estimate an individual's stress level and psychological state and suggest specific actions to reduce stress. The support unit, for example, analyzes life log data to estimate an individual's stress level. For example, it calculates a stress score based on heart rate, sleep patterns, daily activity level, etc., and suggests relaxation methods based on the results. The support unit also suggests stress reduction activities suitable for the individual based on the estimated stress level. For example, it suggests specific actions such as meditation, deep breathing, and light exercise. The support unit also uses life log data to identify causes of stress and provide advice to address those causes. For example, if a person has a heavy work load, it suggests task management methods. This makes it possible to estimate an individual's stress level and psychological state and suggest specific actions to reduce stress.

[0037] The support unit can predict an individual's long-term health risks and suggest preventive measures. For example, the support unit analyzes life log data to predict an individual's health risks. For example, it calculates future disease risks based on dietary habits, exercise habits, sleep patterns, etc. The support unit can also suggest preventive measures appropriate for the individual based on the health risk prediction results. For example, it can recommend dietary improvements and exercise, and communicate the importance of regular health checks. The support unit can also build an early warning system for specific health risks. For example, if it detects abnormalities in blood pressure or blood sugar levels, it can send a notification recommending a doctor's consultation. This makes it possible to predict an individual's long-term health risks and suggest preventive measures.

[0038] The support unit can suggest new hobbies and activities based on an individual's hobbies and interests. For example, the support unit analyzes life log data to identify an individual's hobbies and interests. For example, it understands a user's interests based on past activity history and search history. It also suggests new hobbies and activities based on the hobbies and interests. For example, it can introduce new hiking trails to a user who loves the outdoors. It can also use the life log data to suggest activities that the user has not tried yet but may be interested in. For example, it can introduce new recipes and cooking classes to a user who loves cooking. This makes it possible to suggest new hobbies and activities based on an individual's hobbies and interests.

[0039] The support unit can compare the life log data with other users and introduce communities with common interests and activities. The support unit, for example, analyzes the life log data to find commonalities with other users. For example, it identifies users with the same hobbies and interests. It also introduces communities with common interests and activities. For example, it suggests online forums and events where users with the same hobbies gather. It also suggests community activities that the user can participate in based on the life log data. For example, it introduces local sports clubs and hobby circles. This makes it possible to compare the life log data with other users and introduce communities with common interests and activities.

[0040] The support unit can propose specific menus to optimize the nutritional balance of an individual's diet. The support unit, for example, analyzes life log data to understand the details of an individual's diet. For example, it evaluates nutritional balance based on food records and calorie intake. It also proposes specific menus to optimize nutritional balance. For example, if there is a vitamin or mineral deficiency, it proposes a menu that includes ingredients that supplement it. It also creates a meal plan based on the individual's health condition based on the life log data. For example, it proposes low-calorie menus to a user who is on a diet. This makes it possible to propose specific menus to optimize the nutritional balance of an individual's diet.

[0041] The support unit can create a customized exercise plan to improve an individual's exercise habits. The support unit, for example, analyzes life log data to understand an individual's exercise habits. For example, it evaluates exercise habits based on daily activity level and exercise frequency. It also creates a customized exercise plan to improve exercise habits. For example, it recommends walking or light jogging to a user who is not getting enough exercise. It also provides an exercise plan based on the individual's health condition and goals based on the life log data. For example, it suggests specific menus for strength training and stretching. This makes it possible to create a customized exercise plan to improve an individual's exercise habits.

[0042] The support unit can customize individual travel plans and suggest optimal travel destinations and activities. For example, the support unit analyzes life log data to understand an individual's travel preferences and past travel history. For example, it evaluates travel trends based on places visited and activities participated in. It also customizes travel plans and suggests optimal travel destinations and activities for individuals. For example, it recommends hiking and camping to a user who loves nature. It also provides travel plans based on an individual's health condition and interests based on life log data. For example, it suggests a hot spring trip to a user who wants to relax. This allows it to customize individual travel plans and suggest optimal travel destinations and activities.

[0043] The support unit can analyze an individual's sleep patterns and suggest optimal sleep environments and routines. The support unit, for example, analyzes life log data to understand an individual's sleep patterns. For example, it evaluates sleep patterns based on sleep duration and sleep quality. It also suggests optimal sleep environments, for example, recommending a quiet environment and appropriate temperature settings. It also provides a sleep routine suited to the individual based on the life log data. For example, it suggests ways to relax before bed and a regular sleep schedule. This allows the support unit to analyze an individual's sleep patterns and suggest optimal sleep environments and routines.

[0044] The virtual space generation unit can create a customized virtual space based on the hobbies and preferences of the deceased during their lifetime. The virtual space generation unit, for example, analyzes life log data to identify the hobbies and preferences of the deceased during their lifetime. For example, it designs a virtual space based on the places and activities that the deceased liked. It also creates a customized virtual space based on the hobbies and preferences of the deceased. For example, it provides a virtual space that incorporates the scenery and music that the deceased liked. It also creates a virtual space that reflects the memories of the deceased based on the life log data. For example, it recreates places that the deceased visited and things that the deceased cherished. This makes it possible to create a customized virtual space based on the hobbies and preferences of the deceased during their lifetime.

[0045] The virtual space generation unit can add interactive functions to a funeral in a virtual space that allow attendees to share memories with the deceased. The virtual space generation unit can add interactive functions to a funeral in a virtual space that allow attendees to share memories with the deceased. For example, a space can be provided for attendees to talk about their memories with the deceased. The interactive functions can also be used to build a system that allows attendees to send messages to the deceased. For example, messages can be displayed in the virtual space. An interactive album can also be created that allows attendees to share their memories with the deceased. For example, photos and videos can be uploaded and shared with other attendees. This makes it possible to add interactive functions to a funeral in a virtual space that allow attendees to share memories with the deceased.

[0046] The virtual space generation unit provides a virtual tour based on the life log of the deceased at the virtual space grave, allowing the deceased's life to be looked back on. The virtual space generation unit provides a virtual tour based on the life log of the deceased at the virtual space grave. For example, the activities and places visited by the deceased during their lifetime are reproduced in the virtual space. A system is also constructed that allows mourners to look back on the life of the deceased through the virtual tour. For example, a tour of places that were memorable to the deceased is provided. Furthermore, the life log data is used to provide a virtual tour of the highlights of the deceased's life. For example, important events and achievements are reproduced in the virtual space. This allows the deceased's life to be looked back on by providing a virtual tour based on the life log of the deceased at the virtual space grave.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The life support system can further include an energy management unit. The energy management unit can collect energy consumption data within the home and propose an optimal energy usage plan. For example, it can propose a schedule to avoid peak power consumption times. The energy management unit can also provide advice to promote the use of renewable energy. For example, it can recommend the installation of a solar power generation system or the use of energy-efficient home appliances. The energy management unit can also monitor energy consumption in real time and issue an alert if it detects an abnormal consumption pattern. This makes it possible to optimize energy consumption within the home and reduce environmental impact.

[0049] The life support system can further include a security unit. The security unit provides functions to enhance security both inside and outside the home. For example, it can use sensors on doors and windows to issue an alert if it detects suspicious activity. The security unit can also monitor surveillance camera footage in real time and sound an alarm if it detects an abnormality. The security unit can also have a function to automatically activate the security system when the user leaves the house. For example, it can automatically turn on the security system when the user leaves the house based on location information from a smartphone. The security unit can also collect crime prevention information in the neighborhood and provide it to the user. This can enhance security both inside and outside the home and provide a safe living environment.

[0050] The life support system can further include an education support unit. The education support unit provides functions for supporting the user's learning activities. For example, it can propose an optimal learning plan for an individual based on the user's learning history. The education support unit can also include a function for recommending online courses and learning materials. For example, it can propose courses and learning materials based on the user's interests and learning goals. The education support unit can also monitor the user's learning progress and provide feedback as needed. For example, it can provide additional learning resources if the user's learning progress is lagging behind. The education support unit can also evaluate the user's learning outcomes and provide rewards and recognition according to the level of achievement. This can effectively support the user's learning activities and increase their motivation to learn.

[0051] The life support system can further include a pet care unit. The pet care unit provides functions to support the health management and daily care of pets. For example, it collects pet health data and manages schedules for regular health checks and vaccinations. The pet care unit can also analyze pet behavior data and issue alerts if it detects abnormal behavior. For example, it can send a notification if it detects a loss of appetite or abnormal movements. The pet care unit can also propose diet and exercise plans for pets. For example, it can create diet and exercise schedules based on the pet's weight management and appropriate amount of exercise. The pet care unit can also suggest relaxation methods if the pet is feeling stressed based on the pet's behavior data. This provides effective support for the health management and daily care of pets.

[0052] The life support system may further include a transportation support unit. The transportation support unit provides functions for supporting the user's travel. For example, it may propose an optimal travel route based on the user's location information. The transportation support unit may also provide real-time traffic information and propose routes to avoid congestion and accidents. For example, it may propose a detour route when traffic congestion occurs. The transportation support unit may also provide public transportation schedules and propose an optimal transfer plan. For example, it may propose an optimal transfer route based on train or bus timetables. The transportation support unit may also analyze the user's travel history and optimize travel based on past travel patterns. This effectively supports the user's travel and increases travel efficiency.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The life log collection unit collects an individual's life log. For example, it collects data from a smartphone or wearable device. The life log collection unit can also collect health data, behavioral history, location information, etc. Step 2: The learning unit learns the life log collected by the life log collection unit. For example, it uses a machine learning algorithm to learn the individual's lifestyle patterns and preferences. The learning unit can also use deep learning to perform more advanced analysis. Step 3: The support unit uses the life log learned by the learning unit to support the individual's overall lifestyle. For example, it provides health management, household assistance, and schedule management. The support unit can also analyze the individual's health data and suggest optimal insurance plans. Step 4: The virtual space generator uses the life log to create a funeral or virtual grave in the metaverse. For example, it creates a virtual space based on an individual's hobbies and preferences. The virtual space generator can also automatically generate memorable videos and messages using the deceased person's life log.

[0055] (Example 2) The life support system according to an embodiment of the present invention automatically collects an individual's life log, uses a generation AI to learn, supports all aspects of life, and creates funerals in the metaverse and graves in a virtual space. This allows the life support system to utilize an individual's life log to support all aspects of life and enable continuous memories and memorial services in the metaverse.

[0056] A life support system according to an embodiment includes a life log collection unit, a learning unit, a support unit, and a virtual space generation unit. The life log collection unit collects an individual's life log. For example, it collects data from a smartphone or a wearable device. The life log collection unit can also collect health data, behavioral history, location information, and the like. The learning unit learns the life log collected by the life log collection unit. For example, it uses a machine learning algorithm to learn an individual's lifestyle patterns and preferences. The learning unit can also perform more advanced analysis using deep learning. The support unit supports the individual's overall life based on the life log learned by the learning unit. For example, it provides health management, household assistance, and schedule management. The support unit can also analyze an individual's health data and propose optimal insurance plans. The virtual space generation unit uses the life log to create funerals and virtual graves in the metaverse. For example, it creates virtual spaces based on an individual's hobbies and preferences. The virtual space generation unit can also automatically generate memorable videos and messages using the deceased's life log. As a result, the life support system according to the embodiment can utilize an individual's life log to support their overall life and enable continuous remembrance and memorial services in the metaverse. For example, the system can analyze an individual's life log and propose an optimal insurance plan. The system can also create funerals and virtual graves in the metaverse, allowing family and friends to continuously reminisce and memorialize.

[0057] The life log collection unit can collect data from a smartphone or a wearable device. For example, the life log collection unit collects location information and app usage history from a smartphone. The life log collection unit can also collect data such as heart rate and step count from a wearable device. For example, a smartwatch can be used to collect heart rate and sleep patterns. A fitness tracker can also be used to record daily activity levels. In this way, collecting data from a smartphone or a wearable device can provide a more accurate life log.

[0058] The support unit can analyze an individual's health data and propose the most suitable insurance plan. For example, the support unit analyzes an individual's health data and predicts future illness risks. For example, it calculates health risks based on dietary habits, exercise habits, sleep patterns, etc. The support unit also proposes an insurance plan suitable for the individual based on the predicted health risks. For example, it can propose the most suitable insurance plan using a method for calculating insurance premiums based on health status. The support unit can also build an early warning system for specific health risks. For example, if it detects abnormalities in blood pressure or blood sugar levels, it can send a notification recommending a doctor's consultation. In this way, the most suitable insurance plan can be proposed by analyzing an individual's health data.

[0059] The virtual space generation unit can analyze an individual's life log and create a virtual space based on the individual's hobbies and preferences. For example, the virtual space generation unit can analyze an individual's life log and create a virtual space based on the individual's hobbies and preferences. For example, the virtual space generation unit can provide a virtual space incorporating the deceased's favorite scenery and music. The virtual space generation unit can also use an emotion estimation function to monitor the user's emotional fluctuations in real time and suggest relaxation methods based on the emotions. For example, the emotion estimation function can monitor the user's emotional fluctuations in real time. For example, the emotion estimation function can analyze facial expressions and vocal tone to calculate an emotion score. The virtual space generation unit can also suggest relaxation methods appropriate for the user based on the emotional fluctuations. For example, if stress increases, the virtual space generation unit can recommend meditation or relaxation music. The virtual space generation unit can also provide specific actions to improve the user's emotional state based on the emotion estimation data. For example, the virtual space generation unit can suggest hobbies or activities that will elicit positive emotions. This allows for the creation of a virtual space based on an individual's hobbies and preferences, enabling more familiar and continuous memories and memorial services.

[0060] The support unit can suggest products that match the user's preferences based on the user's personal shopping history. For example, the support unit analyzes the user's shopping history to suggest products that match the user's preferences. For example, the support unit recommends products that the user may be interested in based on past purchase history. The support unit can also use life log data to suggest new hobbies or activities based on the user's preferences. For example, the support unit analyzes life log data to identify the user's hobbies and interests. For example, the support unit understands the user's interests based on past activity history and search history. The support unit can also suggest new hobbies and activities based on the hobbies and interests. For example, the support unit can introduce new hiking trails to a user who loves the outdoors. The support unit can also use life log data to suggest activities that the user has not tried yet but may be interested in. For example, the support unit can introduce new recipes or cooking classes to a user who loves cooking. In this way, the support unit can suggest products that match the user's preferences based on the user's personal shopping history.

[0061] The support unit can support the elderly's daily life and reduce the burden of caregiving. For example, the support unit performs health management, housework assistance, and transportation assistance to support the elderly's daily life. For example, the support unit analyzes health data and performs regular health checks and recommends exercise. Housework assistance can also provide support for shopping, cleaning, and cooking. Transportation assistance can also provide transportation arrangements and transportation support. The support unit can also compare life log data with other users and introduce communities with common interests and activities. For example, the support unit can analyze life log data to find commonalities with other users. For example, the support unit can identify users with the same hobbies and interests. The support unit can also introduce communities with common interests and activities. For example, the support unit can suggest online forums and events where users with the same hobbies gather. The support unit can also suggest community activities that the user can participate in based on the life log data. For example, the support unit can introduce local sports clubs and hobby circles. This can support the elderly's daily life and reduce the burden of caregiving.

[0062] The support unit can compensate for financial shortfalls in retirement by proposing optimal insurance plans and asset management. For example, the support unit can analyze personal health data and life log data to propose optimal insurance plans. For example, the support unit can propose optimal insurance plans using a method for calculating insurance premiums based on health status. The support unit can also propose asset management. For example, the support unit can propose asset management methods such as investment trusts, stock investments, and real estate investments. The support unit can also use an emotion estimation function to identify the time of day and environment in which the user is most relaxed and suggest relaxing activities during that time of day. For example, the support unit can use the emotion estimation function to identify the time of day in which the user is most relaxed. For example, the support unit can analyze past emotion data to find the time of day when the user is most relaxed. The support unit can also identify a relaxing environment and suggest activities suitable for that environment. For example, the support unit can recommend reading or listening to music in a quiet place. The support unit can also suggest relaxation activities during times when the user is most relaxed based on the emotion estimation data. For example, the support unit can recommend meditation or yoga for evening relaxation. In this way, the support unit can compensate for financial shortfalls in retirement by proposing optimal insurance plans and asset management.

[0063] The support unit can estimate an individual's stress level and psychological state and suggest specific actions to reduce stress. The support unit, for example, analyzes life log data to estimate an individual's stress level. For example, it calculates a stress score based on heart rate, sleep patterns, daily activity level, etc., and suggests relaxation methods based on the results. The support unit also suggests stress reduction activities suitable for the individual based on the estimated stress level. For example, it suggests specific actions such as meditation, deep breathing, and light exercise. The support unit also uses life log data to identify causes of stress and provide advice to address those causes. For example, if a person has a heavy work load, it suggests task management methods. This makes it possible to estimate an individual's stress level and psychological state and suggest specific actions to reduce stress.

[0064] The support unit can predict an individual's long-term health risks and suggest preventive measures. For example, the support unit analyzes life log data to predict an individual's health risks. For example, it calculates future disease risks based on dietary habits, exercise habits, sleep patterns, etc. The support unit can also suggest preventive measures appropriate for the individual based on the health risk prediction results. For example, it can recommend dietary improvements and exercise, and communicate the importance of regular health checks. The support unit can also build an early warning system for specific health risks. For example, if it detects abnormalities in blood pressure or blood sugar levels, it can send a notification recommending a doctor's consultation. This makes it possible to predict an individual's long-term health risks and suggest preventive measures.

[0065] The support unit can use the emotion estimation function to monitor the user's emotional fluctuations in real time and suggest relaxation methods according to the emotions. The support unit, for example, uses the emotion estimation function to monitor the user's emotional fluctuations in real time. For example, it analyzes facial expressions and vocal tone to calculate an emotion score. It also suggests relaxation methods suitable for the user according to the emotional fluctuations. For example, if stress increases, it recommends meditation or relaxation music. It also provides specific actions to improve the user's emotional state based on the emotion estimation data. For example, it suggests hobbies or activities that will bring out positive emotions. This makes it possible to monitor the user's emotional fluctuations in real time and suggest relaxation methods according to the emotions.

[0066] The support unit can suggest new hobbies and activities based on an individual's hobbies and interests. For example, the support unit analyzes life log data to identify an individual's hobbies and interests. For example, it understands a user's interests based on past activity history and search history. It also suggests new hobbies and activities based on the hobbies and interests. For example, it can introduce new hiking trails to a user who loves the outdoors. It can also use the life log data to suggest activities that the user has not tried yet but may be interested in. For example, it can introduce new recipes and cooking classes to a user who loves cooking. This makes it possible to suggest new hobbies and activities based on an individual's hobbies and interests.

[0067] The support unit can compare the life log data with other users and introduce communities with common interests and activities. The support unit, for example, analyzes the life log data to find commonalities with other users. For example, it identifies users with the same hobbies and interests. It also introduces communities with common interests and activities. For example, it suggests online forums and events where users with the same hobbies gather. It also suggests community activities that the user can participate in based on the life log data. For example, it introduces local sports clubs and hobby circles. This makes it possible to compare the life log data with other users and introduce communities with common interests and activities.

[0068] The support unit can use the emotion estimation function to identify the time of day and environment in which the user can be most relaxed and suggest relaxing activities for that time of day. The support unit, for example, uses the emotion estimation function to identify the time of day in which the user can be most relaxed. For example, it analyzes past emotion data to find time periods when the level of relaxation is high. It also identifies an environment in which relaxation is possible and suggests activities suitable for that environment. For example, it may recommend reading or listening to music in a quiet place. It also suggests relaxation activities for the time of day in which the user can be most relaxed based on the emotion estimation data. For example, it may recommend meditation or yoga for relaxing in the evening. In this way, it is possible to identify the time of day and environment in which the user can be most relaxed and suggest relaxing activities for that time of day.

[0069] The support unit can propose specific menus to optimize the nutritional balance of an individual's diet. The support unit, for example, analyzes life log data to understand the details of an individual's diet. For example, it evaluates nutritional balance based on food records and calorie intake. It also proposes specific menus to optimize nutritional balance. For example, if there is a vitamin or mineral deficiency, it proposes a menu that includes ingredients that supplement it. It also creates a meal plan based on the individual's health condition based on the life log data. For example, it proposes low-calorie menus to a user who is on a diet. This makes it possible to propose specific menus to optimize the nutritional balance of an individual's diet.

[0070] The support unit can create a customized exercise plan to improve an individual's exercise habits. The support unit, for example, analyzes life log data to understand an individual's exercise habits. For example, it evaluates exercise habits based on daily activity level and exercise frequency. It also creates a customized exercise plan to improve exercise habits. For example, it recommends walking or light jogging to a user who is not getting enough exercise. It also provides an exercise plan based on the individual's health condition and goals based on the life log data. For example, it suggests specific menus for strength training and stretching. This makes it possible to create a customized exercise plan to improve an individual's exercise habits.

[0071] The support unit can use the emotion estimation function to suggest relaxation music or meditation guides that correspond to the user's emotional state. For example, the support unit uses the emotion estimation function to analyze the user's emotional state in real time. For example, when stress levels rise, relaxation music is suggested. The support unit also provides relaxation music that corresponds to the emotional state. For example, gentle music is recommended when you want to relax, and fast-paced music is recommended when you want to concentrate. The support unit also suggests meditation guides that are suitable for the user based on the emotion estimation data. For example, meditation or deep breathing guides for stress reduction are provided. This makes it possible to suggest relaxation music or meditation guides that correspond to the user's emotional state.

[0072] The support unit can customize individual travel plans and suggest optimal travel destinations and activities. For example, the support unit analyzes life log data to understand an individual's travel preferences and past travel history. For example, it evaluates travel trends based on places visited and activities participated in. It also customizes travel plans and suggests optimal travel destinations and activities for individuals. For example, it recommends hiking and camping to a user who loves nature. It also provides travel plans based on an individual's health condition and interests based on life log data. For example, it suggests a hot spring trip to a user who wants to relax. This allows it to customize individual travel plans and suggest optimal travel destinations and activities.

[0073] The support unit can analyze an individual's sleep patterns and suggest optimal sleep environments and routines. The support unit, for example, analyzes life log data to understand an individual's sleep patterns. For example, it evaluates sleep patterns based on sleep duration and sleep quality. It also suggests optimal sleep environments, for example, recommending a quiet environment and appropriate temperature settings. It also provides a sleep routine suited to the individual based on the life log data. For example, it suggests ways to relax before bed and a regular sleep schedule. This allows the support unit to analyze an individual's sleep patterns and suggest optimal sleep environments and routines.

[0074] The support unit can use the emotion estimation function to suggest entertainment content (movies, games, etc.) that matches the user's emotional state. For example, the support unit uses the emotion estimation function to analyze the user's emotional state in real time. For example, when stress levels rise, the support unit suggests a relaxing movie. The support unit also provides entertainment content that matches the user's emotional state. For example, a comedy movie is recommended when the user wants to relax, and a puzzle game is recommended when the user wants to concentrate. The support unit also suggests entertainment content that is suitable for the user based on the emotion estimation data. For example, the support unit selects a movie or game genre depending on the user's emotional fluctuations. This makes it possible to suggest entertainment content that matches the user's emotional state.

[0075] The virtual space generation unit can create a customized virtual space based on the hobbies and preferences of the deceased during their lifetime. The virtual space generation unit, for example, analyzes life log data to identify the hobbies and preferences of the deceased during their lifetime. For example, it designs a virtual space based on the places and activities that the deceased liked. It also creates a customized virtual space based on the hobbies and preferences of the deceased. For example, it provides a virtual space that incorporates the scenery and music that the deceased liked. It also creates a virtual space that reflects the memories of the deceased based on the life log data. For example, it recreates places that the deceased visited and things that the deceased cherished. This makes it possible to create a customized virtual space based on the hobbies and preferences of the deceased during their lifetime.

[0076] The virtual space generation unit can use the emotion estimation function to monitor the emotional state of attendees in real time and suggest memorial messages and music according to their emotions. The virtual space generation unit, for example, uses the emotion estimation function to monitor the emotional state of attendees in real time. For example, it analyzes the facial expressions and voices of attendees and calculates an emotion score. It also suggests memorial messages according to the attendees' emotional state. For example, if the attendees are feeling very sad, it displays an encouraging message. It also suggests music appropriate for the attendees' emotional state based on the emotion estimation data. For example, it selects relaxing music or moving music. This makes it possible to monitor the emotional state of attendees in real time and suggest memorial messages and music according to their emotions.

[0077] The virtual space generation unit can add interactive functions to a funeral in a virtual space that allow attendees to share memories with the deceased. The virtual space generation unit can add interactive functions to a funeral in a virtual space that allow attendees to share memories with the deceased. For example, a space can be provided for attendees to talk about their memories with the deceased. The interactive functions can also be used to build a system that allows attendees to send messages to the deceased. For example, messages can be displayed in the virtual space. An interactive album can also be created that allows attendees to share their memories with the deceased. For example, photos and videos can be uploaded and shared with other attendees. This makes it possible to add interactive functions to a funeral in a virtual space that allow attendees to share memories with the deceased.

[0078] The virtual space generation unit provides a virtual tour based on the life log of the deceased at the virtual space grave, allowing the deceased's life to be looked back on. The virtual space generation unit provides a virtual tour based on the life log of the deceased at the virtual space grave. For example, the activities and places visited by the deceased during their lifetime are reproduced in the virtual space. A system is also constructed that allows mourners to look back on the life of the deceased through the virtual tour. For example, a tour of places that were memorable to the deceased is provided. Furthermore, the life log data is used to provide a virtual tour of the highlights of the deceased's life. For example, important events and achievements are reproduced in the virtual space. This allows the deceased's life to be looked back on by providing a virtual tour based on the life log of the deceased at the virtual space grave.

[0079] The virtual space generation unit uses the emotion estimation function to set up a relaxation space in the virtual space according to the emotional state of the attendee, thereby stabilizing their emotions. The virtual space generation unit, for example, uses the emotion estimation function to analyze the emotional state of the attendee in real time. For example, it calculates an emotion score based on the attendee's facial expression and voice. It also sets up a relaxation space in the virtual space according to the attendee's emotional state. For example, it provides relaxing scenery and music. It also suggests relaxation activities to help attendees stabilize their emotions based on the emotion estimation data. For example, it provides guides for meditation and deep breathing. In this way, it is possible to set up a relaxation space in the virtual space according to the attendee's emotional state, thereby stabilizing their emotions.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The life support system can further include an energy management unit. The energy management unit can collect energy consumption data within the home and propose an optimal energy usage plan. For example, it can propose a schedule to avoid peak power consumption times. The energy management unit can also provide advice to promote the use of renewable energy. For example, it can recommend the installation of a solar power generation system or the use of energy-efficient home appliances. The energy management unit can also monitor energy consumption in real time and issue an alert if it detects an abnormal consumption pattern. This makes it possible to optimize energy consumption within the home and reduce environmental impact.

[0082] The life support system can further include a security unit. The security unit provides functions to enhance security both inside and outside the home. For example, it can use sensors on doors and windows to issue an alert if it detects suspicious activity. The security unit can also monitor surveillance camera footage in real time and sound an alarm if it detects an abnormality. The security unit can also have a function to automatically activate the security system when the user leaves the house. For example, it can automatically turn on the security system when the user leaves the house based on location information from a smartphone. The security unit can also collect crime prevention information in the neighborhood and provide it to the user. This can enhance security both inside and outside the home and provide a safe living environment.

[0083] The life support system can further include an education support unit. The education support unit provides functions for supporting the user's learning activities. For example, it can propose an optimal learning plan for an individual based on the user's learning history. The education support unit can also include a function for recommending online courses and learning materials. For example, it can propose courses and learning materials based on the user's interests and learning goals. The education support unit can also monitor the user's learning progress and provide feedback as needed. For example, it can provide additional learning resources if the user's learning progress is lagging behind. The education support unit can also evaluate the user's learning outcomes and provide rewards and recognition according to the level of achievement. This can effectively support the user's learning activities and increase their motivation to learn.

[0084] The life support system can further include a pet care unit. The pet care unit provides functions to support the health management and daily care of pets. For example, it collects pet health data and manages schedules for regular health checks and vaccinations. The pet care unit can also analyze pet behavior data and issue alerts if it detects abnormal behavior. For example, it can send a notification if it detects a loss of appetite or abnormal movements. The pet care unit can also propose diet and exercise plans for pets. For example, it can create diet and exercise schedules based on the pet's weight management and appropriate amount of exercise. The pet care unit can also suggest relaxation methods if the pet is feeling stressed based on the pet's behavior data. This provides effective support for the health management and daily care of pets.

[0085] The life support system may further include a transportation support unit. The transportation support unit provides functions for supporting the user's travel. For example, it may propose an optimal travel route based on the user's location information. The transportation support unit may also provide real-time traffic information and propose routes to avoid congestion and accidents. For example, it may propose a detour route when traffic congestion occurs. The transportation support unit may also provide public transportation schedules and propose an optimal transfer plan. For example, it may propose an optimal transfer route based on train or bus timetables. The transportation support unit may also analyze the user's travel history and optimize travel based on past travel patterns. This effectively supports the user's travel and increases travel efficiency.

[0086] The lifestyle support system can also use the emotion estimation function to suggest fitness plans tailored to the user's emotional state. For example, if the user is feeling stressed, it can suggest yoga and stretching plans for relaxation. If the user is feeling energetic, it can suggest high-intensity interval training (HIIT) or running plans. Furthermore, the emotion estimation function can be used to set fitness goals tailored to the user's emotional state. For example, it can set a fitness goal aimed at stress reduction and monitor the progress of that goal. It can also suggest fitness activities to improve the user's emotional state based on the emotion estimation data. This allows the system to provide fitness plans tailored to the user's emotional state and effectively support health management.

[0087] The life support system can further use the emotion estimation function to suggest a meal plan based on the user's emotional state. For example, if the user is feeling stressed, a menu using ingredients with a relaxing effect can be suggested. Alternatively, if the user is feeling energetic, a nutritious energy-replenishing menu can be suggested. Furthermore, the emotion estimation function can be used to adjust meal timing based on the user's emotional state. For example, if the user is feeling stressed, a light meal can be recommended, and if energy is needed, a balanced meal can be suggested. Furthermore, based on the emotion estimation data, a meal plan to improve the user's emotional state can be provided. This allows for the provision of a meal plan based on the user's emotional state, effectively supporting health management.

[0088] The life support system can further use the emotion estimation function to suggest a sleeping environment that matches the user's emotional state. For example, if the user is feeling stressed, the system can suggest a sleeping environment using relaxing music or aromas. Alternatively, if the user is feeling energetic, the system can recommend a quiet environment or an appropriate temperature setting. Furthermore, the emotion estimation function can be used to adjust a sleep routine according to the user's emotional state. For example, if the user is feeling stressed, the system can suggest a pre-bedtime relaxation method. Alternatively, if the user needs energy, the system can provide a schedule to ensure adequate sleep time. Furthermore, the system can provide a sleeping environment that improves the user's emotional state based on the emotion estimation data. This allows the system to provide a sleeping environment that matches the user's emotional state and effectively support health management.

[0089] The life support system can further use the emotion estimation function to suggest entertainment content according to the user's emotional state. For example, if the user is feeling stressed, it can suggest relaxing movies or music. If the user is feeling energetic, it can suggest action movies or upbeat music. Furthermore, it can use the emotion estimation function to suggest games or activities according to the user's emotional state. For example, if the user is feeling stressed, it can recommend relaxation games or puzzle games, and if the user needs energy, it can suggest action games or sports games. It can also provide entertainment content to improve the user's emotional state based on the emotion estimation data. This makes it possible to provide entertainment content according to the user's emotional state and effectively support relaxation and energy replenishment.

[0090] The life support system can further use the emotion estimation function to suggest communication methods according to the user's emotional state. For example, if the user is feeling stressed, the system can suggest conversation topics and communication methods that will help them relax. Also, if the user is in an energetic state, the system can suggest lively discussions and activities. Furthermore, the emotion estimation function can be used to adjust the timing of communication according to the user's emotional state. For example, if the user is feeling stressed, the system can recommend communication during times when they can relax, and if they need energy, the system can suggest communication during times when they can be active. The emotion estimation data can also be used to provide communication methods to improve the user's emotional state. This makes it possible to provide communication methods according to the user's emotional state and support effective communication.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The life log collection unit collects an individual's life log. For example, it collects data from a smartphone or wearable device. The life log collection unit can also collect health data, behavioral history, location information, etc. Step 2: The learning unit learns the life log collected by the life log collection unit. For example, it uses a machine learning algorithm to learn the individual's lifestyle patterns and preferences. The learning unit can also use deep learning to perform more advanced analysis. Step 3: The support unit uses the life log learned by the learning unit to support the individual's overall lifestyle. For example, it provides health management, household assistance, and schedule management. The support unit can also analyze the individual's health data and suggest optimal insurance plans. Step 4: The virtual space generator uses the life log to create a funeral or virtual grave in the metaverse. For example, it creates a virtual space based on an individual's hobbies and preferences. The virtual space generator can also automatically generate memorable videos and messages using the deceased person's life log.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0159] 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]

[0160] 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 life log collection unit that collects life logs; a learning unit that learns the life log collected by the life log collecting unit; a support unit that supports the individual's overall life based on the life log learned by the learning unit; A virtual space generation unit that uses the life log to create a funeral or a virtual grave in the metaverse. A system characterized by:

2. The life log collection unit Collect data from smartphones or wearable devices 2. The system of claim 1.

3. The support portion is Analyze the individual's health data and propose the most suitable insurance plan 2. The system of claim 1.

4. The virtual space generation unit Analyzing the life log of the individual and creating the virtual space based on the individual's hobbies or preferences 2. The system of claim 1.

5. The support portion is Based on the shopping history of the individual, products that match the user's preferences are suggested.

2. The system of claim 1.

Images