system

Abstract

An object of a system according to an embodiment is to relieve loneliness of elderly people and support daily life.SOLUTION: A system according to an embodiment includes an avatar generator, a personality forming part, an information provider, and a health manager. The avatar generation unit generates a customizable avatar based on a user's instruction. The personality forming section forms a personality of the avatar generated by the avatar generation section on the basis of a preference of the user and a past interaction history. The information providing unit provides information necessary for the user's daily life. The health manager may monitor a health state of the user in association with the IOT device.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not provide sufficient effective means to alleviate the sense of loneliness of the elderly, and there is room for improvement.

[0005] The system according to the embodiment aims to alleviate the sense of loneliness felt by elderly people and to support their daily lives. [Means for solving the problem]

[0006] The system according to the embodiment includes an avatar generation unit, a personality development unit, an information provision unit, and a health management unit. The avatar generation unit generates a customizable avatar based on a user's instructions. The personality development unit develops the personality of the avatar generated by the avatar generation unit based on the user's preferences and past interaction history. The information provision unit provides information needed by the user in their daily life. The health management unit monitors the user's health condition in cooperation with an IoT device. [Effects of the Invention]

[0007] The system according to the embodiment can alleviate the sense of loneliness felt by elderly people and support their daily lives. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The Lifemate Communication System of the present invention is a system that fully utilizes AI and virtual avatars to alleviate the sense of loneliness felt by the elderly, combining functions such as avatar customization, personality development, and image generation according to the user's needs and preferences. This allows the Lifemate Communication System to alleviate the sense of loneliness felt by the elderly and support their daily lives.

[0029] The lifemate communication system according to the embodiment includes an avatar generation unit, a personality development unit, an information provision unit, and a health management unit. The avatar generation unit generates a customizable avatar based on a user's instructions. For example, if a user requests a "pet avatar that looks like a dog," the avatar generation unit generates an avatar with the appearance of a dog based on the user's instructions. The avatar generation unit can also analyze the user's tone of voice and speaking style and customize the avatar's voice and speaking style based on the analysis. For example, if the user speaks in a calm tone, the avatar can be configured to speak in a similar tone. The avatar generation unit can also learn the user's past preferences and behavioral history and automatically generate future avatar customization suggestions. For example, new suggestions can be made based on the style and color of avatars previously selected by the user. The personality development unit develops the avatar's personality based on the user's preferences and past interaction history. For example, if a user requests a "kind and easy-to-talk-to friend," an avatar with a kind personality can be generated based on the user's instructions. The personality development unit can also analyze the user's past conversation history and develop a personality to enable more natural conversations. For example, it can reflect the user's frequently discussed topics and phrases in the avatar. Furthermore, the personality development unit can use the emotion estimation function to dynamically change the avatar's personality according to the user's emotional state. For example, if the user is sad, the avatar can offer words of comfort. The information provision unit provides information needed in the user's daily life. For example, if the user instructs the unit to "tell me tomorrow's plans," the unit checks the user's schedule based on that instruction and notifies the user of the plans. The information provision unit can also execute a reminder function to set reminders for medication or important events and notify the user. For example, if the user wakes up at 7:00 a.m. every morning, the unit can set a reminder for that time. Furthermore, the information provision unit can use the emotion estimation function to adjust the content and timing of reminders according to the user's emotional state. For example, if the user is feeling stressed, the unit can make the reminder more gentle. The health management unit works in conjunction with IoT devices to monitor the user's health status.For example, the health management unit analyzes data acquired from IoT devices such as smartwatches and blood pressure monitors to understand the user's health status. The health management unit can also analyze the user's health data over the long term to understand health trends. For example, it analyzes fluctuations in the user's blood pressure and heart rate. Furthermore, the health management unit can use an emotion estimation function to provide health advice based on the user's emotional state. For example, if the user is feeling stressed, it can provide advice on how to relax. This allows the life mate communication system according to the embodiment to alleviate feelings of loneliness among the elderly and support their daily lives. For example, through customizable avatars, users can interact with their own special friends or pets. Furthermore, the information provision, reminder function, and health management function can make the user's daily life more convenient and secure.

[0030] The avatar generation unit can analyze the user's voice tone and speaking style and customize the avatar's voice and speaking style based on the data. The avatar generation unit, for example, analyzes the user's voice tone and speaking style and generates the avatar's voice based on the data. For example, if the user speaks in a calm tone, the avatar is set to speak in a similar tone. The avatar generation unit can also analyze the user's voice tone and speaking style and customize the avatar's speaking style based on the data. For example, if the user speaks quickly, the avatar is set to speak quickly as well. This allows the avatar's voice and speaking style to be customized based on the user's voice tone and speaking style.

[0031] The avatar generation unit can learn the user's past preferences and behavioral history and automatically generate future avatar customization suggestions. The avatar generation unit, for example, stores the user's past preferences and behavioral history in a database and makes avatar customization suggestions based on that data. For example, new suggestions are made based on the style and color of avatars previously selected by the user. The avatar generation unit can also learn the user's past preferences and behavioral history and automatically generate future avatar customization suggestions based on that data. For example, a new avatar is suggested based on the features of avatars previously selected by the user. This allows future avatar customization suggestions to be automatically generated based on the user's past preferences and behavioral history.

[0032] The avatar generation unit can generate a more realistic appearance using photos and videos taken by the user. The avatar generation unit builds a system that generates the appearance of an avatar based on, for example, photos taken by the user. For example, when a user uploads a photo of their face, the avatar's face is generated based on that photo. The avatar generation unit can also generate the appearance of an avatar based on a video taken by the user. For example, when a user uploads a video of their pet, a pet avatar is generated based on that video. This allows a more realistic appearance to be generated using photos and videos taken by the user.

[0033] The avatar generation unit generates avatars that incorporate characteristics of different cultures and regions, making it possible to accommodate international users. For example, the avatar generation unit registers characteristics of different cultures and regions in a database and builds a system that generates avatars based on that data. For example, it generates an avatar that incorporates traditional Japanese clothing or casual American clothing. The avatar generation unit can also generate avatars that incorporate characteristics of different cultures and regions, making it possible to accommodate international users. For example, it customizes the avatar based on the characteristics of the culture or region selected by the user. This makes it possible to generate avatars that incorporate characteristics of different cultures and regions, making it possible to accommodate international users.

[0034] The personality development unit can analyze the user's past conversation history and develop a personality to achieve more natural conversations. The personality development unit, for example, stores the user's past conversation history in a database and builds a system that develops an avatar's personality based on that data. For example, the avatar is made to reflect the topics and phrases that the user often talks about. The personality development unit can also analyze the user's past conversation history and develop a personality based on that data to achieve more natural conversations. For example, if the user is interested in a particular topic, an avatar that is knowledgeable about that topic can be generated. This allows the user's past conversation history to be analyzed and a personality to be developed to achieve more natural conversations.

[0035] The personality development unit can develop a personality by referring to the user's social media posts and comments. The personality development unit, for example, analyzes the user's social media posts and comments and builds a system that develops an avatar's personality based on that data. For example, the system reflects the user's frequently used words and expressions in the avatar. The personality development unit can also develop a personality by referring to the user's social media posts and comments. For example, if a user frequently posts about a particular topic, an avatar that is knowledgeable about that topic can be generated. This allows the personality development to be developed by referring to the user's social media posts and comments.

[0036] The personality development unit can develop a personality by reflecting the user's movie and music preferences. The personality development unit, for example, builds a system that develops the personality of an avatar based on data on movies and music selected by the user. For example, the personality development unit can reflect the characteristics of characters from movies the user likes in the avatar. The personality development unit can also develop a personality by reflecting the user's movie and music preferences. For example, if the user likes a particular music genre, an avatar that is knowledgeable about that genre can be generated. This allows the personality development to be developed by reflecting the user's movie and music preferences.

[0037] The personality creation unit generates avatar personalities according to different age groups and genders, making it possible to accommodate a wide range of users. For example, the personality creation unit registers the characteristics of different age groups and genders in a database and builds a system that generates avatar personalities based on that data. For example, an avatar for children can be set to have a bright and lively personality, while an avatar for adults can be set to have a calm personality. The personality creation unit can also generate avatar personalities according to different age groups and genders, making it possible to accommodate a wide range of users. For example, the avatar can be customized based on the age group and gender selected by the user. This makes it possible to generate avatar personalities according to different age groups and genders, making it possible to accommodate a wide range of users.

[0038] The information providing unit can learn the user's past behavioral patterns and provide reminders at the optimal timing. For example, the information providing unit stores the user's past behavioral patterns in a database and builds a system that provides reminders at the optimal timing based on that data. For example, if the user wakes up at 7:00 every morning, a reminder is set for that time. The information providing unit can also learn the user's past behavioral patterns and provide reminders at the optimal timing based on that data. For example, if the user takes medicine at a specific time, a reminder is set for that time. In this way, the system can learn the user's past behavioral patterns and provide reminders at the optimal timing.

[0039] The information providing unit can dynamically change the content of the reminder depending on the user's current situation and environment. For example, the information providing unit builds a system that analyzes the user's current situation and environment in real time and dynamically changes the content of the reminder based on that data. For example, if the user is out, the content of the reminder can be made simple. The information providing unit can also analyze the user's current situation and environment in real time and dynamically change the content of the reminder based on that data. For example, if the user is in a meeting, the reminder notification can be made less frequent. This makes it possible to dynamically change the content of the reminder depending on the user's current situation and environment.

[0040] The information providing unit can incorporate messages from the user's family and friends into the reminder function. The information providing unit, for example, builds a system that incorporates messages from the user's family and friends into the reminder function. For example, a message of encouragement from family members is displayed as a reminder. The information providing unit can also incorporate messages from the user's family and friends into the reminder function. For example, a birthday message from a friend is displayed as a reminder. This makes it possible to incorporate messages from the user's family and friends into the reminder function.

[0041] The information providing unit can synchronize reminders between different devices, allowing the user to receive notifications regardless of which device they use. The information providing unit, for example, builds a system for synchronizing reminders between different devices. For example, the information providing unit can enable the user to receive reminders on multiple devices, such as a smartphone, a tablet, and a smartwatch. The information providing unit can also synchronize reminders between different devices, allowing the user to receive notifications regardless of which device they use. For example, even if the user is using a smartphone, the user can receive reminders on a tablet or a smartwatch. This allows the reminders to be synchronized between different devices, allowing the user to receive notifications regardless of which device they use.

[0042] The health management unit can analyze the user's health data over the long term and grasp trends in the health condition. For example, the health management unit stores the user's health data in a database over the long term and builds a system that grasps trends in the health condition based on that data. For example, it analyzes fluctuations in the user's blood pressure and heart rate. The health management unit can also analyze the user's health data over the long term and grasp trends in the health condition based on that data. For example, it analyzes fluctuations in the user's weight and amount of exercise. This allows the user's health data to be analyzed over the long term and grasp trends in the health condition.

[0043] The health management unit can incorporate the user's diet and exercise history into the health management function and provide comprehensive health advice. The health management unit, for example, stores the user's diet and exercise history in a database and builds a system that provides comprehensive health advice based on that data. For example, health advice is provided based on the user's diet and exercise amount. The health management unit can also incorporate the user's diet and exercise history into the health management function and provide comprehensive health advice based on that data. For example, if the user prefers a particular diet, health advice based on that diet is provided. In this way, the user's diet and exercise history can be incorporated into the health management function and comprehensive health advice can be provided.

[0044] The health management unit can incorporate the user's sleep data into the health management function and provide advice to improve sleep quality. The health management unit, for example, stores the user's sleep data in a database and builds a system that provides advice to improve sleep quality based on that data. For example, it analyzes the user's sleep time and sleep depth. The health management unit can also incorporate the user's sleep data into the health management function and provide advice to improve sleep quality based on that data. For example, if the user goes to bed at a specific time, advice tailored to that time can be provided. In this way, the user's sleep data can be incorporated into the health management function and advice to improve sleep quality can be provided.

[0045] The deep connection building unit can develop a conversation based on the user's hobbies and interests and provide common topics. The deep connection building unit, for example, stores the user's hobbies and interests in a database and builds a system in which an avatar develops a conversation based on that data. For example, when a user talks about their favorite sport, the avatar also becomes knowledgeable about that topic. The deep connection building unit can also develop a conversation based on the user's hobbies and interests and provide common topics based on that data. For example, if the user has a specific hobby, topics related to that hobby can be provided. This makes it possible to develop a conversation based on the user's hobbies and interests and provide common topics.

[0046] The deep connection building unit can analyze the user's past interaction history and create a personality that will allow for more natural conversations. The deep connection building unit, for example, stores the user's past interaction history in a database and builds a system that creates an avatar's personality based on that data. For example, the avatar can reflect the topics and phrases that the user often talks about. The deep connection building unit can also analyze the user's past interaction history and create a personality based on that data that will allow for more natural conversations. For example, if the user is interested in a particular topic, an avatar that is knowledgeable about that topic can be generated. This allows the user's past interaction history to be analyzed and a personality to be created that will allow for more natural conversations.

[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 Lifemate Communication System can also customize the appearance and clothing of an avatar based on the user's hobbies and interests. For example, if a user likes a particular sport, the avatar can be outfitted in uniforms and accessories related to that sport. Also, if the user is a fan of a particular movie or anime, the avatar can be dressed in a costume of that character. Furthermore, if the user prefers clothing appropriate for the season or an event, the avatar's clothing can be changed accordingly. This makes it possible to customize an avatar based on the user's hobbies and interests, thereby providing a more unique and approachable avatar.

[0049] The Lifemate Communication System can also suggest hobbies and special skills for the avatar based on the user's past preferences and behavioral history. For example, if the user has shown an interest in cooking in the past, the avatar can suggest recipes or simulate cooking together. If the user is interested in music, the avatar can teach the user how to play an instrument or provide musical topics. Furthermore, if the user is interested in traveling, the avatar can provide information about travel destinations and help plan the trip. This allows the avatar to make more personalized suggestions based on the user's past preferences and behavioral history.

[0050] The Lifemate Communication System can also customize the background and environment of an avatar using photos and videos taken by the user. For example, by setting a photo taken at a travel destination as the background, the avatar can appear to be in that location. Also, by setting a video taken at home as the background, the avatar can appear to blend into the user's living space. Furthermore, by setting a photo from a particular event or party as the background, the avatar can appear to be participating in that event. This allows the user to customize the background and environment of the avatar to be more realistic and familiar using photos and videos taken by the user.

[0051] The Lifemate Communication System also allows users to customize the language and accent of their avatar to incorporate different cultural and regional characteristics. For example, if the user speaks Japanese, the avatar can also speak in Japanese. If the user speaks English, the avatar can also speak in English. Furthermore, if a user prefers a particular regional accent, that accent can be reflected in the avatar's speaking style. This allows users to customize the language and accent of their avatar to incorporate different cultural and regional characteristics, making it possible to accommodate international users.

[0052] The Lifemate Communication System can also customize the appearance and clothing of an avatar by taking into account the user's social media posts and comments. For example, if a user likes a particular fashion style on social media, that style can be reflected in the avatar's clothing. Also, if a user is attending a particular event or party, the avatar can be dressed in clothing appropriate for that event. Furthermore, if a user likes a particular character or idol, the avatar can be dressed in the costume of that character or idol. This makes it possible to customize the appearance and clothing of an avatar to be more unique and friendly by taking into account the user's social media posts and comments.

[0053] The Lifemate Communication System also allows the avatar to provide health advice based on the user's health data. For example, the avatar can provide advice on appropriate exercise and diet based on the user's blood pressure and heart rate data. The avatar can also provide advice on improving sleep quality based on the user's sleep data. Furthermore, the avatar can suggest healthy lifestyle habits based on the user's weight and exercise data. This allows the avatar to provide comprehensive health advice based on the user's health data and support the user's health management.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The avatar generator generates a customizable avatar based on the user's instructions. For example, if the user requests a "pet avatar that looks like a dog," the generator generates an avatar with the appearance of a dog based on that instruction. The generator can also analyze the user's tone of voice and speaking style and customize the avatar's voice and speaking style based on that analysis. Furthermore, the generator can learn the user's past preferences and behavioral history and automatically generate suggestions for future avatar customization. Step 2: The personality creation module creates the avatar's personality based on the user's preferences and past interaction history. For example, if the user requests a "kind and easy-to-talk-to friend," an avatar with a kind personality is created based on that request. The module can also analyze the user's past conversation history to create a personality that allows for more natural conversations. Furthermore, the emotion estimation function can dynamically change the avatar's personality according to the user's emotional state. Step 3: The information provider provides information needed in the user's daily life. For example, if the user instructs the device to "tell me what's on the schedule for tomorrow," the device checks the user's schedule based on that instruction and notifies them of the schedule. It can also execute a reminder function, set reminders for medication or important events, and notify the user. Furthermore, it can use an emotion estimation function to adjust the content and timing of reminders according to the user's emotional state. Step 4: The health management unit monitors the user's health status in cooperation with IoT devices. For example, it analyzes data obtained from IoT devices such as smartwatches and blood pressure monitors to understand the user's health status. It can also analyze the user's health data over the long term to understand health trends. Furthermore, it can use emotion estimation functions to provide health advice according to the user's emotional state.

[0056] (Example 2) The Lifemate Communication System of the present invention is a system that fully utilizes AI and virtual avatars to alleviate the sense of loneliness felt by the elderly, combining functions such as avatar customization, personality development, and image generation according to the user's needs and preferences. This allows the Lifemate Communication System to alleviate the sense of loneliness felt by the elderly and support their daily lives.

[0057] The lifemate communication system according to the embodiment includes an avatar generation unit, a personality development unit, an information provision unit, and a health management unit. The avatar generation unit generates a customizable avatar based on a user's instructions. For example, if a user requests a "pet avatar that looks like a dog," the avatar generation unit generates an avatar with the appearance of a dog based on the user's instructions. The avatar generation unit can also analyze the user's tone of voice and speaking style and customize the avatar's voice and speaking style based on the analysis. For example, if the user speaks in a calm tone, the avatar can be configured to speak in a similar tone. The avatar generation unit can also learn the user's past preferences and behavioral history and automatically generate future avatar customization suggestions. For example, new suggestions can be made based on the style and color of avatars previously selected by the user. The personality development unit develops the avatar's personality based on the user's preferences and past interaction history. For example, if a user requests a "kind and easy-to-talk-to friend," an avatar with a kind personality can be generated based on the user's instructions. The personality development unit can also analyze the user's past conversation history and develop a personality to enable more natural conversations. For example, it can reflect the user's frequently discussed topics and phrases in the avatar. Furthermore, the personality development unit can use the emotion estimation function to dynamically change the avatar's personality according to the user's emotional state. For example, if the user is sad, the avatar can offer words of comfort. The information provision unit provides information needed in the user's daily life. For example, if the user instructs the unit to "tell me tomorrow's plans," the unit checks the user's schedule based on that instruction and notifies the user of the plans. The information provision unit can also execute a reminder function to set reminders for medication or important events and notify the user. For example, if the user wakes up at 7:00 a.m. every morning, the unit can set a reminder for that time. Furthermore, the information provision unit can use the emotion estimation function to adjust the content and timing of reminders according to the user's emotional state. For example, if the user is feeling stressed, the unit can make the reminder more gentle. The health management unit works in conjunction with IoT devices to monitor the user's health status.For example, the health management unit analyzes data acquired from IoT devices such as smartwatches and blood pressure monitors to understand the user's health status. The health management unit can also analyze the user's health data over the long term to understand health trends. For example, it analyzes fluctuations in the user's blood pressure and heart rate. Furthermore, the health management unit can use an emotion estimation function to provide health advice based on the user's emotional state. For example, if the user is feeling stressed, it can provide advice on how to relax. This allows the life mate communication system according to the embodiment to alleviate feelings of loneliness among the elderly and support their daily lives. For example, through customizable avatars, users can interact with their own special friends or pets. Furthermore, the information provision, reminder function, and health management function can make the user's daily life more convenient and secure.

[0058] The avatar generation unit can analyze the user's voice tone and speaking style and customize the avatar's voice and speaking style based on the data. The avatar generation unit, for example, analyzes the user's voice tone and speaking style and generates the avatar's voice based on the data. For example, if the user speaks in a calm tone, the avatar is set to speak in a similar tone. The avatar generation unit can also analyze the user's voice tone and speaking style and customize the avatar's speaking style based on the data. For example, if the user speaks quickly, the avatar is set to speak quickly as well. This allows the avatar's voice and speaking style to be customized based on the user's voice tone and speaking style.

[0059] The avatar generation unit can learn the user's past preferences and behavioral history and automatically generate future avatar customization suggestions. The avatar generation unit, for example, stores the user's past preferences and behavioral history in a database and makes avatar customization suggestions based on that data. For example, new suggestions are made based on the style and color of avatars previously selected by the user. The avatar generation unit can also learn the user's past preferences and behavioral history and automatically generate future avatar customization suggestions based on that data. For example, a new avatar is suggested based on the features of avatars previously selected by the user. This allows future avatar customization suggestions to be automatically generated based on the user's past preferences and behavioral history.

[0060] The avatar generation unit can use the emotion estimation function to change the appearance and facial expression of the avatar in real time according to the emotional state of the user. For example, the avatar generation unit uses the emotion estimation function to analyze the emotional state of the user in real time and change the facial expression of the avatar based on the analysis result. For example, if the user is sad, the avatar will also have a sad expression. The avatar generation unit can also use the emotion estimation function to change the appearance of the avatar in real time according to the emotional state of the user. For example, if the user is happy, the avatar's appearance will also become brighter. This allows the appearance and facial expression of the avatar to be changed in real time according to the emotional state of the user.

[0061] The avatar generation unit can generate a more realistic appearance using photos and videos taken by the user. The avatar generation unit builds a system that generates the appearance of an avatar based on, for example, photos taken by the user. For example, when a user uploads a photo of their face, the avatar's face is generated based on that photo. The avatar generation unit can also generate the appearance of an avatar based on a video taken by the user. For example, when a user uploads a video of their pet, a pet avatar is generated based on that video. This allows a more realistic appearance to be generated using photos and videos taken by the user.

[0062] The avatar generation unit generates avatars that incorporate characteristics of different cultures and regions, making it possible to accommodate international users. For example, the avatar generation unit registers characteristics of different cultures and regions in a database and builds a system that generates avatars based on that data. For example, it generates an avatar that incorporates traditional Japanese clothing or casual American clothing. The avatar generation unit can also generate avatars that incorporate characteristics of different cultures and regions, making it possible to accommodate international users. For example, it customizes the avatar based on the characteristics of the culture or region selected by the user. This makes it possible to generate avatars that incorporate characteristics of different cultures and regions, making it possible to accommodate international users.

[0063] The avatar generation unit can use the emotion estimation function to make suggestions to reduce stress felt by the user while customizing the avatar. For example, the avatar generation unit uses the emotion estimation function to analyze in real time the stress felt by the user while customizing the avatar, and makes suggestions to reduce stress based on the results. For example, if the user is feeling stressed, simple customization options are presented. The avatar generation unit can also use the emotion estimation function to make suggestions to reduce stress felt by the user while customizing the avatar. For example, if the user is tired, a suggestion is made to encourage the user to take a break. This makes it possible to make suggestions to reduce stress felt by the user while customizing the avatar.

[0064] The personality development unit can analyze the user's past conversation history and develop a personality to achieve more natural conversations. The personality development unit, for example, stores the user's past conversation history in a database and builds a system that develops an avatar's personality based on that data. For example, the avatar is made to reflect the topics and phrases that the user often talks about. The personality development unit can also analyze the user's past conversation history and develop a personality based on that data to achieve more natural conversations. For example, if the user is interested in a particular topic, an avatar that is knowledgeable about that topic can be generated. This allows the user's past conversation history to be analyzed and a personality to be developed to achieve more natural conversations.

[0065] The personality development unit can develop a personality by referring to the user's social media posts and comments. The personality development unit, for example, analyzes the user's social media posts and comments and builds a system that develops an avatar's personality based on that data. For example, the system reflects the user's frequently used words and expressions in the avatar. The personality development unit can also develop a personality by referring to the user's social media posts and comments. For example, if a user frequently posts about a particular topic, an avatar that is knowledgeable about that topic can be generated. This allows the personality development to be developed by referring to the user's social media posts and comments.

[0066] The personality development unit can develop a personality by reflecting the user's movie and music preferences. The personality development unit, for example, builds a system that develops the personality of an avatar based on data on movies and music selected by the user. For example, the personality development unit can reflect the characteristics of characters from movies the user likes in the avatar. The personality development unit can also develop a personality by reflecting the user's movie and music preferences. For example, if the user likes a particular music genre, an avatar that is knowledgeable about that genre can be generated. This allows the personality development to be developed by reflecting the user's movie and music preferences.

[0067] The personality creation unit generates avatar personalities according to different age groups and genders, making it possible to accommodate a wide range of users. For example, the personality creation unit registers the characteristics of different age groups and genders in a database and builds a system that generates avatar personalities based on that data. For example, an avatar for children can be set to have a bright and lively personality, while an avatar for adults can be set to have a calm personality. The personality creation unit can also generate avatar personalities according to different age groups and genders, making it possible to accommodate a wide range of users. For example, the avatar can be customized based on the age group and gender selected by the user. This makes it possible to generate avatar personalities according to different age groups and genders, making it possible to accommodate a wide range of users.

[0068] The personality development unit can use the emotion estimation function to analyze the emotions felt by the user while interacting with the avatar in real time and adjust the content of the interaction. For example, the personality development unit uses the emotion estimation function to analyze the emotions felt by the user while interacting with the avatar in real time and build a system that adjusts the content of the interaction based on the results. For example, if the user is feeling anxious, the avatar can say words to reassure the user. The personality development unit can also use the emotion estimation function to analyze the emotions felt by the user while interacting with the avatar in real time and adjust the content of the interaction based on the results. For example, if the user is happy, the avatar can say words that empathize with the user. In this way, the emotions felt by the user while interacting with the avatar can be analyzed in real time and the content of the interaction can be adjusted.

[0069] The information providing unit can learn the user's past behavioral patterns and provide reminders at the optimal timing. For example, the information providing unit stores the user's past behavioral patterns in a database and builds a system that provides reminders at the optimal timing based on that data. For example, if the user wakes up at 7:00 every morning, a reminder is set for that time. The information providing unit can also learn the user's past behavioral patterns and provide reminders at the optimal timing based on that data. For example, if the user takes medicine at a specific time, a reminder is set for that time. In this way, the system can learn the user's past behavioral patterns and provide reminders at the optimal timing.

[0070] The information providing unit can dynamically change the content of the reminder depending on the user's current situation and environment. For example, the information providing unit builds a system that analyzes the user's current situation and environment in real time and dynamically changes the content of the reminder based on that data. For example, if the user is out, the content of the reminder can be made simple. The information providing unit can also analyze the user's current situation and environment in real time and dynamically change the content of the reminder based on that data. For example, if the user is in a meeting, the reminder notification can be made less frequent. This makes it possible to dynamically change the content of the reminder depending on the user's current situation and environment.

[0071] The information providing unit can use the emotion estimation function to adjust the content and timing of reminders according to the user's emotional state. For example, the information providing unit uses the emotion estimation function to analyze the user's emotional state in real time and build a system that adjusts the content and timing of reminders based on the results. For example, if the user is feeling stressed, the content of the reminder can be made gentler. The information providing unit can also use the emotion estimation function to adjust the content and timing of reminders according to the user's emotional state. For example, if the user is relaxed, the reminder notification can be made less frequent. In this way, the emotion estimation function can be used to adjust the content and timing of reminders according to the user's emotional state.

[0072] The information providing unit can incorporate messages from the user's family and friends into the reminder function. The information providing unit, for example, builds a system that incorporates messages from the user's family and friends into the reminder function. For example, a message of encouragement from family members is displayed as a reminder. The information providing unit can also incorporate messages from the user's family and friends into the reminder function. For example, a birthday message from a friend is displayed as a reminder. This makes it possible to incorporate messages from the user's family and friends into the reminder function.

[0073] The information providing unit can synchronize reminders between different devices, allowing the user to receive notifications regardless of which device they use. The information providing unit, for example, builds a system for synchronizing reminders between different devices. For example, the information providing unit can enable the user to receive reminders on multiple devices, such as a smartphone, a tablet, and a smartwatch. The information providing unit can also synchronize reminders between different devices, allowing the user to receive notifications regardless of which device they use. For example, even if the user is using a smartphone, the user can receive reminders on a tablet or a smartwatch. This allows the reminders to be synchronized between different devices, allowing the user to receive notifications regardless of which device they use.

[0074] The information providing unit can use the emotion estimation function to analyze the emotional reaction of the user when receiving a reminder and improve the content of the next reminder. For example, the information providing unit can use the emotion estimation function to analyze the emotional reaction of the user when receiving a reminder in real time and build a system that improves the content of the next reminder based on the data. For example, if the user has a positive reaction to the reminder, the same content will be used next time. The information providing unit can also use the emotion estimation function to analyze the emotional reaction of the user when receiving a reminder and improve the content of the next reminder based on the data. For example, if the user has a negative reaction, the content will be changed. In this way, the emotion estimation function can be used to analyze the emotional reaction of the user when receiving a reminder and improve the content of the next reminder.

[0075] The health management unit can analyze the user's health data over the long term and grasp trends in the health condition. For example, the health management unit stores the user's health data in a database over the long term and builds a system that grasps trends in the health condition based on that data. For example, it analyzes fluctuations in the user's blood pressure and heart rate. The health management unit can also analyze the user's health data over the long term and grasp trends in the health condition based on that data. For example, it analyzes fluctuations in the user's weight and amount of exercise. This allows the user's health data to be analyzed over the long term and grasp trends in the health condition.

[0076] The health management unit can incorporate the user's diet and exercise history into the health management function and provide comprehensive health advice. The health management unit, for example, stores the user's diet and exercise history in a database and builds a system that provides comprehensive health advice based on that data. For example, health advice is provided based on the user's diet and exercise amount. The health management unit can also incorporate the user's diet and exercise history into the health management function and provide comprehensive health advice based on that data. For example, if the user prefers a particular diet, health advice based on that diet is provided. In this way, the user's diet and exercise history can be incorporated into the health management function and comprehensive health advice can be provided.

[0077] The health management unit can use the emotion estimation function to provide health advice according to the user's emotional state. For example, the health management unit uses the emotion estimation function to analyze the user's emotional state in real time and build a system that provides health advice based on the data. For example, if the user is feeling stressed, the health management unit can provide advice to relax. The health management unit can also use the emotion estimation function to provide health advice according to the user's emotional state. For example, if the user is relaxed, the health management unit can provide advice to encourage exercise. In this way, the emotion estimation function can be used to provide health advice according to the user's emotional state.

[0078] The health management unit can incorporate the user's sleep data into the health management function and provide advice to improve sleep quality. The health management unit, for example, stores the user's sleep data in a database and builds a system that provides advice to improve sleep quality based on that data. For example, it analyzes the user's sleep time and sleep depth. The health management unit can also incorporate the user's sleep data into the health management function and provide advice to improve sleep quality based on that data. For example, if the user goes to bed at a specific time, advice tailored to that time can be provided. In this way, the user's sleep data can be incorporated into the health management function and advice to improve sleep quality can be provided.

[0079] The health management unit uses the emotion estimation function to analyze the emotional reaction of the user when receiving health advice and can improve the content of the next advice. For example, the health management unit uses the emotion estimation function to analyze the emotional reaction of the user when receiving health advice in real time and builds a system to improve the content of the next advice based on the data. For example, advice to which the user had a positive reaction is used again next time. The health management unit can also use the emotion estimation function to analyze the emotional reaction of the user when receiving health advice and improve the content of the next advice based on the data. For example, if the user had a negative reaction, the content of the advice is changed. In this way, the emotion estimation function can be used to analyze the emotional reaction of the user when receiving health advice and improve the content of the next advice.

[0080] The deep connection building unit can develop a conversation based on the user's hobbies and interests and provide common topics. The deep connection building unit, for example, stores the user's hobbies and interests in a database and builds a system in which an avatar develops a conversation based on that data. For example, when a user talks about their favorite sport, the avatar also becomes knowledgeable about that topic. The deep connection building unit can also develop a conversation based on the user's hobbies and interests and provide common topics based on that data. For example, if the user has a specific hobby, topics related to that hobby can be provided. This makes it possible to develop a conversation based on the user's hobbies and interests and provide common topics.

[0081] The deep connection building unit can use the emotion estimation function to analyze the user's emotions and mood and respond accordingly. The deep connection building unit, for example, uses the emotion estimation function to analyze the user's emotions and mood in real time and builds a system in which an avatar responds based on that data. For example, if the user is sad, the avatar will offer words of comfort. The deep connection building unit can also use the emotion estimation function to analyze the user's emotions and mood and respond based on that data. For example, if the user is happy, the avatar will offer words of empathy. In this way, the emotion estimation function can be used to analyze the user's emotions and mood and respond accordingly.

[0082] The deep connection building unit can analyze the user's past interaction history and create a personality that will allow for more natural conversations. The deep connection building unit, for example, stores the user's past interaction history in a database and builds a system that creates an avatar's personality based on that data. For example, the avatar can reflect the topics and phrases that the user often talks about. The deep connection building unit can also analyze the user's past interaction history and create a personality based on that data that will allow for more natural conversations. For example, if the user is interested in a particular topic, an avatar that is knowledgeable about that topic can be generated. This allows the user's past interaction history to be analyzed and a personality to be created that will allow for more natural conversations.

[0083] The deep connection building unit can make the avatar respond in an emotionally empathetic manner based on the emotion estimation data. The deep connection building unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and builds a system in which the avatar responds in an emotionally empathetic manner based on that data. For example, if the user is happy, the avatar also expresses joy. The deep connection building unit can also make the avatar respond in an emotionally empathetic manner based on the emotion estimation data. For example, if the user is sad, the avatar will offer words of comfort. This allows the avatar to respond in an emotionally empathetic manner based on the emotion estimation data.

[0084] The deep connection building unit can make the avatar respond in an emotionally empathetic manner based on the emotion estimation data. The deep connection building unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and builds a system in which the avatar responds in an emotionally empathetic manner based on that data. For example, if the user is happy, the avatar also expresses joy. The deep connection building unit can also make the avatar respond in an emotionally empathetic manner based on the emotion estimation data. For example, if the user is sad, the avatar will offer words of comfort. This allows the avatar to respond in an emotionally empathetic manner based on the emotion estimation data.

[0085] The deep connection building unit can make the avatar respond in an emotionally empathetic manner based on the emotion estimation data. The deep connection building unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and builds a system in which the avatar responds in an emotionally empathetic manner based on that data. For example, if the user is happy, the avatar also expresses joy. The deep connection building unit can also make the avatar respond in an emotionally empathetic manner based on the emotion estimation data. For example, if the user is sad, the avatar will offer words of comfort. This allows the avatar to respond in an emotionally empathetic manner based on the emotion estimation data.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The Lifemate Communication System can also customize the appearance and clothing of an avatar based on the user's hobbies and interests. For example, if a user likes a particular sport, the avatar can be outfitted in uniforms and accessories related to that sport. Also, if the user is a fan of a particular movie or anime, the avatar can be dressed in a costume of that character. Furthermore, if the user prefers clothing appropriate for the season or an event, the avatar's clothing can be changed accordingly. This makes it possible to customize an avatar based on the user's hobbies and interests, thereby providing a more unique and approachable avatar.

[0088] The Lifemate interaction system can also dynamically change the avatar's voice tone and speaking style based on the user's emotional state. For example, if the user is relaxed, the avatar's voice can be set to a calm and soothing tone. If the user is excited, the avatar's voice can be set to a lively tone. Furthermore, if the user is tired, the avatar's voice can be set to a gentle and encouraging tone. This allows the avatar's voice tone and speaking style to be provided according to the user's emotional state, resulting in more natural and friendly interactions.

[0089] The Lifemate Communication System can also suggest hobbies and special skills for the avatar based on the user's past preferences and behavioral history. For example, if the user has shown an interest in cooking in the past, the avatar can suggest recipes or simulate cooking together. If the user is interested in music, the avatar can teach the user how to play an instrument or provide musical topics. Furthermore, if the user is interested in traveling, the avatar can provide information about travel destinations and help plan the trip. This allows the avatar to make more personalized suggestions based on the user's past preferences and behavioral history.

[0090] The Lifemate Communication System can also change the appearance and facial expression of the avatar in real time based on the user's emotional state. For example, if the user is happy, the avatar can have a smiling, cheerful expression. If the user is sad, the avatar can have a comforting expression. Furthermore, if the user is surprised, the avatar can have a surprised expression. This allows the appearance and facial expression of the avatar to be changed in real time according to the user's emotional state, providing a more empathetic and friendly avatar.

[0091] The Lifemate Communication System can also customize the background and environment of an avatar using photos and videos taken by the user. For example, by setting a photo taken at a travel destination as the background, the avatar can appear to be in that location. Also, by setting a video taken at home as the background, the avatar can appear to blend into the user's living space. Furthermore, by setting a photo from a particular event or party as the background, the avatar can appear to be participating in that event. This allows the user to customize the background and environment of the avatar to be more realistic and familiar using photos and videos taken by the user.

[0092] The Lifemate Communication System also allows users to customize the language and accent of their avatar to incorporate different cultural and regional characteristics. For example, if the user speaks Japanese, the avatar can also speak in Japanese. If the user speaks English, the avatar can also speak in English. Furthermore, if a user prefers a particular regional accent, that accent can be reflected in the avatar's speaking style. This allows users to customize the language and accent of their avatar to incorporate different cultural and regional characteristics, making it possible to accommodate international users.

[0093] The Lifemate interaction system can also make suggestions to reduce stress felt during avatar customization based on the user's emotional state. For example, if the user feels stressed during customization, the avatar can suggest relaxing music. If the user feels tired, the avatar can suggest taking a break. Furthermore, if the user is unsure, the avatar can present simple customization options. This allows the system to make suggestions to reduce stress felt during avatar customization, providing a more comfortable customization experience.

[0094] The Lifemate Communication System can also customize the appearance and clothing of an avatar by taking into account the user's social media posts and comments. For example, if a user likes a particular fashion style on social media, that style can be reflected in the avatar's clothing. Also, if a user is attending a particular event or party, the avatar can be dressed in clothing appropriate for that event. Furthermore, if a user likes a particular character or idol, the avatar can be dressed in the costume of that character or idol. This makes it possible to customize the appearance and clothing of an avatar to be more unique and friendly by taking into account the user's social media posts and comments.

[0095] The Lifemate Communication System can also dynamically change the content of conversations with the avatar based on the user's emotional state. For example, if the user is feeling anxious, the avatar can speak reassuring words. If the user is happy, the avatar can speak empathetic words. Furthermore, if the user is angry, the avatar can respond calmly. This allows the content of conversations with the avatar to dynamically change based on the user's emotional state, resulting in more natural and friendly conversations.

[0096] The Lifemate Communication System also allows the avatar to provide health advice based on the user's health data. For example, the avatar can provide advice on appropriate exercise and diet based on the user's blood pressure and heart rate data. The avatar can also provide advice on improving sleep quality based on the user's sleep data. Furthermore, the avatar can suggest healthy lifestyle habits based on the user's weight and exercise data. This allows the avatar to provide comprehensive health advice based on the user's health data and support the user's health management.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The avatar generator generates a customizable avatar based on the user's instructions. For example, if the user requests a "pet avatar that looks like a dog," the generator generates an avatar with the appearance of a dog based on that instruction. The generator can also analyze the user's tone of voice and speaking style and customize the avatar's voice and speaking style based on that analysis. Furthermore, the generator can learn the user's past preferences and behavioral history and automatically generate suggestions for future avatar customization. Step 2: The personality creation module creates the avatar's personality based on the user's preferences and past interaction history. For example, if the user requests a "kind and easy-to-talk-to friend," an avatar with a kind personality is created based on that request. The module can also analyze the user's past conversation history to create a personality that allows for more natural conversations. Furthermore, the emotion estimation function can dynamically change the avatar's personality according to the user's emotional state. Step 3: The information provider provides information needed in the user's daily life. For example, if the user instructs the device to "tell me what's on the schedule for tomorrow," the device checks the user's schedule based on that instruction and notifies them of the schedule. It can also execute a reminder function, set reminders for medication or important events, and notify the user. Furthermore, it can use an emotion estimation function to adjust the content and timing of reminders according to the user's emotional state. Step 4: The health management unit monitors the user's health status in cooperation with IoT devices. For example, it analyzes data obtained from IoT devices such as smartwatches and blood pressure monitors to understand the user's health status. It can also analyze the user's health data over the long term to understand health trends. Furthermore, it can use emotion estimation functions to provide health advice according to the user's emotional state.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0143] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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]

[0166] 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. an avatar generator that generates a customizable avatar based on a user's instructions; a personality formation unit that forms a personality of the avatar generated by the avatar generation unit based on the user's preferences and past interaction history; an information providing unit that provides information needed in the user's daily life; A health management unit that monitors the user's health condition in cooperation with an IOT device. A system characterized by:

2. The avatar generation unit Changing the appearance and facial expression of the avatar in real time according to the emotional state of the user.

2. The system of claim 1.

3. The personality development department Dynamically changing the personality of the avatar according to the user's emotions.

2. The system of claim 1.

4. The information providing unit Adjusting the content and timing of reminders according to the user's emotional state 2. The system of claim 1.

5. The health management department Providing health advice according to the user's emotional state 2. The system of claim 1.

6. The deep connection builder Analyzing the user's emotions and moods and responding accordingly 2. The system of claim 1.

7. The information providing unit Analyzing the user's emotional response when receiving a reminder and improving the content of the next reminder 2. The system of claim 1.

8. The health management department Analyzing the user's emotional response when receiving health advice and improving the content of the next advice 2. The system of claim 1.

Images