System

Abstract

An object of the system according to the embodiment is to perform mental care by behaving like a friend on the basis of a user's background and personality.SOLUTION: A system according to an embodiment includes a friend function unit and a mental care function unit. The friend function unit learns the user's background and personality and behaves like a friend based on the user's background and personality. The mental care function unit grasps the user's mental condition by having a conversation with the user on a daily basis.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] In conventional technologies, when providing mental care to users, the background and personality of each individual user are not sufficiently taken into account, and there is room for improvement.

[0005] The system according to the embodiment aims to act like a friend and provide mental care based on the user's background and personality. [Means for solving the problem]

[0006] The system according to the embodiment includes a friend function unit and a mental care function unit. The friend function unit learns the user's background and personality and behaves like a friend based on that information. The mental care function unit grasps the user's mental state by having daily conversations with the user. [Effects of the Invention]

[0007] The system according to the embodiment can act like a friend and provide mental care based on the user's background and personality. [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 mental care system according to the embodiment of the present invention is a system that provides mental care to a user by using a generative AI that learns the user's background and personality and behaves like a friend based on that information. This allows the mental care system to provide mental care to the user.

[0029] The mental care system according to the embodiment includes a friend function unit and a mental care function unit. The friend function unit learns the user's background and personality and behaves like a friend based on that information. For example, when the user talks about their favorite hobbies or past experiences, the friend function unit remembers that information and can bring up that topic in the next conversation. The friend function unit generates a response appropriate for the user based on prompts containing information about the user's background and personality. The mental care function unit grasps the user's mental state by having daily conversations with the user. For example, if the user is feeling stressed, the mental care function unit reads signs of stress from the conversation and provides advice on how to relax. The mental care function unit suggests an appropriate solution based on prompts containing information about the user's mental state. Background refers to information such as the user's birthplace, family structure, and educational background. Personality refers to the results of a personality diagnostic test and an analysis of the user's behavioral patterns. Mental state refers to the user's stress level, emotional state, psychological health, and the like. This enables the mental care system according to the embodiment to provide mental care to the user.

[0030] The friend function unit can store the user's hobbies and past experiences and bring up those topics in the next conversation. For example, the friend function unit can store information about the user's hobbies and bring up those topics in the next conversation. For example, if the user is interested in music, the friend function unit can provide the latest music information in the next conversation. The friend function unit can also store the user's past experiences and bring up those topics in the next conversation. For example, if the user talks about their travel experience, the friend function unit can provide a topic about that travel destination in the next conversation. In this way, by storing the user's hobbies and past experiences and bringing up those topics in the next conversation, the user can feel more like a friend.

[0031] If the user is feeling stressed, the mental care function unit can read signs of stress from the conversation and provide advice for relaxation. For example, if the user is feeling stressed, the mental care function unit reads signs of stress from the conversation. For example, if the user makes a negative comment, the mental care function unit detects the comment and provides advice for relaxation. Furthermore, if the user is feeling stressed, the mental care function unit reads signs of stress from the conversation and provides advice for relaxation. For example, if the user is feeling stressed, the mental care function unit suggests deep breathing or relaxation techniques. In this way, if the user is feeling stressed, the mental care function unit can read signs of stress from the conversation and provide advice for relaxation, thereby providing mental care for the user.

[0032] The mental care function unit can analyze the user's comments in real time and provide immediate feedback if any inappropriate comments are made. The mental care function unit, for example, analyzes the user's comments in real time and detects inappropriate comments. For example, if the user makes a self-deprecating comment, the mental care function unit detects the comment and provides a positive perspective. The mental care function unit also analyzes the user's comments in real time and provides immediate feedback if any inappropriate comments are made. For example, if the user makes a negative comment, the mental care function unit makes a suggestion to change the comment into a positive one. In this way, the user's comments can be analyzed in real time and immediate feedback can be provided if any inappropriate comments are made, thereby providing mental care for the user.

[0033] The mental care function unit can support the user as a conversation partner when learning a language other than their native language. The mental care function unit, for example, supports the user as a conversation partner when learning a language other than their native language. For example, if the user is learning English, the mental care function unit provides conversation in English and gives appropriate feedback. The mental care function unit also supports the user as a conversation partner when learning a language other than their native language. For example, if the user is learning French, the mental care function unit provides conversation in French and gives appropriate feedback. This makes it possible to support the user as a conversation partner when learning a language other than their native language, thereby assisting in language learning.

[0034] The friend function unit analyzes the user's past conversation history, learns the user's reaction to a specific topic, and can avoid or delve deeper into that topic in the next conversation. The friend function unit, for example, analyzes the user's past conversation history and learns the user's emotional reaction to a specific topic. For example, a topic to which the user had a negative reaction is avoided in the next conversation. The friend function unit also analyzes the user's past conversation history, learns the user's reaction to a specific topic, and avoids or delves deeper into that topic in the next conversation. For example, a topic to which the user had a positive reaction is delved deeper into in the next conversation. In this way, by analyzing the user's past conversation history and learning the user's reaction to a specific topic, the user can feel more like a friend.

[0035] The friend function unit can suggest related events and news based on the user's hobbies and interests. For example, the friend function unit learns the user's hobbies and interests and suggests related events and news based on them. For example, if the user is interested in music, the friend function unit provides the latest concert information. The friend function unit also suggests related events and news based on the user's hobbies and interests. For example, if the user is interested in sports, the friend function unit provides the latest game information. In this way, by suggesting related events and news based on the user's hobbies and interests, the user can feel more like a friend.

[0036] The friend function unit can learn information about the user's friends and family and provide advice to support the relationships with them. The friend function unit, for example, learns information about the user's friends and family and provides advice to support the relationships with them. For example, it remembers a friend's birthday and suggests a gift. The friend function unit also learns information about the user's friends and family and provides advice to support the relationships with them. For example, it suggests ways to improve communication with family. In this way, the user's interpersonal relationships can be supported by learning information about the user's friends and family and providing advice to support the relationships with them.

[0037] The mental care function unit can track the user's mental state over the long term and predict the future mental state based on past data. The mental care function unit, for example, tracks the user's mental state over the long term and predicts the future mental state based on past data. For example, it predicts that the user will be prone to stress at a specific time and suggests measures in advance. The mental care function unit also tracks the user's mental state over the long term and predicts the future mental state based on past data. For example, it predicts that the user will be prone to stress in response to a specific event and provides advice on how to relax in advance. In this way, the user's mental state can be tracked over the long term and predicted based on past data to provide mental care for the user.

[0038] The mental care function unit can suggest relaxation techniques and meditation methods that are useful for the user's mental care. The mental care function unit, for example, analyzes the user's mental state and suggests relaxation techniques and meditation methods. For example, if the user is feeling stressed, it teaches the user how to take deep breaths or meditate. The mental care function unit also suggests relaxation techniques and meditation methods that are useful for the user's mental care. For example, it teaches the user how to do yoga to relax. In this way, the user's mental care can be provided by suggesting relaxation techniques and meditation methods that are useful for the user's mental care.

[0039] The mental care function unit can suggest music and art that are useful for the user's mental care. For example, the mental care function unit analyzes the user's mental state and suggests relaxing music and art. For example, if the user is feeling stressed, it plays relaxing music. The mental care function unit also suggests music and art that are useful for the user's mental care. For example, it suggests art therapy to help the user relax. In this way, the user's mental care can be provided by suggesting music and art that are useful for the user's mental care.

[0040] The comment monitoring function unit can analyze the user's comment history, identify patterns of self-negative comments, and provide advice to improve those patterns. The comment monitoring function unit, for example, analyzes the user's comment history and identifies patterns of self-negative comments. For example, if the user frequently makes self-negative comments, it provides advice to improve those patterns. The comment monitoring function unit also analyzes the user's comment history, identifies patterns of self-negative comments, and provides advice to improve those patterns. For example, it provides specific advice to help the user reduce self-negative comments. In this way, by analyzing the user's comment history, identifying patterns of self-negative comments, and providing advice to improve those patterns, it is possible to appropriately guide the user's comments.

[0041] The speech monitoring function unit can analyze the user's comments and provide advice to support the improvement of communication skills. The speech monitoring function unit, for example, analyzes the user's comments and provides advice to support the improvement of communication skills. For example, it provides specific advice to help the user learn more effective ways of expressing themselves. The speech monitoring function unit also analyzes the user's comments and provides advice to support the improvement of communication skills. For example, it provides specific advice to help the user communicate more smoothly. In this way, by analyzing the user's comments and providing advice to support the improvement of communication skills, the user's communication ability can be improved.

[0042] The comment monitoring function unit can analyze the user's comments and provide advice to improve interpersonal relationships at work or school. The comment monitoring function unit, for example, analyzes the user's comments and provides advice to improve interpersonal relationships at work or school. For example, it provides specific advice to help the user communicate more smoothly. The comment monitoring function unit also analyzes the user's comments and provides advice to improve interpersonal relationships at work or school. For example, it provides specific advice to help the user improve communication at work. In this way, by analyzing the user's comments and providing advice to improve interpersonal relationships at work or school, the user's interpersonal relationships can be improved.

[0043] The language learning support function unit can track the user's learning progress and provide support that focuses on areas where the user is particularly weak. The language learning support function unit, for example, tracks the user's learning progress and identifies areas where the user is particularly weak. For example, if the user is struggling with a particular grammar item, support is provided that focuses on that item. The language learning support function unit also tracks the user's learning progress and provides support that focuses on areas where the user is particularly weak. For example, if the user is conscious of their pronunciation as a weakness, support is provided that focuses on pronunciation practice. In this way, by tracking the user's learning progress and providing support that focuses on areas where the user is particularly weak, the user's language learning can be effectively supported.

[0044] The language learning support function unit can provide a customized learning plan according to the user's learning style. For example, the language learning support function unit analyzes the user's learning style and provides a customized learning plan according to that style. For example, visual learning materials are provided for a visual learner. The language learning support function unit also provides a customized learning plan according to the user's learning style. For example, audio learning materials are provided for an auditory learner. In this way, by providing a customized learning plan according to the user's learning style, the user's language learning can be effectively supported.

[0045] The language learning support functional unit can compare the user's learning progress with other learners and provide feedback to stimulate a competitive spirit. The language learning support functional unit, for example, compares the user's learning progress with other learners and provides feedback to stimulate a competitive spirit. For example, if the user is progressing faster than other learners, the language learning support functional unit notifies the user of this. The language learning support functional unit also compares the user's learning progress with other learners and provides feedback to stimulate a competitive spirit. For example, if the user is progressing slower than other learners, the language learning support functional unit provides words of encouragement. In this way, by comparing the user's learning progress with other learners and providing feedback to stimulate a competitive spirit, the language learning support functional unit can effectively support the user's language learning.

[0046] The language learning support function unit can provide a customized learning plan according to the user's learning style. For example, the language learning support function unit analyzes the user's learning style and provides a customized learning plan according to that style. For example, visual learning materials are provided for a visual learner. The language learning support function unit also provides a customized learning plan according to the user's learning style. For example, audio learning materials are provided for an auditory learner. In this way, by providing a customized learning plan according to the user's learning style, the user's language learning can be effectively supported.

[0047] The language learning support functional unit can compare the user's learning progress with other learners and provide feedback to stimulate a competitive spirit. The language learning support functional unit, for example, compares the user's learning progress with other learners and provides feedback to stimulate a competitive spirit. For example, if the user is progressing faster than other learners, the language learning support functional unit notifies the user of this. The language learning support functional unit also compares the user's learning progress with other learners and provides feedback to stimulate a competitive spirit. For example, if the user is progressing slower than other learners, the language learning support functional unit provides words of encouragement. In this way, by comparing the user's learning progress with other learners and providing feedback to stimulate a competitive spirit, the language learning support functional unit can effectively support the user's language learning.

[0048] The language learning support function unit can track the user's learning progress and provide support that focuses on areas where the user is particularly weak. The language learning support function unit, for example, tracks the user's learning progress and identifies areas where the user is particularly weak. For example, if the user is struggling with a particular grammar item, support is provided that focuses on that item. The language learning support function unit also tracks the user's learning progress and provides support that focuses on areas where the user is particularly weak. For example, if the user is conscious of their pronunciation as a weakness, support is provided that focuses on pronunciation practice. In this way, by tracking the user's learning progress and providing support that focuses on areas where the user is particularly weak, the user's language learning can be effectively supported.

[0049] The language learning support function unit can provide a customized learning plan according to the user's learning style. For example, the language learning support function unit analyzes the user's learning style and provides a customized learning plan according to that style. For example, visual learning materials are provided for a visual learner. The language learning support function unit also provides a customized learning plan according to the user's learning style. For example, audio learning materials are provided for an auditory learner. In this way, by providing a customized learning plan according to the user's learning style, the user's language learning can be effectively supported.

[0050] The language learning support functional unit can compare the user's learning progress with other learners and provide feedback to stimulate a competitive spirit. The language learning support functional unit, for example, compares the user's learning progress with other learners and provides feedback to stimulate a competitive spirit. For example, if the user is progressing faster than other learners, the language learning support functional unit notifies the user of this. The language learning support functional unit also compares the user's learning progress with other learners and provides feedback to stimulate a competitive spirit. For example, if the user is progressing slower than other learners, the language learning support functional unit provides words of encouragement. In this way, by comparing the user's learning progress with other learners and providing feedback to stimulate a competitive spirit, the language learning support functional unit can effectively support the user's language learning.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The mental care system can also be equipped with a sleep management unit that monitors the user's sleep patterns and provides appropriate sleep advice. For example, if the user stays up late, the system can explain the importance of going to bed early and getting up early, and suggest specific measures to improve the situation. If the user suffers from insomnia, the system can suggest relaxing music or meditation techniques. This can improve the user's sleep quality and support overall mental care.

[0053] The mental care system can also include a nutrition management unit that monitors the user's eating patterns and supports healthy eating habits. For example, if the user has an unbalanced diet, the system can explain the importance of a balanced diet and suggest a specific meal plan. Also, if the user is lacking in a specific nutrient, the system can suggest foods to supplement that nutrient. This can improve the user's eating habits and overall health.

[0054] The mental care system can also include an exercise management unit that monitors the user's exercise habits and provides appropriate exercise advice. For example, if the user is not getting enough exercise, the system can suggest an exercise method that is easy to incorporate into daily life. Also, if the user likes a particular exercise, the system can provide advice on how to perform that exercise effectively. This can improve the user's exercise habits and overall health.

[0055] The mental care system can also include a hobby suggestion unit that suggests new hobbies and activities based on the user's hobbies and interests. For example, if the user is interested in music, the system can suggest new musical instruments to play. If the user enjoys outdoor activities, the system can suggest new hiking trails and campsites. This can provide new enjoyment to the user's life and support their mental care.

[0056] The mental care system can also include a social support unit to strengthen the user's social connections. For example, if the user feels lonely, it can suggest local community events or online social gatherings. If the user has a particular hobby, it can introduce groups or circles related to that hobby. This strengthens the user's social connections and supports mental care.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The friend function learns about the user's background and personality and behaves like a friend based on that information. For example, when a user talks about their favorite hobbies or past experiences, the function remembers them and can bring up those topics in the next conversation. The friend function also generates responses appropriate to the user based on prompts containing information about the user's background and personality. Step 2: The mental care function unit grasps the user's mental state by talking to them on a daily basis. For example, if the user is feeling stressed, it will read signs of stress from the conversation and provide advice on how to relax. The mental care function unit also suggests appropriate ways to deal with the situation based on prompts containing information about the user's mental state.

[0059] (Example 2) The mental care system according to the embodiment of the present invention is a system that provides mental care to a user by using a generative AI that learns the user's background and personality and behaves like a friend based on that information. This allows the mental care system to provide mental care to the user.

[0060] The mental care system according to the embodiment includes a friend function unit and a mental care function unit. The friend function unit learns the user's background and personality and behaves like a friend based on that information. For example, when the user talks about their favorite hobbies or past experiences, the friend function unit remembers that information and can bring up that topic in the next conversation. The friend function unit generates a response appropriate for the user based on prompts containing information about the user's background and personality. The mental care function unit grasps the user's mental state by having daily conversations with the user. For example, if the user is feeling stressed, the mental care function unit reads signs of stress from the conversation and provides advice on how to relax. The mental care function unit suggests an appropriate solution based on prompts containing information about the user's mental state. Background refers to information such as the user's birthplace, family structure, and educational background. Personality refers to the results of a personality diagnostic test and an analysis of the user's behavioral patterns. Mental state refers to the user's stress level, emotional state, psychological health, and the like. This enables the mental care system according to the embodiment to provide mental care to the user.

[0061] The friend function unit can store the user's hobbies and past experiences and bring up those topics in the next conversation. For example, the friend function unit can store information about the user's hobbies and bring up those topics in the next conversation. For example, if the user is interested in music, the friend function unit can provide the latest music information in the next conversation. The friend function unit can also store the user's past experiences and bring up those topics in the next conversation. For example, if the user talks about their travel experience, the friend function unit can provide a topic about that travel destination in the next conversation. In this way, by storing the user's hobbies and past experiences and bringing up those topics in the next conversation, the user can feel more like a friend.

[0062] If the user is feeling stressed, the mental care function unit can read signs of stress from the conversation and provide advice for relaxation. For example, if the user is feeling stressed, the mental care function unit reads signs of stress from the conversation. For example, if the user makes a negative comment, the mental care function unit detects the comment and provides advice for relaxation. Furthermore, if the user is feeling stressed, the mental care function unit reads signs of stress from the conversation and provides advice for relaxation. For example, if the user is feeling stressed, the mental care function unit suggests deep breathing or relaxation techniques. In this way, if the user is feeling stressed, the mental care function unit can read signs of stress from the conversation and provide advice for relaxation, thereby providing mental care for the user.

[0063] The mental care function unit can analyze the user's comments in real time and provide immediate feedback if any inappropriate comments are made. The mental care function unit, for example, analyzes the user's comments in real time and detects inappropriate comments. For example, if the user makes a self-deprecating comment, the mental care function unit detects the comment and provides a positive perspective. The mental care function unit also analyzes the user's comments in real time and provides immediate feedback if any inappropriate comments are made. For example, if the user makes a negative comment, the mental care function unit makes a suggestion to change the comment into a positive one. In this way, the user's comments can be analyzed in real time and immediate feedback can be provided if any inappropriate comments are made, thereby providing mental care for the user.

[0064] The mental care function unit can support the user as a conversation partner when learning a language other than their native language. The mental care function unit, for example, supports the user as a conversation partner when learning a language other than their native language. For example, if the user is learning English, the mental care function unit provides conversation in English and gives appropriate feedback. The mental care function unit also supports the user as a conversation partner when learning a language other than their native language. For example, if the user is learning French, the mental care function unit provides conversation in French and gives appropriate feedback. This makes it possible to support the user as a conversation partner when learning a language other than their native language, thereby assisting in language learning.

[0065] The friend function unit can estimate the user's emotions in real time and select an appropriate topic according to those emotions. The friend function unit, for example, analyzes the user's facial expressions and voice tone to estimate emotions in real time. For example, if the user looks sad, the friend function unit will provide topics about the user's favorite hobbies or happy memories. The friend function unit also estimates the user's emotions in real time and selects an appropriate topic according to those emotions. For example, if the user is relaxed, the friend function unit will provide a relaxing topic. In this way, by estimating the user's emotions in real time and selecting an appropriate topic according to those emotions, the user can feel more like a friend.

[0066] The friend function unit analyzes the user's past conversation history, learns the user's reaction to a specific topic, and can avoid or delve deeper into that topic in the next conversation. The friend function unit, for example, analyzes the user's past conversation history and learns the user's emotional reaction to a specific topic. For example, a topic to which the user had a negative reaction is avoided in the next conversation. The friend function unit also analyzes the user's past conversation history, learns the user's reaction to a specific topic, and avoids or delves deeper into that topic in the next conversation. For example, a topic to which the user had a positive reaction is delved deeper into in the next conversation. In this way, by analyzing the user's past conversation history and learning the user's reaction to a specific topic, the user can feel more like a friend.

[0067] The friend function unit can use the emotion estimation function to prioritize topics about which the user has expressed particularly positive emotions. The friend function unit, for example, analyzes the user's emotions in real time and prioritizes topics about which the user has expressed positive emotions. For example, a topic about which the user has smiled may be brought up again in the next conversation. The friend function unit also uses the emotion estimation function to prioritize topics about which the user has expressed particularly positive emotions. For example, a topic that the user is enjoying may be brought up again in the next conversation. In this way, by prioritizing topics about which the user has expressed particularly positive emotions, the user can feel more like a friend.

[0068] The friend function unit can suggest related events and news based on the user's hobbies and interests. For example, the friend function unit learns the user's hobbies and interests and suggests related events and news based on them. For example, if the user is interested in music, the friend function unit provides the latest concert information. The friend function unit also suggests related events and news based on the user's hobbies and interests. For example, if the user is interested in sports, the friend function unit provides the latest game information. In this way, by suggesting related events and news based on the user's hobbies and interests, the user can feel more like a friend.

[0069] The friend function unit can learn information about the user's friends and family and provide advice to support the relationships with them. The friend function unit, for example, learns information about the user's friends and family and provides advice to support the relationships with them. For example, it remembers a friend's birthday and suggests a gift. The friend function unit also learns information about the user's friends and family and provides advice to support the relationships with them. For example, it suggests ways to improve communication with family. In this way, the user's interpersonal relationships can be supported by learning information about the user's friends and family and providing advice to support the relationships with them.

[0070] The friend function unit can use the emotion estimation function to analyze how a user feels about a specific topic and adjust the direction of the conversation based on that emotion. The friend function unit, for example, analyzes the user's emotions in real time and analyzes their emotions about a specific topic. For example, if the user shows negative emotions, the topic is avoided. The friend function unit also uses the emotion estimation function to analyze how a user feels about a specific topic and adjust the direction of the conversation based on that emotion. For example, it digs deeper into topics about which the user showed positive emotions. This allows the user to feel more like a friend by analyzing how the user feels about a specific topic and adjusting the direction of the conversation based on that emotion.

[0071] The mental care function unit can estimate the user's emotions in real time and provide mental care advice according to those emotions. The mental care function unit, for example, analyzes the user's facial expressions and voice tone to estimate the user's emotions in real time. For example, if the user is feeling stressed, it provides advice to relax. The mental care function unit also estimates the user's emotions in real time and provides mental care advice according to those emotions. For example, if the user is relaxed, it provides advice to maintain relaxation. In this way, the user's emotions can be estimated in real time and mental care advice according to those emotions can be provided, thereby providing mental care for the user.

[0072] The mental care function unit can track the user's mental state over the long term and predict the future mental state based on past data. The mental care function unit, for example, tracks the user's mental state over the long term and predicts the future mental state based on past data. For example, it predicts that the user will be prone to stress at a specific time and suggests measures in advance. The mental care function unit also tracks the user's mental state over the long term and predicts the future mental state based on past data. For example, it predicts that the user will be prone to stress in response to a specific event and provides advice on how to relax in advance. In this way, the user's mental state can be tracked over the long term and predicted based on past data to provide mental care for the user.

[0073] The mental care function unit can use the emotion estimation function to identify situations in which the user feels particularly stressed and provide advice to avoid those situations. The mental care function unit, for example, analyzes the user's emotions in real time and identifies situations in which the user feels particularly stressed. For example, if the user feels stressed about a particular topic, advice is provided to avoid that topic. The mental care function unit can also use the emotion estimation function to identify situations in which the user feels particularly stressed and provide advice to avoid those situations. For example, if the user feels stressed in a particular environment, advice is provided to avoid that environment. In this way, the mental care of the user can be provided by identifying situations in which the user feels particularly stressed and providing advice to avoid those situations.

[0074] The mental care function unit can suggest relaxation techniques and meditation methods that are useful for the user's mental care. The mental care function unit, for example, analyzes the user's mental state and suggests relaxation techniques and meditation methods. For example, if the user is feeling stressed, it teaches the user how to take deep breaths or meditate. The mental care function unit also suggests relaxation techniques and meditation methods that are useful for the user's mental care. For example, it teaches the user how to do yoga to relax. In this way, the user's mental care can be provided by suggesting relaxation techniques and meditation methods that are useful for the user's mental care.

[0075] The mental care function unit can suggest music and art that are useful for the user's mental care. For example, the mental care function unit analyzes the user's mental state and suggests relaxing music and art. For example, if the user is feeling stressed, it plays relaxing music. The mental care function unit also suggests music and art that are useful for the user's mental care. For example, it suggests art therapy to help the user relax. In this way, the user's mental care can be provided by suggesting music and art that are useful for the user's mental care.

[0076] The mental care function unit can use the emotion estimation function to identify activities that are particularly relaxing for the user and recommend those activities. The mental care function unit, for example, analyzes the user's emotions in real time and identifies activities that are particularly relaxing for the user. For example, it suggests hobbies or activities that are relaxing for the user. The mental care function unit also uses the emotion estimation function to identify activities that are particularly relaxing for the user and recommends those activities. For example, it suggests taking a walk or reading, which is relaxing for the user. In this way, by identifying activities that are particularly relaxing for the user and recommending those activities, it is possible to provide mental care for the user.

[0077] The comment monitoring function unit can analyze user comments in real time and provide immediate feedback if any inappropriate comments are made. The comment monitoring function unit, for example, analyzes user comments in real time and detects inappropriate comments. For example, if a user makes a self-deprecating comment, the comment monitoring function unit detects the comment and provides a positive perspective. The comment monitoring function unit also analyzes user comments in real time and provides immediate feedback if any inappropriate comments are made. For example, if a user makes a negative comment, the function unit makes a suggestion to change the comment into a positive one. In this way, by analyzing user comments in real time and providing immediate feedback if any inappropriate comments are made, the user's comments can be appropriately guided.

[0078] The comment monitoring function unit can analyze the user's comment history, identify patterns of self-negative comments, and provide advice to improve those patterns. The comment monitoring function unit, for example, analyzes the user's comment history and identifies patterns of self-negative comments. For example, if the user frequently makes self-negative comments, it provides advice to improve those patterns. The comment monitoring function unit also analyzes the user's comment history, identifies patterns of self-negative comments, and provides advice to improve those patterns. For example, it provides specific advice to help the user reduce self-negative comments. In this way, by analyzing the user's comment history, identifying patterns of self-negative comments, and providing advice to improve those patterns, it is possible to appropriately guide the user's comments.

[0079] The comment monitoring function unit can use the emotion estimation function to identify comments that the user makes that show particularly negative emotions and make suggestions to convert those comments into positive ones. The comment monitoring function unit, for example, analyzes the user's comments in real time to identify comments that show negative emotions. For example, if the user makes a self-deprecating comment, the unit makes suggestions to convert that comment into a positive one. The comment monitoring function unit also uses the emotion estimation function to identify comments that the user makes that show particularly negative emotions and makes suggestions to convert that comment into a positive one. For example, if the user makes a negative comment, the unit makes specific suggestions to convert that comment into a positive one. In this way, by identifying comments that the user makes that show particularly negative emotions and making suggestions to convert that comment into a positive one, the user's comments can be appropriately guided.

[0080] The speech monitoring function unit can analyze the user's comments and provide advice to support the improvement of communication skills. The speech monitoring function unit, for example, analyzes the user's comments and provides advice to support the improvement of communication skills. For example, it provides specific advice to help the user learn more effective ways of expressing themselves. The speech monitoring function unit also analyzes the user's comments and provides advice to support the improvement of communication skills. For example, it provides specific advice to help the user communicate more smoothly. In this way, by analyzing the user's comments and providing advice to support the improvement of communication skills, the user's communication ability can be improved.

[0081] The comment monitoring function unit can analyze the user's comments and provide advice to improve interpersonal relationships at work or school. The comment monitoring function unit, for example, analyzes the user's comments and provides advice to improve interpersonal relationships at work or school. For example, it provides specific advice to help the user communicate more smoothly. The comment monitoring function unit also analyzes the user's comments and provides advice to improve interpersonal relationships at work or school. For example, it provides specific advice to help the user improve communication at work. In this way, by analyzing the user's comments and providing advice to improve interpersonal relationships at work or school, the user's interpersonal relationships can be improved.

[0082] The comment monitoring function unit can use the emotion estimation function to highlight comments that the user makes that show particularly positive emotions and provide advice to increase the number of such comments. The comment monitoring function unit, for example, analyzes the user's comments in real time and highlights comments that show positive emotions. For example, if the user makes a positive comment, the comment is praised. The comment monitoring function unit also uses the emotion estimation function to highlight comments that the user makes that show particularly positive emotions and provide advice to increase the number of such comments. For example, if the user makes a positive comment, specific advice is given to increase the number of such comments. In this way, by highlighting comments that the user makes that show particularly positive emotions and providing advice to increase the number of such comments, the user's comments can be appropriately guided.

[0083] The language learning support function unit can estimate the user's emotions in real time and provide appropriate feedback according to those emotions. The language learning support function unit, for example, analyzes the user's facial expressions and voice tone to estimate the user's emotions in real time. For example, if the user feels stressed while learning, the language learning support function unit provides words of encouragement. The language learning support function unit can also estimate the user's emotions in real time and provide appropriate feedback according to those emotions. For example, if the user is relaxed, the language learning support function unit provides feedback to help the user maintain relaxation. In this way, the user's language learning can be supported by estimating the user's emotions in real time and providing appropriate feedback according to those emotions.

[0084] The language learning support function unit can track the user's learning progress and provide support that focuses on areas where the user is particularly weak. The language learning support function unit, for example, tracks the user's learning progress and identifies areas where the user is particularly weak. For example, if the user is struggling with a particular grammar item, support is provided that focuses on that item. The language learning support function unit also tracks the user's learning progress and provides support that focuses on areas where the user is particularly weak. For example, if the user is conscious of their pronunciation as a weakness, support is provided that focuses on pronunciation practice. In this way, by tracking the user's learning progress and providing support that focuses on areas where the user is particularly weak, the user's language learning can be effectively supported.

[0085] The language learning support function unit can use the emotion estimation function to identify a learning method that the user finds particularly motivating and recommend that method. The language learning support function unit, for example, analyzes the user's emotions in real time to identify a learning method that the user finds particularly motivating. For example, it recommends a learning method that the user enjoys. The language learning support function unit also uses the emotion estimation function to identify a learning method that the user finds particularly motivating and recommends that method. For example, if the user enjoys game-style learning, it recommends that method. In this way, by identifying a learning method that the user finds particularly motivating and recommending that method, it is possible to effectively support the user's language learning.

[0086] The language learning support function unit can provide a customized learning plan according to the user's learning style. For example, the language learning support function unit analyzes the user's learning style and provides a customized learning plan according to that style. For example, visual learning materials are provided for a visual learner. The language learning support function unit also provides a customized learning plan according to the user's learning style. For example, audio learning materials are provided for an auditory learner. In this way, by providing a customized learning plan according to the user's learning style, the user's language learning can be effectively supported.

[0087] The language learning support functional unit can compare the user's learning progress with other learners and provide feedback to stimulate a competitive spirit. The language learning support functional unit, for example, compares the user's learning progress with other learners and provides feedback to stimulate a competitive spirit. For example, if the user is progressing faster than other learners, the language learning support functional unit notifies the user of this. The language learning support functional unit also compares the user's learning progress with other learners and provides feedback to stimulate a competitive spirit. For example, if the user is progressing slower than other learners, the language learning support functional unit provides words of encouragement. In this way, by comparing the user's learning progress with other learners and providing feedback to stimulate a competitive spirit, the language learning support functional unit can effectively support the user's language learning.

[0088] The language learning support function unit can use the emotion estimation function to identify learning activities that the user particularly enjoys and recommend those activities. For example, the language learning support function unit analyzes the user's emotions in real time to identify learning activities that the user particularly enjoys. For example, it recommends learning games that the user enjoys. The language learning support function unit also uses the emotion estimation function to identify learning activities that the user particularly enjoys and recommends those activities. For example, it recommends learning apps that the user enjoys. In this way, by identifying learning activities that the user particularly enjoys and recommending those activities, it is possible to effectively support the user's language learning.

[0089] The language learning support function unit can provide a customized learning plan according to the user's learning style. For example, the language learning support function unit analyzes the user's learning style and provides a customized learning plan according to that style. For example, visual learning materials are provided for a visual learner. The language learning support function unit also provides a customized learning plan according to the user's learning style. For example, audio learning materials are provided for an auditory learner. In this way, by providing a customized learning plan according to the user's learning style, the user's language learning can be effectively supported.

[0090] The language learning support functional unit can compare the user's learning progress with other learners and provide feedback to stimulate a competitive spirit. The language learning support functional unit, for example, compares the user's learning progress with other learners and provides feedback to stimulate a competitive spirit. For example, if the user is progressing faster than other learners, the language learning support functional unit notifies the user of this. The language learning support functional unit also compares the user's learning progress with other learners and provides feedback to stimulate a competitive spirit. For example, if the user is progressing slower than other learners, the language learning support functional unit provides words of encouragement. In this way, by comparing the user's learning progress with other learners and providing feedback to stimulate a competitive spirit, the language learning support functional unit can effectively support the user's language learning.

[0091] The language learning support function unit can use the emotion estimation function to identify learning activities that the user particularly enjoys and recommend those activities. For example, the language learning support function unit analyzes the user's emotions in real time to identify learning activities that the user particularly enjoys. For example, it recommends learning games that the user enjoys. The language learning support function unit also uses the emotion estimation function to identify learning activities that the user particularly enjoys and recommends those activities. For example, it recommends learning apps that the user enjoys. In this way, by identifying learning activities that the user particularly enjoys and recommending those activities, it is possible to effectively support the user's language learning.

[0092] The language learning support function unit can estimate the user's emotions in real time and provide appropriate feedback according to those emotions. The language learning support function unit, for example, analyzes the user's facial expressions and voice tone to estimate the user's emotions in real time. For example, if the user feels stressed while learning, it provides words of encouragement. The language learning support function unit can also estimate the user's emotions in real time and provide appropriate feedback according to those emotions. For example, if the user is relaxed, it provides feedback to help the user maintain relaxation. In this way, by estimating the user's emotions in real time and providing appropriate feedback according to those emotions, it is possible to effectively support the user's language learning.

[0093] The language learning support function unit can track the user's learning progress and provide support that focuses on areas where the user is particularly weak. The language learning support function unit, for example, tracks the user's learning progress and identifies areas where the user is particularly weak. For example, if the user is struggling with a particular grammar item, support is provided that focuses on that item. The language learning support function unit also tracks the user's learning progress and provides support that focuses on areas where the user is particularly weak. For example, if the user is conscious of their pronunciation as a weakness, support is provided that focuses on pronunciation practice. In this way, by tracking the user's learning progress and providing support that focuses on areas where the user is particularly weak, the user's language learning can be effectively supported.

[0094] The language learning support function unit can use the emotion estimation function to identify a learning method that the user finds particularly motivating and recommend that method. The language learning support function unit, for example, analyzes the user's emotions in real time to identify a learning method that the user finds particularly motivating. For example, it recommends a learning method that the user enjoys. The language learning support function unit also uses the emotion estimation function to identify a learning method that the user finds particularly motivating and recommends that method. For example, if the user enjoys game-style learning, it recommends that method. In this way, by identifying a learning method that the user finds particularly motivating and recommending that method, it is possible to effectively support the user's language learning.

[0095] The language learning support function unit can provide a customized learning plan according to the user's learning style. For example, the language learning support function unit analyzes the user's learning style and provides a customized learning plan according to that style. For example, visual learning materials are provided for a visual learner. The language learning support function unit also provides a customized learning plan according to the user's learning style. For example, audio learning materials are provided for an auditory learner. In this way, by providing a customized learning plan according to the user's learning style, the user's language learning can be effectively supported.

[0096] The language learning support functional unit can compare the user's learning progress with other learners and provide feedback to stimulate a competitive spirit. The language learning support functional unit, for example, compares the user's learning progress with other learners and provides feedback to stimulate a competitive spirit. For example, if the user is progressing faster than other learners, the language learning support functional unit notifies the user of this. The language learning support functional unit also compares the user's learning progress with other learners and provides feedback to stimulate a competitive spirit. For example, if the user is progressing slower than other learners, the language learning support functional unit provides words of encouragement. In this way, by comparing the user's learning progress with other learners and providing feedback to stimulate a competitive spirit, the language learning support functional unit can effectively support the user's language learning.

[0097] The language learning support function unit can use the emotion estimation function to identify learning activities that the user particularly enjoys and recommend those activities. For example, the language learning support function unit analyzes the user's emotions in real time to identify learning activities that the user particularly enjoys. For example, it recommends learning games that the user enjoys. The language learning support function unit also uses the emotion estimation function to identify learning activities that the user particularly enjoys and recommends those activities. For example, it recommends learning apps that the user enjoys. In this way, by identifying learning activities that the user particularly enjoys and recommending those activities, it is possible to effectively support the user's language learning.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The mental care system can also be equipped with a sleep management unit that monitors the user's sleep patterns and provides appropriate sleep advice. For example, if the user stays up late, the system can explain the importance of going to bed early and getting up early, and suggest specific measures to improve the situation. If the user suffers from insomnia, the system can suggest relaxing music or meditation techniques. This can improve the user's sleep quality and support overall mental care.

[0100] The mental care system can also include a nutrition management unit that monitors the user's eating patterns and supports healthy eating habits. For example, if the user has an unbalanced diet, the system can explain the importance of a balanced diet and suggest a specific meal plan. Also, if the user is lacking in a specific nutrient, the system can suggest foods to supplement that nutrient. This can improve the user's eating habits and overall health.

[0101] The mental care system can also include an exercise management unit that monitors the user's exercise habits and provides appropriate exercise advice. For example, if the user is not getting enough exercise, the system can suggest an exercise method that is easy to incorporate into daily life. Also, if the user likes a particular exercise, the system can provide advice on how to perform that exercise effectively. This can improve the user's exercise habits and overall health.

[0102] The mental care system can also include a hobby suggestion unit that suggests new hobbies and activities based on the user's hobbies and interests. For example, if the user is interested in music, the system can suggest new musical instruments to play. If the user enjoys outdoor activities, the system can suggest new hiking trails and campsites. This can provide new enjoyment to the user's life and support their mental care.

[0103] The mental care system can also include a social support unit to strengthen the user's social connections. For example, if the user feels lonely, it can suggest local community events or online social gatherings. If the user has a particular hobby, it can introduce groups or circles related to that hobby. This strengthens the user's social connections and supports mental care.

[0104] The mental care system can also include a music providing unit that estimates the user's emotions in real time and provides music that corresponds to those emotions. For example, if the user is feeling stressed, relaxing music can be played. Alternatively, if the user wants to cheer up, energetic music can be provided. In this way, mental care can be supported by providing music that corresponds to the user's emotions.

[0105] The mental care system can also include an art therapy unit that estimates the user's emotions in real time and provides art therapy tailored to those emotions. For example, if the user is sad, the system can suggest colorful art to brighten the mood. Or, if the user wants to relax, the system can suggest painting a calming landscape. This allows the system to support mental care by providing art therapy tailored to the user's emotions.

[0106] The mental care system can also include a meditation guide unit that estimates the user's emotions in real time and provides a meditation guide that corresponds to those emotions. For example, if the user is feeling stressed, the system can suggest a meditation method to help them relax. If the user wants to improve their concentration, the system can suggest a meditation method to improve their concentration. In this way, the system can support mental care by providing a meditation guide that corresponds to the user's emotions.

[0107] The mental care system can also be equipped with a relaxation technology unit that estimates the user's emotions in real time and provides relaxation techniques according to those emotions. For example, if the user is tense, it can suggest deep breathing or stretching techniques. If the user wants to relax, it can suggest aromatherapy or massage techniques. In this way, it can support mental care by providing relaxation techniques according to the user's emotions.

[0108] The mental care system can also include a reading list unit that estimates the user's emotions in real time and provides a reading list that corresponds to those emotions. For example, if the user wants to relax, the system can suggest relaxing novels or essays. If the user wants to cheer up, the system can suggest business books or self-help books to boost motivation. In this way, the system can support mental care by providing a reading list that corresponds to the user's emotions.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The friend function learns about the user's background and personality and behaves like a friend based on that information. For example, when a user talks about their favorite hobbies or past experiences, the function remembers them and can bring up those topics in the next conversation. The friend function also generates responses appropriate to the user based on prompts containing information about the user's background and personality. Step 2: The mental care function unit grasps the user's mental state by talking to them on a daily basis. For example, if the user is feeling stressed, it will read signs of stress from the conversation and provide advice on how to relax. The mental care function unit also suggests appropriate ways to deal with the situation based on prompts containing information about the user's mental state.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 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.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The 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.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A friend function unit that learns the user's background and personality and behaves like a friend based on that information; a mental care function unit that grasps the mental state of the user by talking with the user on a daily basis; A system characterized by:

2. The mental care function unit includes: If the user is feeling stressed, the system reads signs of this from the conversation and provides advice on how to relax.

2. The system of claim 1.

3. The mental care function unit includes: To support the user as a conversation partner when learning a language other than the user's native language 2. The system of claim 1.

4. The friend function unit Learn about the user's friends and family and provide advice to support their relationships 2. The system of claim 1.

5. The mental care function unit includes: The user's emotions are estimated in real time, and mental health advice is provided according to those emotions.

2. The system of claim 1.

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