System

Abstract

An object of the system according to the embodiment is to allow lonely people to find a conversation partner suitable for the people and reduce a sense of loneliness.SOLUTION: A system includes an avatar generation unit, a conversation control unit, a personal information analysis unit, and a growth control unit. The avatar generation unit generates a dialogue-enabled avatar on the basis of input information of a user. The conversation control unit grasps hobbies and interests through a conversation with the user using the avatar generated by the avatar generation unit. The personal information analysis unit analyzes personal information of a user. A growth control part grows the avatar on the basis of the conversation history by the conversation control part and the lapse of time.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have made it difficult for lonely people to find a suitable conversation partner, and there has been a lack of effective means to alleviate loneliness.

[0005] The system according to the embodiment aims to help lonely people find suitable conversation partners and reduce their feelings of loneliness. [Means for solving the problem]

[0006] The system according to the embodiment includes an avatar generation unit, a conversation control unit, a personal information analysis unit, and a growth control unit. The avatar generation unit generates an avatar that can interact with the user based on information input by the user. The conversation control unit uses the avatar generated by the avatar generation unit to understand the user's hobbies and interests through conversation with the user. The personal information analysis unit analyzes the user's personal information. The growth control unit grows the avatar based on the conversation history and the passage of time obtained by the conversation control unit. [Effects of the Invention]

[0007] The system according to the embodiment allows lonely people to find suitable conversation partners and reduce their feelings of loneliness. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The interactive avatar system according to an embodiment of the present invention generates an interactive avatar based on user input information, and the generation AI grasps the user's hobbies and interests through conversation, allowing the avatar to grow over time and based on the conversation history. This allows the interactive avatar system to generate and grow an interactive avatar based on the user's hobbies and interests.

[0029] An interactive avatar system according to an embodiment includes an avatar generation unit, a conversation control unit, a personal information analysis unit, and a growth control unit. The avatar generation unit generates an interactive avatar based on user input information. For example, the avatar generation unit sets the avatar's appearance and personality based on text information entered by the user. The avatar generation unit can also customize the avatar's appearance by analyzing image information provided by the user. The avatar generation unit can also set the avatar's voice based on the user's voice information. The conversation control unit understands the user's hobbies and interests through conversation with the user using the avatar generated by the avatar generation unit. For example, the conversation control unit analyzes the user's utterances using a generation AI to extract information about the user's hobbies and interests. The conversation control unit can also save a conversation history with the user and update the conversation content over time. The conversation control unit can also generate appropriate responses based on the user's utterances. The personal information analysis unit analyzes the user's personal information. For example, the personal information analysis unit analyzes the user's text information to understand the user's hobbies and interests. The personal information analysis unit can also analyze image information provided by the user to identify the user's preferences and interests. Furthermore, the personal information analysis unit can analyze the user's voice information to grasp the user's emotional state. The growth control unit grows the avatar based on the conversation history generated by the conversation control unit and the passage of time. For example, the growth control unit analyzes the conversation history with the user to update the avatar's personality and hobbies. The growth control unit can also expand the avatar's knowledge in accordance with the user's new hobbies and interests. Furthermore, the growth control unit can adjust the avatar's response in accordance with the user's emotional state. In this way, the interactive avatar system according to the embodiment can generate and grow an interactive avatar based on the user's hobbies and interests.

[0030] The avatar generation unit can customize the avatar's appearance and voice by analyzing the user's past preference data. For example, the avatar generation unit collects data on the appearance and voice of avatars previously selected by the user, and the generation AI analyzes that data to generate a new avatar. For example, the avatar generation unit may reflect the user's preferred hairstyle, clothing, and tone of voice. The avatar generation unit also collects preference data from the user's social media and other online activities, and the generation AI customizes the avatar based on that data. For example, it may reflect information about the videos the user frequently watches and the accounts the user follows. The avatar generation unit also analyzes surveys and feedback the user has completed in the past, and the generation AI adjusts the avatar's appearance and voice based on the results. For example, it may reflect the user's preferred colors and styles. This allows the avatar's appearance and voice to be customized based on the user's past preference data.

[0031] The conversation control unit can automatically adjust the schedule to match the user's lifestyle and start a conversation at the appropriate time. The conversation control unit, for example, works in conjunction with the user's calendar or schedule app, and the generation AI analyzes the user's lifestyle and adjusts the avatar's conversation timing. For example, the conversation can start when the user returns home from work. The conversation control unit also analyzes the user's activity patterns based on data collected from the user's smart device and sets the optimal conversation timing. For example, the conversation can start when the user is relaxing. The conversation control unit also analyzes the user's past conversation history, and the generation AI learns the user's preferred conversation timing and adjusts the avatar's schedule accordingly. For example, if the user prefers to talk in the evening, the conversation can start at that time. This allows the conversation timing to be adjusted to match the user's lifestyle.

[0032] The growth control unit functions as a pet or a member of the user's family and can provide information about pet care and family events. For example, the growth control unit uses an avatar to support the user's pet's health management and provide the pet's meal and exercise schedule. For example, it reminds the pet of meal times and walk times. The growth control unit also uses an avatar to record the user's family's birthdays and anniversaries and provide event reminders and gift suggestions. For example, it provides gift ideas for family members' birthdays. The growth control unit also uses an avatar to monitor the health status of the user's family and provide health advice and reminders. For example, it manages the family's health checkup schedule. In this way, the growth control unit can function as a pet or a member of the user's family and provide related information.

[0033] The growth control unit can monitor the user's health status and provide health advice and reminders. For example, the growth control unit allows the avatar to connect with the user's fitness tracker or smartwatch, collect health data, and provide advice. For example, it can detect lack of exercise and suggest exercise. The growth control unit can also allow the avatar to analyze the user's food records and provide nutritional balance advice and meal reminders. For example, it can suggest eating more vegetables if the user's vegetable intake is low. The growth control unit can also allow the avatar to monitor the user's sleep data and provide advice and reminders to improve sleep quality. For example, it can remind the user to go to bed and encourage a regular lifestyle. This allows the user's health status to be monitored and health advice and reminders to be provided.

[0034] The conversation control unit can analyze the content of the conversation, evaluate the user's stress level and psychological state, and provide appropriate advice. The conversation control unit, for example, analyzes the content of the user's conversation and develops an algorithm to evaluate the stress level. For example, it may determine that stress is high if there are a lot of negative words. The conversation control unit also builds a system that evaluates the psychological state based on the content of the user's conversation and provides appropriate advice. For example, it may suggest ways to relax if the user is tired. The conversation control unit also analyzes the user's conversation history and develops a system that tracks changes in stress level and psychological state. For example, it may issue an alert if stress has increased compared to past conversation content. This makes it possible to evaluate the user's stress level and psychological state and provide appropriate advice.

[0035] The conversation control unit uses a generation AI to predict a user's preferences and interests based on the user's past conversation history, and can suggest the next conversation topic. For example, the conversation control unit analyzes a user's past conversation history and develops an algorithm for the generation AI to predict the user's preferences and interests. For example, the generation AI suggests the next conversation topic based on topics the user often talks about. The conversation control unit also builds a system in which the generation AI automatically generates the next conversation topic based on the user's conversation history. For example, it suggests topics that the user has recently become interested in. The conversation control unit also analyzes the user's past conversation data, and the generation AI learns the user's interests and suggests the next conversation topic. For example, it could suggest topics about the user's favorite movies or music. This makes it possible to suggest the next conversation topic based on the user's past conversation history.

[0036] The conversation control unit allows the generative AI to introduce relevant experts and communities when a user wants to have an in-depth conversation about a specific topic. For example, the conversation control unit develops a system where the generative AI introduces relevant experts when a user wants to have an in-depth conversation about a specific topic. For example, if a user wants to talk about health, it will introduce medical experts. The conversation control unit also builds a system where the generative AI introduces relevant online communities when a user wants to have an in-depth conversation about a specific topic. For example, if a user wants to talk about hobbies, it will introduce relevant forums. The conversation control unit also develops an algorithm where the generative AI automatically recommends relevant experts and communities based on the user's interests and concerns. For example, if a user wants to talk about technology, it will introduce technical experts. This allows it to introduce relevant experts and communities when a user wants to have an in-depth conversation about a specific topic.

[0037] The personal information analysis unit analyzes photos and text entered by the user, allowing the generation AI to generate conversations based on the user's past experiences and memories. For example, the personal information analysis unit develops a system in which the generation AI analyzes photos entered by the user and generates conversations based on those photos. For example, talking about travel memories based on travel photos. The personal information analysis unit also analyzes text entered by the user, building a system in which the generation AI generates conversations based on that text. For example, talking about past events based on diary text. The personal information analysis unit also analyzes photos and text entered by the user, allowing the generation AI to generate conversations that draw out the user's past experiences and memories based on that data. For example, talking about family memories based on family photos. This makes it possible to generate conversations based on past experiences and memories based on the photos and text entered by the user.

[0038] The personal information analysis unit links the user's social media accounts, allowing the generation AI to update conversations based on the latest information and trends. For example, the personal information analysis unit develops a system in which the user's social media accounts are linked and the generation AI generates conversations based on the latest posts and trends. For example, the user talks about articles they recently shared. The personal information analysis unit also analyzes the user's social media following list, building a system in which the generation AI updates conversations based on that information. For example, the user talks about the latest posts from influencers they follow. The personal information analysis unit also analyzes the user's social media activity, allowing the generation AI to generate conversations that incorporate the latest trends and topics based on that data. For example, the user talks about events they attended. This allows the user's social media accounts to be linked and conversations to be updated based on the latest information and trends.

[0039] The personal information analysis unit enables the generation AI to propose personalized travel plans and events based on information entered by the user. For example, the personal information analysis unit analyzes travel photos and text entered by the user, and develops a system in which the generation AI proposes personalized travel plans based on that information. For example, the next travel destination is suggested based on the user's favorite tourist spots. The personal information analysis unit also analyzes event information entered by the user, and builds a system in which the generation AI proposes personalized events based on that information. For example, the next event is suggested based on events in which the user is interested. The personal information analysis unit also analyzes data on the user's past travels and events, and the generation AI proposes personalized travel plans and events based on that data. For example, a new travel destination is suggested based on places the user has visited in the past. This makes it possible to propose personalized travel plans and events based on the information entered by the user.

[0040] The personal information analysis unit enables the generation AI to introduce online communities related to the user's hobbies and interests based on the user's personal information. For example, the personal information analysis unit analyzes information on hobbies and interests entered by the user, and develops a system in which the generation AI introduces relevant online communities based on that information. For example, the unit introduces forums related to the user's favorite sports. The personal information analysis unit also builds a system in which the generation AI recommends online communities that match the user's interests based on the user's personal information. For example, the unit introduces groups related to topics that interest the user. The personal information analysis unit also analyzes data on the user's hobbies and interests, and develops an algorithm in which the generation AI automatically recommends relevant online communities based on that data. For example, the unit introduces communities related to the user's favorite music genre. This makes it possible to introduce online communities related to the user's hobbies and interests based on the user's personal information.

[0041] The growth control unit can learn new skills and knowledge in response to the user's growth and changes, thereby broadening the scope of conversation. For example, the growth control unit will develop a system that learns about the user's new hobbies and interests, and the generation AI will use that information to update the avatar's skills and knowledge. For example, if the user starts playing a new instrument, the generation AI will learn about that instrument. The growth control unit will also build a system that allows the generation AI to update the avatar's conversation content in response to the user's growth and changes. For example, if the user starts a new job, the generation AI will learn about that job. The growth control unit will also analyze the user's past conversation history, and develop an algorithm that allows the generation AI to continuously update the avatar's skills and knowledge based on that data. For example, if the user starts learning a new language, the generation AI will learn about that language. This allows the user to learn new skills and knowledge in response to their growth and changes, thereby broadening the scope of conversation.

[0042] The growth control unit can record the user's life events and provide special messages and surprises. For example, the growth control unit develops a system in which the generation AI records the user's birthdays and anniversaries and generates special messages based on that information. For example, it sends a congratulatory message on the user's birthday. The growth control unit also builds a system in which the generation AI records the user's life events and provides surprises based on that information. For example, it suggests a special gift for the user's anniversary. The growth control unit also analyzes the user's past event data and develops an algorithm in which the generation AI provides special messages and surprises based on that data. For example, it sends a special message on the user's wedding anniversary. In this way, the user's life events can be recorded and special messages and surprises can be provided.

[0043] The growth control unit can support the user's relationships with friends and family and provide advice to deepen those relationships. For example, the growth control unit develops a system that analyzes the user's relationships with friends and family and allows the generation AI to provide advice to deepen those relationships based on that information. For example, it suggests ways to communicate with friends. The growth control unit also builds a system that analyzes the user's conversation history with friends and family and allows the generation AI to provide advice to deepen those relationships based on that data. For example, it suggests common hobbies with family. The growth control unit also develops an algorithm that allows the generation AI to provide appropriate advice to deepen those relationships based on relationship data with the user's friends and family. For example, it suggests regular contact with friends. This makes it possible to support the user's relationships with friends and family and provide advice to deepen those relationships.

[0044] The growth control unit can support the user's activities at work or school and provide advice on work or studies. For example, the growth control unit analyzes the user's activity data at work or school and develops a system in which a generation AI provides advice on work or studies based on that information. For example, it can suggest efficient ways to work. The growth control unit can also analyze the user's conversation history at work or school and build a system in which a generation AI provides advice on work or studies based on that data. For example, it can suggest study tips. The growth control unit can also develop an algorithm in which a generation AI provides appropriate advice on work or studies based on the user's activity data at work or school. For example, it can suggest methods for managing project progress. This can support the user's activities at work or school and provide advice on work or studies.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The avatar generation unit generates a conversational avatar based on user input information. For example, the avatar generation unit sets the avatar's appearance and personality based on text information entered by the user. The avatar generation unit can also customize the avatar's appearance by analyzing image information provided by the user. The avatar generation unit can also set the avatar's voice based on the user's voice information. The conversation control unit uses the avatar generated by the avatar generation unit to understand the user's hobbies and interests through conversation with the user. For example, the conversation control unit uses a generation AI to analyze the user's utterances and extract information about the user's hobbies and interests. The conversation control unit can also save a conversation history with the user and update the conversation content over time. The conversation control unit can also generate appropriate responses based on the user's utterances. The personal information analysis unit analyzes the user's personal information. For example, the personal information analysis unit analyzes the user's text information to understand the user's hobbies and interests. The personal information analysis unit can also analyze the user's image information provided by the user to identify the user's preferences and interests. Furthermore, the personal information analysis unit can analyze the user's voice information and grasp the user's emotional state. The growth control unit grows the avatar based on the conversation history generated by the conversation control unit and the passage of time. For example, the growth control unit analyzes the conversation history with the user and updates the avatar's personality and hobbies. The growth control unit can also expand the avatar's knowledge in accordance with the user's new hobbies and interests. Furthermore, the growth control unit can adjust the avatar's response in accordance with the user's emotional state. In this way, the interactive avatar system according to the embodiment can generate and grow an interactive avatar based on the user's hobbies and interests.

[0047] The avatar generation unit can customize the avatar's appearance and voice by analyzing the user's past preference data. For example, it collects data on the appearance and voice of avatars previously selected by the user, and the generation AI analyzes that data to generate a new avatar. For example, it can reflect the user's preferred hairstyle, clothing, and tone of voice. The avatar generation unit also collects preference data from the user's social media and other online activities, and the generation AI customizes the avatar based on that data. For example, it can reflect information about the videos the user frequently watches and the accounts the user follows. The avatar generation unit also analyzes surveys and feedback the user has completed in the past, and the generation AI can adjust the avatar's appearance and voice based on the results. For example, it can reflect the user's preferred colors and styles. This allows the avatar's appearance and voice to be customized based on the user's past preference data.

[0048] The conversation control unit can automatically adjust the schedule to match the user's lifestyle and start a conversation at the appropriate time. For example, the conversation control unit works in conjunction with the user's calendar or schedule app, and the generation AI analyzes the user's lifestyle and adjusts the avatar's conversation timing. For example, the conversation can start when the user returns home from work. The conversation control unit also analyzes the user's activity patterns based on data collected from the user's smart device and sets the optimal conversation timing. For example, the conversation can start when the user is relaxing. The conversation control unit also analyzes the user's past conversation history, and the generation AI learns the user's preferred conversation timing and adjusts the avatar's schedule accordingly. For example, if the user prefers to talk in the evening, the conversation can start at that time. This allows the conversation timing to be adjusted to match the user's lifestyle.

[0049] The growth control unit functions as a pet or a member of the user's family and can provide information about pet care and family events. For example, the growth control unit allows an avatar to support the user's pet's health management and provide the pet's meal and exercise schedule. For example, it may remind the pet of meal times and walk times. The growth control unit may also allow an avatar to record the user's family's birthdays and anniversaries and provide event reminders and gift suggestions. For example, it may provide gift ideas for family members' birthdays. The growth control unit may also allow an avatar to monitor the health status of the user's family and provide health advice and reminders. For example, it may manage the family's health checkup schedule. In this way, the growth control unit can function as a pet or a member of the user's family and provide related information.

[0050] The growth control unit can monitor the user's health status and provide health advice and reminders. For example, the growth control unit allows the avatar to connect with the user's fitness tracker or smartwatch, collect health data, and provide advice. For example, it can detect lack of exercise and suggest exercise. The growth control unit can also allow the avatar to analyze the user's food records and provide nutritional balance advice and meal reminders. For example, it can suggest eating more vegetables if the user's vegetable intake is low. The growth control unit can also allow the avatar to monitor the user's sleep data and provide advice and reminders to improve sleep quality. For example, it can remind the user to go to bed and encourage a regular lifestyle. This allows the user's health status to be monitored and health advice and reminders to be provided.

[0051] The conversation control unit can analyze the content of the conversation, evaluate the user's stress level and psychological state, and provide appropriate advice. For example, the conversation control unit analyzes the content of the user's conversation and develops an algorithm to evaluate the stress level. For example, it may determine that stress is high if there are a lot of negative words. The conversation control unit also builds a system that evaluates the psychological state based on the content of the user's conversation and provides appropriate advice. For example, it may suggest ways to relax if the user is tired. The conversation control unit also analyzes the user's conversation history and develops a system that tracks changes in stress level and psychological state. For example, it issues an alert if stress has increased compared to past conversation content. This makes it possible to evaluate the user's stress level and psychological state and provide appropriate advice.

[0052] The conversation control system uses a generation AI to predict a user's preferences and interests based on the user's past conversation history, and can suggest the next conversation topic. For example, the conversation control system analyzes a user's past conversation history, and the generation AI develops an algorithm to predict the user's preferences and interests. For example, the generation AI suggests the next conversation topic based on topics the user often talks about. The conversation control system also builds a system in which the generation AI automatically generates the next conversation topic based on the user's conversation history. For example, the generation AI suggests topics that the user has recently become interested in. The conversation control system also analyzes the user's past conversation data, and the generation AI learns the user's interests and suggests the next conversation topic. For example, the generation AI could suggest topics about the user's favorite movies or music. This makes it possible to suggest the next conversation topic based on the user's past conversation history.

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

[0054] Step 1: The avatar generator generates a conversational avatar based on user input. For example, the avatar's appearance and personality can be set based on text information entered by the user, and the avatar's appearance can be customized by analyzing provided image information. The avatar's voice can also be set based on the user's voice information. Step 2: The conversation control unit uses the avatar generated by the avatar generation unit to understand the user's hobbies and interests through conversation with the user. For example, it can use the generation AI to analyze the user's comments and extract information about the user's hobbies and interests. It can also save the conversation history with the user and update the conversation content over time. It can also generate appropriate responses based on the user's comments. Step 3: The personal information analysis unit analyzes the user's personal information. For example, it analyzes text information provided by the user to identify the user's hobbies and interests. It can also analyze image information provided to identify the user's preferences and interests. It can also analyze the user's voice information to identify the user's emotional state. Step 4: The growth control unit grows the avatar based on the conversation history and the passage of time generated by the conversation control unit. For example, it analyzes the conversation history with the user and updates the avatar's personality and hobbies. It can also expand the avatar's knowledge based on new hobbies and interests. It can also adjust the avatar's response based on the user's emotional state.

[0055] (Example 2) The interactive avatar system according to an embodiment of the present invention generates an interactive avatar based on user input information, and the generation AI grasps the user's hobbies and interests through conversation, allowing the avatar to grow over time and based on the conversation history. This allows the interactive avatar system to generate and grow an interactive avatar based on the user's hobbies and interests.

[0056] An interactive avatar system according to an embodiment includes an avatar generation unit, a conversation control unit, a personal information analysis unit, and a growth control unit. The avatar generation unit generates an interactive avatar based on user input information. For example, the avatar generation unit sets the avatar's appearance and personality based on text information entered by the user. The avatar generation unit can also customize the avatar's appearance by analyzing image information provided by the user. The avatar generation unit can also set the avatar's voice based on the user's voice information. The conversation control unit understands the user's hobbies and interests through conversation with the user using the avatar generated by the avatar generation unit. For example, the conversation control unit analyzes the user's utterances using a generation AI to extract information about the user's hobbies and interests. The conversation control unit can also save a conversation history with the user and update the conversation content over time. The conversation control unit can also generate appropriate responses based on the user's utterances. The personal information analysis unit analyzes the user's personal information. For example, the personal information analysis unit analyzes the user's text information to understand the user's hobbies and interests. The personal information analysis unit can also analyze image information provided by the user to identify the user's preferences and interests. Furthermore, the personal information analysis unit can analyze the user's voice information to grasp the user's emotional state. The growth control unit grows the avatar based on the conversation history generated by the conversation control unit and the passage of time. For example, the growth control unit analyzes the conversation history with the user to update the avatar's personality and hobbies. The growth control unit can also expand the avatar's knowledge in accordance with the user's new hobbies and interests. Furthermore, the growth control unit can adjust the avatar's response in accordance with the user's emotional state. In this way, the interactive avatar system according to the embodiment can generate and grow an interactive avatar based on the user's hobbies and interests.

[0057] The avatar generation unit can customize the avatar's appearance and voice by analyzing the user's past preference data. For example, the avatar generation unit collects data on the appearance and voice of avatars previously selected by the user, and the generation AI analyzes that data to generate a new avatar. For example, the avatar generation unit may reflect the user's preferred hairstyle, clothing, and tone of voice. The avatar generation unit also collects preference data from the user's social media and other online activities, and the generation AI customizes the avatar based on that data. For example, it may reflect information about the videos the user frequently watches and the accounts the user follows. The avatar generation unit also analyzes surveys and feedback the user has completed in the past, and the generation AI adjusts the avatar's appearance and voice based on the results. For example, it may reflect the user's preferred colors and styles. This allows the avatar's appearance and voice to be customized based on the user's past preference data.

[0058] The conversation control unit can automatically adjust the schedule to match the user's lifestyle and start a conversation at the appropriate time. The conversation control unit, for example, works in conjunction with the user's calendar or schedule app, and the generation AI analyzes the user's lifestyle and adjusts the avatar's conversation timing. For example, the conversation can start when the user returns home from work. The conversation control unit also analyzes the user's activity patterns based on data collected from the user's smart device and sets the optimal conversation timing. For example, the conversation can start when the user is relaxing. The conversation control unit also analyzes the user's past conversation history, and the generation AI learns the user's preferred conversation timing and adjusts the avatar's schedule accordingly. For example, if the user prefers to talk in the evening, the conversation can start at that time. This allows the conversation timing to be adjusted to match the user's lifestyle.

[0059] The conversation control unit can use the emotion estimation function to change the avatar's facial expression and tone in real time according to the user's emotional state. For example, the conversation control unit analyzes the user's facial expression using a camera, and the generation AI changes the avatar's facial expression in real time according to the user's emotional state. For example, when the user smiles, the avatar also smiles. The conversation control unit also analyzes the user's voice tone, and the generation AI adjusts the avatar's voice tone according to the user's emotional state. For example, when the user is depressed, the avatar's voice becomes gentler. The conversation control unit also analyzes the user's text messages, and the generation AI changes the avatar's facial expression and tone according to the user's emotional state. For example, when the user is excited, the avatar also shows an excited expression. This allows the avatar's facial expression and tone to change in real time according to the user's emotional state.

[0060] The growth control unit functions as a pet or a member of the user's family and can provide information about pet care and family events. For example, the growth control unit uses an avatar to support the user's pet's health management and provide the pet's meal and exercise schedule. For example, it reminds the pet of meal times and walk times. The growth control unit also uses an avatar to record the user's family's birthdays and anniversaries and provide event reminders and gift suggestions. For example, it provides gift ideas for family members' birthdays. The growth control unit also uses an avatar to monitor the health status of the user's family and provide health advice and reminders. For example, it manages the family's health checkup schedule. In this way, the growth control unit can function as a pet or a member of the user's family and provide related information.

[0061] The growth control unit can monitor the user's health status and provide health advice and reminders. For example, the growth control unit allows the avatar to connect with the user's fitness tracker or smartwatch, collect health data, and provide advice. For example, it can detect lack of exercise and suggest exercise. The growth control unit can also allow the avatar to analyze the user's food records and provide nutritional balance advice and meal reminders. For example, it can suggest eating more vegetables if the user's vegetable intake is low. The growth control unit can also allow the avatar to monitor the user's sleep data and provide advice and reminders to improve sleep quality. For example, it can remind the user to go to bed and encourage a regular lifestyle. This allows the user's health status to be monitored and health advice and reminders to be provided.

[0062] The growth control unit can use the emotion estimation function to recommend music and movies based on the user's emotions. For example, the growth control unit analyzes the user's emotional state, and the generation AI recommends music that matches that emotion. For example, when the user wants to relax, it suggests relaxing music. The growth control unit also analyzes the user's emotional state, and the generation AI recommends movies that match that emotion. For example, when the user wants to cheer up, it suggests uplifting movies. The growth control unit also analyzes the user's emotional state, and the generation AI recommends entertainment content that matches that emotion. For example, when the user is sad, it suggests content that will brighten up their mood. This makes it possible to recommend music and movies based on the user's emotions.

[0063] The conversation control unit can analyze the content of the conversation, evaluate the user's stress level and psychological state, and provide appropriate advice. The conversation control unit, for example, analyzes the content of the user's conversation and develops an algorithm to evaluate the stress level. For example, it may determine that stress is high if there are a lot of negative words. The conversation control unit also builds a system that evaluates the psychological state based on the content of the user's conversation and provides appropriate advice. For example, it may suggest ways to relax if the user is tired. The conversation control unit also analyzes the user's conversation history and develops a system that tracks changes in stress level and psychological state. For example, it may issue an alert if stress has increased compared to past conversation content. This makes it possible to evaluate the user's stress level and psychological state and provide appropriate advice.

[0064] The conversation control unit uses a generation AI to predict a user's preferences and interests based on the user's past conversation history, and can suggest the next conversation topic. For example, the conversation control unit analyzes a user's past conversation history and develops an algorithm for the generation AI to predict the user's preferences and interests. For example, the generation AI suggests the next conversation topic based on topics the user often talks about. The conversation control unit also builds a system in which the generation AI automatically generates the next conversation topic based on the user's conversation history. For example, it suggests topics that the user has recently become interested in. The conversation control unit also analyzes the user's past conversation data, and the generation AI learns the user's interests and suggests the next conversation topic. For example, it could suggest topics about the user's favorite movies or music. This makes it possible to suggest the next conversation topic based on the user's past conversation history.

[0065] The conversation control unit can use the emotion estimation function to generate words of encouragement or comfort according to the user's emotions. For example, the conversation control unit develops a system in which a generation AI analyzes the user's emotional state and generates words of encouragement according to that emotion. For example, sending an encouraging message when the user is feeling down. The conversation control unit also builds a system in which a generation AI analyzes the user's emotional state and generates words of comfort according to that emotion. For example, sending a comforting message when the user is sad. The conversation control unit also develops an algorithm in which a generation AI generates appropriate words according to the user's emotions based on the user's emotional data. For example, providing words of reassurance when the user is anxious. This makes it possible to generate words of encouragement or comfort according to the user's emotions.

[0066] The conversation control unit can analyze the tone and speed of the user's voice during a conversation and provide feedback on the user's emotional state in real time. For example, the conversation control unit will develop a system in which the conversation control unit analyzes the tone and speed of the user's voice and the generation AI uses that data to provide feedback on the user's emotional state in real time. For example, when the user is excited, that emotion will be provided as feedback. The conversation control unit will also analyze the tone and speed of the user's voice and build an algorithm in which the generation AI uses that data to evaluate the user's emotional state. For example, when the user is calm, that emotion will be provided as feedback. The conversation control unit will also develop a system in which the conversation control unit analyzes the user's voice data in real time and the generation AI uses the results to provide feedback on the user's emotional state. For example, when the user is nervous, that emotion will be provided as feedback. This makes it possible to provide feedback on the user's emotional state in real time based on the tone and speed of the user's voice.

[0067] The conversation control unit allows the generative AI to introduce relevant experts and communities when a user wants to have an in-depth conversation about a specific topic. For example, the conversation control unit develops a system where the generative AI introduces relevant experts when a user wants to have an in-depth conversation about a specific topic. For example, if a user wants to talk about health, it will introduce medical experts. The conversation control unit also builds a system where the generative AI introduces relevant online communities when a user wants to have an in-depth conversation about a specific topic. For example, if a user wants to talk about hobbies, it will introduce relevant forums. The conversation control unit also develops an algorithm where the generative AI automatically recommends relevant experts and communities based on the user's interests and concerns. For example, if a user wants to talk about technology, it will introduce technical experts. This allows it to introduce relevant experts and communities when a user wants to have an in-depth conversation about a specific topic.

[0068] The conversation control unit can use the emotion estimation function to provide relaxation techniques and meditation guides based on the user's emotions. For example, the conversation control unit develops a system in which a generation AI analyzes the user's emotional state and provides relaxation techniques based on those emotions. For example, it suggests a relaxation method when the user is feeling stressed. The conversation control unit also builds a system in which a generation AI analyzes the user's emotional state and provides meditation guides based on those emotions. For example, it suggests a meditation method when the user is feeling anxious. The conversation control unit also develops an algorithm in which a generation AI provides relaxation techniques and meditation guides based on the user's emotions based on the user's emotional data. For example, it provides meditation guides when the user wants to relax. This makes it possible to provide relaxation techniques and meditation guides based on the user's emotions.

[0069] The personal information analysis unit analyzes photos and text entered by the user, allowing the generation AI to generate conversations based on the user's past experiences and memories. For example, the personal information analysis unit develops a system in which the generation AI analyzes photos entered by the user and generates conversations based on those photos. For example, talking about travel memories based on travel photos. The personal information analysis unit also analyzes text entered by the user, building a system in which the generation AI generates conversations based on that text. For example, talking about past events based on diary text. The personal information analysis unit also analyzes photos and text entered by the user, allowing the generation AI to generate conversations that draw out the user's past experiences and memories based on that data. For example, talking about family memories based on family photos. This makes it possible to generate conversations based on past experiences and memories based on the photos and text entered by the user.

[0070] The personal information analysis unit links the user's social media accounts, allowing the generation AI to update conversations based on the latest information and trends. For example, the personal information analysis unit develops a system in which the user's social media accounts are linked and the generation AI generates conversations based on the latest posts and trends. For example, the user talks about articles they recently shared. The personal information analysis unit also analyzes the user's social media following list, building a system in which the generation AI updates conversations based on that information. For example, the user talks about the latest posts from influencers they follow. The personal information analysis unit also analyzes the user's social media activity, allowing the generation AI to generate conversations that incorporate the latest trends and topics based on that data. For example, the user talks about events they attended. This allows the user's social media accounts to be linked and conversations to be updated based on the latest information and trends.

[0071] The personal information analysis unit uses the emotion estimation function to analyze the emotional response to information entered by the user and can provide feedback based on the emotion. The personal information analysis unit, for example, analyzes the emotional response to photos or text entered by the user, and develops a system in which the generation AI provides feedback based on that emotion. For example, if the user enters a happy photo, positive feedback is provided. The personal information analysis unit also analyzes the emotional response to information entered by the user in real time, and builds a system in which the generation AI provides feedback according to the emotion based on the results. For example, if the user enters sad text, words of comfort are provided. The personal information analysis unit also develops an algorithm in which the generation AI provides appropriate feedback according to the emotion based on the user's emotional response data. For example, if the user enters information that makes them excited, words of empathy are provided. This makes it possible to analyze the emotional response to information entered by the user and provide feedback based on the emotion.

[0072] The personal information analysis unit enables the generation AI to propose personalized travel plans and events based on information entered by the user. For example, the personal information analysis unit analyzes travel photos and text entered by the user, and develops a system in which the generation AI proposes personalized travel plans based on that information. For example, the next travel destination is suggested based on the user's favorite tourist spots. The personal information analysis unit also analyzes event information entered by the user, and builds a system in which the generation AI proposes personalized events based on that information. For example, the next event is suggested based on events in which the user is interested. The personal information analysis unit also analyzes data on the user's past travels and events, and the generation AI proposes personalized travel plans and events based on that data. For example, a new travel destination is suggested based on places the user has visited in the past. This makes it possible to propose personalized travel plans and events based on the information entered by the user.

[0073] The personal information analysis unit enables the generation AI to introduce online communities related to the user's hobbies and interests based on the user's personal information. For example, the personal information analysis unit analyzes information on hobbies and interests entered by the user, and develops a system in which the generation AI introduces relevant online communities based on that information. For example, the unit introduces forums related to the user's favorite sports. The personal information analysis unit also builds a system in which the generation AI recommends online communities that match the user's interests based on the user's personal information. For example, the unit introduces groups related to topics that interest the user. The personal information analysis unit also analyzes data on the user's hobbies and interests, and develops an algorithm in which the generation AI automatically recommends relevant online communities based on that data. For example, the unit introduces communities related to the user's favorite music genre. This makes it possible to introduce online communities related to the user's hobbies and interests based on the user's personal information.

[0074] The personal information analysis unit can use the emotion estimation function to generate emotional support and encouraging messages based on information entered by the user. For example, the personal information analysis unit analyzes the emotional response to information entered by the user, and develops a system in which the generation AI generates messages of support and encouragement based on those emotions. For example, if a user enters a sad photo, a comforting message is sent. The personal information analysis unit also analyzes the emotional response to information entered by the user in real time, and builds a system in which the generation AI provides messages of support and encouragement that correspond to the user's emotions based on the results. For example, if a user enters happy text, a message of empathy is sent. The personal information analysis unit also develops an algorithm that uses the user's emotional response data to enable the generation AI to generate appropriate messages of support and encouragement that correspond to the user's emotions. For example, if a user enters anxious information, a reassuring message is sent. This makes it possible to generate emotional support and encouraging messages based on the information entered by the user.

[0075] The growth control unit can learn new skills and knowledge in response to the user's growth and changes, thereby broadening the scope of conversation. For example, the growth control unit will develop a system that learns about the user's new hobbies and interests, and the generation AI will use that information to update the avatar's skills and knowledge. For example, if the user starts playing a new instrument, the generation AI will learn about that instrument. The growth control unit will also build a system that allows the generation AI to update the avatar's conversation content in response to the user's growth and changes. For example, if the user starts a new job, the generation AI will learn about that job. The growth control unit will also analyze the user's past conversation history, and develop an algorithm that allows the generation AI to continuously update the avatar's skills and knowledge based on that data. For example, if the user starts learning a new language, the generation AI will learn about that language. This allows the user to learn new skills and knowledge in response to their growth and changes, thereby broadening the scope of conversation.

[0076] The growth control unit can record the user's life events and provide special messages and surprises. For example, the growth control unit develops a system in which the generation AI records the user's birthdays and anniversaries and generates special messages based on that information. For example, it sends a congratulatory message on the user's birthday. The growth control unit also builds a system in which the generation AI records the user's life events and provides surprises based on that information. For example, it suggests a special gift for the user's anniversary. The growth control unit also analyzes the user's past event data and develops an algorithm in which the generation AI provides special messages and surprises based on that data. For example, it sends a special message on the user's wedding anniversary. In this way, the user's life events can be recorded and special messages and surprises can be provided.

[0077] The growth control unit uses the emotion estimation function to allow the avatar to grow in response to changes in the user's emotions, enabling more appropriate responses. The growth control unit, for example, develops a system that analyzes the user's emotional state and allows the generation AI to adjust the avatar's response according to those emotions. For example, when the user is sad, the avatar provides words of comfort. The growth control unit also builds a system that analyzes changes in the user's emotions in real time and allows the generation AI to update the avatar's response based on the results. For example, when the user is excited, the avatar provides words of empathy. The growth control unit also develops an algorithm that allows the generation AI to respond appropriately according to the user's emotions, based on the user's emotional data. For example, when the user is anxious, the avatar provides words of reassurance. This allows the avatar to grow in response to changes in the user's emotions, enabling more appropriate responses.

[0078] The growth control unit can support the user's relationships with friends and family and provide advice to deepen those relationships. For example, the growth control unit develops a system that analyzes the user's relationships with friends and family and allows the generation AI to provide advice to deepen those relationships based on that information. For example, it suggests ways to communicate with friends. The growth control unit also builds a system that analyzes the user's conversation history with friends and family and allows the generation AI to provide advice to deepen those relationships based on that data. For example, it suggests common hobbies with family. The growth control unit also develops an algorithm that allows the generation AI to provide appropriate advice to deepen those relationships based on relationship data with the user's friends and family. For example, it suggests regular contact with friends. This makes it possible to support the user's relationships with friends and family and provide advice to deepen those relationships.

[0079] The growth control unit can support the user's activities at work or school and provide advice on work or studies. For example, the growth control unit analyzes the user's activity data at work or school and develops a system in which a generation AI provides advice on work or studies based on that information. For example, it can suggest efficient ways to work. The growth control unit can also analyze the user's conversation history at work or school and build a system in which a generation AI provides advice on work or studies based on that data. For example, it can suggest study tips. The growth control unit can also develop an algorithm in which a generation AI provides appropriate advice on work or studies based on the user's activity data at work or school. For example, it can suggest methods for managing project progress. This can support the user's activities at work or school and provide advice on work or studies.

[0080] The growth control unit can use the emotion estimation function to support long-term goal setting and achievement based on the user's emotions. The growth control unit, for example, analyzes the user's emotional state and develops a system in which the generation AI supports long-term goal setting based on those emotions. For example, it suggests goals that motivate the user. The growth control unit also builds a system in which the generation AI analyzes changes in the user's emotions in real time and supports long-term goal achievement based on the results. For example, it suggests steps for the user to move toward their goal. The growth control unit also develops an algorithm based on the user's emotional data that enables the generation AI to support appropriate goal setting and achievement based on the user's emotions. For example, it sets goals that give the user a sense of accomplishment and supports the user in achieving those goals. This makes it possible to support long-term goal setting and achievement based on the user's emotions.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The avatar generation unit generates a conversational avatar based on user input information. For example, the avatar generation unit sets the avatar's appearance and personality based on text information entered by the user. The avatar generation unit can also customize the avatar's appearance by analyzing image information provided by the user. The avatar generation unit can also set the avatar's voice based on the user's voice information. The conversation control unit uses the avatar generated by the avatar generation unit to understand the user's hobbies and interests through conversation with the user. For example, the conversation control unit uses a generation AI to analyze the user's utterances and extract information about the user's hobbies and interests. The conversation control unit can also save a conversation history with the user and update the conversation content over time. The conversation control unit can also generate appropriate responses based on the user's utterances. The personal information analysis unit analyzes the user's personal information. For example, the personal information analysis unit analyzes the user's text information to understand the user's hobbies and interests. The personal information analysis unit can also analyze the user's image information provided by the user to identify the user's preferences and interests. Furthermore, the personal information analysis unit can analyze the user's voice information and grasp the user's emotional state. The growth control unit grows the avatar based on the conversation history generated by the conversation control unit and the passage of time. For example, the growth control unit analyzes the conversation history with the user and updates the avatar's personality and hobbies. The growth control unit can also expand the avatar's knowledge in accordance with the user's new hobbies and interests. Furthermore, the growth control unit can adjust the avatar's response in accordance with the user's emotional state. In this way, the interactive avatar system according to the embodiment can generate and grow an interactive avatar based on the user's hobbies and interests.

[0083] The avatar generation unit can customize the avatar's appearance and voice by analyzing the user's past preference data. For example, it collects data on the appearance and voice of avatars previously selected by the user, and the generation AI analyzes that data to generate a new avatar. For example, it can reflect the user's preferred hairstyle, clothing, and tone of voice. The avatar generation unit also collects preference data from the user's social media and other online activities, and the generation AI customizes the avatar based on that data. For example, it can reflect information about the videos the user frequently watches and the accounts the user follows. The avatar generation unit also analyzes surveys and feedback the user has completed in the past, and the generation AI can adjust the avatar's appearance and voice based on the results. For example, it can reflect the user's preferred colors and styles. This allows the avatar's appearance and voice to be customized based on the user's past preference data.

[0084] The conversation control unit can automatically adjust the schedule to match the user's lifestyle and start a conversation at the appropriate time. For example, the conversation control unit works in conjunction with the user's calendar or schedule app, and the generation AI analyzes the user's lifestyle and adjusts the avatar's conversation timing. For example, the conversation can start when the user returns home from work. The conversation control unit also analyzes the user's activity patterns based on data collected from the user's smart device and sets the optimal conversation timing. For example, the conversation can start when the user is relaxing. The conversation control unit also analyzes the user's past conversation history, and the generation AI learns the user's preferred conversation timing and adjusts the avatar's schedule accordingly. For example, if the user prefers to talk in the evening, the conversation can start at that time. This allows the conversation timing to be adjusted to match the user's lifestyle.

[0085] The conversation control unit can use the emotion estimation function to change the avatar's facial expression and tone in real time according to the user's emotional state. For example, the conversation control unit analyzes the user's facial expression using a camera, and the generation AI changes the avatar's facial expression in real time according to the user's emotional state. For example, when the user smiles, the avatar also smiles. The conversation control unit also analyzes the user's voice tone, and the generation AI adjusts the avatar's voice tone according to the user's emotional state. For example, when the user is depressed, the avatar's voice becomes gentler. The conversation control unit also analyzes the user's text messages, and the generation AI changes the avatar's facial expression and tone according to the user's emotional state. For example, when the user is excited, the avatar also shows an excited expression. This makes it possible to change the avatar's facial expression and tone in real time according to the user's emotional state.

[0086] The growth control unit functions as a pet or a member of the user's family and can provide information about pet care and family events. For example, the growth control unit allows an avatar to support the user's pet's health management and provide the pet's meal and exercise schedule. For example, it may remind the pet of meal times and walk times. The growth control unit may also allow an avatar to record the user's family's birthdays and anniversaries and provide event reminders and gift suggestions. For example, it may provide gift ideas for family members' birthdays. The growth control unit may also allow an avatar to monitor the health status of the user's family and provide health advice and reminders. For example, it may manage the family's health checkup schedule. In this way, the growth control unit can function as a pet or a member of the user's family and provide related information.

[0087] The growth control unit can monitor the user's health status and provide health advice and reminders. For example, the growth control unit allows the avatar to connect with the user's fitness tracker or smartwatch, collect health data, and provide advice. For example, it can detect lack of exercise and suggest exercise. The growth control unit can also allow the avatar to analyze the user's food records and provide nutritional balance advice and meal reminders. For example, it can suggest eating more vegetables if the user's vegetable intake is low. The growth control unit can also allow the avatar to monitor the user's sleep data and provide advice and reminders to improve sleep quality. For example, it can remind the user to go to bed and encourage a regular lifestyle. This allows the user's health status to be monitored and health advice and reminders to be provided.

[0088] The growth control unit can use the emotion estimation function to recommend music and movies based on the user's emotions. For example, the growth control unit analyzes the user's emotional state, and the generation AI recommends music that matches that emotion. For example, when the user wants to relax, it suggests relaxing music. The growth control unit also analyzes the user's emotional state, and the generation AI recommends movies that match that emotion. For example, when the user wants to cheer up, it suggests uplifting movies. The growth control unit also analyzes the user's emotional state, and the generation AI recommends entertainment content that matches that emotion. For example, when the user is sad, it suggests content that will brighten up their mood. This makes it possible to recommend music and movies based on the user's emotions.

[0089] The conversation control unit can analyze the content of the conversation, evaluate the user's stress level and psychological state, and provide appropriate advice. For example, the conversation control unit analyzes the content of the user's conversation and develops an algorithm to evaluate the stress level. For example, it may determine that stress is high if there are a lot of negative words. The conversation control unit also builds a system that evaluates the psychological state based on the content of the user's conversation and provides appropriate advice. For example, it may suggest ways to relax if the user is tired. The conversation control unit also analyzes the user's conversation history and develops a system that tracks changes in stress level and psychological state. For example, it issues an alert if stress has increased compared to past conversation content. This makes it possible to evaluate the user's stress level and psychological state and provide appropriate advice.

[0090] The conversation control system uses a generation AI to predict a user's preferences and interests based on the user's past conversation history, and can suggest the next conversation topic. For example, the conversation control system analyzes a user's past conversation history, and the generation AI develops an algorithm to predict the user's preferences and interests. For example, the generation AI suggests the next conversation topic based on topics the user often talks about. The conversation control system also builds a system in which the generation AI automatically generates the next conversation topic based on the user's conversation history. For example, the generation AI suggests topics that the user has recently become interested in. The conversation control system also analyzes the user's past conversation data, and the generation AI learns the user's interests and suggests the next conversation topic. For example, the generation AI could suggest topics about the user's favorite movies or music. This makes it possible to suggest the next conversation topic based on the user's past conversation history.

[0091] The conversation control unit can use the emotion estimation function to generate words of encouragement or comfort according to the user's emotions. For example, the conversation control unit develops a system in which the conversation control unit analyzes the user's emotional state and the generation AI generates words of encouragement according to that emotion. For example, sending an encouraging message when the user is feeling down. The conversation control unit also builds a system in which the conversation control unit analyzes the user's emotional state and the generation AI generates words of comfort according to that emotion. For example, sending a comforting message when the user is sad. The conversation control unit also develops an algorithm in which the generation AI generates appropriate words according to the user's emotions based on the user's emotional data. For example, providing words of reassurance when the user is anxious. This makes it possible to generate words of encouragement or comfort according to the user's emotions.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The avatar generator generates a conversational avatar based on user input. For example, the avatar's appearance and personality can be set based on text information entered by the user, and the avatar's appearance can be customized by analyzing provided image information. The avatar's voice can also be set based on the user's voice information. Step 2: The conversation control unit uses the avatar generated by the avatar generation unit to understand the user's hobbies and interests through conversation with the user. For example, it can use the generation AI to analyze the user's comments and extract information about the user's hobbies and interests. It can also save the conversation history with the user and update the conversation content over time. It can also generate appropriate responses based on the user's comments. Step 3: The personal information analysis unit analyzes the user's personal information. For example, it analyzes text information provided by the user to identify the user's hobbies and interests. It can also analyze image information provided to identify the user's preferences and interests. It can also analyze the user's voice information to identify the user's emotional state. Step 4: The growth control unit grows the avatar based on the conversation history and the passage of time generated by the conversation control unit. For example, it analyzes the conversation history with the user and updates the avatar's personality and hobbies. It can also expand the avatar's knowledge based on new hobbies and interests. It can also adjust the avatar's response based on the user's emotional state.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0122] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0138] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an avatar generation unit that generates an interactive avatar based on user input information; a conversation control unit that grasps hobbies and interests of a user through conversation with the user using the avatar generated by the avatar generation unit; a personal information analysis unit that analyzes personal information of a user; a growth control unit that grows the avatar based on the conversation history and the passage of time by the conversation control unit. A system characterized by:

2. The avatar generation unit Analyzing the user's past preference data to customize the appearance and voice of the avatar 2. The system of claim 1.

3. The conversation control unit The system automatically adjusts the schedule to match the user's daily rhythm and starts conversations at appropriate times.

2. The system of claim 1.

4. The conversation control unit Changing the facial expression and tone of the avatar in real time according to the emotional state of the user 2. The system of claim 1.

5. The growth control unit Acting as a pet or family member of the user and providing information about the care of the pet and events for the family 2. The system of claim 1.

6. The growth control unit Monitor the user's health and provide health advice and reminders 2. The system of claim 1.

Images