System

Abstract

The system according to the embodiment aims to build deep connections through customizable virtual avatars in order to ease the loneliness of the elderly.SOLUTION: A system according to an embodiment includes a collection unit, a generation unit, an interaction unit, a learning unit, and an image generation unit. The collection unit collects basic information of the user. The generating unit generates a customizable virtual avatar based on the information collected by the collecting unit. The avatar generated by the generation unit interacts with the user. The learning unit learns the behavior pattern of the user obtained by the interaction unit and forms the personality of the avatar. The image generation unit generates an image based on the interaction scenario performed by the interaction unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

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

[0005] The system of the embodiment aims to alleviate feelings of loneliness among seniors by building deeper connections through customizable virtual avatars. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a generation unit, an interaction unit, a learning unit, and an image generation unit. The collection unit collects basic information about a user. The generation unit generates a customizable virtual avatar based on the information collected by the collection unit. The interaction unit allows the avatar generated by the generation unit to interact with the user. The learning unit learns the user's behavioral patterns obtained by the interaction unit and forms the avatar's personality. The image generation unit generates an image based on an interaction scenario performed by the interaction unit. [Effects of the Invention]

[0007] In embodiments, the system can build deep connections through customizable virtual avatars to help seniors feel less alone. [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) An interaction system according to an embodiment of the present invention collects basic information about a user, generates a customizable virtual avatar, interacts with the user, learns, and generates images. The interaction system can build a deep connection with the user by collecting basic information about the user, generating a customizable virtual avatar, interacting with the user, learning, and generating images. For example, the interaction system allows a user to register for a service and input basic information. The interaction system then uses AI to generate a customizable virtual avatar based on the user's needs and preferences. The avatar reflects the user's selected appearance and personality and interacts with the user. The AI ​​learns the user's past conversations and behavioral patterns to create the avatar's personality. For example, if a user selects a pet avatar, the avatar will behave and react like a pet. Similarly, if a user selects a best friend or companion avatar, the avatar will behave and react accordingly. Furthermore, the interaction system uses AI to generate images to visually enrich interactions with the user. For example, if a user selects a scenario in which they travel with their avatar, the AI ​​generates images based on that scenario and provides them to the user. This allows the user to feel a more realistic interaction with the avatar. This allows the social interaction system to not only alleviate loneliness among the elderly, but also support the mental health of users. For example, by interacting with avatars, users can reduce stress and anxiety in their daily lives and live more fulfilling lives.

[0029] The communication system according to the embodiment includes a collection unit, a generation unit, a communication unit, a learning unit, and an image generation unit. The collection unit collects basic information about a user. The basic information includes, but is not limited to, the user's name, age, gender, and hobbies. The collection unit stores the information entered by the user in a database. The collection unit can also estimate the user's emotions and adjust the timing of collecting the basic information based on the estimated emotions. For example, the collection unit may smoothly collect the basic information when the user is relaxed. The generation unit generates a customizable virtual avatar based on the information collected by the collection unit. The generation unit generates an avatar with an appearance and personality that meets the user's needs and preferences. The generation unit uses a generation AI to set the avatar's appearance and personality based on the user's selection. For example, the generation AI generates a 3D model or 2D character that reflects the user's selected appearance and personality. The communication unit allows the avatar generated by the generation unit to interact with the user. The communication unit allows the user to interact with the avatar through methods such as chat, voice calls, and in-game interactions. The interaction unit can also use AI to estimate the user's emotions and adjust interaction standards based on the estimated emotions. For example, the interaction unit may engage in gentle interaction when the user is relaxed. The learning unit learns the user's behavioral patterns obtained by the interaction unit and forms the avatar's personality. For example, the learning unit may analyze the user's past conversations and behavioral patterns and form the avatar's personality traits and behavioral tendencies. The learning unit uses a generation AI to learn the user's behavioral patterns and form the avatar's personality. For example, the generation AI may set the avatar's personality based on the user's past conversation data. The image generation unit generates images based on interaction scenarios performed by the interaction unit. For example, if the user selects a scenario in which they travel with the avatar, the image generation unit generates images based on that scenario. The image generation unit uses the generation AI to generate images based on the interaction scenario. For example, the generation AI may generate an image depicting a scene in which the user and avatar are traveling together.This allows the interaction system of the embodiment to build deep connections with users by collecting basic information about the user, generating a customizable virtual avatar, interacting with the user, learning from the user, and generating images.

[0030] The collection unit can analyze the user's past behavioral history and select an appropriate information collection method. For example, the collection unit prioritizes selecting information collection methods (such as questionnaires and interviews) that the user has frequently used in the past. The collection unit can also select the most efficient information collection method from the user's past behavioral history. Furthermore, the collection unit can analyze the user's past behavioral history and select a method that allows the user to be most relaxed when collecting information. In this way, the optimal information collection method can be selected by analyzing the user's past behavioral history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's behavioral history data into a generation AI and cause the generation AI to select the optimal information collection method.

[0031] When collecting basic information, the collection unit can filter the information based on the user's current living situation and areas of interest. For example, the collection unit filters the information to be collected based on the user's current living situation (work, family, health, etc.). The collection unit can also filter the information to be collected based on the user's areas of interest (hobbies, interests, concerns, etc.). Furthermore, the collection unit can combine the user's current living situation and areas of interest to collect the most relevant information. In this way, by filtering information based on the user's current living situation and areas of interest, more relevant information can be collected. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's living situation data and area of ​​interest data to a generation AI and have the generation AI perform information filtering.

[0032] When collecting basic information, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit can preferentially use voice input to collect basic information. Furthermore, if the user prefers text input, the collection unit can preferentially use text input to collect basic information. Furthermore, if the user prefers image input, the collection unit can preferentially use image input to collect basic information. This improves the efficiency of information collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data into a generation AI and have the generation AI select the optimal collection means.

[0033] When collecting basic information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. The collection unit, for example, prioritizes collecting highly relevant information based on the user's current location. The collection unit can also prioritize collecting highly relevant information by referring to the user's past location information. Furthermore, the collection unit can combine the user's geographical location information with areas of interest to collect the most relevant information. In this way, highly relevant information can be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's location information data into a generation AI and cause the generation AI to select highly relevant information.

[0034] When collecting basic information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit can analyze the content of the user's social media posts and collect related information. The collection unit can also collect related information by referring to the activities of the user's friends on social media. Furthermore, the collection unit can also collect related information based on the user's social media check-in information. In this way, related information can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related information.

[0035] When collecting basic information, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, selects the optimal collection method based on feedback provided by the user in the past. The collection unit can also customize the collection method by reflecting the user's past feedback. Furthermore, the collection unit can analyze the user's past feedback and improve the collection method. In this way, the collection method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's feedback data into a generation AI and cause the generation AI to customize the collection method.

[0036] When generating an avatar, the generation unit can adjust the level of detail of the avatar based on the user's important needs. For example, if the user desires a detailed avatar, the generation unit can set the avatar's appearance and personality in detail. Alternatively, if the user desires a simple avatar, the generation unit can simplify the avatar's appearance and personality. Furthermore, the generation unit can adjust the level of detail of the avatar according to the user's needs. By adjusting the level of detail of the avatar based on the user's important needs, a more appropriate avatar can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user needs data into the generation AI and cause the generation AI to adjust the level of detail of the avatar.

[0037] The generation unit can apply different generation algorithms depending on the user's category when generating an avatar. For example, if the user selects an avatar as a pet, the generation unit can apply a generation algorithm for pets. Furthermore, if the user selects an avatar as a best friend, the generation unit can also apply a generation algorithm for best friends. Furthermore, if the user selects an avatar as a companion, the generation unit can also apply a generation algorithm for companions. In this way, by applying different generation algorithms depending on the user's category, a more appropriate avatar can be generated. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input user category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0038] When generating an avatar, the generation unit can improve the accuracy of generation by referring to the user's past avatar selection results. The generation unit can improve the accuracy of generation by referring to, for example, the appearance and personality of avatars selected by the user in the past. The generation unit can also analyze the user's past avatar selection results and generate an optimal avatar. Furthermore, the generation unit can optimize the generation algorithm based on the user's past avatar selection results. In this way, the accuracy of generation can be improved by referring to the user's past avatar selection results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past avatar selection data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0039] When generating an avatar, the generation unit can determine the generation priority based on the time of the user's selection. For example, if the user is in a hurry, the generation unit can quickly generate the avatar. Alternatively, if the user is relaxed, the generation unit can slowly generate the avatar. Furthermore, the generation unit can determine the generation priority based on the time of the user's selection. This allows for the generation of a more appropriate avatar. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user selection time data into the generation AI and have the generation AI determine the generation priority.

[0040] When generating avatars, the generation unit can adjust the order of generation based on the user's relevance. For example, if the user selects an avatar for a best friend, the generation unit can prioritize generating an avatar for the best friend. Also, if the user selects an avatar for a pet, the generation unit can prioritize generating an avatar for the pet. Furthermore, the generation unit can adjust the order of generation of avatars based on the user's relevance. In this way, by adjusting the order of generation based on the user's relevance, more appropriate avatars can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user relevance data into the generation AI and cause the generation AI to adjust the order of generation.

[0041] The generation unit can adjust the use of technical terminology when generating an avatar according to the user's level of expertise. For example, if the user has technical expertise, the generation unit can generate an avatar that uses a lot of technical terminology. Alternatively, if the user does not have technical expertise, the generation unit can generate an avatar that avoids technical terminology. Furthermore, the generation unit can adjust the technical terminology used when generating the avatar according to the user's level of expertise. This allows for the generation of a more appropriate avatar by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0042] The communication unit can improve the accuracy of communication by taking into account the interrelationships between avatars during communication. The communication unit can improve the accuracy of communication by taking into account the interrelationships between avatars, for example. The communication unit can also analyze the interrelationships between avatars and propose an optimal communication method. Furthermore, the communication unit can improve the accuracy of communication based on the interrelationships between avatars. In this way, the accuracy of communication can be improved by taking into account the interrelationships between avatars. Some or all of the above-described processing in the communication unit may be performed using AI, for example, or may be performed without using AI. For example, the communication unit can input avatar interrelationship data into a generation AI and cause the generation AI to improve the accuracy of communication.

[0043] The communication unit can conduct communication taking into account the user's attribute information. The communication unit selects the optimal communication method based on, for example, the user's age and gender. The communication unit can also select the optimal communication method based on the user's hobbies and interests. Furthermore, the communication unit can suggest the optimal communication method based on the user's attribute information. This enables more appropriate communication by taking the user's attribute information into consideration. Some or all of the above-mentioned processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input the user's attribute information data into the generation AI and cause the generation AI to select the optimal communication method.

[0044] The interaction unit can weight interactions based on the frequency of the user's interactions during interactions. For example, the interaction unit prioritizes interactions with avatars with which the user frequently interacts. The interaction unit can also reduce interactions with avatars with which the user does not interact often. Furthermore, the interaction unit can adjust the weighting of interactions based on the frequency of the user's interactions. This allows for more appropriate interactions by weighting interactions based on the frequency of the user's interactions. Some or all of the above-described processing in the interaction unit may be performed using, or without, AI, for example. For example, the interaction unit can input the user's interaction frequency data into a generation AI and cause the generation AI to weight the interactions.

[0045] The communication unit can conduct communication taking into account the geographical distribution of users. For example, the communication unit selects the optimal communication method based on the user's current location. The communication unit can also select the optimal communication method by referring to the user's past location information. Furthermore, the communication unit can also suggest the optimal communication method based on the user's geographical distribution. This enables more appropriate communication by taking the user's geographical distribution into consideration. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input user geographical distribution data into the generation AI and cause the generation AI to select the optimal communication method.

[0046] The communication unit can improve the accuracy of the communication by referring to the user's related literature during communication. For example, the communication unit can improve the accuracy of the communication by referring to related literature in a field in which the user is interested. The communication unit can also improve the accuracy of the communication by referring to related literature based on the user's past communication history. Furthermore, the communication unit can also improve the accuracy of the communication by referring to related literature based on the user's interests. In this way, the accuracy of the communication can be improved by referring to the user's related literature. Some or all of the above-mentioned processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input the user's related literature data into the generation AI and cause the generation AI to improve the accuracy of the communication.

[0047] The communication unit can conduct communication taking into account the user's market value. For example, the communication unit selects an optimal communication method based on the user's market value. The communication unit can also analyze the user's market value and propose an optimal communication method. Furthermore, the communication unit can adjust the weighting of interactions based on the user's market value. This allows for more appropriate communication by taking the user's market value into consideration. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input the user's market value data into a generation AI and have the generation AI select an optimal communication method.

[0048] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit, for example, selects an optimal learning algorithm based on past learning data. The learning unit can also analyze past learning data and optimize the learning algorithm. Furthermore, the learning unit can also improve the accuracy of the learning algorithm by referring to past learning data. In this way, the learning algorithm can be optimized by referring to past learning data. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data to a generation AI and cause the generation AI to optimize the learning algorithm.

[0049] During learning, the learning unit can update the learning data by reflecting user feedback. The learning unit updates the learning data based on, for example, user feedback. The learning unit can also improve the learning data by reflecting past user feedback. Furthermore, the learning unit can analyze user feedback and optimize the learning data. This allows the learning data to be updated by reflecting user feedback. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input user feedback data into a generation AI and cause the generation AI to update the learning data.

[0050] The learning unit can analyze the user's behavioral patterns during learning and improve the accuracy of the learning. The learning unit can improve the accuracy of the learning based on, for example, the user's behavioral patterns. The learning unit can also analyze the user's past behavioral patterns and optimize the learning algorithm. Furthermore, the learning unit can improve the learning data by referring to the user's behavioral patterns. In this way, the accuracy of the learning can be improved by analyzing the user's behavioral patterns. Some or all of the above-mentioned processing in the learning unit can be performed using, for example, AI, or can be performed without using AI. For example, the learning unit can input the user's behavioral pattern data into the generation AI and cause the generation AI to improve the accuracy of the learning.

[0051] During learning, the learning unit can weight the learning data based on the time of user submission. For example, the learning unit prioritizes learning of data recently submitted by the user. The learning unit can also adjust the weighting of the learning data based on the time of user submission. Furthermore, the learning unit can determine the priority of the learning data taking into account the time of user submission. This enables more appropriate learning by weighting the learning data based on the time of user submission. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the user's submission time data into the generation AI and cause the generation AI to weight the learning data.

[0052] During learning, the learning unit can integrate information from different data sources to enrich the training data. For example, the learning unit integrates information from different data sources to enrich the training data. The learning unit can also refer to different data sources to improve the accuracy of the training data. Furthermore, the learning unit can analyze information from different data sources and optimize the learning algorithm. This allows the training data to be enriched by integrating information from different data sources. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input information from different data sources into a generation AI and cause the generation AI to integrate the information.

[0053] During learning, the learning unit can adjust the learning algorithm by reflecting the user's past feedback. The learning unit adjusts the learning algorithm based on, for example, the user's past feedback. The learning unit can also optimize the learning algorithm by reflecting the user's feedback. Furthermore, the learning unit can analyze the user's past feedback and improve the accuracy of the learning algorithm. In this way, the learning algorithm can be adjusted by reflecting the user's past feedback. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the user's past feedback data into the generation AI and cause the generation AI to adjust the learning algorithm.

[0054] The image generation unit can adjust the level of detail of the image based on the importance of the interaction scenario when generating an image. For example, in the case of an important interaction scenario, the image generation unit generates a detailed image. In addition, in the case of a less important interaction scenario, the image generation unit can also generate a simplified image. Furthermore, the image generation unit can adjust the level of detail of the image based on the importance of the interaction scenario. In this way, by adjusting the level of detail of the image based on the importance of the interaction scenario, a more appropriate image can be generated. Some or all of the above-mentioned processing in the image generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the image generation unit can input importance data of the interaction scenario to the generation AI and cause the generation AI to adjust the level of detail of the image.

[0055] The image generation unit can apply different generation algorithms depending on the scenario category when generating an image. For example, in the case of a travel scenario, the image generation unit applies a generation algorithm for travel. Furthermore, in the case of a daily life scenario, the image generation unit can also apply a generation algorithm for daily life. Furthermore, in the case of a specific event scenario, the image generation unit can also apply a generation algorithm for events. In this way, by applying different generation algorithms depending on the scenario category, more appropriate images can be generated. Some or all of the above-mentioned processing in the image generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the image generation unit can input scenario category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0056] When generating an image, the image generation unit can improve the accuracy of generation by referring to the user's past image generation results. The image generation unit improves the accuracy of generation, for example, based on the user's past image generation results. The image generation unit can also analyze the user's past image generation results and generate an optimal image. Furthermore, the image generation unit can optimize the generation algorithm based on the user's past image generation results. In this way, the accuracy of generation can be improved by referring to the user's past image generation results. Some or all of the above-mentioned processing in the image generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the image generation unit can input the user's past image generation data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0057] When generating images, the image generation unit can determine the generation priority based on the submission time of the scenario. For example, the image generation unit generates images with priority given to the most recently submitted scenario. The image generation unit can also adjust the generation priority based on the submission time of the scenario. Furthermore, the image generation unit can determine the order of image generation taking into account the submission time of the scenario. In this way, by determining the generation priority based on the submission time of the scenario, more appropriate images can be generated. Some or all of the above-mentioned processing in the image generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the image generation unit can input scenario submission time data into the generation AI and have the generation AI determine the generation priority.

[0058] The image generation unit can adjust the order of generation based on the relevance of the scenarios when generating images. For example, the image generation unit generates images with priority given to important scenarios. The image generation unit can also adjust the order of generation based on the relevance of the scenarios. Furthermore, the image generation unit can determine the order of image generation taking into account the relevance of the scenarios. In this way, by adjusting the order of generation based on the relevance of the scenarios, more appropriate images can be generated. Some or all of the above-described processing in the image generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the image generation unit can input scenario relevance data to the generation AI and cause the generation AI to adjust the order of generation.

[0059] The image generation unit can adjust the use of technical terminology when generating an image according to the user's level of expertise. For example, if the user has technical expertise, the image generation unit generates an image that uses a lot of technical terminology. Also, if the user does not have technical expertise, the image generation unit can generate an image that avoids technical terminology. Furthermore, the image generation unit can adjust the technical terminology used when generating an image according to the user's level of expertise. This allows for the generation of more appropriate images by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the image generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the image generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The collection unit can analyze the user's past purchase history and select an appropriate information collection method. For example, the collection unit can select the optimal information collection method based on products and services the user has purchased in the past. The collection unit can also select the information collection method that makes the user most relaxed based on the user's purchase history. Furthermore, the collection unit can analyze the user's purchase history and select the method that the user is most interested in when collecting information. In this way, the optimal information collection method can be selected by analyzing the user's purchase history.

[0062] The collection unit can collect basic information based on the user's current health condition. For example, the collection unit can monitor the user's health condition (e.g., blood pressure, heart rate, body temperature, etc.) and filter the information to be collected based on that information. If the user is healthy, the collection unit can collect detailed information. On the other hand, if the user is in poor health, the collection unit can prioritize collecting basic information. Furthermore, the collection unit can combine the user's health condition and areas of interest to collect the most relevant information. This allows more relevant information to be collected by filtering information based on the user's health condition.

[0063] The collection unit can adjust the format of the information to be collected depending on the user's input method. For example, if the user prefers voice input, the collection unit can collect information in a format suitable for voice input. If the user prefers text input, the collection unit can collect information in a format suitable for text input. Furthermore, if the user prefers image input, the collection unit can collect information in a format suitable for image input. This improves the efficiency of information collection by collecting information in an optimal format depending on the user's input method.

[0064] The collection unit can improve the accuracy of the collected information by taking into account the user's geographical location information. For example, the collection unit can prioritize collecting highly relevant information based on the user's current location. Also, the collection unit can prioritize collecting highly relevant information by referring to the user's past location information. Furthermore, the collection unit can combine the user's geographical location information with areas of interest to collect the most relevant information. In this way, by taking into account the user's geographical location information, it is possible to prioritize collecting highly relevant information.

[0065] The collection unit can analyze the user's social media activities and collect related information. For example, the collection unit can analyze the content posted by the user on social media and collect related information. The collection unit can also collect related information by referring to the activities of the user's friends on social media. Furthermore, the collection unit can collect related information based on the user's check-in information on social media. In this way, related information can be collected by analyzing the user's social media activities.

[0066] The collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit can select the optimal collection method based on feedback provided by the user in the past. The collection method can also be customized by reflecting the user's past feedback. Furthermore, the collection unit can analyze the user's past feedback and improve the collection method. In this way, the collection method can be customized by reflecting the user's past feedback.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The collection unit collects basic information about the user. The basic information includes, for example, name, age, gender, and hobbies. The collection unit stores the information entered by the user in a database. The collection unit can also estimate the user's emotions and adjust the timing of collecting the basic information based on the estimated emotions. For example, the basic information can be collected smoothly when the user is relaxed. Step 2: The generator generates a customizable virtual avatar based on the information collected by the collector. The generator generates an avatar with an appearance and personality that meets the user's needs and preferences. The generator uses a generation AI to set the avatar's appearance and personality based on the user's selections. For example, the generator AI generates a 3D model or 2D character that reflects the user's selected appearance and personality. Step 3: The interaction unit allows the avatar generated by the generation unit to interact with the user. The interaction unit allows the user and avatar to interact through chat, voice calls, in-game dialogue, etc. The interaction unit can also use AI to estimate the user's emotions and adjust the criteria for interaction based on the estimated emotions. For example, the interaction unit will be gentle when the user is relaxed. Step 4: The learning unit learns the user's behavioral patterns obtained by the interaction unit and forms the avatar's personality. The learning unit analyzes the user's past conversations and behavioral patterns and forms the avatar's personality traits and behavioral tendencies. The generation AI learns the user's behavioral patterns and forms the avatar's personality. For example, the generation AI sets the avatar's personality based on the user's past conversation data. Step 5: The image generation unit generates an image based on the interaction scenario performed by the interaction unit. If the user selects a scenario in which the user travels with the avatar, the image generation unit generates an image based on that scenario. The image generation unit uses the generation AI to generate an image based on the interaction scenario. For example, the generation AI generates an image depicting a scene in which the user and avatar are traveling together.

[0069] (Example 2) An interaction system according to an embodiment of the present invention collects basic information about a user, generates a customizable virtual avatar, interacts with the user, learns, and generates images. The interaction system can build a deep connection with the user by collecting basic information about the user, generating a customizable virtual avatar, interacting with the user, learning, and generating images. For example, the interaction system allows a user to register for a service and input basic information. The interaction system then uses AI to generate a customizable virtual avatar based on the user's needs and preferences. The avatar reflects the user's selected appearance and personality and interacts with the user. The AI ​​learns the user's past conversations and behavioral patterns to create the avatar's personality. For example, if a user selects a pet avatar, the avatar will behave and react like a pet. Similarly, if a user selects a best friend or companion avatar, the avatar will behave and react accordingly. Furthermore, the interaction system uses AI to generate images to visually enrich interactions with the user. For example, if a user selects a scenario in which they travel with their avatar, the AI ​​generates images based on that scenario and provides them to the user. This allows the user to feel a more realistic interaction with the avatar. This allows the social interaction system to not only alleviate loneliness among the elderly, but also support the mental health of users. For example, by interacting with avatars, users can reduce stress and anxiety in their daily lives and live more fulfilling lives.

[0070] The communication system according to the embodiment includes a collection unit, a generation unit, a communication unit, a learning unit, and an image generation unit. The collection unit collects basic information about a user. The basic information includes, but is not limited to, the user's name, age, gender, and hobbies. The collection unit stores the information entered by the user in a database. The collection unit can also estimate the user's emotions and adjust the timing of collecting the basic information based on the estimated emotions. For example, the collection unit may smoothly collect the basic information when the user is relaxed. The generation unit generates a customizable virtual avatar based on the information collected by the collection unit. The generation unit generates an avatar with an appearance and personality that meets the user's needs and preferences. The generation unit uses a generation AI to set the avatar's appearance and personality based on the user's selection. For example, the generation AI generates a 3D model or 2D character that reflects the user's selected appearance and personality. The communication unit allows the avatar generated by the generation unit to interact with the user. The communication unit allows the user to interact with the avatar through methods such as chat, voice calls, and in-game interactions. The interaction unit can also use AI to estimate the user's emotions and adjust interaction standards based on the estimated emotions. For example, the interaction unit may engage in gentle interaction when the user is relaxed. The learning unit learns the user's behavioral patterns obtained by the interaction unit and forms the avatar's personality. For example, the learning unit may analyze the user's past conversations and behavioral patterns and form the avatar's personality traits and behavioral tendencies. The learning unit uses a generation AI to learn the user's behavioral patterns and form the avatar's personality. For example, the generation AI may set the avatar's personality based on the user's past conversation data. The image generation unit generates images based on interaction scenarios performed by the interaction unit. For example, if the user selects a scenario in which they travel with the avatar, the image generation unit generates images based on that scenario. The image generation unit uses the generation AI to generate images based on the interaction scenario. For example, the generation AI may generate an image depicting a scene in which the user and avatar are traveling together.This allows the interaction system of the embodiment to build deep connections with users by collecting basic information about the user, generating a customizable virtual avatar, interacting with the user, learning from the user, and generating images.

[0071] The collection unit can estimate the user's emotions and adjust the timing of collecting basic information based on the estimated user emotions. For example, if the user is relaxed, the collection unit adjusts the collection timing to smoothly collect basic information. Furthermore, if the user is feeling stressed, the collection unit can delay the collection timing to allow the user to provide information when they are calm. Furthermore, if the user is excited, the collection unit can advance the collection timing to collect information before the user's excitement subsides. This allows information to be collected at a more appropriate time by adjusting the timing of collecting basic information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0072] The collection unit can analyze the user's past behavioral history and select an appropriate information collection method. For example, the collection unit prioritizes selecting information collection methods (such as questionnaires and interviews) that the user has frequently used in the past. The collection unit can also select the most efficient information collection method from the user's past behavioral history. Furthermore, the collection unit can analyze the user's past behavioral history and select a method that allows the user to be most relaxed when collecting information. In this way, the optimal information collection method can be selected by analyzing the user's past behavioral history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's behavioral history data into a generation AI and cause the generation AI to select the optimal information collection method.

[0073] When collecting basic information, the collection unit can filter the information based on the user's current living situation and areas of interest. For example, the collection unit filters the information to be collected based on the user's current living situation (work, family, health, etc.). The collection unit can also filter the information to be collected based on the user's areas of interest (hobbies, interests, concerns, etc.). Furthermore, the collection unit can combine the user's current living situation and areas of interest to collect the most relevant information. In this way, by filtering information based on the user's current living situation and areas of interest, more relevant information can be collected. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's living situation data and area of ​​interest data to a generation AI and have the generation AI perform information filtering.

[0074] When collecting basic information, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit can preferentially use voice input to collect basic information. Furthermore, if the user prefers text input, the collection unit can preferentially use text input to collect basic information. Furthermore, if the user prefers image input, the collection unit can preferentially use image input to collect basic information. This improves the efficiency of information collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data into a generation AI and have the generation AI select the optimal collection means.

[0075] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, when the user is relaxed, the collection unit can prioritize collecting detailed information. Furthermore, when the user is stressed, the collection unit can prioritize collecting basic information. Furthermore, when the user is excited, the collection unit can prioritize collecting information related to the cause of the user's excitement. This allows for more appropriate information to be collected by determining the priority of information to be collected according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0076] When collecting basic information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. The collection unit, for example, prioritizes collecting highly relevant information based on the user's current location. The collection unit can also prioritize collecting highly relevant information by referring to the user's past location information. Furthermore, the collection unit can combine the user's geographical location information with areas of interest to collect the most relevant information. In this way, highly relevant information can be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's location information data into a generation AI and cause the generation AI to select highly relevant information.

[0077] When collecting basic information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit can analyze the content of the user's social media posts and collect related information. The collection unit can also collect related information by referring to the activities of the user's friends on social media. Furthermore, the collection unit can also collect related information based on the user's social media check-in information. In this way, related information can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related information.

[0078] When collecting basic information, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, selects the optimal collection method based on feedback provided by the user in the past. The collection unit can also customize the collection method by reflecting the user's past feedback. Furthermore, the collection unit can analyze the user's past feedback and improve the collection method. In this way, the collection method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's feedback data into a generation AI and cause the generation AI to customize the collection method.

[0079] The generation unit can estimate the user's emotions and adjust the avatar's expression based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can calm the avatar's facial expressions and movements. Furthermore, if the user is feeling stressed, the generation unit can calm the avatar's facial expressions and movements. Furthermore, if the user is excited, the generation unit can make the avatar's facial expressions and movements more active. This allows for the generation of a more appropriate avatar by adjusting the avatar's expression based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or without an AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to adjust the avatar's expression.

[0080] When generating an avatar, the generation unit can adjust the level of detail of the avatar based on the user's important needs. For example, if the user desires a detailed avatar, the generation unit can set the avatar's appearance and personality in detail. Alternatively, if the user desires a simple avatar, the generation unit can simplify the avatar's appearance and personality. Furthermore, the generation unit can adjust the level of detail of the avatar according to the user's needs. By adjusting the level of detail of the avatar based on the user's important needs, a more appropriate avatar can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user needs data into the generation AI and cause the generation AI to adjust the level of detail of the avatar.

[0081] The generation unit can apply different generation algorithms depending on the user's category when generating an avatar. For example, if the user selects an avatar as a pet, the generation unit can apply a generation algorithm for pets. Furthermore, if the user selects an avatar as a best friend, the generation unit can also apply a generation algorithm for best friends. Furthermore, if the user selects an avatar as a companion, the generation unit can also apply a generation algorithm for companions. In this way, by applying different generation algorithms depending on the user's category, a more appropriate avatar can be generated. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input user category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0082] When generating an avatar, the generation unit can improve the accuracy of generation by referring to the user's past avatar selection results. The generation unit can improve the accuracy of generation by referring to, for example, the appearance and personality of avatars selected by the user in the past. The generation unit can also analyze the user's past avatar selection results and generate an optimal avatar. Furthermore, the generation unit can optimize the generation algorithm based on the user's past avatar selection results. In this way, the accuracy of generation can be improved by referring to the user's past avatar selection results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past avatar selection data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0083] The generation unit can estimate the user's emotions and adjust the size of the avatar based on the estimated user emotions. For example, if the user is relaxed, the generation unit can make the avatar's conversation and movements at a relaxed pace. Furthermore, if the user is stressed, the generation unit can shorten and concise the avatar's conversation and movements. Furthermore, if the user is excited, the generation unit can make the avatar's conversation and movements more lively. This allows for the generation of a more appropriate avatar by adjusting the size of the avatar according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to adjust the size of the avatar.

[0084] When generating an avatar, the generation unit can determine the generation priority based on the time of the user's selection. For example, if the user is in a hurry, the generation unit can quickly generate the avatar. Alternatively, if the user is relaxed, the generation unit can slowly generate the avatar. Furthermore, the generation unit can determine the generation priority based on the time of the user's selection. This allows for the generation of a more appropriate avatar. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user selection time data into the generation AI and have the generation AI determine the generation priority.

[0085] When generating avatars, the generation unit can adjust the order of generation based on the user's relevance. For example, if the user selects an avatar for a best friend, the generation unit can prioritize generating an avatar for the best friend. Also, if the user selects an avatar for a pet, the generation unit can prioritize generating an avatar for the pet. Furthermore, the generation unit can adjust the order of generation of avatars based on the user's relevance. In this way, by adjusting the order of generation based on the user's relevance, more appropriate avatars can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user relevance data into the generation AI and cause the generation AI to adjust the order of generation.

[0086] The generation unit can adjust the use of technical terminology when generating an avatar according to the user's level of expertise. For example, if the user has technical expertise, the generation unit can generate an avatar that uses a lot of technical terminology. Alternatively, if the user does not have technical expertise, the generation unit can generate an avatar that avoids technical terminology. Furthermore, the generation unit can adjust the technical terminology used when generating the avatar according to the user's level of expertise. This allows for the generation of a more appropriate avatar by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0087] The interaction unit can estimate the user's emotions and adjust the interaction criteria based on the estimated user emotions. For example, if the user is relaxed, the interaction unit can calm the user's interaction with the avatar. Furthermore, if the user is stressed, the interaction unit can calm the user's interaction with the avatar. Furthermore, if the user is excited, the interaction unit can increase the user's interaction with the avatar. This allows for more appropriate interaction by adjusting the interaction criteria according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the interaction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the interaction unit can input the user's facial expression data into the generation AI and cause the generation AI to adjust the interaction criteria.

[0088] The communication unit can improve the accuracy of communication by taking into account the interrelationships between avatars during communication. The communication unit can improve the accuracy of communication by taking into account the interrelationships between avatars, for example. The communication unit can also analyze the interrelationships between avatars and propose an optimal communication method. Furthermore, the communication unit can improve the accuracy of communication based on the interrelationships between avatars. In this way, the accuracy of communication can be improved by taking into account the interrelationships between avatars. Some or all of the above-described processing in the communication unit may be performed using AI, for example, or may be performed without using AI. For example, the communication unit can input avatar interrelationship data into a generation AI and cause the generation AI to improve the accuracy of communication.

[0089] The communication unit can conduct communication taking into account the user's attribute information. The communication unit selects the optimal communication method based on, for example, the user's age and gender. The communication unit can also select the optimal communication method based on the user's hobbies and interests. Furthermore, the communication unit can suggest the optimal communication method based on the user's attribute information. This enables more appropriate communication by taking the user's attribute information into consideration. Some or all of the above-mentioned processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input the user's attribute information data into the generation AI and cause the generation AI to select the optimal communication method.

[0090] The interaction unit can weight interactions based on the frequency of the user's interactions during interactions. For example, the interaction unit prioritizes interactions with avatars with which the user frequently interacts. The interaction unit can also reduce interactions with avatars with which the user does not interact often. Furthermore, the interaction unit can adjust the weighting of interactions based on the frequency of the user's interactions. This allows for more appropriate interactions by weighting interactions based on the frequency of the user's interactions. Some or all of the above-described processing in the interaction unit may be performed using, or without, AI, for example. For example, the interaction unit can input the user's interaction frequency data into a generation AI and cause the generation AI to weight the interactions.

[0091] The communication unit can estimate the user's emotions and adjust the order in which the interaction results are displayed based on the estimated user's emotions. For example, if the user is relaxed, the communication unit can display the interaction results in a relaxed order. Furthermore, if the user is stressed, the communication unit can also display the interaction results concisely. Furthermore, if the user is excited, the communication unit can also display the interaction results in a lively order. This allows for more appropriate interaction by adjusting the order in which the interaction results are displayed according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the communication unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the communication unit can input the user's facial expression data into the generation AI and cause the generation AI to adjust the display order of the interaction results.

[0092] The communication unit can conduct communication taking into account the geographical distribution of users. For example, the communication unit selects the optimal communication method based on the user's current location. The communication unit can also select the optimal communication method by referring to the user's past location information. Furthermore, the communication unit can also suggest the optimal communication method based on the user's geographical distribution. This enables more appropriate communication by taking the user's geographical distribution into consideration. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input user geographical distribution data into the generation AI and cause the generation AI to select the optimal communication method.

[0093] The communication unit can improve the accuracy of the communication by referring to the user's related literature during communication. For example, the communication unit can improve the accuracy of the communication by referring to related literature in a field in which the user is interested. The communication unit can also improve the accuracy of the communication by referring to related literature based on the user's past communication history. Furthermore, the communication unit can also improve the accuracy of the communication by referring to related literature based on the user's interests. In this way, the accuracy of the communication can be improved by referring to the user's related literature. Some or all of the above-mentioned processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input the user's related literature data into the generation AI and cause the generation AI to improve the accuracy of the communication.

[0094] The communication unit can conduct communication taking into account the user's market value. For example, the communication unit selects an optimal communication method based on the user's market value. The communication unit can also analyze the user's market value and propose an optimal communication method. Furthermore, the communication unit can adjust the weighting of interactions based on the user's market value. This allows for more appropriate communication by taking the user's market value into consideration. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input the user's market value data into a generation AI and have the generation AI select an optimal communication method.

[0095] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is relaxed, the learning unit selects calm training data. Furthermore, if the user is stressed, the learning unit can select training data that alleviates stress. Furthermore, if the user is excited, the learning unit can select training data that alleviates excitement. This enables more appropriate learning by selecting training data according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit can input the user's facial expression data into the generation AI and have the generation AI select the training data.

[0096] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit, for example, selects an optimal learning algorithm based on past learning data. The learning unit can also analyze past learning data and optimize the learning algorithm. Furthermore, the learning unit can also improve the accuracy of the learning algorithm by referring to past learning data. In this way, the learning algorithm can be optimized by referring to past learning data. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data to a generation AI and cause the generation AI to optimize the learning algorithm.

[0097] During learning, the learning unit can update the learning data by reflecting user feedback. The learning unit updates the learning data based on, for example, user feedback. The learning unit can also improve the learning data by reflecting past user feedback. Furthermore, the learning unit can analyze user feedback and optimize the learning data. This allows the learning data to be updated by reflecting user feedback. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input user feedback data into a generation AI and cause the generation AI to update the learning data.

[0098] The learning unit can analyze the user's behavioral patterns during learning and improve the accuracy of the learning. The learning unit can improve the accuracy of the learning based on, for example, the user's behavioral patterns. The learning unit can also analyze the user's past behavioral patterns and optimize the learning algorithm. Furthermore, the learning unit can improve the learning data by referring to the user's behavioral patterns. In this way, the accuracy of the learning can be improved by analyzing the user's behavioral patterns. Some or all of the above-mentioned processing in the learning unit can be performed using, for example, AI, or can be performed without using AI. For example, the learning unit can input the user's behavioral pattern data into the generation AI and cause the generation AI to improve the accuracy of the learning.

[0099] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. For example, the learning unit can increase the frequency of learning when the user is relaxed. The learning unit can also decrease the frequency of learning when the user is stressed. Furthermore, the learning unit can adjust the frequency of learning when the user is excited. This allows for more appropriate learning by adjusting the frequency of learning according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the learning unit can be performed using an AI, for example, or without an AI. For example, the learning unit can input the user's facial expression data into the generation AI and cause the generation AI to adjust the frequency of learning.

[0100] During learning, the learning unit can weight the learning data based on the time of user submission. For example, the learning unit prioritizes learning of data recently submitted by the user. The learning unit can also adjust the weighting of the learning data based on the time of user submission. Furthermore, the learning unit can determine the priority of the learning data taking into account the time of user submission. This enables more appropriate learning by weighting the learning data based on the time of user submission. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the user's submission time data into the generation AI and cause the generation AI to weight the learning data.

[0101] During learning, the learning unit can integrate information from different data sources to enrich the training data. For example, the learning unit integrates information from different data sources to enrich the training data. The learning unit can also refer to different data sources to improve the accuracy of the training data. Furthermore, the learning unit can analyze information from different data sources and optimize the learning algorithm. This allows the training data to be enriched by integrating information from different data sources. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input information from different data sources into a generation AI and cause the generation AI to integrate the information.

[0102] During learning, the learning unit can adjust the learning algorithm by reflecting the user's past feedback. The learning unit adjusts the learning algorithm based on, for example, the user's past feedback. The learning unit can also optimize the learning algorithm by reflecting the user's feedback. Furthermore, the learning unit can analyze the user's past feedback and improve the accuracy of the learning algorithm. In this way, the learning algorithm can be adjusted by reflecting the user's past feedback. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the user's past feedback data into the generation AI and cause the generation AI to adjust the learning algorithm.

[0103] The image generation unit can estimate the user's emotions and adjust the image generation method based on the estimated user's emotions. For example, if the user is relaxed, the image generation unit generates a calm image. Furthermore, if the user is stressed, the image generation unit can also generate an image that reduces stress. Furthermore, if the user is excited, the image generation unit can also generate an image that reduces excitement. This allows for adjusting the image generation method according to the user's emotions to generate more appropriate images. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the image generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the image generation unit can input the user's facial expression data into the generation AI and have the generation AI adjust the image generation method.

[0104] The image generation unit can adjust the level of detail of the image based on the importance of the interaction scenario when generating an image. For example, in the case of an important interaction scenario, the image generation unit generates a detailed image. In addition, in the case of a less important interaction scenario, the image generation unit can also generate a simplified image. Furthermore, the image generation unit can adjust the level of detail of the image based on the importance of the interaction scenario. In this way, by adjusting the level of detail of the image based on the importance of the interaction scenario, a more appropriate image can be generated. Some or all of the above-mentioned processing in the image generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the image generation unit can input importance data of the interaction scenario to the generation AI and cause the generation AI to adjust the level of detail of the image.

[0105] The image generation unit can apply different generation algorithms depending on the scenario category when generating an image. For example, in the case of a travel scenario, the image generation unit applies a generation algorithm for travel. Furthermore, in the case of a daily life scenario, the image generation unit can also apply a generation algorithm for daily life. Furthermore, in the case of a specific event scenario, the image generation unit can also apply a generation algorithm for events. In this way, by applying different generation algorithms depending on the scenario category, more appropriate images can be generated. Some or all of the above-mentioned processing in the image generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the image generation unit can input scenario category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0106] When generating an image, the image generation unit can improve the accuracy of generation by referring to the user's past image generation results. The image generation unit improves the accuracy of generation, for example, based on the user's past image generation results. The image generation unit can also analyze the user's past image generation results and generate an optimal image. Furthermore, the image generation unit can optimize the generation algorithm based on the user's past image generation results. In this way, the accuracy of generation can be improved by referring to the user's past image generation results. Some or all of the above-mentioned processing in the image generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the image generation unit can input the user's past image generation data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0107] The image generation unit can estimate the user's emotion and adjust the size of the image based on the estimated user's emotion. For example, if the user is relaxed, the image generation unit can generate a longer image. Furthermore, if the user is feeling stressed, the image generation unit can also generate a shorter image. Furthermore, if the user is excited, the image generation unit can generate an image that reduces the excitement. This allows for the generation of a more appropriate image by adjusting the size of the image according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the image generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the image generation unit can input the user's facial expression data into the generation AI and have the generation AI adjust the size of the image.

[0108] When generating images, the image generation unit can determine the generation priority based on the submission time of the scenario. For example, the image generation unit generates images with priority given to the most recently submitted scenario. The image generation unit can also adjust the generation priority based on the submission time of the scenario. Furthermore, the image generation unit can determine the order of image generation taking into account the submission time of the scenario. In this way, by determining the generation priority based on the submission time of the scenario, more appropriate images can be generated. Some or all of the above-mentioned processing in the image generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the image generation unit can input scenario submission time data into the generation AI and have the generation AI determine the generation priority.

[0109] The image generation unit can adjust the order of generation based on the relevance of the scenarios when generating images. For example, the image generation unit generates images with priority given to important scenarios. The image generation unit can also adjust the order of generation based on the relevance of the scenarios. Furthermore, the image generation unit can determine the order of image generation taking into account the relevance of the scenarios. In this way, by adjusting the order of generation based on the relevance of the scenarios, more appropriate images can be generated. Some or all of the above-described processing in the image generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the image generation unit can input scenario relevance data to the generation AI and cause the generation AI to adjust the order of generation.

[0110] The image generation unit can adjust the use of technical terminology when generating an image according to the user's level of expertise. For example, if the user has technical expertise, the image generation unit generates an image that uses a lot of technical terminology. Also, if the user does not have technical expertise, the image generation unit can generate an image that avoids technical terminology. Furthermore, the image generation unit can adjust the technical terminology used when generating an image according to the user's level of expertise. This allows for the generation of more appropriate images by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the image generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the image generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, generation unit, interaction unit, learning unit, and image generation unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects basic information about the user using the reception device 38 of the smart device 14 and stores the collected information in the database 24 via the specific processing unit 290 of the data processing device 12. The generation unit generates a customizable virtual avatar based on the information collected by the specific processing unit 290 of the data processing device 12. The interaction unit allows the generated avatar to interact with the user using the control unit 46A of the smart device 14. The learning unit learns the user's behavioral patterns via the specific processing unit 290 of the data processing device 12 and creates a personality for the avatar. The image generation unit generates an image based on an interaction scenario via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, generation unit, interaction unit, learning unit, and image generation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects basic information about the user using the microphone 238 of the smart glasses 214 and stores it in the database 24 by the specific processing unit 290 of the data processing device 12. The generation unit generates a customizable virtual avatar based on the information collected by the specific processing unit 290 of the data processing device 12. The interaction unit allows the generated avatar to interact with the user using the control unit 46A of the smart glasses 214. The learning unit learns the user's behavioral patterns by the specific processing unit 290 of the data processing device 12 and forms the avatar's personality. The image generation unit generates an image based on an interaction scenario by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, generation unit, interaction unit, learning unit, and image generation unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects basic information about the user using the microphone 238 of the headset-type terminal 314 and stores the collected information in the database 24 by the specific processing unit 290 of the data processing device 12. The generation unit generates a customizable virtual avatar based on the information collected by the specific processing unit 290 of the data processing device 12. The interaction unit allows the avatar generated using the control unit 46A of the headset-type terminal 314 to interact with the user. The learning unit learns the user's behavioral patterns by the specific processing unit 290 of the data processing device 12 and forms the avatar's personality. The image generation unit generates an image based on an interaction scenario by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, generation unit, interaction unit, learning unit, and image generation unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects basic information about the user using the microphone 238 of the robot 414 and stores the collected information in the database 24 by the specific processing unit 290 of the data processing device 12. The generation unit generates a customizable virtual avatar based on the information collected by the specific processing unit 290 of the data processing device 12. The interaction unit allows the generated avatar to interact with the user using the control unit 46A of the robot 414. The learning unit learns the user's behavioral patterns by the specific processing unit 290 of the data processing device 12 and forms the avatar's personality. The image generation unit generates an image based on an interaction scenario by the specific processing unit 290 of the data processing device 12.

[0111] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0112] The interaction system may further include a voice recognition unit. The voice recognition unit can analyze the user's voice in real time and estimate the user's emotions and intentions. For example, if the user is excited, the voice recognition unit can detect that excitement and adjust the avatar's response. Alternatively, if the user is calm, the voice recognition unit can detect that calmness and calm the avatar's response. Furthermore, the voice recognition unit can extract specific keywords from the user's voice and determine the avatar's behavior based on those keywords. This allows for more natural interaction by analyzing the user's voice.

[0113] The collection unit can collect biometric information of the user and adjust the timing of collecting basic information based on that information. For example, the collection unit can monitor the user's heart rate and electrodermal activity to determine whether the user is relaxed. If the user is relaxed, the collection unit can smoothly collect basic information. If the user is feeling stressed, the collection unit can delay the timing of collection. Furthermore, the collection unit can analyze the user's biometric information and select the optimal collection method. This allows information to be collected at a more appropriate time by utilizing the user's biometric information.

[0114] The collection unit can analyze the user's past purchase history and select an appropriate information collection method. For example, the collection unit can select the optimal information collection method based on products and services the user has purchased in the past. The collection unit can also select the information collection method that makes the user most relaxed based on the user's purchase history. Furthermore, the collection unit can analyze the user's purchase history and select the method that the user is most interested in when collecting information. In this way, the optimal information collection method can be selected by analyzing the user's purchase history.

[0115] The collection unit can collect basic information based on the user's current health condition. For example, the collection unit can monitor the user's health condition (e.g., blood pressure, heart rate, body temperature, etc.) and filter the information to be collected based on that information. If the user is healthy, the collection unit can collect detailed information. On the other hand, if the user is in poor health, the collection unit can prioritize collecting basic information. Furthermore, the collection unit can combine the user's health condition and areas of interest to collect the most relevant information. This allows more relevant information to be collected by filtering information based on the user's health condition.

[0116] The collection unit can adjust the format of the information to be collected depending on the user's input method. For example, if the user prefers voice input, the collection unit can collect information in a format suitable for voice input. If the user prefers text input, the collection unit can collect information in a format suitable for text input. Furthermore, if the user prefers image input, the collection unit can collect information in a format suitable for image input. This improves the efficiency of information collection by collecting information in an optimal format depending on the user's input method.

[0117] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user's emotions. For example, when the user is relaxed, the collection unit can prioritize collecting detailed information. When the user is feeling stressed, the collection unit can prioritize collecting basic information. When the user is excited, the collection unit can prioritize collecting information related to the cause of the excitement. In this way, by determining the priority of information to be collected according to the user's emotions, more appropriate information can be collected.

[0118] The collection unit can improve the accuracy of the collected information by taking into account the user's geographical location information. For example, the collection unit can prioritize collecting highly relevant information based on the user's current location. Also, the collection unit can prioritize collecting highly relevant information by referring to the user's past location information. Furthermore, the collection unit can combine the user's geographical location information with areas of interest to collect the most relevant information. In this way, by taking into account the user's geographical location information, it is possible to prioritize collecting highly relevant information.

[0119] The collection unit can analyze the user's social media activities and collect related information. For example, the collection unit can analyze the content posted by the user on social media and collect related information. The collection unit can also collect related information by referring to the activities of the user's friends on social media. Furthermore, the collection unit can collect related information based on the user's check-in information on social media. In this way, related information can be collected by analyzing the user's social media activities.

[0120] The collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit can select the optimal collection method based on feedback provided by the user in the past. The collection method can also be customized by reflecting the user's past feedback. Furthermore, the collection unit can analyze the user's past feedback and improve the collection method. In this way, the collection method can be customized by reflecting the user's past feedback.

[0121] The generation unit can estimate the user's emotions and adjust the avatar's expression based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can calm the avatar's facial expressions and movements. If the user is stressed, the generation unit can calm the avatar's facial expressions and movements. If the user is excited, the generation unit can make the avatar's facial expressions and movements more active. This allows the generation of a more appropriate avatar by adjusting the avatar's expression based on the user's emotions.

[0122] The processing flow of the second embodiment will be briefly explained below.

[0123] Step 1: The collection unit collects basic information about the user. The basic information includes, for example, name, age, gender, and hobbies. The collection unit stores the information entered by the user in a database. The collection unit can also estimate the user's emotions and adjust the timing of collecting the basic information based on the estimated emotions. For example, the basic information can be collected smoothly when the user is relaxed. Step 2: The generator generates a customizable virtual avatar based on the information collected by the collector. The generator generates an avatar with an appearance and personality that meets the user's needs and preferences. The generator uses a generation AI to set the avatar's appearance and personality based on the user's selections. For example, the generator AI generates a 3D model or 2D character that reflects the user's selected appearance and personality. Step 3: The interaction unit allows the avatar generated by the generation unit to interact with the user. The interaction unit allows the user and avatar to interact through chat, voice calls, in-game dialogue, etc. The interaction unit can also use AI to estimate the user's emotions and adjust the criteria for interaction based on the estimated emotions. For example, the interaction unit will be gentle when the user is relaxed. Step 4: The learning unit learns the user's behavioral patterns obtained by the interaction unit and forms the avatar's personality. The learning unit analyzes the user's past conversations and behavioral patterns and forms the avatar's personality traits and behavioral tendencies. The generation AI learns the user's behavioral patterns and forms the avatar's personality. For example, the generation AI sets the avatar's personality based on the user's past conversation data. Step 5: The image generation unit generates an image based on the interaction scenario performed by the interaction unit. If the user selects a scenario in which the user travels with the avatar, the image generation unit generates an image based on that scenario. The image generation unit uses the generation AI to generate an image based on the interaction scenario. For example, the generation AI generates an image depicting a scene in which the user and avatar are traveling together.

[0124] 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.

[0125] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] 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.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0129] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[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 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.

[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 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.

[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 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.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0145] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0146] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0147] The 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.

[0148] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0150] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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 identification processing unit 290 using these models.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0161] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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).

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] [Explanation of symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects basic information of users; a generating unit that generates a customizable virtual avatar based on the information collected by the collecting unit; an interaction unit through which the avatar generated by the generation unit interacts with a user; a learning unit that learns the user's behavioral patterns obtained by the communication unit and forms a personality of an avatar; an image generation unit that generates an image based on the AC scenario performed by the AC unit; A system characterized by:

2. The collecting unit The system estimates the user's emotions and adjusts the timing of collecting basic information based on the estimated user emotions.

2. The system of claim 1.

3. The collecting unit Analyze users' past behavioral history and select the appropriate information collection method 2. The system of claim 1.

4. The collecting unit When collecting basic information, filter it based on the user's current life situation and areas of interest.

2. The system of claim 1.

5. The collecting unit When collecting basic information, select the appropriate collection method depending on the user's input method.

2. The system of claim 1.

6. The collecting unit Estimate the user's emotions and prioritize the information to be collected based on the estimated user emotions.

2. The system of claim 1.

7. The collecting unit When collecting basic information, prioritize collecting highly relevant information by taking into account the user's geographic location.

2. The system of claim 1.

8. The collecting unit When collecting basic information, we analyze your social media activity and collect related information.

2. The system of claim 1.

Images