system

Abstract

An object of a system according to an embodiment is to make SNS distribution efficient by using a AI avatar that reflects the personality of a user and to enable a one to-one conversation.SOLUTION: A system according to an embodiment includes a AI double generation part, a distribution substitute part, and a conversation processing part. The AI avatar generating unit generates a AI avatar reflecting the personality of the user. The distribution substitute part substitutes distribution on the SNS by using the AI copy generated by the AI copy generation part. The conversation processing unit allows the AI avatar generated by the AI avatar generation unit to have a conversation with another AI avatar.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] With conventional technology, social media distribution had the problem of taking up the broadcaster's time and only allowing one-to-many conversations.

[0005] The system of the embodiment aims to improve the efficiency of SNS distribution by using an AI avatar that reflects the user's personality and enable one-on-one conversations. [Means for solving the problem]

[0006] The system according to the embodiment includes an AI avatar generation unit, a distribution agent unit, and a conversation processing unit. The AI ​​avatar generation unit generates an AI avatar that reflects the user's personality. The distribution agent unit uses the AI ​​avatar generated by the AI ​​avatar generation unit to distribute on SNS. The conversation processing unit enables the AI ​​avatar generated by the AI ​​avatar generation unit to converse with other AI avatars. [Effects of the Invention]

[0007] The system according to the embodiment can streamline SNS distribution by using an AI avatar that reflects the user's personality, enabling one-on-one conversations. [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 SNS platform according to an embodiment of the present invention is a SNS platform that allows users to automatically converse with their own AI shadow avatars, which are created using a generation AI. This platform allows users to create AI avatars that reflect their own personalities, and these AI avatars then act as "broadcasters" on their behalf, thereby saving users time and enabling them to converse individually with more people. This allows the SNS platform to save users time and enable them to converse individually with more people.

[0029] An SNS platform according to an embodiment includes an AI avatar generation unit, a distribution agent unit, and a conversation processing unit. The AI ​​avatar generation unit generates an AI avatar that reflects the user's personality. For example, when a user inputs their favorite hobbies, special skills, speaking style, etc., the generation AI analyzes that information and creates an AI avatar that resembles the user. The generation AI receives input in the form of prompts containing instructions on what the user wants the generation AI to do, and the generation AI generates an AI avatar based on the prompts. The distribution agent unit uses the AI ​​avatar generated by the AI ​​avatar generation unit to distribute content on the SNS on behalf of the user. For example, the AI ​​avatar posts content on behalf of the user and replies to comments. This allows the user to continue their activities on the SNS while saving time. The conversation processing unit allows the AI ​​avatar generated by the AI ​​avatar generation unit to converse with other AI avatars. For example, the user can chat with the AI ​​avatars of friends and family, or exchange information with the AI ​​avatars of people who share their hobbies. As a result, the SNS platform according to the embodiment generates an AI avatar that reflects the user's personality, and the AI ​​avatar acts as an agent for distribution on the SNS and can converse with other AI avatars.

[0030] The AI ​​avatar generation unit analyzes a user's past SNS posts and message history to more accurately reflect their personality. For example, the AI ​​avatar generation unit analyzes a user's past SNS posts to extract frequently used words and phrases. For example, it reflects the user's frequently used expressions and catchphrases in the AI ​​avatar. The AI ​​avatar generation unit also analyzes the user's message history to extract conversation patterns and topic trends. For example, it reflects topics that the user is interested in in the AI ​​avatar. The AI ​​avatar generation unit also analyzes emotional changes and tone from the user's SNS posts and message history. For example, if there are many positive posts, the AI ​​avatar will also generate positive responses. This makes it possible to more accurately reflect a user's personality by analyzing the user's past SNS posts and message history.

[0031] The AI ​​avatar generation unit can analyze the user's voice data and generate an AI avatar that reflects the user's tone of voice and speaking rhythm. For example, the AI ​​avatar generation unit collects the user's voice data and analyzes the user's tone of voice and pitch. For example, it reflects the user's vocal characteristics in the AI ​​avatar. The AI ​​avatar generation unit also analyzes the rhythm and tempo of the user's speaking style to reproduce natural conversation. For example, it generates responses that match the user's speaking style. The AI ​​avatar generation unit also analyzes changes in emotions from the user's voice data. For example, it reflects the tone of voice when the user is emotional in the AI ​​avatar. In this way, by analyzing the user's voice data, it is possible to generate an AI avatar that reflects the user's tone of voice and speaking rhythm.

[0032] The AI ​​avatar generation unit can analyze the user's physical movements and gestures and generate a 3D avatar AI avatar that reflects them. The AI ​​avatar generation unit, for example, uses motion capture technology to capture the user's physical movements. For example, it analyzes the user's movements in real time and reflects them in the 3D avatar. The AI ​​avatar generation unit also analyzes the user's gestures and reproduces natural movements. For example, it reflects hand movements and facial expressions in the 3D avatar. The AI ​​avatar generation unit also collects the user's physical movement data and learns movement patterns. For example, a specific movement can trigger the AI ​​avatar to make a specific response. In this way, by analyzing the user's physical movements and gestures, it can generate a 3D avatar AI avatar that reflects them.

[0033] The AI ​​avatar generation unit can make it possible to share an AI avatar that reflects a user's personality across different SNS platforms. The AI ​​avatar generation unit, for example, develops an API for sharing a user's AI avatar across different SNS platforms. For example, it enables the same AI avatar to be used across multiple platforms, such as Facebook (registered trademark) and Twitter (registered trademark). The AI ​​avatar generation unit also builds a system for synchronizing AI avatar data across different SNS platforms. For example, it integrates user profile information and conversation history. The AI ​​avatar generation unit also standardizes data formats to enable consistent use of a user's AI avatar across different SNS platforms. For example, it generates an AI avatar using a common data format. This allows AI avatars that reflect a user's personality to be shared across different SNS platforms, providing a unified experience.

[0034] The distribution agency can analyze the user's schedule and automatically determine the optimal distribution timing. For example, the distribution agency will build a system in which an AI avatar analyzes the user's schedule and automatically determines the optimal distribution timing. For example, distribution will be performed to avoid times when the user is busy. The distribution agency will also analyze the user's calendar or planner to select the optimal distribution timing. For example, distribution will be performed at a time when the user is free. The distribution agency will also develop a system in which an AI avatar learns the user's past distribution data and predicts the optimal distribution timing. For example, the timing will be determined based on past distribution success rates. This will enable effective distribution by automatically determining the optimal distribution timing based on the user's schedule.

[0035] The distribution agency can learn from a user's past distribution data and generate more effective distribution content. For example, the distribution agency will build a system in which an AI avatar learns a user's past distribution data and automatically generates effective distribution content. For example, it will create new distributions based on past successful distribution content. The distribution agency will also analyze a user's past distribution data and optimize distribution content based on viewer reactions. For example, it will reuse content that has high viewer engagement. The distribution agency will also develop a system in which an AI avatar learns from past distribution data and generates distribution content tailored to viewer preferences. For example, it will prioritize distribution of topics that viewers like. In this way, more effective distribution content can be generated by learning from a user's past distribution data.

[0036] The distribution agency can simultaneously distribute content on multiple SNS platforms on behalf of the user. The distribution agency, for example, builds a system in which an AI avatar distributes content on multiple SNS platforms simultaneously on behalf of the user. For example, it may simultaneously live stream on Facebook (registered trademark) and Twitter (registered trademark). The distribution agency also develops an API for simultaneously distributing content on multiple SNS platforms. For example, it synchronizes the content distributed between different platforms. The distribution agency also develops a system that generates content tailored to the characteristics of each platform when the AI ​​avatar distributes content on multiple SNS platforms simultaneously. For example, it distributes different messages for each platform. This allows the system to reach a wide audience by simultaneously distributing content on multiple SNS platforms on behalf of the user.

[0037] The conversation processing unit can analyze the conversation history of the conversation partner and generate more personalized responses. For example, the conversation processing unit uses an AI avatar to analyze the conversation partner's past conversation history and extract frequently used words and phrases. For example, it reflects expressions frequently used by the conversation partner in the response. The conversation processing unit also analyzes the conversation partner's past conversation history and extracts topics of interest and topic trends. For example, it generates a response based on themes that interest the conversation partner. The conversation processing unit also analyzes emotional changes and tone from the conversation partner's past conversation history. For example, it reflects words used by the conversation partner when they are positive in the response. In this way, by analyzing the conversation partner's past conversation history, more personalized responses can be generated.

[0038] The conversation processing unit can learn the interests and concerns of the conversation partner and generate conversation content based on that. For example, the conversation processing unit will build a system in which an AI avatar learns the interests and concerns of the conversation partner and generates conversation content based on that. For example, it will respond based on the conversation partner's favorite hobbies or topics. The conversation processing unit will also analyze the conversation partner's interests and concerns in real time and select appropriate conversation content. For example, it will reflect topics that the conversation partner has recently been interested in in the response. The conversation processing unit will also develop a system that learns the conversation partner's past conversation data and generates conversation content based on those interests and concerns. For example, it will incorporate topics that the conversation partner frequently talks about into the response. In this way, it will be possible to learn the interests and concerns of the conversation partner and generate conversation content based on them.

[0039] The conversation processing unit can translate conversations between users who speak different languages ​​in real time to support communication. For example, the conversation processing unit builds a system in which an AI avatar translates conversations between users who speak different languages ​​in real time. For example, it instantly translates conversations between Japanese and English. The conversation processing unit also uses the real-time translation function to achieve smooth communication between users who speak different languages. For example, it translates without interrupting the flow of conversation. The conversation processing unit also develops a system in which the AI ​​avatar understands the context of the conversation and provides appropriate translation. For example, it performs translation that takes cultural nuances into consideration. This makes it possible to support smooth communication by translating conversations between users who speak different languages ​​in real time.

[0040] The conversation processing unit can simultaneously converse with the AI ​​avatars of multiple users, realizing group chats. The conversation processing unit, for example, builds a system in which an AI avatar converses with the AI ​​avatars of multiple users simultaneously. For example, multiple conversations can proceed simultaneously in a group chat format. The conversation processing unit also allows the AI ​​avatar to appropriately manage each user's comments in a group chat, realizing smooth conversations. For example, it organizes the order and content of comments. The conversation processing unit also develops a system in which the AI ​​avatar analyzes the emotions of multiple users and responds according to the atmosphere of the group chat. For example, it responds in line with the overall emotional state. This makes it possible to simultaneously converse with the AI ​​avatars of multiple users, realizing group chats.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] Social media platforms can also be equipped with a health management module that monitors the user's health status and adjusts the AI ​​avatar's responses based on that data. For example, it can analyze the user's sleep patterns and provide relaxing conversations if the user is sleep-deprived. The health management module can also collect the user's exercise data and send encouraging messages after exercise. Furthermore, the health management module can analyze the user's dietary data and generate messages recommending a balanced diet. This allows for personalized responses based on the user's health status.

[0043] The SNS platform may further include a location information unit that uses the user's geographic location information to provide local events and news. For example, if the user is in a specific area, the location information unit may provide information about events taking place in that area. The location information unit may also recommend nearby restaurants and tourist attractions based on the user's location information. Furthermore, the location information unit may analyze the user's movement history and suggest recommended places based on places visited in the past. This allows for personalized information to be provided based on the user's geographic location information.

[0044] The SNS platform may further include an advertisement serving unit that analyzes a user's purchase history and provides individually customized advertisements. For example, advertisements for related products may be displayed based on products the user has previously purchased. The advertisement serving unit may also analyze the user's interests and generate advertisements based on the results. Furthermore, the advertisement serving unit may provide discount information or sale information at specific times based on the user's purchase history. This makes it possible to provide personalized advertisements based on the user's purchase history.

[0045] The SNS platform may further include a learning support unit that analyzes a user's learning history and provides individually customized learning content. For example, the learning support unit may provide relevant learning materials based on topics the user has previously studied. The learning support unit may also analyze the user's learning progress and propose a learning plan based on that analysis. Furthermore, the learning support unit may provide practice questions to strengthen specific skills or knowledge based on the user's learning history. This enables personalized learning support based on the user's learning history.

[0046] The SNS platform may further include a music recommendation unit that analyzes a user's music playback history and provides personalized music recommendations. For example, the music recommendation unit may recommend related artists and songs based on songs the user has played in the past. The music recommendation unit may also analyze the user's playback history and generate playlists tailored to specific moods or situations. The music recommendation unit may also provide information about new releases and concerts based on the user's music playback history. This enables personalized music recommendations based on the user's music playback history.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The AI ​​avatar generation unit generates an AI avatar that reflects the user's personality. For example, when a user inputs their favorite hobbies, special skills, and speaking style, the generation AI analyzes this information and creates an AI avatar that resembles the user. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates an AI avatar based on that prompt. Step 2: The distribution agent uses the AI ​​avatar generated by the AI ​​avatar generation unit to distribute content on the SNS. For example, the AI ​​avatar posts content on the user's behalf and replies to comments. This allows the user to continue their activities on the SNS while saving time. Step 3: The conversation processing unit allows the AI ​​avatar generated by the AI ​​avatar generation unit to converse with other AI avatars. For example, the user can chat with the AI ​​avatars of friends and family, or exchange information with the AI ​​avatars of people who share common hobbies.

[0049] (Example 2) An SNS platform according to an embodiment of the present invention is a SNS platform that allows users to automatically converse with their own AI shadow avatars, which are created using a generation AI. This platform allows users to create AI avatars that reflect their own personalities, and these AI avatars then act as "broadcasters" on their behalf, thereby saving users time and enabling them to converse individually with more people. This allows the SNS platform to save users time and enable them to converse individually with more people.

[0050] An SNS platform according to an embodiment includes an AI avatar generation unit, a distribution agent unit, and a conversation processing unit. The AI ​​avatar generation unit generates an AI avatar that reflects the user's personality. For example, when a user inputs their favorite hobbies, special skills, speaking style, etc., the generation AI analyzes that information and creates an AI avatar that resembles the user. The generation AI receives input in the form of prompts containing instructions on what the user wants the generation AI to do, and the generation AI generates an AI avatar based on the prompts. The distribution agent unit uses the AI ​​avatar generated by the AI ​​avatar generation unit to distribute content on the SNS on behalf of the user. For example, the AI ​​avatar posts content on behalf of the user and replies to comments. This allows the user to continue their activities on the SNS while saving time. The conversation processing unit allows the AI ​​avatar generated by the AI ​​avatar generation unit to converse with other AI avatars. For example, the user can chat with the AI ​​avatars of friends and family, or exchange information with the AI ​​avatars of people who share their hobbies. As a result, the SNS platform according to the embodiment generates an AI avatar that reflects the user's personality, and the AI ​​avatar acts as an agent for distribution on the SNS and can converse with other AI avatars.

[0051] The AI ​​avatar generation unit can monitor the user's emotional state in real time and generate an AI avatar response based on that emotion. The AI ​​avatar generation unit, for example, uses facial expression recognition technology to monitor the user's emotional state in real time. For example, it analyzes the user's facial expressions through a camera to estimate the user's emotional state. The AI ​​avatar generation unit also uses voice analysis technology to monitor the user's emotional state in real time. For example, it analyzes the tone and rhythm of the user's voice to estimate the user's emotional state. The AI ​​avatar generation unit also uses biosensors to monitor the user's emotional state in real time. For example, it measures heart rate and electrodermal activity to estimate the user's emotional state. This enables more personalized conversations by generating responses based on the user's emotional state.

[0052] The AI ​​avatar generation unit analyzes a user's past SNS posts and message history to more accurately reflect their personality. For example, the AI ​​avatar generation unit analyzes a user's past SNS posts to extract frequently used words and phrases. For example, it reflects the user's frequently used expressions and catchphrases in the AI ​​avatar. The AI ​​avatar generation unit also analyzes the user's message history to extract conversation patterns and topic trends. For example, it reflects topics that the user is interested in in the AI ​​avatar. The AI ​​avatar generation unit also analyzes emotional changes and tone from the user's SNS posts and message history. For example, if there are many positive posts, the AI ​​avatar will also generate positive responses. This makes it possible to more accurately reflect a user's personality by analyzing the user's past SNS posts and message history.

[0053] The AI ​​avatar generation unit can analyze the user's voice data and generate an AI avatar that reflects the user's tone of voice and speaking rhythm. For example, the AI ​​avatar generation unit collects the user's voice data and analyzes the user's tone of voice and pitch. For example, it reflects the user's vocal characteristics in the AI ​​avatar. The AI ​​avatar generation unit also analyzes the rhythm and tempo of the user's speaking style to reproduce natural conversation. For example, it generates responses that match the user's speaking style. The AI ​​avatar generation unit also analyzes changes in emotions from the user's voice data. For example, it reflects the tone of voice when the user is emotional in the AI ​​avatar. In this way, by analyzing the user's voice data, it is possible to generate an AI avatar that reflects the user's tone of voice and speaking rhythm.

[0054] The AI ​​avatar generation unit can analyze the user's physical movements and gestures and generate a 3D avatar AI avatar that reflects them. The AI ​​avatar generation unit, for example, uses motion capture technology to capture the user's physical movements. For example, it analyzes the user's movements in real time and reflects them in the 3D avatar. The AI ​​avatar generation unit also analyzes the user's gestures and reproduces natural movements. For example, it reflects hand movements and facial expressions in the 3D avatar. The AI ​​avatar generation unit also collects the user's physical movement data and learns movement patterns. For example, a specific movement can trigger the AI ​​avatar to make a specific response. In this way, by analyzing the user's physical movements and gestures, it can generate a 3D avatar AI avatar that reflects them.

[0055] The AI ​​avatar generation unit can make it possible to share an AI avatar that reflects a user's personality across different SNS platforms. The AI ​​avatar generation unit, for example, develops an API for sharing a user's AI avatar across different SNS platforms. For example, it enables the same AI avatar to be used across multiple platforms, such as Facebook (registered trademark) and Twitter (registered trademark). The AI ​​avatar generation unit also builds a system for synchronizing AI avatar data across different SNS platforms. For example, it integrates user profile information and conversation history. The AI ​​avatar generation unit also standardizes data formats to enable consistent use of a user's AI avatar across different SNS platforms. For example, it generates an AI avatar using a common data format. This allows AI avatars that reflect a user's personality to be shared across different SNS platforms, providing a unified experience.

[0056] The AI ​​avatar generation unit can use the emotion estimation function to automatically change the appearance and clothing of the AI ​​avatar based on the user's emotions. For example, the AI ​​avatar generation unit uses the emotion estimation function to build a system that automatically changes the appearance and clothing according to the user's emotional state. For example, when the user is happy, the AI ​​avatar generation unit changes the user's clothing to bright colors. The AI ​​avatar generation unit also analyzes the user's emotional state in real time and dynamically changes the appearance of the AI ​​avatar. For example, when the user is angry, the AI ​​avatar generation unit makes the user's expression stern. The AI ​​avatar generation unit also develops a system that automatically selects the AI ​​avatar's clothing and accessories based on the emotion estimation data. For example, when the user is relaxed, the AI ​​avatar's clothing is changed to casual clothing. This allows the AI ​​avatar's appearance and clothing to be automatically changed based on the user's emotions, providing a more personalized experience.

[0057] The delivery agent unit can estimate the user's emotions and automatically generate delivery content according to those emotions. For example, the delivery agent unit will build a system in which an AI avatar analyzes the user's emotions in real time and automatically generates delivery content according to those emotions. For example, when the user is happy, a positive message will be delivered. The delivery agent unit will also use the emotion estimation function to generate delivery content based on the user's emotional state. For example, when the user is sad, an encouraging message will be delivered. The delivery agent unit will also develop a system in which the AI ​​avatar selects appropriate delivery content based on the user's emotional data. For example, when the user is excited, an energetic message will be delivered. This will enable more effective delivery by automatically generating delivery content according to the user's emotions.

[0058] The distribution agency can analyze the user's schedule and automatically determine the optimal distribution timing. For example, the distribution agency will build a system in which an AI avatar analyzes the user's schedule and automatically determines the optimal distribution timing. For example, distribution will be performed to avoid times when the user is busy. The distribution agency will also analyze the user's calendar or planner to select the optimal distribution timing. For example, distribution will be performed at a time when the user is free. The distribution agency will also develop a system in which an AI avatar learns the user's past distribution data and predicts the optimal distribution timing. For example, the timing will be determined based on past distribution success rates. This will enable effective distribution by automatically determining the optimal distribution timing based on the user's schedule.

[0059] The distribution agency can learn from a user's past distribution data and generate more effective distribution content. For example, the distribution agency will build a system in which an AI avatar learns a user's past distribution data and automatically generates effective distribution content. For example, it will create new distributions based on past successful distribution content. The distribution agency will also analyze a user's past distribution data and optimize distribution content based on viewer reactions. For example, it will reuse content that has high viewer engagement. The distribution agency will also develop a system in which an AI avatar learns from past distribution data and generates distribution content tailored to viewer preferences. For example, it will prioritize distribution of topics that viewers like. In this way, more effective distribution content can be generated by learning from a user's past distribution data.

[0060] The distribution agent unit can perform live streaming on behalf of the user and realize real-time interaction with viewers. For example, the distribution agent unit will build a system in which an AI avatar performs live streaming on behalf of the user and realizes real-time interaction with viewers. For example, it will respond immediately to viewer comments. The distribution agent unit will also add a function that allows the AI ​​avatar to answer viewer questions in real time during live streaming. For example, when a viewer inputs a question, the AI ​​avatar will immediately respond. The distribution agent unit will also develop a system in which the AI ​​avatar analyzes the viewer's emotions during live streaming and responds according to those emotions. For example, if the viewer is happy, it will respond positively. This will allow live streaming on behalf of the user and realize real-time interaction with viewers.

[0061] The distribution agency can simultaneously distribute content on multiple SNS platforms on behalf of the user. The distribution agency, for example, builds a system in which an AI avatar distributes content on multiple SNS platforms simultaneously on behalf of the user. For example, it may simultaneously live stream on Facebook (registered trademark) and Twitter (registered trademark). The distribution agency also develops an API for simultaneously distributing content on multiple SNS platforms. For example, it synchronizes the content distributed between different platforms. The distribution agency also develops a system that generates content tailored to the characteristics of each platform when the AI ​​avatar distributes content on multiple SNS platforms simultaneously. For example, it distributes different messages for each platform. This allows the system to reach a wide audience by simultaneously distributing content on multiple SNS platforms on behalf of the user.

[0062] The distribution agent unit can use the emotion estimation function to adjust the distribution content in real time according to the viewer's emotions. The distribution agent unit, for example, uses the emotion estimation function to build a system that adjusts the distribution content in real time according to the viewer's emotions. For example, when the viewer is excited, the content is changed to energetic content. The distribution agent unit also analyzes the viewer's emotion data in real time and dynamically changes the distribution content. For example, when the viewer is relaxed, the content is changed to calm content. The distribution agent unit also develops a system that automatically generates distribution content that matches the viewer's emotions based on the emotion estimation data. For example, when the viewer is sad, an encouraging message is delivered. In this way, viewer engagement can be increased by adjusting the distribution content in real time according to the viewer's emotions.

[0063] The conversation processing unit can estimate the emotions of the conversation partner and generate a response according to those emotions. For example, the conversation processing unit will build a system in which an AI avatar analyzes the emotions of the conversation partner in real time and generates a response according to those emotions. For example, if the conversation partner is happy, it will make a positive response. The conversation processing unit will also use the emotion estimation function to generate a response based on the conversation partner's emotional state. For example, if the conversation partner is sad, it will offer words of encouragement. The conversation processing unit will also develop a system in which the AI ​​avatar selects an appropriate response based on the conversation partner's emotional data. For example, if the conversation partner is angry, it will make a calm response. This will enable more natural and personalized conversations by generating responses according to the conversation partner's emotions.

[0064] The conversation processing unit can analyze the conversation history of the conversation partner and generate more personalized responses. For example, the conversation processing unit uses an AI avatar to analyze the conversation partner's past conversation history and extract frequently used words and phrases. For example, it reflects expressions frequently used by the conversation partner in the response. The conversation processing unit also analyzes the conversation partner's past conversation history and extracts topics of interest and topic trends. For example, it generates a response based on themes that interest the conversation partner. The conversation processing unit also analyzes emotional changes and tone from the conversation partner's past conversation history. For example, it reflects words used by the conversation partner when they are positive in the response. In this way, by analyzing the conversation partner's past conversation history, more personalized responses can be generated.

[0065] The conversation processing unit can learn the interests and concerns of the conversation partner and generate conversation content based on that. For example, the conversation processing unit will build a system in which an AI avatar learns the interests and concerns of the conversation partner and generates conversation content based on that. For example, it will respond based on the conversation partner's favorite hobbies or topics. The conversation processing unit will also analyze the conversation partner's interests and concerns in real time and select appropriate conversation content. For example, it will reflect topics that the conversation partner has recently been interested in in the response. The conversation processing unit will also develop a system that learns the conversation partner's past conversation data and generates conversation content based on those interests and concerns. For example, it will incorporate topics that the conversation partner frequently talks about into the response. In this way, it will be possible to learn the interests and concerns of the conversation partner and generate conversation content based on them.

[0066] The conversation processing unit can translate conversations between users who speak different languages ​​in real time to support communication. For example, the conversation processing unit builds a system in which an AI avatar translates conversations between users who speak different languages ​​in real time. For example, it instantly translates conversations between Japanese and English. The conversation processing unit also uses the real-time translation function to achieve smooth communication between users who speak different languages. For example, it translates without interrupting the flow of conversation. The conversation processing unit also develops a system in which the AI ​​avatar understands the context of the conversation and provides appropriate translation. For example, it performs translation that takes cultural nuances into consideration. This makes it possible to support smooth communication by translating conversations between users who speak different languages ​​in real time.

[0067] The conversation processing unit can simultaneously converse with the AI ​​avatars of multiple users, realizing group chats. The conversation processing unit, for example, builds a system in which an AI avatar converses with the AI ​​avatars of multiple users simultaneously. For example, multiple conversations can proceed simultaneously in a group chat format. The conversation processing unit also allows the AI ​​avatar to appropriately manage each user's comments in a group chat, realizing smooth conversations. For example, it organizes the order and content of comments. The conversation processing unit also develops a system in which the AI ​​avatar analyzes the emotions of multiple users and responds according to the atmosphere of the group chat. For example, it responds in line with the overall emotional state. This makes it possible to simultaneously converse with the AI ​​avatars of multiple users, realizing group chats.

[0068] The conversation processing unit can use the emotion estimation function to automatically suggest conversation topics according to the emotions of the conversation partner. The conversation processing unit, for example, uses the emotion estimation function to build a system that automatically suggests conversation topics according to the emotions of the conversation partner. For example, when the conversation partner is happy, it suggests a fun topic. The conversation processing unit also analyzes the emotional state of the conversation partner in real time and selects an appropriate conversation topic. For example, when the conversation partner is depressed, it suggests an encouraging topic. The conversation processing unit also develops a system that automatically generates conversation topics according to the emotions of the conversation partner based on the emotion estimation data. For example, when the conversation partner is excited, it suggests an energetic topic. This automatically suggests conversation topics according to the emotions of the conversation partner, enabling more natural and personalized conversations.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] Social media platforms can also be equipped with a health management module that monitors the user's health status and adjusts the AI ​​avatar's responses based on that data. For example, it can analyze the user's sleep patterns and provide relaxing conversations if the user is sleep-deprived. The health management module can also collect the user's exercise data and send encouraging messages after exercise. Furthermore, the health management module can analyze the user's dietary data and generate messages recommending a balanced diet. This allows for personalized responses based on the user's health status.

[0071] The SNS platform may further include a location information unit that uses the user's geographic location information to provide local events and news. For example, if the user is in a specific area, the location information unit may provide information about events taking place in that area. The location information unit may also recommend nearby restaurants and tourist attractions based on the user's location information. Furthermore, the location information unit may analyze the user's movement history and suggest recommended places based on places visited in the past. This allows for personalized information to be provided based on the user's geographic location information.

[0072] The SNS platform may further include an advertisement serving unit that analyzes a user's purchase history and provides individually customized advertisements. For example, advertisements for related products may be displayed based on products the user has previously purchased. The advertisement serving unit may also analyze the user's interests and generate advertisements based on the results. Furthermore, the advertisement serving unit may provide discount information or sale information at specific times based on the user's purchase history. This makes it possible to provide personalized advertisements based on the user's purchase history.

[0073] The SNS platform may further include a learning support unit that analyzes a user's learning history and provides individually customized learning content. For example, the learning support unit may provide relevant learning materials based on topics the user has previously studied. The learning support unit may also analyze the user's learning progress and propose a learning plan based on that analysis. Furthermore, the learning support unit may provide practice questions to strengthen specific skills or knowledge based on the user's learning history. This enables personalized learning support based on the user's learning history.

[0074] The SNS platform may further include a music recommendation unit that analyzes a user's music playback history and provides personalized music recommendations. For example, the music recommendation unit may recommend related artists and songs based on songs the user has played in the past. The music recommendation unit may also analyze the user's playback history and generate playlists tailored to specific moods or situations. The music recommendation unit may also provide information about new releases and concerts based on the user's music playback history. This enables personalized music recommendations based on the user's music playback history.

[0075] The SNS platform can also estimate the user's emotions and provide relaxation content based on those emotions. For example, when a user is feeling stressed, it can provide relaxing music or meditation guides. Also, when a user is tired, it can use the emotion estimation function to recommend short videos that will refresh the user. Furthermore, when a user is feeling anxious, it can use the emotion estimation function to provide messages or content that will give a sense of security. This makes it possible to provide relaxation content based on the user's emotions.

[0076] The SNS platform can also estimate the user's emotions and provide an exercise plan based on the user's emotions. For example, if the user is energetic, a high-intensity exercise can be recommended. Also, using the emotion estimation function, if the user wants to relax, a yoga or stretching plan can be provided. Furthermore, using the emotion estimation function, if the user is feeling stressed, a relaxation exercise can be recommended. This makes it possible to provide an exercise plan based on the user's emotions.

[0077] The SNS platform can also estimate the user's emotions and provide meal plans based on those emotions. For example, when a user is tired, it can recommend meals to replenish energy. Also, using the emotion estimation function, when a user wants to relax, it can provide recipes using ingredients that have a relaxing effect. Furthermore, when a user is feeling stressed, it can also use the emotion estimation function to recommend meals that have a stress-reducing effect. This makes it possible to provide meal plans based on the user's emotions.

[0078] The SNS platform can also estimate the user's emotions and provide travel plans based on those emotions. For example, when a user is feeling adventurous, it can recommend active travel plans. Also, using the emotion estimation function, it can recommend travel plans to resorts and hot springs when the user wants to relax. Furthermore, it can also use the emotion estimation function to recommend travel plans that allow users to experience nature when they are feeling stressed. This makes it possible to provide travel plans based on the user's emotions.

[0079] The SNS platform can also estimate the user's emotions and recommend books based on those emotions. For example, when a user wants to relax, it can recommend relaxing novels or essays. Also, by using the emotion estimation function, it can provide action or adventure novels when the user is energetic. Furthermore, by using the emotion estimation function, it can recommend self-help books that have a stress-reducing effect when the user is feeling stressed. This makes it possible to recommend books based on the user's emotions.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The AI ​​avatar generation unit generates an AI avatar that reflects the user's personality. For example, when a user inputs their favorite hobbies, special skills, and speaking style, the generation AI analyzes this information and creates an AI avatar that resembles the user. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates an AI avatar based on that prompt. Step 2: The distribution agent uses the AI ​​avatar generated by the AI ​​avatar generation unit to distribute content on the SNS. For example, the AI ​​avatar posts content on the user's behalf and replies to comments. This allows the user to continue their activities on the SNS while saving time. Step 3: The conversation processing unit allows the AI ​​avatar generated by the AI ​​avatar generation unit to converse with other AI avatars. For example, the user can chat with the AI ​​avatars of friends and family, or exchange information with the AI ​​avatars of people who share common hobbies.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an AI avatar generation unit that generates an AI avatar that reflects the user's personality; a distribution agent that uses the AI ​​avatar generated by the AI ​​avatar generation unit to distribute on an SNS; a conversation processing unit that enables the AI ​​avatar generated by the AI ​​avatar generation unit to converse with other AI avatars; A system characterized by:

2. The AI ​​avatar generation unit: Monitoring the emotional state of the user in real time and generating a response from the AI ​​avatar in accordance with the emotional state.

2. The system of claim 1.

3. The AI ​​avatar generation unit: Analyze the user's past SNS posts and message history to more accurately reflect their personality.

2. The system of claim 1.

4. The AI ​​avatar generation unit: Analyzing the user's voice data and generating the AI ​​avatar that reflects the tone of voice and the rhythm of the speaking style 2. The system of claim 1.

5. The AI ​​avatar generation unit: Analyzing the user's body movements and gestures and generating the AI ​​avatar that reflects them.

2. The system of claim 1.

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