system

Abstract

The system according to the embodiment aims to generate an AI avatar that reflects the user's personality and automatically converse with other users. [Solution] A system according to an embodiment includes an input unit, a generation unit, and a conversation unit. The input unit inputs information to reflect the user's personality. The generation unit analyzes the information input by the input unit and generates an AI avatar that reflects the user's personality. The conversation unit allows the AI ​​avatar generated by the generation unit to converse with other users.

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 according to the embodiment aims to generate an AI avatar that reflects the user's personality and automatically converse with other users. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, a generation unit, and a conversation unit. The input unit inputs information to reflect the user's personality. The generation unit analyzes the information input by the input unit and generates an AI avatar that reflects the user's personality. The conversation unit allows the AI ​​avatar generated by the generation unit to converse with other users. [Effects of the Invention]

[0007] The system according to the embodiment can generate an AI avatar that reflects the user's personality and automatically converse with other users. [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 system that automatically converses with an AI avatar created using a generation AI. The SNS platform allows users to create AI avatars that reflect their own personalities and engage in conversations with other users. For example, the SNS platform allows users to input information that reflects their personalities. The generation AI then creates an AI avatar based on this information. The created AI avatar then "broadcasts" on the user's behalf. Users can converse with AI avatars created by other people. For example, users can converse with their friends' AI avatars or discuss topics of interest with other users' AI avatars. Furthermore, users can have one-on-one conversations with AI avatars that reflect the personalities of celebrities. This allows users to communicate with others without taking up their own time. The SNS platform generates AI avatars that reflect the user's personalities and can converse with other users. For example, users can communicate with others without taking up their own time. Furthermore, the SNS platform can solve the problems of current SNS distribution services, such as only being able to have one-to-many conversations and the consuming of broadcasters' time.

[0029] An SNS platform according to an embodiment includes an input unit, a generation unit, and a conversation unit. The input unit inputs information to reflect a user's individuality. Examples of the user's individuality include, but are not limited to, personality, hobbies, and behavioral patterns. The input unit can input information in the form of, for example, text, audio, or image. The generation unit uses a generation AI to analyze the information input by the input unit and generate an AI avatar that reflects the user's individuality. The generation AI generates a conversation model that reflects the user's individuality using technologies such as deep learning and generative models. For example, the generation AI constructs a dialogue flow and a response generation algorithm based on the user's input information. The conversation unit allows the AI ​​avatar generated by the generation unit to converse with other users. The conversation unit can conduct conversations using methods such as voice dialogue and text chat. The conversation unit can converse with the AI ​​avatars of other users. For example, the conversation unit can hold discussions with the AI ​​avatars of other users. The conversation unit can also have one-on-one conversations with AI avatars that reflect the personalities of celebrities. As a result, the SNS platform according to the embodiment can generate an AI avatar that reflects the user's personality and can have conversations with other users. As a result, the SNS platform can generate an AI avatar that reflects the user's personality and can have conversations with other users. For example, users can communicate with others without taking up their own time. Furthermore, the SNS platform can solve the problems of current SNS distribution services, such as only being able to have one-to-many conversations and having to take up the distributor's time.

[0030] The input unit can input information including the user's preferences, speaking style, and topics of interest. Examples of user preferences include, but are not limited to, music preferences and food preferences. Examples of speaking style include, but are not limited to, tone of voice, choice of words, and speaking speed. Examples of topics of interest include, but are not limited to, sports, technology, and art. The input unit can input, for example, the user's preferences, speaking style, and topics of interest. By inputting information such as the user's preferences, speaking style, and topics of interest, an AI avatar that more closely reflects the user's individuality can be generated. Some or all of the above-described processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, when inputting the user's preferences, speaking style, and topics of interest, the input unit can analyze the information using AI and optimize the input content.

[0031] The generation unit can use a generation AI to generate a conversation model that reflects the user's personality. The generation AI generates a conversation model that reflects the user's personality, for example, using technologies such as deep learning and generative models. The generation unit, for example, uses the generation AI to construct a dialogue flow and a response generation algorithm based on user input information. The generation unit, for example, uses the generation AI to generate a conversation model that reflects the user's personality. This enables more natural conversations by generating a conversation model that reflects the user's personality through the generation AI. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can optimize the algorithm of the generation AI when using the generation AI to generate a conversation model that reflects the user's personality.

[0032] The conversation unit can have a conversation with the AI ​​avatar of another user. The conversation unit can have a conversation using methods such as voice dialogue or text chat. The conversation unit can have a conversation with the AI ​​avatar of another user. For example, the conversation unit can have a discussion with the AI ​​avatar of another user. This promotes communication between users by having a conversation with the AI ​​avatar of another user. Some or all of the above-mentioned processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, when having a conversation with the AI ​​avatar of another user, the conversation unit can use AI to analyze the content of the conversation and generate an optimal response.

[0033] The conversation unit can have a one-on-one conversation with an AI avatar that reflects the personality of a famous person. Famous people include, but are not limited to, historical figures and modern celebrities. The conversation unit can conduct a conversation using methods such as voice dialogue and text chat. The conversation unit can have a one-on-one conversation with an AI avatar that reflects the personality of a famous person. This allows the user to have a special experience by having a one-on-one conversation with an AI avatar that reflects the personality of a famous person. Some or all of the above-mentioned processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, when having a conversation with an AI avatar that reflects the personality of a famous person, the conversation unit can use AI to analyze the content of the conversation and generate an optimal response.

[0034] The input unit can analyze the user's past input history and suggest an appropriate input method. For example, the input unit automatically displays topics that the user has frequently input in the past as candidates. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The input unit can also predict and suggest topics to be used in a specific time period from the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can analyze the user's past input history using AI and suggest the optimal input method.

[0035] The input unit can filter input content based on the user's current activity status and areas of interest when inputting. The input unit can display related topics as input candidates based on, for example, an article or news item the user is currently reading. The input unit can also display related topics as input candidates based on an event or activity the user is currently participating in. The input unit can also display related topics as input candidates based on keywords recently searched by the user. This allows the user to input more relevant information by filtering the input content based on the user's current activity status and areas of interest. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or can be performed without using AI. For example, the input unit can analyze the user's current activity status and areas of interest using AI and present optimal input content.

[0036] The input unit can select the optimal input means depending on the user's input method when inputting data. For example, if the user selects voice input, the input unit converts the input content into text using voice recognition technology. Furthermore, if the user selects text input, the input unit can also accept the input content using a keyboard or touch panel. Furthermore, if the user selects image input, the input unit can analyze the input content using image recognition technology and extract related information. This improves input efficiency by selecting the optimal input means depending on the user's input method. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can analyze the user's input method using AI and select the optimal input means.

[0037] The input unit can prioritize input of highly relevant information based on the user's geographical location information during input. For example, when the user is in a specific area, the input unit can prioritize displaying topics related to that area as input candidates. Furthermore, when the user is traveling, the input unit can also prioritize displaying information related to the travel destination as input candidates. Furthermore, when the user is participating in a specific event, the input unit can also prioritize displaying information related to the event as input candidates. In this way, highly relevant information can be input preferentially by taking the user's geographical location information into consideration. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can analyze the user's geographical location information using AI and present optimal input content.

[0038] The input unit can analyze the user's social media activity and input related information at the time of input. For example, the input unit can display related topics as input candidates based on articles and posts shared by the user on social media. The input unit can also display related information as input candidates by referring to the activities of the user's friends on social media. The input unit can also analyze the content of the user's past posts on social media and display related information as input candidates. In this way, related information can be input by analyzing the user's social media activity. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can analyze the user's social media activity using AI and present optimal input content.

[0039] The input unit can customize the input method by reflecting the user's past feedback when inputting. The input unit can, for example, suggest the optimal input method based on feedback provided by the user in the past. The input unit can also preferentially suggest a specific input method (voice, text, etc.) based on the user's past feedback. The input unit can also analyze the user's past feedback and customize the input interface. In this way, the input method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can analyze the user's past feedback using AI and suggest the optimal input method.

[0040] The generation unit can apply different generation algorithms based on the user's personality during generation. For example, if the user likes humor, the generation AI can apply a conversation algorithm that includes humor. Furthermore, if the user has specialized knowledge, the generation unit can apply a conversation algorithm that includes technical terms. Furthermore, if the user likes emotional expression, the generation unit can apply an emotionally rich conversation algorithm. By applying different generation algorithms based on the user's personality, a more unique AI avatar can be generated. Some or all of the above-described processing in the generation unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the generation unit can analyze the user's personality using the generation AI and apply the optimal generation algorithm.

[0041] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, the generation unit allows the generation AI to improve accuracy based on the conversation history of AI avatars previously generated by the user. The generation unit can also extract specific patterns from the user's past generation results, allowing the generation AI to improve accuracy. The generation unit can also analyze the user's past generation results, allowing the generation AI to select the optimal algorithm. In this way, the accuracy of generation can be improved by referring to the user's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can analyze the user's past generation results using the generation AI and apply the optimal generation algorithm.

[0042] At the time of generation, the generation unit can adjust the level of detail of the AI ​​avatar to be generated according to the level of detail of the information input by the user. For example, if the user inputs detailed information, the generation unit causes the generation AI to generate a detailed conversation model. Furthermore, if the user inputs brief information, the generation unit can also cause the generation AI to generate a brief conversation model. Furthermore, the generation unit can cause the generation AI to generate an optimal conversation model according to the level of detail of the information input by the user. In this way, by adjusting the level of detail of the AI ​​avatar according to the level of detail of the information input by the user, a more appropriate AI avatar can be generated. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can use the generation AI to analyze the information input by the user and generate an AI avatar with an optimal level of detail.

[0043] The generation unit can determine the generation priority based on the user's submission time at the time of generation. For example, if the user is in a hurry, the generation AI can prioritize processing that information and quickly generate an AI avatar. Furthermore, if the user sets a specific deadline, the generation unit can also have the generation AI determine the priority based on that deadline. Furthermore, if the user does not specify a submission time, the generation unit can have the generation AI determine the priority by taking into account the submission times of other users. This allows for the rapid generation of AI avatars by determining the generation priority based on the user's submission time. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can use the generation AI to analyze the user's submission time and determine the optimal generation priority.

[0044] The generation unit can adjust the order of the AI ​​avatars to be generated based on the user's relevance at the time of generation. For example, if the user inputs information related to a specific topic, the generation unit causes the generation AI to prioritize generating AI avatars related to that topic. Furthermore, if the user inputs information related to a specific person, the generation unit can also cause the generation AI to prioritize generating AI avatars related to that person. Furthermore, if the user inputs information related to a specific event, the generation unit can also prioritize generating AI avatars related to that event. By adjusting the order of the AI ​​avatars based on the user's relevance, more relevant AI avatars can be generated. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can use the generation AI to analyze the user's relevance and determine the optimal generation order.

[0045] The generation unit can adjust the use of technical terms in the AI ​​avatar to be generated according to the user's level of expertise at the time of generation. For example, if the user has technical knowledge, the generation unit generates an AI avatar that uses a lot of technical terms. Furthermore, if the user has general knowledge, the generation unit can also generate an AI avatar that uses less technical terms. Furthermore, if the user is a beginner, the generation unit can generate an AI avatar that avoids technical terms. This allows for the generation of a more appropriate AI avatar by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can analyze the user's level of expertise using the generation AI and adjust the use of optimal technical terms.

[0046] During a conversation, the conversation unit can provide optimal conversation content by referring to the AI ​​avatar's past conversation history. For example, the conversation unit selects relevant topics based on the AI ​​avatar's past conversation history. The conversation unit can also select topics that will interest the user from the AI ​​avatar's past conversation history. The conversation unit can also analyze the AI ​​avatar's past conversation history and suggest optimal conversation flow. In this way, by referring to the AI ​​avatar's past conversation history, more relevant conversation content can be provided. Some or all of the above-mentioned processing in the conversation unit may be performed using AI, for example, or may be performed without using AI. For example, the conversation unit can analyze the AI ​​avatar's past conversation history using AI to provide optimal conversation content.

[0047] The conversation unit can conduct a conversation based on attribute information of the other party of the AI ​​avatar during a conversation. For example, the conversation unit selects an appropriate topic based on the age and gender of the other party. The conversation unit can also select a relevant topic based on the occupation and interests of the other party. The conversation unit can also provide optimal conversation content by referring to the other party's past conversation history. This makes it possible to provide more appropriate conversation content by taking into account the attribute information of the other party of the AI ​​avatar. Some or all of the above-mentioned processing in the conversation unit may be performed using AI, for example, or may be performed without using AI. For example, the conversation unit can analyze the attribute information of the other party of the AI ​​avatar using AI to provide optimal conversation content.

[0048] During a conversation, the conversation unit can weight the conversation based on the frequency of submissions by the AI ​​avatar's partner. For example, if the AI ​​avatar's partner submits frequently, the conversation unit can prioritize important topics. Furthermore, if the AI ​​avatar's partner submits infrequently, the conversation unit can also focus on general topics. Furthermore, the conversation unit can adjust the depth and level of detail of the conversation based on the frequency of submissions by the AI ​​avatar. Thus, by weighting the conversation based on the frequency of submissions by the AI ​​avatar's partner, more important topics can be prioritized. Some or all of the above-described processing in the conversation unit may be performed using, or without, AI. For example, the conversation unit can analyze the frequency of submissions by the AI ​​avatar's partner using AI to provide optimal conversation content.

[0049] The conversation unit can conduct a conversation while taking into account the geographical distribution of the AI ​​avatar. For example, the conversation unit selects relevant topics based on the geographical location information of the other party. The conversation unit can also provide appropriate conversation content based on the culture and customs of the other party's region. The conversation unit can also suggest an optimal conversation flow by taking into account the other party's geographical background. In this way, more appropriate conversation content can be provided by taking into account the geographical distribution of the AI ​​avatar. Some or all of the above-mentioned processing in the conversation unit may be performed using AI, for example, or may be performed without using AI. For example, the conversation unit can analyze the geographical distribution of the AI ​​avatars using AI and provide optimal conversation content.

[0050] The conversation unit can improve the accuracy of the conversation by referring to literature related to the AI ​​avatar during the conversation. For example, the conversation unit can refer to academic papers related to the AI ​​avatar to provide accurate information. The conversation unit can also refer to news articles related to the AI ​​avatar to provide the latest information. The conversation unit can also refer to books related to the AI ​​avatar to provide in-depth knowledge. In this way, more accurate information can be provided by referring to literature related to the AI ​​avatar. Some or all of the above-mentioned processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can use AI to analyze literature related to the AI ​​avatar to provide optimal conversation content.

[0051] The conversation unit can conduct a conversation while taking into account the market value of the AI ​​avatar. For example, the conversation unit can prioritize important topics based on the market value of the other person. The conversation unit can also provide appropriate conversation content based on the market value of the other person. The conversation unit can also adjust the depth and level of detail of the conversation based on the market value of the other person. This allows the AI ​​avatar to prioritize more important topics by taking into account the market value of the AI ​​avatar. Some or all of the above-mentioned processing in the conversation unit can be performed using AI, for example, or can be performed without using AI. For example, the conversation unit can analyze the market value of the AI ​​avatar using AI and provide optimal conversation content.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The SNS platform can further include a learning management unit that manages the user's learning progress. The learning management unit records the content and progress of the user's learning and suggests a study plan based on this data. For example, if the user has difficulty in a particular subject, the learning management unit can provide resources to focus on that part. The learning management unit can also adjust the content of the AI ​​avatar's conversations based on the user's learning progress. For example, if the user is studying a specific topic, the learning management unit can suggest conversations related to that topic. This can effectively support the user's learning.

[0054] The SNS platform can further include a hobby management unit that digs deeper into the user's hobbies and interests. The hobby management unit records the user's areas of interest and activities and suggests new hobbies and activities based on this data. For example, if the user is interested in music, the hobby management unit can suggest new artists and songs. The hobby management unit can also tailor the conversation content of the AI ​​avatar based on the user's hobbies. For example, if the user is interested in a particular genre of movie, the hobby management unit can suggest conversations related to that genre. This allows the user's hobbies and interests to be further explored.

[0055] The SNS platform may further include a lifestyle rhythm management unit that manages the user's lifestyle rhythm. The lifestyle rhythm management unit records the user's sleep patterns, meal timings, and other data, and provides advice to optimize the user's lifestyle rhythm based on this data. For example, if the user has an irregular sleep pattern, the lifestyle rhythm management unit can provide advice to promote regular sleep. The lifestyle rhythm management unit can also adjust the content of the AI ​​avatar's conversation based on the user's lifestyle rhythm. For example, if the user is active in the morning, the lifestyle rhythm management unit can provide conversation content appropriate for the morning. This makes it possible to provide appropriate support according to the user's lifestyle rhythm.

[0056] The SNS platform can further include a purchase history analysis unit that analyzes a user's purchasing history. The purchase history analysis unit records the products and services a user has purchased in the past and suggests new products and services based on this data. For example, if a user frequently purchases products from a particular brand, the purchase history analysis unit can suggest new products from that brand. The purchase history analysis unit can also adjust the content of the AI ​​avatar's conversations based on the user's purchasing history. For example, it can suggest conversations about products the user has recently purchased. This makes it possible to provide appropriate support based on the user's purchasing history.

[0057] The SNS platform can further include a travel planning unit that supports users' travel plans. The travel planning unit records the destinations and itineraries of the trips the user is planning and provides travel advice based on this data. For example, if the user plans to visit a specific city, the travel planning unit can suggest tourist spots and restaurants in that city. The travel planning unit can also adjust the content of the conversation of the AI ​​avatar based on the user's travel plans. For example, the travel planning unit can suggest conversations about activities the user will do at their travel destination. This can effectively support the user's travel plans.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The input unit inputs information that reflects the user's individuality. The user's individuality includes, but is not limited to, for example, personality, hobbies, and behavioral patterns. The input unit can input information in the form of, for example, text, audio, or image. Step 2: The generation unit uses a generation AI to analyze the information input by the input unit and generate an AI avatar that reflects the user's personality. The generation AI uses technologies such as deep learning and generative models to generate a conversation model that reflects the user's personality. For example, the generation unit constructs a dialogue flow and a response generation algorithm based on the user's input information. Step 3: The conversation unit allows the AI ​​avatar generated by the generation unit to converse with other users. The conversation unit can conduct conversations using methods such as voice dialogue or text chat. The conversation unit can converse with the AI ​​avatar of another user. For example, the conversation unit can hold discussions with the AI ​​avatar of another user. The conversation unit can also have one-on-one conversations with an AI avatar that reflects the personality of a celebrity.

[0060] (Example 2) An SNS platform according to an embodiment of the present invention is a system that automatically converses with an AI avatar created using a generation AI. The SNS platform allows users to create AI avatars that reflect their own personalities and engage in conversations with other users. For example, the SNS platform allows users to input information that reflects their personalities. The generation AI then creates an AI avatar based on this information. The created AI avatar then "broadcasts" on the user's behalf. Users can converse with AI avatars created by other people. For example, users can converse with their friends' AI avatars or discuss topics of interest with other users' AI avatars. Furthermore, users can have one-on-one conversations with AI avatars that reflect the personalities of celebrities. This allows users to communicate with others without taking up their own time. The SNS platform generates AI avatars that reflect the user's personalities and can converse with other users. For example, users can communicate with others without taking up their own time. Furthermore, the SNS platform can solve the problems of current SNS distribution services, such as only being able to have one-to-many conversations and the consuming of broadcasters' time.

[0061] An SNS platform according to an embodiment includes an input unit, a generation unit, and a conversation unit. The input unit inputs information to reflect a user's individuality. Examples of the user's individuality include, but are not limited to, personality, hobbies, and behavioral patterns. The input unit can input information in the form of, for example, text, audio, or image. The generation unit uses a generation AI to analyze the information input by the input unit and generate an AI avatar that reflects the user's individuality. The generation AI generates a conversation model that reflects the user's individuality using technologies such as deep learning and generative models. For example, the generation AI constructs a dialogue flow and a response generation algorithm based on the user's input information. The conversation unit allows the AI ​​avatar generated by the generation unit to converse with other users. The conversation unit can conduct conversations using methods such as voice dialogue and text chat. The conversation unit can converse with the AI ​​avatars of other users. For example, the conversation unit can hold discussions with the AI ​​avatars of other users. The conversation unit can also have one-on-one conversations with AI avatars that reflect the personalities of celebrities. As a result, the SNS platform according to the embodiment can generate an AI avatar that reflects the user's personality and can have conversations with other users. As a result, the SNS platform can generate an AI avatar that reflects the user's personality and can have conversations with other users. For example, users can communicate with others without taking up their own time. Furthermore, the SNS platform can solve the problems of current SNS distribution services, such as only being able to have one-to-many conversations and having to take up the distributor's time.

[0062] The input unit can input information including the user's preferences, speaking style, and topics of interest. Examples of user preferences include, but are not limited to, music preferences and food preferences. Examples of speaking style include, but are not limited to, tone of voice, choice of words, and speaking speed. Examples of topics of interest include, but are not limited to, sports, technology, and art. The input unit can input, for example, the user's preferences, speaking style, and topics of interest. By inputting information such as the user's preferences, speaking style, and topics of interest, an AI avatar that more closely reflects the user's individuality can be generated. Some or all of the above-described processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, when inputting the user's preferences, speaking style, and topics of interest, the input unit can analyze the information using AI and optimize the input content.

[0063] The generation unit can use a generation AI to generate a conversation model that reflects the user's personality. The generation AI generates a conversation model that reflects the user's personality, for example, using technologies such as deep learning and generative models. The generation unit, for example, uses the generation AI to construct a dialogue flow and a response generation algorithm based on user input information. The generation unit, for example, uses the generation AI to generate a conversation model that reflects the user's personality. This enables more natural conversations by generating a conversation model that reflects the user's personality through the generation AI. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can optimize the algorithm of the generation AI when using the generation AI to generate a conversation model that reflects the user's personality.

[0064] The conversation unit can have a conversation with the AI ​​avatar of another user. The conversation unit can have a conversation using methods such as voice dialogue or text chat. The conversation unit can have a conversation with the AI ​​avatar of another user. For example, the conversation unit can have a discussion with the AI ​​avatar of another user. This promotes communication between users by having a conversation with the AI ​​avatar of another user. Some or all of the above-mentioned processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, when having a conversation with the AI ​​avatar of another user, the conversation unit can use AI to analyze the content of the conversation and generate an optimal response.

[0065] The conversation unit can have a one-on-one conversation with an AI avatar that reflects the personality of a famous person. Famous people include, but are not limited to, historical figures and modern celebrities. The conversation unit can conduct a conversation using methods such as voice dialogue and text chat. The conversation unit can have a one-on-one conversation with an AI avatar that reflects the personality of a famous person. This allows the user to have a special experience by having a one-on-one conversation with an AI avatar that reflects the personality of a famous person. Some or all of the above-mentioned processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, when having a conversation with an AI avatar that reflects the personality of a famous person, the conversation unit can use AI to analyze the content of the conversation and generate an optimal response.

[0066] The input unit can estimate the user's emotions and prioritize input information based on the estimated user emotions. For example, when the user is excited, the input unit uses an emotion engine to estimate the user's emotions and prioritize input of topics of interest. Furthermore, when the user is calm, the input unit can also estimate the user's emotions and prioritize input of detailed information. Furthermore, when the user is stressed, the input unit can also estimate the user's emotions and prioritize input of simple items. This allows more appropriate information to be input by prioritizing input information based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or without AI. For example, when the input unit estimates the user's emotions and prioritizes input information based on the estimated emotions, it can use AI to analyze emotion data and present optimal input content.

[0067] The input unit can analyze the user's past input history and suggest an appropriate input method. For example, the input unit automatically displays topics that the user has frequently input in the past as candidates. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The input unit can also predict and suggest topics to be used in a specific time period from the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can analyze the user's past input history using AI and suggest the optimal input method.

[0068] The input unit can filter input content based on the user's current activity status and areas of interest when inputting. The input unit can display related topics as input candidates based on, for example, an article or news item the user is currently reading. The input unit can also display related topics as input candidates based on an event or activity the user is currently participating in. The input unit can also display related topics as input candidates based on keywords recently searched by the user. This allows the user to input more relevant information by filtering the input content based on the user's current activity status and areas of interest. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or can be performed without using AI. For example, the input unit can analyze the user's current activity status and areas of interest using AI and present optimal input content.

[0069] The input unit can select the optimal input means depending on the user's input method when inputting data. For example, if the user selects voice input, the input unit converts the input content into text using voice recognition technology. Furthermore, if the user selects text input, the input unit can also accept the input content using a keyboard or touch panel. Furthermore, if the user selects image input, the input unit can analyze the input content using image recognition technology and extract related information. This improves input efficiency by selecting the optimal input means depending on the user's input method. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can analyze the user's input method using AI and select the optimal input means.

[0070] The input unit can estimate the user's emotion and adjust the expression method of the input content based on the estimated user emotion. For example, if the user is nervous, the input unit uses an emotion engine to estimate the emotion and provide a simple, highly visible expression method. Furthermore, if the user is relaxed, the input unit can use an emotion engine to estimate the emotion and provide a detailed expression method. Furthermore, if the user is excited, the input unit can use an emotion engine to estimate the emotion and provide a visually stimulating expression method. This allows for adjusting the expression method of the input content based on the user's emotion to provide a more appropriate expression method. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, when the input unit estimates the user's emotion and adjusts the expression method of the input content based on the estimated emotion, it can use AI to analyze emotion data and present an optimal expression method.

[0071] The input unit can prioritize input of highly relevant information based on the user's geographical location information during input. For example, when the user is in a specific area, the input unit can prioritize displaying topics related to that area as input candidates. Furthermore, when the user is traveling, the input unit can also prioritize displaying information related to the travel destination as input candidates. Furthermore, when the user is participating in a specific event, the input unit can also prioritize displaying information related to the event as input candidates. In this way, highly relevant information can be input preferentially by taking the user's geographical location information into consideration. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can analyze the user's geographical location information using AI and present optimal input content.

[0072] The input unit can analyze the user's social media activity and input related information at the time of input. For example, the input unit can display related topics as input candidates based on articles and posts shared by the user on social media. The input unit can also display related information as input candidates by referring to the activities of the user's friends on social media. The input unit can also analyze the content of the user's past posts on social media and display related information as input candidates. In this way, related information can be input by analyzing the user's social media activity. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can analyze the user's social media activity using AI and present optimal input content.

[0073] The input unit can customize the input method by reflecting the user's past feedback when inputting. The input unit can, for example, suggest the optimal input method based on feedback provided by the user in the past. The input unit can also preferentially suggest a specific input method (voice, text, etc.) based on the user's past feedback. The input unit can also analyze the user's past feedback and customize the input interface. In this way, the input method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can analyze the user's past feedback using AI and suggest the optimal input method.

[0074] The generation unit can estimate the user's emotions and adjust the personality of the generated AI avatar based on the estimated user emotions. For example, if the user is relaxed, the generation AI can reflect the user's emotions and generate an AI avatar with a calm personality. Furthermore, if the user is excited, the generation AI can reflect the user's emotions and generate an AI avatar with a lively personality. Furthermore, if the user is stressed, the generation AI can reflect the user's emotions and generate an AI avatar with a calm personality. This allows for more natural conversation by adjusting the personality of the AI ​​avatar based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. Examples of the generation AI include, but are not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can analyze the user's emotions using the generation AI and generate an AI avatar with an optimal personality.

[0075] The generation unit can apply different generation algorithms based on the user's personality during generation. For example, if the user likes humor, the generation AI can apply a conversation algorithm that includes humor. Furthermore, if the user has specialized knowledge, the generation unit can apply a conversation algorithm that includes technical terms. Furthermore, if the user likes emotional expression, the generation unit can apply an emotionally rich conversation algorithm. By applying different generation algorithms based on the user's personality, a more unique AI avatar can be generated. Some or all of the above-described processing in the generation unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the generation unit can analyze the user's personality using the generation AI and apply the optimal generation algorithm.

[0076] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, the generation unit allows the generation AI to improve accuracy based on the conversation history of AI avatars previously generated by the user. The generation unit can also extract specific patterns from the user's past generation results, allowing the generation AI to improve accuracy. The generation unit can also analyze the user's past generation results, allowing the generation AI to select the optimal algorithm. In this way, the accuracy of generation can be improved by referring to the user's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can analyze the user's past generation results using the generation AI and apply the optimal generation algorithm.

[0077] At the time of generation, the generation unit can adjust the level of detail of the AI ​​avatar to be generated according to the level of detail of the information input by the user. For example, if the user inputs detailed information, the generation unit causes the generation AI to generate a detailed conversation model. Furthermore, if the user inputs brief information, the generation unit can also cause the generation AI to generate a brief conversation model. Furthermore, the generation unit can cause the generation AI to generate an optimal conversation model according to the level of detail of the information input by the user. In this way, by adjusting the level of detail of the AI ​​avatar according to the level of detail of the information input by the user, a more appropriate AI avatar can be generated. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can use the generation AI to analyze the information input by the user and generate an AI avatar with an optimal level of detail.

[0078] The generation unit can estimate the user's emotions and adjust the appearance of the generated AI avatar based on the estimated user's emotions. For example, if the user is relaxed, the generation AI can reflect the user's emotions and generate an AI avatar with a calm appearance. Furthermore, if the user is excited, the generation AI can reflect the user's emotions and generate an AI avatar with a lively appearance. Furthermore, if the user is stressed, the generation AI can reflect the user's emotions and generate an AI avatar with a calm appearance. By adjusting the appearance of the AI ​​avatar based on the user's emotions, an AI avatar with a more natural appearance can be generated. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. Examples of the generation AI include, but are not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can analyze the user's emotions using the generation AI and generate an AI avatar with an optimal appearance.

[0079] The generation unit can determine the generation priority based on the user's submission time at the time of generation. For example, if the user is in a hurry, the generation AI can prioritize processing that information and quickly generate an AI avatar. Furthermore, if the user sets a specific deadline, the generation unit can also have the generation AI determine the priority based on that deadline. Furthermore, if the user does not specify a submission time, the generation unit can have the generation AI determine the priority by taking into account the submission times of other users. This allows for the rapid generation of AI avatars by determining the generation priority based on the user's submission time. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can use the generation AI to analyze the user's submission time and determine the optimal generation priority.

[0080] The generation unit can adjust the order of the AI ​​avatars to be generated based on the user's relevance at the time of generation. For example, if the user inputs information related to a specific topic, the generation unit causes the generation AI to prioritize generating AI avatars related to that topic. Furthermore, if the user inputs information related to a specific person, the generation unit can also cause the generation AI to prioritize generating AI avatars related to that person. Furthermore, if the user inputs information related to a specific event, the generation unit can also prioritize generating AI avatars related to that event. By adjusting the order of the AI ​​avatars based on the user's relevance, more relevant AI avatars can be generated. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can use the generation AI to analyze the user's relevance and determine the optimal generation order.

[0081] The generation unit can adjust the use of technical terms in the AI ​​avatar to be generated according to the user's level of expertise at the time of generation. For example, if the user has technical knowledge, the generation unit generates an AI avatar that uses a lot of technical terms. Furthermore, if the user has general knowledge, the generation unit can also generate an AI avatar that uses less technical terms. Furthermore, if the user is a beginner, the generation unit can generate an AI avatar that avoids technical terms. This allows for the generation of a more appropriate AI avatar by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can analyze the user's level of expertise using the generation AI and adjust the use of optimal technical terms.

[0082] The conversation unit can estimate the user's emotions and adjust the conversation topic based on the estimated user's emotions. For example, if the user is relaxed, the conversation unit's emotion engine can estimate the user's emotions and select a calm topic. Furthermore, if the user is excited, the conversation unit's emotion engine can estimate the user's emotions and select a lively topic. Furthermore, if the user is stressed, the conversation unit's emotion engine can estimate the user's emotions and select a relaxing topic. This allows for more appropriate conversation by adjusting the conversation topic based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the conversation unit may be performed using, for example, AI, or without AI. For example, when estimating the user's emotions and adjusting the conversation topic based on the estimated emotions, the conversation unit can use AI to analyze emotion data and present an optimal topic.

[0083] During a conversation, the conversation unit can provide optimal conversation content by referring to the AI ​​avatar's past conversation history. For example, the conversation unit selects relevant topics based on the AI ​​avatar's past conversation history. The conversation unit can also select topics that will interest the user from the AI ​​avatar's past conversation history. The conversation unit can also analyze the AI ​​avatar's past conversation history and suggest optimal conversation flow. In this way, by referring to the AI ​​avatar's past conversation history, more relevant conversation content can be provided. Some or all of the above-mentioned processing in the conversation unit may be performed using AI, for example, or may be performed without using AI. For example, the conversation unit can analyze the AI ​​avatar's past conversation history using AI to provide optimal conversation content.

[0084] The conversation unit can conduct a conversation based on attribute information of the other party of the AI ​​avatar during a conversation. For example, the conversation unit selects an appropriate topic based on the age and gender of the other party. The conversation unit can also select a relevant topic based on the occupation and interests of the other party. The conversation unit can also provide optimal conversation content by referring to the other party's past conversation history. This makes it possible to provide more appropriate conversation content by taking into account the attribute information of the other party of the AI ​​avatar. Some or all of the above-mentioned processing in the conversation unit may be performed using AI, for example, or may be performed without using AI. For example, the conversation unit can analyze the attribute information of the other party of the AI ​​avatar using AI to provide optimal conversation content.

[0085] During a conversation, the conversation unit can weight the conversation based on the frequency of submissions by the AI ​​avatar's partner. For example, if the AI ​​avatar's partner submits frequently, the conversation unit can prioritize important topics. Furthermore, if the AI ​​avatar's partner submits infrequently, the conversation unit can also focus on general topics. Furthermore, the conversation unit can adjust the depth and level of detail of the conversation based on the frequency of submissions by the AI ​​avatar. Thus, by weighting the conversation based on the frequency of submissions by the AI ​​avatar's partner, more important topics can be prioritized. Some or all of the above-described processing in the conversation unit may be performed using, or without, AI. For example, the conversation unit can analyze the frequency of submissions by the AI ​​avatar's partner using AI to provide optimal conversation content.

[0086] The conversation unit can estimate the user's emotions and adjust the conversational expression method based on the estimated user's emotions. For example, if the user is nervous, the conversation unit uses an emotion engine to estimate the user's emotions and provide a simple, highly visible expression method. Furthermore, if the user is relaxed, the conversation unit can use an emotion engine to estimate the user's emotions and provide a detailed expression method. Furthermore, if the user is excited, the conversation unit can use an emotion engine to estimate the user's emotions and provide a visually stimulating expression method. This allows the conversational expression method to be adjusted based on the user's emotions, thereby providing a more appropriate expression method. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the conversation unit may be performed using, for example, AI, or without AI. For example, when the conversation unit estimates the user's emotions and adjusts the conversational expression method based on the estimated emotions, it can use AI to analyze emotion data and present an optimal expression method.

[0087] The conversation unit can conduct a conversation while taking into account the geographical distribution of the AI ​​avatar. For example, the conversation unit selects relevant topics based on the geographical location information of the other party. The conversation unit can also provide appropriate conversation content based on the culture and customs of the other party's region. The conversation unit can also suggest an optimal conversation flow by taking into account the other party's geographical background. In this way, more appropriate conversation content can be provided by taking into account the geographical distribution of the AI ​​avatar. Some or all of the above-mentioned processing in the conversation unit may be performed using AI, for example, or may be performed without using AI. For example, the conversation unit can analyze the geographical distribution of the AI ​​avatars using AI and provide optimal conversation content.

[0088] The conversation unit can improve the accuracy of the conversation by referring to literature related to the AI ​​avatar during the conversation. For example, the conversation unit can refer to academic papers related to the AI ​​avatar to provide accurate information. The conversation unit can also refer to news articles related to the AI ​​avatar to provide the latest information. The conversation unit can also refer to books related to the AI ​​avatar to provide in-depth knowledge. In this way, more accurate information can be provided by referring to literature related to the AI ​​avatar. Some or all of the above-mentioned processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can use AI to analyze literature related to the AI ​​avatar to provide optimal conversation content.

[0089] The conversation unit can conduct a conversation while taking into account the market value of the AI ​​avatar. For example, the conversation unit can prioritize important topics based on the market value of the other person. The conversation unit can also provide appropriate conversation content based on the market value of the other person. The conversation unit can also adjust the depth and level of detail of the conversation based on the market value of the other person. This allows the AI ​​avatar to prioritize more important topics by taking into account the market value of the AI ​​avatar. Some or all of the above-mentioned processing in the conversation unit can be performed using AI, for example, or can be performed without using AI. For example, the conversation unit can analyze the market value of the AI ​​avatar using AI and provide optimal conversation content. === Hard Collateral 1-1 === Each of the multiple elements including the input unit, generation unit, and conversation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit is realized by the reception device 38 of the smart device 14, and inputs information to reflect the user's personality in the form of text, voice, image, or the like. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an AI avatar that reflects the user's personality using a generation AI. The conversation unit is realized, for example, by the control unit 46A of the smart device 14, and the generated AI avatar converses with other users by voice dialogue, text chat, or the like. === Hard Collateral 1-2 === Each of the multiple elements including the input unit, generation unit, and conversation unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the smart glasses 214, and inputs information to reflect the user's personality by voice. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an AI avatar that reflects the user's personality using a generation AI. The conversation unit is realized, for example, by the control unit 46A of the smart glasses 214, and the generated AI avatar converses with other users by voice dialogue, text chat, or other methods. === Hard Collateral 1-3 === Each of the multiple elements including the input unit, generation unit, and conversation unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the headset-type terminal 314, and inputs information to reflect the user's personality by voice. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an AI avatar that reflects the user's personality using a generation AI. The conversation unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and the generated AI avatar converses with other users by voice dialogue, text chat, or other methods. === Hard Collateral 1-4 === Each of the multiple elements including the input unit, generation unit, and conversation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the robot 414, and inputs information to reflect the user's personality by voice. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an AI avatar that reflects the user's personality using a generation AI. The conversation unit is realized, for example, by the control unit 46A of the robot 414, and the generated AI avatar converses with other users by voice dialogue, text chat, or other methods.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The SNS platform can further include a health management unit that monitors the user's health condition. The health management unit collects health data such as the user's heart rate, blood pressure, and stress level, and evaluates the user's health condition based on this data. For example, if the user is under high stress, the health management unit can provide advice on how to relax. The health management unit can also adjust the content of the AI ​​avatar's conversation based on the user's health condition. For example, if the user is tired, the health management unit can select topics that will help them relax. This makes it possible to provide appropriate support according to the user's health condition.

[0092] The SNS platform can further include a learning management unit that manages the user's learning progress. The learning management unit records the content and progress of the user's learning and suggests a study plan based on this data. For example, if the user has difficulty in a particular subject, the learning management unit can provide resources to focus on that part. The learning management unit can also adjust the content of the AI ​​avatar's conversations based on the user's learning progress. For example, if the user is studying a specific topic, the learning management unit can suggest conversations related to that topic. This can effectively support the user's learning.

[0093] The SNS platform can further include a hobby management unit that digs deeper into the user's hobbies and interests. The hobby management unit records the user's areas of interest and activities and suggests new hobbies and activities based on this data. For example, if the user is interested in music, the hobby management unit can suggest new artists and songs. The hobby management unit can also tailor the conversation content of the AI ​​avatar based on the user's hobbies. For example, if the user is interested in a particular genre of movie, the hobby management unit can suggest conversations related to that genre. This allows the user's hobbies and interests to be further explored.

[0094] The SNS platform may further include an emotion monitoring unit that monitors a user's emotions in real time. The emotion monitoring unit analyzes the user's facial expressions and voice tone to estimate the user's emotions in real time. For example, if the user is sad, the emotion monitoring unit can detect that emotion and provide comforting conversation content. The emotion monitoring unit may also adjust the AI ​​avatar's expression style based on the user's emotions. For example, if the user is excited, the emotion monitoring unit can provide a visually stimulating expression style. This allows the system to provide appropriate support according to the user's emotions.

[0095] The SNS platform may further include a lifestyle rhythm management unit that manages the user's lifestyle rhythm. The lifestyle rhythm management unit records the user's sleep patterns, meal timings, and other data, and provides advice to optimize the user's lifestyle rhythm based on this data. For example, if the user has an irregular sleep pattern, the lifestyle rhythm management unit can provide advice to promote regular sleep. The lifestyle rhythm management unit can also adjust the content of the AI ​​avatar's conversation based on the user's lifestyle rhythm. For example, if the user is active in the morning, the lifestyle rhythm management unit can provide conversation content appropriate for the morning. This makes it possible to provide appropriate support according to the user's lifestyle rhythm.

[0096] The SNS platform can further include a purchase history analysis unit that analyzes a user's purchasing history. The purchase history analysis unit records the products and services a user has purchased in the past and suggests new products and services based on this data. For example, if a user frequently purchases products from a particular brand, the purchase history analysis unit can suggest new products from that brand. The purchase history analysis unit can also adjust the content of the AI ​​avatar's conversations based on the user's purchasing history. For example, it can suggest conversations about products the user has recently purchased. This makes it possible to provide appropriate support based on the user's purchasing history.

[0097] The SNS platform can further include a travel planning unit that supports users' travel plans. The travel planning unit records the destinations and itineraries of the trips the user is planning and provides travel advice based on this data. For example, if the user plans to visit a specific city, the travel planning unit can suggest tourist spots and restaurants in that city. The travel planning unit can also adjust the content of the conversation of the AI ​​avatar based on the user's travel plans. For example, the travel planning unit can suggest conversations about activities the user will do at their travel destination. This can effectively support the user's travel plans.

[0098] The SNS platform can further estimate the user's emotions and adjust the appearance of the AI ​​avatar based on the estimated emotions. For example, if the user is relaxed, the generation AI can reflect that emotion and generate an AI avatar with a calm appearance. Alternatively, if the user is excited, the generation AI can reflect that emotion and generate an AI avatar with a lively appearance. Alternatively, if the user is stressed, the generation AI can reflect that emotion and generate an AI avatar with a calm appearance. In this way, by adjusting the appearance of the AI ​​avatar based on the user's emotions, it is possible to generate an AI avatar with a more natural appearance.

[0099] The SNS platform can further estimate the user's emotions and adjust the tone of voice of the AI ​​avatar based on the estimated emotions. For example, if the user is relaxed, the generation AI can reflect that emotion and generate an AI avatar with a calm tone of voice. Alternatively, if the user is excited, the generation AI can reflect that emotion and generate an AI avatar with a lively tone of voice. Alternatively, if the user is stressed, the generation AI can reflect that emotion and generate an AI avatar with a calm tone of voice. This allows for more natural conversations by adjusting the tone of voice of the AI ​​avatar based on the user's emotions.

[0100] The SNS platform can further estimate the user's emotions and adjust the gestures of the AI ​​avatar based on the estimated emotions. For example, if the user is relaxed, the generation AI can reflect that emotion and generate an AI avatar with calm gestures. Alternatively, if the user is excited, the generation AI can reflect that emotion and generate an AI avatar with lively gestures. Alternatively, if the user is stressed, the generation AI can reflect that emotion and generate an AI avatar with calm gestures. This allows for more natural conversations by adjusting the gestures of the AI ​​avatar based on the user's emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The input unit inputs information that reflects the user's individuality. The user's individuality includes, but is not limited to, for example, personality, hobbies, and behavioral patterns. The input unit can input information in the form of, for example, text, audio, or image. Step 2: The generation unit uses a generation AI to analyze the information input by the input unit and generate an AI avatar that reflects the user's personality. The generation AI uses technologies such as deep learning and generative models to generate a conversation model that reflects the user's personality. For example, the generation unit constructs a dialogue flow and a response generation algorithm based on the user's input information. Step 3: The conversation unit allows the AI ​​avatar generated by the generation unit to converse with other users. The conversation unit can conduct conversations using methods such as voice dialogue or text chat. The conversation unit can converse with the AI ​​avatar of another user. For example, the conversation unit can hold discussions with the AI ​​avatar of another user. The conversation unit can also have one-on-one conversations with an AI avatar that reflects the personality of a celebrity.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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 input unit for inputting information that reflects the user's individuality; a generation unit that analyzes the information input by the input unit and generates an AI avatar that reflects the user's individuality; a conversation unit in which the AI ​​avatar generated by the generation unit converses with other users; Equipped with A system characterized by:

2. The input unit Enter information including your preferences, speaking style, and topics of interest 2. The system of claim 1.

3. The generation unit Generative AI generates a conversation model that reflects the user's personality 2. The system of claim 1.

4. The conversation unit is Have conversations with other users' AI avatars 2. The system of claim 1.

5. The conversation unit is Have one-on-one conversations with AI avatars that reflect the personalities of famous people 2. The system of claim 1.

6. The input unit Estimate the user's emotions and prioritize input information based on the estimated user emotions.

2. The system of claim 1.

7. The input unit Analyzes the user's past input history and suggests appropriate input methods 2. The system of claim 1.

8. The input unit As you type, filter your input based on your current activity and interests 2. The system of claim 1.

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