System

Abstract

An object of the system according to the embodiment is to enable a user to create a character that suits the user's preferences, interact with the character, and sell the created character to another user.SOLUTION: A system according to an embodiment includes a character creation unit, a dialogue unit, a sales unit, and a use unit. The character creation component generates a character on the basis of a user instruction. The interaction component implements an interaction between the user and the character created by the character creator. The sales unit sells the character created by the character creation unit. The utilization unit realizes a dialogue between the character sold by the sales unit and the purchaser.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that it is difficult for users to create characters of their choice, interact with them, or sell the characters they have created to other users.

[0005] The system according to the embodiment aims to allow users to create characters of their choice, interact with them, and sell the characters they have created to other users. [Means for solving the problem]

[0006] The system according to the embodiment includes a character creation unit, a dialogue unit, a sales unit, and a utilization unit. The character creation unit generates a character based on a user's instructions. The dialogue unit enables a dialogue between the user and the character created by the character creation unit. The sales unit sells the character created by the character creation unit. The utilization unit enables a dialogue between the purchaser and the character sold by the sales unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to create their own characters, interact with them, and sell the characters they have created to 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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A chatbot platform according to an embodiment of the present invention is a system that utilizes a generation AI to allow users to interact with their own personalized characters. This system allows users to create characters according to their preferences and enjoy interacting with them. Furthermore, characters created by users can be sold to other users, who can then use the characters as their own chat partners. This allows the chatbot platform to allow users to enjoy interacting with their own personalized characters and also earn revenue by selling the characters to other users.

[0029] A chatbot platform according to an embodiment includes a character creation unit, a dialogue unit, a sales unit, and a utilization unit. The character creation unit generates a character based on a user's instructions. For example, when a user inputs a prompt such as "I want to create a kind and knowledgeable character," the generation AI generates a character based on the user's instructions. The generation AI can generate a character using a text generation AI (e.g., LLM) or a multimodal generation AI. The dialogue unit enables a dialogue between the character generated by the character creation unit and the user. For example, the generation AI analyzes the user's utterances and generates an appropriate response. When a user asks the character, "What book did you read today?", the generation AI generates a response such as, "I read a mystery novel today. It was very interesting!" The sales unit sells the character generated by the character creation unit. For example, when a user instructs the generation AI, "I want to sell this character," the generation AI saves the character's information and publishes it on a sales platform. The utilization unit enables a dialogue between the character sold by the sales unit and the buyer. For example, if a purchaser asks the character, "What movie did you see today?", the generation AI generates a response such as, "I saw an action movie today. It was very exciting!". As a result, the chatbot platform according to the embodiment allows users to enjoy conversations with their own characters and can also earn revenue by selling the characters to other users. For example, a user can create a character according to their preferences and enjoy daily conversations with that character. Other users can also purchase that character and use it as their own chat partner. This is expected to promote interaction between users and revitalize the entire platform.

[0030] The character creation unit can analyze the user's past dialogue history and automatically adjust the character's personality and speaking style based on the history. The character creation unit, for example, analyzes the user's past dialogue history and automatically adjusts the character's personality and speaking style based on the content of the history. For example, if the user has asked many questions in the past, the character is set to have a knowledgeable personality and is good at explaining things. The dialogue history is saved in the form of a text log, an audio log, dialogue metadata, etc. The personality is adjusted based on specific definitions such as friendly, cool, and humorous. The speaking style is adjusted based on criteria such as tone, speed, and accent. This makes it possible to automatically adjust the character's personality and speaking style based on the user's past dialogue history.

[0031] The character creation unit can generate a character's appearance and personality from images and audio data uploaded by a user. The character creation unit, for example, analyzes images uploaded by a user and generates a character's appearance based on the images. For example, if a user uploads a photo of their pet, a character resembling the pet is generated. The character creation unit also analyzes audio data uploaded by a user and generates a character's personality based on the audio. For example, if a user uploads audio data speaking in a calm voice, the character is set to have a calm personality. Images are uploaded in formats such as JPEG, PNG, and GIF. Audio data is uploaded in formats such as WAV, MP3, and AAC. Appearance is generated based on criteria such as facial features, clothing, and accessories. This allows a character's appearance and personality to be generated from images and audio data uploaded by a user.

[0032] The character creation unit can automatically generate a character's background story based on a theme or scenario selected by a user. The character creation unit automatically generates a character's background story based on, for example, a theme selected by a user. For example, if a user selects the theme "fantasy," the character has a background story of an adventure in a magical world. The character creation unit also automatically generates a character's background story based on a scenario selected by a user. For example, if a user selects "detective" as the scenario, the character has a background story of a detective who solves mysteries. The theme is selected based on a specific type, such as fantasy, science fiction, or modern. The scenario is generated based on specific content, such as a storyboard, plot, and events. The background story is generated based on criteria such as the character's past, motivation, and goals. This allows a character's background story to be automatically generated based on a theme or scenario selected by a user.

[0033] The character creation unit can generate a new character by combining parts of characters created by other users. The character creation unit generates a new character by combining, for example, parts of the appearance and personality of characters created by other users. For example, the character creation unit combines the hairstyle of one character with the personality of another character. The character creation unit also generates a new character by combining parts of characters created by other users. For example, the character creation unit combines the clothing of one character with the accessories of another character. The character parts are combined based on specific definitions such as facial features, clothing, and accessories. The new character is generated based on a method for combining parts of existing characters. This allows a new character to be generated by combining parts of characters created by other users.

[0034] The dialogue unit can suggest new topics based on the user's interests and concerns. For example, the dialogue unit analyzes the user's past dialogue history, and a character suggests new topics based on that history. For example, if the user talks a lot about movies, the character suggests new movie topics. The dialogue unit also suggests new topics based on the user's interests and concerns. For example, if the user is interested in sports, the character suggests the latest sports news. Interests and concerns are estimated based on specific definitions such as past dialogue history and user profile. New topics are suggested based on topic selection criteria based on the user's interests and concerns. This makes it possible to suggest new topics based on the user's interests and concerns.

[0035] The dialogue unit can make the dialogue more interactive by having the character suggest a quiz or game to the user during the dialogue. The dialogue unit can make the dialogue more interactive, for example, by having the character suggest a quiz to the user during the dialogue. For example, the dialogue unit can ask a quiz in the form of, "Try answering the next question!" The dialogue unit can also make the dialogue more interactive by having the character suggest a game to the user during the dialogue. For example, the dialogue unit can suggest a game in the form of, "Let's solve a puzzle together!" The quiz is suggested based on specific content such as a knowledge quiz or a puzzle quiz. The game is suggested based on specific content such as a mini-game or an interactive game. This allows the character to suggest a quiz or game to the user during the dialogue, making the dialogue more interactive.

[0036] The dialogue unit allows a character to manage the user's schedule and tasks and send reminders. For example, the dialogue unit allows a character to manage the user's schedule and remind them of important appointments. For example, the character may send a reminder in the form of, "Don't forget about tomorrow's meeting!". The dialogue unit also allows a character to manage the user's tasks and send reminders. For example, the character may send a reminder in the form of, "Check your tasks for today!". Schedules are managed based on specific management methods such as calendar integration and notification methods. Tasks are managed based on specific management methods such as task lists and priority settings. Reminders are sent based on specific sending methods such as notification timing and notification method. This allows the character to manage the user's schedule and tasks and send reminders.

[0037] The sales department can implement an algorithm that automatically adjusts prices based on the popularity and ratings of the characters being sold. For example, the sales department implements an algorithm that analyzes the popularity and ratings of the characters being sold in real time and automatically adjusts prices based on the results. For example, the price of a highly popular character is increased. The sales department also implements an algorithm that automatically adjusts prices based on ratings of the characters being sold. For example, the price of a highly rated character is increased. Popularity is analyzed based on specific evaluation criteria such as sales volume and user ratings. Ratings are analyzed based on specific criteria such as user reviews and star ratings. Prices are adjusted based on specific adjustment methods such as supply and demand and popularity-based pricing. The algorithm is implemented based on specific content such as a machine learning algorithm or a rule-based algorithm. In this way, prices can be automatically adjusted based on the popularity and ratings of the characters being sold.

[0038] The sales department can analyze the sales history of the character and propose an optimal sales strategy to the user. For example, the sales department analyzes the sales history of the character and builds a system that proposes an optimal sales strategy to the user based on that data. For example, if sales are concentrated during a specific time period, sales are promoted during that time period. The sales department also analyzes the sales history of the character and proposes an optimal sales strategy to the user based on that data. For example, sales are concentrated during periods when a specific character is popular. The sales history is analyzed based on specific details such as sales volume, sales period, and user attributes. The sales strategy is proposed based on specific proposal methods such as pricing and promotion methods. In this way, the sales history of the character can be analyzed and an optimal sales strategy can be proposed to the user.

[0039] The sales department may add a demo function to the character sales platform that allows purchasers to try out characters. For example, the sales department may add a demo function to the character sales platform that allows purchasers to try out characters. For example, the sales department may provide a function that allows purchasers to interact with a character for a short period of time before purchasing. The sales department may also add a demo function to the character sales platform that allows purchasers to try out characters. For example, the sales department may provide a demo that allows purchasers to check the character's functions before purchasing. The demo function is implemented based on specific details such as a trial period, trial scope, and trial method. In this way, the sales department may add a demo function to the character sales platform that allows purchasers to try out characters.

[0040] The sales department can add a function to compare characters created by other users, making it easier for purchasers to select the most suitable character. The sales department, for example, can add a function to compare characters created by other users, making it easier for purchasers to select the most suitable character. For example, the characteristics and ratings of multiple characters can be displayed side by side. The sales department can also add a function to compare characters created by other users, making it easier for purchasers to select the most suitable character. For example, the sales department can provide a function to compare characters' appearances and personalities. The comparison function is implemented based on specific content such as comparison items, comparison method, and display method. In this way, the sales department can add a function to compare characters created by other users, making it easier for purchasers to select the most suitable character.

[0041] The utilization unit can analyze the dialogue history when the purchaser uses the character and further personalize the character's responses. For example, when the purchaser uses the character, the utilization unit analyzes the dialogue history and personalizes the character's responses based on the content of the dialogue history. For example, the character returns an appropriate response based on the content of past dialogue. Furthermore, when the purchaser uses the character, the utilization unit analyzes the dialogue history and personalizes the character's responses based on the content of the dialogue history. For example, if the purchaser likes a particular topic, the utilization unit generates a response related to that topic. The dialogue history is saved based on specific content such as a text log, an audio log, and dialogue metadata. Personalization is performed based on user preferences and an adjustment method based on past dialogue history. In this way, when the purchaser uses the character, the utilization unit can analyze the dialogue history and further personalize the character's responses.

[0042] The utilization unit can add a function to generate a dialogue log that can be shared with other users when a purchaser uses a character. The utilization unit, for example, adds a function to automatically generate a dialogue log and share it with other users when a purchaser uses a character. For example, the utilization unit saves the dialogue content in text format and generates a sharing link. The utilization unit also adds a function to automatically generate a dialogue log and share it with other users when a purchaser uses a character. For example, the utilization unit saves the dialogue content in audio format and generates a sharing link. The dialogue log is saved based on specific content such as a text log, an audio log, and dialogue metadata. The sharing function is implemented based on specific methods such as the scope of sharing, the sharing method, and privacy settings. This makes it possible to add a function to generate a dialogue log that can be shared with other users when a purchaser uses a character.

[0043] The utilization unit adds a function that allows the purchaser to customize the character, thereby realizing more personalized dialogue. The utilization unit adds, for example, a function that allows the purchaser to customize the character's appearance and personality, thereby realizing more personalized dialogue. For example, the purchaser can change the character's clothing and hairstyle. The utilization unit also adds a function that allows the purchaser to customize the character's personality, thereby realizing more personalized dialogue. For example, the purchaser can change the character's way of speaking and tone of voice. The customization function is implemented based on specific methods such as changing the appearance, adjusting the personality, and adding functions. Personalized dialogue is realized based on a method of adjusting the dialogue based on the user's preferences and past dialogue history. This allows the purchaser to add a function that allows the purchaser to customize the character, thereby realizing more personalized dialogue.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The character creation unit can automatically generate a character's background story based on a theme or scenario selected by a user. For example, the character's background story is automatically generated based on a theme selected by a user. For example, if a user selects the theme "fantasy," the character has a background story of an adventure in a magical world. The character creation unit also automatically generates a character's background story based on a scenario selected by a user. For example, if a user selects "detective" as the scenario, the character has a background story of a detective who solves mysteries. The theme is selected based on a specific type, such as fantasy, science fiction, or modern. The scenario is generated based on specific content, such as a storyboard, plot, and events. The background story is generated based on criteria such as the character's past, motivation, and goals. This allows the character's background story to be automatically generated based on a theme or scenario selected by a user.

[0046] The character creation unit can generate a new character by combining parts of characters created by other users. For example, a new character can be generated by combining parts of the appearance and personality of characters created by other users. For example, the hairstyle of one character can be combined with the personality of another character. The character creation unit can also generate a new character by combining parts of characters created by other users. For example, the clothing of one character can be combined with the accessories of another character. The parts of a character can be combined based on specific definitions such as facial features, clothing, and accessories. A new character can be generated based on a method for combining parts of existing characters. This allows a new character to be generated by combining parts of characters created by other users.

[0047] The dialogue unit can suggest new topics based on the user's interests and concerns. For example, the dialogue unit analyzes the user's past dialogue history, and a character suggests new topics based on that history. For example, if the user talks a lot about movies, the character suggests new movie topics. The dialogue unit also suggests new topics based on the user's interests and concerns. For example, if the user is interested in sports, the character suggests the latest sports news. Interests and concerns are estimated based on specific definitions such as past dialogue history and user profile. New topics are suggested based on topic selection criteria based on the user's interests and concerns. This makes it possible to suggest new topics based on the user's interests and concerns.

[0048] The dialogue unit can make the dialogue more interactive by having the character suggest a quiz or game to the user during the dialogue. For example, the character can suggest a quiz to the user during the dialogue, making the dialogue more interactive. For example, the quiz can be asked in the form of, "Try answering the next question!" The dialogue unit can also make the dialogue more interactive by having the character suggest a game to the user during the dialogue. For example, the dialogue unit can suggest a game in the form of, "Let's solve a puzzle together!" The quiz can be suggested based on specific content such as a knowledge quiz or a puzzle quiz. The game can be suggested based on specific content such as a mini-game or an interactive game. This allows the character to suggest a quiz or game to the user during the dialogue, making the dialogue more interactive.

[0049] The sales department can implement an algorithm that automatically adjusts prices based on the popularity and ratings of the characters being sold. For example, an algorithm that analyzes the popularity and ratings of the characters being sold in real time and automatically adjusts prices based on the results may be implemented. For example, the price of highly popular characters may be increased. The sales department can also implement an algorithm that automatically adjusts prices based on ratings of the characters being sold. For example, the price of highly rated characters may be increased. Popularity is analyzed based on specific evaluation criteria such as sales volume and user ratings. Ratings are analyzed based on specific criteria such as user reviews and star ratings. Prices are adjusted based on specific adjustment methods such as supply and demand and popularity-based pricing. The algorithm is implemented based on specific content such as a machine learning algorithm or a rule-based algorithm. This makes it possible to automatically adjust prices based on the popularity and ratings of the characters being sold.

[0050] The utilization unit can add a function to generate a dialogue log that can be shared with other users when a purchaser uses a character. For example, a function is added to automatically generate a dialogue log and share it with other users when a purchaser uses a character. For example, the dialogue content is saved in text format and a sharing link is generated. The utilization unit also adds a function to automatically generate a dialogue log and share it with other users when a purchaser uses a character. For example, the dialogue content is saved in audio format and a sharing link is generated. The dialogue log is saved based on specific content such as a text log, an audio log, and dialogue metadata. The sharing function is implemented based on specific methods such as the scope of sharing, the sharing method, and privacy settings. This makes it possible to add a function to generate a dialogue log that can be shared with other users when a purchaser uses a character.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The character creation unit generates a character based on the user's instructions. For example, when a user inputs a prompt such as "I want to create a kind and knowledgeable character," the generation AI generates a character based on the user's instructions. The generation AI can generate a character using text generation AI (e.g., LLM) or multimodal generation AI. Step 2: The dialogue unit enables dialogue between the character generated by the character creation unit and the user. For example, the generation AI analyzes the user's utterances and generates an appropriate response. If the user asks the character, "What book did you read today?", the generation AI will generate a response such as, "I read a mystery novel today. It was very interesting!" Step 3: The sales department sells the characters generated by the character creation department. For example, if a user instructs the generation AI that they want to sell a character, the generation AI saves the character's information and makes it available on a sales platform. Step 4: The user unit enables the conversation between the character sold by the sales unit and the buyer. For example, if the buyer asks the character, "What movie did you see today?", the generation AI generates a response such as, "I saw an action movie today. It was very exciting!"

[0053] (Example 2) A chatbot platform according to an embodiment of the present invention is a system that utilizes a generation AI to allow users to interact with their own personalized characters. This system allows users to create characters according to their preferences and enjoy interacting with them. Furthermore, characters created by users can be sold to other users, who can then use the characters as their own chat partners. This allows the chatbot platform to allow users to enjoy interacting with their own personalized characters and also earn revenue by selling the characters to other users.

[0054] A chatbot platform according to an embodiment includes a character creation unit, a dialogue unit, a sales unit, and a utilization unit. The character creation unit generates a character based on a user's instructions. For example, when a user inputs a prompt such as "I want to create a kind and knowledgeable character," the generation AI generates a character based on the user's instructions. The generation AI can generate a character using a text generation AI (e.g., LLM) or a multimodal generation AI. The dialogue unit enables a dialogue between the character generated by the character creation unit and the user. For example, the generation AI analyzes the user's utterances and generates an appropriate response. When a user asks the character, "What book did you read today?", the generation AI generates a response such as, "I read a mystery novel today. It was very interesting!" The sales unit sells the character generated by the character creation unit. For example, when a user instructs the generation AI, "I want to sell this character," the generation AI saves the character's information and publishes it on a sales platform. The utilization unit enables a dialogue between the character sold by the sales unit and the buyer. For example, if a purchaser asks the character, "What movie did you see today?", the generation AI generates a response such as, "I saw an action movie today. It was very exciting!". As a result, the chatbot platform according to the embodiment allows users to enjoy conversations with their own characters and can also earn revenue by selling the characters to other users. For example, a user can create a character according to their preferences and enjoy daily conversations with that character. Other users can also purchase that character and use it as their own chat partner. This is expected to promote interaction between users and revitalize the entire platform.

[0055] The character creation unit can analyze the user's past dialogue history and automatically adjust the character's personality and speaking style based on the history. The character creation unit, for example, analyzes the user's past dialogue history and automatically adjusts the character's personality and speaking style based on the content of the history. For example, if the user has asked many questions in the past, the character is set to have a knowledgeable personality and is good at explaining things. The dialogue history is saved in the form of a text log, an audio log, dialogue metadata, etc. The personality is adjusted based on specific definitions such as friendly, cool, and humorous. The speaking style is adjusted based on criteria such as tone, speed, and accent. This makes it possible to automatically adjust the character's personality and speaking style based on the user's past dialogue history.

[0056] The character creation unit can generate a character's appearance and personality from images and audio data uploaded by a user. The character creation unit, for example, analyzes images uploaded by a user and generates a character's appearance based on the images. For example, if a user uploads a photo of their pet, a character resembling the pet is generated. The character creation unit also analyzes audio data uploaded by a user and generates a character's personality based on the audio. For example, if a user uploads audio data speaking in a calm voice, the character is set to have a calm personality. Images are uploaded in formats such as JPEG, PNG, and GIF. Audio data is uploaded in formats such as WAV, MP3, and AAC. Appearance is generated based on criteria such as facial features, clothing, and accessories. This allows a character's appearance and personality to be generated from images and audio data uploaded by a user.

[0057] The character creation unit can use the emotion estimation function to suggest a character according to the user's emotional state. The character creation unit, for example, analyzes the user's emotional state in real time and suggests a character based on the results. For example, if the user is feeling stressed, a character with a personality that will relax the user is suggested. The emotion estimation function is realized using technologies such as facial expression recognition, voice analysis, and text analysis. The emotional state is estimated based on specific definitions such as joy, sadness, and anger. Character suggestions are made based on character selection criteria according to the user's emotions. This makes it possible to suggest characters according to the user's emotional state.

[0058] The character creation unit can automatically generate a character's background story based on a theme or scenario selected by a user. The character creation unit automatically generates a character's background story based on, for example, a theme selected by a user. For example, if a user selects the theme "fantasy," the character has a background story of an adventure in a magical world. The character creation unit also automatically generates a character's background story based on a scenario selected by a user. For example, if a user selects "detective" as the scenario, the character has a background story of a detective who solves mysteries. The theme is selected based on a specific type, such as fantasy, science fiction, or modern. The scenario is generated based on specific content, such as a storyboard, plot, and events. The background story is generated based on criteria such as the character's past, motivation, and goals. This allows a character's background story to be automatically generated based on a theme or scenario selected by a user.

[0059] The character creation unit can generate a new character by combining parts of characters created by other users. The character creation unit generates a new character by combining, for example, parts of the appearance and personality of characters created by other users. For example, the character creation unit combines the hairstyle of one character with the personality of another character. The character creation unit also generates a new character by combining parts of characters created by other users. For example, the character creation unit combines the clothing of one character with the accessories of another character. The character parts are combined based on specific definitions such as facial features, clothing, and accessories. The new character is generated based on a method for combining parts of existing characters. This allows a new character to be generated by combining parts of characters created by other users.

[0060] The character creation unit uses the emotion estimation function to provide real-time feedback on emotions felt by the user while creating a character, and can suggest the most suitable character. For example, when the user creates a character, the character creation unit uses the emotion estimation function to analyze emotions in real time and provide feedback on the results. For example, if the user is having fun, the character creation unit suggests a character that reinforces that emotion. The character creation unit also provides real-time feedback on emotions felt by the user while creating a character, and suggests the most suitable character. For example, if the user is feeling stressed, the character creation unit suggests a character that will relax the user. Emotions are estimated based on specific definitions such as enjoyment, excitement, and stress. The real-time feedback is provided based on a method for displaying the immediate emotion analysis results. The most suitable character is suggested based on character selection criteria based on the user's emotional state. This allows the user to provide real-time feedback on emotions felt by the user while creating a character, and suggest the most suitable character.

[0061] The dialogue unit can analyze the user's facial expression and tone of voice during a dialogue and adjust the character's responses based on the analysis. For example, the dialogue unit analyzes the user's facial expression using a camera and adjusts the character's responses based on the facial expression. For example, if the user is smiling, the character will also return a cheerful response. The dialogue unit can also analyze the user's tone of voice and adjust the character's responses based on the tone. For example, if the user speaks in a calm voice, the character will also return a calm response. The facial expression is analyzed based on a specific analysis method such as facial recognition technology or the type of facial expression. The tone of voice is analyzed based on a specific analysis method such as voice analysis technology or the type of tone. The response is adjusted based on a response adjustment method based on the user's facial expression and tone of voice. This makes it possible to analyze the user's facial expression and tone of voice during a dialogue and adjust the character's responses based on the analysis.

[0062] The dialogue unit can suggest new topics based on the user's interests and concerns. For example, the dialogue unit analyzes the user's past dialogue history, and a character suggests new topics based on that history. For example, if the user talks a lot about movies, the character suggests new movie topics. The dialogue unit also suggests new topics based on the user's interests and concerns. For example, if the user is interested in sports, the character suggests the latest sports news. Interests and concerns are estimated based on specific definitions such as past dialogue history and user profile. New topics are suggested based on topic selection criteria based on the user's interests and concerns. This makes it possible to suggest new topics based on the user's interests and concerns.

[0063] The dialogue unit can make the dialogue more interactive by having the character suggest a quiz or game to the user during the dialogue. The dialogue unit can make the dialogue more interactive, for example, by having the character suggest a quiz to the user during the dialogue. For example, the dialogue unit can ask a quiz in the form of, "Try answering the next question!" The dialogue unit can also make the dialogue more interactive by having the character suggest a game to the user during the dialogue. For example, the dialogue unit can suggest a game in the form of, "Let's solve a puzzle together!" The quiz is suggested based on specific content such as a knowledge quiz or a puzzle quiz. The game is suggested based on specific content such as a mini-game or an interactive game. This allows the character to suggest a quiz or game to the user during the dialogue, making the dialogue more interactive.

[0064] The dialogue unit allows a character to manage the user's schedule and tasks and send reminders. For example, the dialogue unit allows a character to manage the user's schedule and remind them of important appointments. For example, the character may send a reminder in the form of, "Don't forget about tomorrow's meeting!". The dialogue unit also allows a character to manage the user's tasks and send reminders. For example, the character may send a reminder in the form of, "Check your tasks for today!". Schedules are managed based on specific management methods such as calendar integration and notification methods. Tasks are managed based on specific management methods such as task lists and priority settings. Reminders are sent based on specific sending methods such as notification timing and notification method. This allows the character to manage the user's schedule and tasks and send reminders.

[0065] The dialogue unit can use the emotion estimation function to analyze the emotions felt by the user during a dialogue in real time and adjust the content of the dialogue. For example, the dialogue unit can use the emotion estimation function to analyze the emotions felt by the user during a dialogue in real time and adjust the character's response based on the results. For example, if the user is angry, the character responds calmly. The dialogue unit can also analyze the emotions felt by the user during a dialogue in real time and adjust the content of the dialogue based on the results. For example, if the user is excited, the character adjusts the content of the dialogue to share that excitement. Emotions are estimated based on specific definitions such as joy, sadness, and anger. The content of the dialogue is adjusted based on a method for adjusting the content of the dialogue based on the emotion estimation result. This makes it possible to analyze the emotions felt by the user during a dialogue in real time and adjust the content of the dialogue.

[0066] The sales department can implement an algorithm that automatically adjusts prices based on the popularity and ratings of the characters being sold. For example, the sales department implements an algorithm that analyzes the popularity and ratings of the characters being sold in real time and automatically adjusts prices based on the results. For example, the price of a highly popular character is increased. The sales department also implements an algorithm that automatically adjusts prices based on ratings of the characters being sold. For example, the price of a highly rated character is increased. Popularity is analyzed based on specific evaluation criteria such as sales volume and user ratings. Ratings are analyzed based on specific criteria such as user reviews and star ratings. Prices are adjusted based on specific adjustment methods such as supply and demand and popularity-based pricing. The algorithm is implemented based on specific content such as a machine learning algorithm or a rule-based algorithm. In this way, prices can be automatically adjusted based on the popularity and ratings of the characters being sold.

[0067] The sales department can analyze the sales history of the character and propose an optimal sales strategy to the user. For example, the sales department analyzes the sales history of the character and builds a system that proposes an optimal sales strategy to the user based on that data. For example, if sales are concentrated during a specific time period, sales are promoted during that time period. The sales department also analyzes the sales history of the character and proposes an optimal sales strategy to the user based on that data. For example, sales are concentrated during periods when a specific character is popular. The sales history is analyzed based on specific details such as sales volume, sales period, and user attributes. The sales strategy is proposed based on specific proposal methods such as pricing and promotion methods. In this way, the sales history of the character can be analyzed and an optimal sales strategy can be proposed to the user.

[0068] The sales department can use the emotion estimation function to analyze the emotions that buyers have toward characters and reflect the results on the sales page. For example, the sales department can use the emotion estimation function to analyze the emotions that buyers have toward characters in real time and reflect the results on the sales page. For example, characters about which buyers have positive emotions can be prominently displayed. The sales department can also use the emotion estimation function to analyze the emotions that buyers have toward characters and reflect the results on the sales page. For example, characters in which buyers are interested can be prominently displayed. Emotions are estimated based on specific definitions such as liking, interest, and dislike. The sales page can be adjusted based on specific content such as layout, color usage, and information placement. In this way, the emotion estimation function can be used to analyze the emotions that buyers have toward characters and reflect the results on the sales page.

[0069] The sales department may add a demo function to the character sales platform that allows purchasers to try out characters. For example, the sales department may add a demo function to the character sales platform that allows purchasers to try out characters. For example, the sales department may provide a function that allows purchasers to interact with a character for a short period of time before purchasing. The sales department may also add a demo function to the character sales platform that allows purchasers to try out characters. For example, the sales department may provide a demo that allows purchasers to check the character's functions before purchasing. The demo function is implemented based on specific details such as a trial period, trial scope, and trial method. In this way, the sales department may add a demo function to the character sales platform that allows purchasers to try out characters.

[0070] The sales department can add a function to compare characters created by other users, making it easier for purchasers to select the most suitable character. The sales department, for example, can add a function to compare characters created by other users, making it easier for purchasers to select the most suitable character. For example, the characteristics and ratings of multiple characters can be displayed side by side. The sales department can also add a function to compare characters created by other users, making it easier for purchasers to select the most suitable character. For example, the sales department can provide a function to compare characters' appearances and personalities. The comparison function is implemented based on specific content such as comparison items, comparison method, and display method. In this way, the sales department can add a function to compare characters created by other users, making it easier for purchasers to select the most suitable character.

[0071] The sales department can use the emotion estimation function to analyze the emotions felt by buyers while viewing a character's sales page and adjust the design and content of the page. For example, the sales department can use the emotion estimation function to analyze the emotions felt by buyers while viewing a character's sales page in real time and adjust the design and content of the page based on the results. For example, elements that evoke positive emotions can be emphasized. The sales department can also use the emotion estimation function to analyze the emotions felt by buyers while viewing a character's sales page and adjust the design and content of the page based on the results. For example, elements that interest buyers can be emphasized. Emotions are estimated based on specific definitions such as interest, doubt, and expectation. The page design is adjusted based on specific adjustment methods such as layout, color usage, and information placement. The content is adjusted based on specific adjustment methods such as product description, images, and reviews. In this way, the emotion estimation function can be used to analyze the emotions felt by buyers while viewing a character's sales page and adjust the design and content of the page.

[0072] The utilization unit can analyze the dialogue history when the purchaser uses the character and further personalize the character's responses. For example, when the purchaser uses the character, the utilization unit analyzes the dialogue history and personalizes the character's responses based on the content of the dialogue history. For example, the character returns an appropriate response based on the content of past dialogue. Furthermore, when the purchaser uses the character, the utilization unit analyzes the dialogue history and personalizes the character's responses based on the content of the dialogue history. For example, if the purchaser likes a particular topic, the utilization unit generates a response related to that topic. The dialogue history is saved based on specific content such as a text log, an audio log, and dialogue metadata. Personalization is performed based on user preferences and an adjustment method based on past dialogue history. In this way, when the purchaser uses the character, the utilization unit can analyze the dialogue history and further personalize the character's responses.

[0073] The utilization unit uses the emotion estimation function to generate a response according to the emotion of the buyer, thereby improving the quality of the dialogue. The utilization unit, for example, uses the emotion estimation function to analyze the emotional state of the buyer in real time and generate a character's response based on the result. For example, if the buyer is sad, the character returns a comforting response. The utilization unit also uses the emotion estimation function to generate a response according to the buyer's emotion, thereby improving the quality of the dialogue. For example, if the buyer is excited, the character returns a response that shares the buyer's excitement. Emotions are estimated based on specific definitions such as joy, sadness, and anger. Responses are generated based on a response generation method based on the emotion estimation result. The quality of the dialogue is improved based on specific evaluation criteria such as user satisfaction and naturalness of the dialogue. In this way, the emotion estimation function can be used to generate a response according to the buyer's emotion, thereby improving the quality of the dialogue.

[0074] The utilization unit can add a function to generate a dialogue log that can be shared with other users when a purchaser uses a character. The utilization unit, for example, adds a function to automatically generate a dialogue log and share it with other users when a purchaser uses a character. For example, the utilization unit saves the dialogue content in text format and generates a sharing link. The utilization unit also adds a function to automatically generate a dialogue log and share it with other users when a purchaser uses a character. For example, the utilization unit saves the dialogue content in audio format and generates a sharing link. The dialogue log is saved based on specific content such as a text log, an audio log, and dialogue metadata. The sharing function is implemented based on specific methods such as the scope of sharing, the sharing method, and privacy settings. This makes it possible to add a function to generate a dialogue log that can be shared with other users when a purchaser uses a character.

[0075] The utilization unit adds a function that allows the purchaser to customize the character, thereby realizing more personalized dialogue. The utilization unit adds, for example, a function that allows the purchaser to customize the character's appearance and personality, thereby realizing more personalized dialogue. For example, the purchaser can change the character's clothing and hairstyle. The utilization unit also adds a function that allows the purchaser to customize the character's personality, thereby realizing more personalized dialogue. For example, the purchaser can change the character's way of speaking and tone of voice. The customization function is implemented based on specific methods such as changing the appearance, adjusting the personality, and adding functions. Personalized dialogue is realized based on a method of adjusting the dialogue based on the user's preferences and past dialogue history. This allows the purchaser to add a function that allows the purchaser to customize the character, thereby realizing more personalized dialogue.

[0076] The utilization unit can use the emotion estimation function to analyze in real time the emotions felt by the purchaser during a conversation with a character and adjust the content of the conversation. For example, the utilization unit can use the emotion estimation function to analyze in real time the emotions felt by the purchaser during a conversation with a character and adjust the character's response based on the results. For example, if the purchaser is angry, the character responds calmly. The utilization unit can also use the emotion estimation function to analyze in real time the emotions felt by the purchaser during a conversation with a character and adjust the content of the conversation based on the results. For example, if the purchaser is excited, the character adjusts the content of the conversation to share that excitement. Emotions are estimated based on specific definitions such as joy, sadness, and anger. The content of the conversation is adjusted based on a method for adjusting the content of the conversation based on the emotion estimation result. In this way, the utilization unit can use the emotion estimation function to analyze in real time the emotions felt by the purchaser during a conversation with a character and adjust the content of the conversation.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The character creation unit can automatically generate a character's background story based on a theme or scenario selected by a user. For example, the character's background story is automatically generated based on a theme selected by a user. For example, if a user selects the theme "fantasy," the character has a background story of an adventure in a magical world. The character creation unit also automatically generates a character's background story based on a scenario selected by a user. For example, if a user selects "detective" as the scenario, the character has a background story of a detective who solves mysteries. The theme is selected based on a specific type, such as fantasy, science fiction, or modern. The scenario is generated based on specific content, such as a storyboard, plot, and events. The background story is generated based on criteria such as the character's past, motivation, and goals. This allows the character's background story to be automatically generated based on a theme or scenario selected by a user.

[0079] The character creation unit can generate a new character by combining parts of characters created by other users. For example, a new character can be generated by combining parts of the appearance and personality of characters created by other users. For example, the hairstyle of one character can be combined with the personality of another character. The character creation unit can also generate a new character by combining parts of characters created by other users. For example, the clothing of one character can be combined with the accessories of another character. The parts of a character can be combined based on specific definitions such as facial features, clothing, and accessories. A new character can be generated based on a method for combining parts of existing characters. This allows a new character to be generated by combining parts of characters created by other users.

[0080] The character creation unit can use the emotion estimation function to suggest characters according to the user's emotional state. For example, the emotional state of the user can be analyzed in real time, and a character can be suggested based on the results. For example, if the user is feeling stressed, a character with a personality that will relax the user can be suggested. The emotion estimation function is realized using technologies such as facial expression recognition, voice analysis, and text analysis. The emotional state is estimated based on specific definitions such as joy, sadness, and anger. Character suggestions are made based on character selection criteria according to the user's emotions. This makes it possible to suggest characters according to the user's emotional state.

[0081] The dialogue unit can analyze the user's facial expression and tone of voice during a dialogue and adjust the character's responses based on the analysis. For example, the user's facial expression is analyzed using a camera and the character's responses are adjusted based on the facial expression. For example, if the user is smiling, the character will respond cheerfully. The dialogue unit can also analyze the user's tone of voice and adjust the character's responses based on the tone. For example, if the user speaks in a calm voice, the character will respond calmly. Facial expressions are analyzed based on specific analysis methods such as facial recognition technology and types of facial expression. Voice tones are analyzed based on specific analysis methods such as voice analysis technology and types of tone. Responses are adjusted based on a response adjustment method based on the user's facial expression and tone of voice. This allows the user's facial expression and tone of voice to be analyzed during a dialogue and the character's responses to be adjusted based on the analysis.

[0082] The dialogue unit can suggest new topics based on the user's interests and concerns. For example, the dialogue unit analyzes the user's past dialogue history, and a character suggests new topics based on that history. For example, if the user talks a lot about movies, the character suggests new movie topics. The dialogue unit also suggests new topics based on the user's interests and concerns. For example, if the user is interested in sports, the character suggests the latest sports news. Interests and concerns are estimated based on specific definitions such as past dialogue history and user profile. New topics are suggested based on topic selection criteria based on the user's interests and concerns. This makes it possible to suggest new topics based on the user's interests and concerns.

[0083] The dialogue unit can make the dialogue more interactive by having the character suggest a quiz or game to the user during the dialogue. For example, the character can suggest a quiz to the user during the dialogue, making the dialogue more interactive. For example, the quiz can be asked in the form of, "Try answering the next question!" The dialogue unit can also make the dialogue more interactive by having the character suggest a game to the user during the dialogue. For example, the dialogue unit can suggest a game in the form of, "Let's solve a puzzle together!" The quiz can be suggested based on specific content such as a knowledge quiz or a puzzle quiz. The game can be suggested based on specific content such as a mini-game or an interactive game. This allows the character to suggest a quiz or game to the user during the dialogue, making the dialogue more interactive.

[0084] The dialogue unit can use the emotion estimation function to analyze the emotions felt by the user during a dialogue in real time and adjust the content of the dialogue. For example, the emotion estimation function can be used to analyze the emotions felt by the user during a dialogue in real time and adjust the character's response based on the results. For example, if the user is angry, the character responds calmly. The dialogue unit can also analyze the emotions felt by the user during a dialogue in real time and adjust the content of the dialogue based on the results. For example, if the user is excited, the character adjusts the content of the dialogue to share that excitement. Emotions are estimated based on specific definitions such as joy, sadness, and anger. The content of the dialogue is adjusted based on a method for adjusting the content of the dialogue based on the emotion estimation result. This allows the emotions felt by the user during a dialogue to be analyzed in real time and the content of the dialogue to be adjusted.

[0085] The sales department can implement an algorithm that automatically adjusts prices based on the popularity and ratings of the characters being sold. For example, an algorithm that analyzes the popularity and ratings of the characters being sold in real time and automatically adjusts prices based on the results may be implemented. For example, the price of highly popular characters may be increased. The sales department can also implement an algorithm that automatically adjusts prices based on ratings of the characters being sold. For example, the price of highly rated characters may be increased. Popularity is analyzed based on specific evaluation criteria such as sales volume and user ratings. Ratings are analyzed based on specific criteria such as user reviews and star ratings. Prices are adjusted based on specific adjustment methods such as supply and demand and popularity-based pricing. The algorithm is implemented based on specific content such as a machine learning algorithm or a rule-based algorithm. This makes it possible to automatically adjust prices based on the popularity and ratings of the characters being sold.

[0086] The sales department can use the emotion estimation function to analyze the emotions that buyers have toward characters and reflect the emotions on the sales page. For example, the emotion estimation function can be used to analyze the emotions that buyers have toward characters in real time and reflect the results on the sales page. For example, characters about which buyers have positive emotions can be prominently displayed. The sales department can also use the emotion estimation function to analyze the emotions that buyers have toward characters and reflect the results on the sales page. For example, characters that buyers are interested in can be prominently displayed. Emotions are estimated based on specific definitions such as liking, interest, and dislike. The sales page can be adjusted based on specific content such as layout, color usage, and information placement. In this way, the emotion estimation function can be used to analyze the emotions that buyers have toward characters and reflect the emotions on the sales page.

[0087] The utilization unit can add a function to generate a dialogue log that can be shared with other users when a purchaser uses a character. For example, a function is added to automatically generate a dialogue log and share it with other users when a purchaser uses a character. For example, the dialogue content is saved in text format and a sharing link is generated. The utilization unit also adds a function to automatically generate a dialogue log and share it with other users when a purchaser uses a character. For example, the dialogue content is saved in audio format and a sharing link is generated. The dialogue log is saved based on specific content such as a text log, an audio log, and dialogue metadata. The sharing function is implemented based on specific methods such as the scope of sharing, the sharing method, and privacy settings. This makes it possible to add a function to generate a dialogue log that can be shared with other users when a purchaser uses a character.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: The character creation unit generates a character based on the user's instructions. For example, when a user inputs a prompt such as "I want to create a kind and knowledgeable character," the generation AI generates a character based on the user's instructions. The generation AI can generate a character using text generation AI (e.g., LLM) or multimodal generation AI. Step 2: The dialogue unit enables dialogue between the character generated by the character creation unit and the user. For example, the generation AI analyzes the user's utterances and generates an appropriate response. If the user asks the character, "What book did you read today?", the generation AI will generate a response such as, "I read a mystery novel today. It was very interesting!" Step 3: The sales department sells the characters generated by the character creation department. For example, if a user instructs the generation AI that they want to sell a character, the generation AI saves the character's information and makes it available on a sales platform. Step 4: The user unit enables the conversation between the character sold by the sales unit and the buyer. For example, if the buyer asks the character, "What movie did you see today?", the generation AI generates a response such as, "I saw an action movie today. It was very exciting!"

[0090] 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.

[0091] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0092] 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.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0103] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0109] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0116] 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.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0118] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0124] 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.

[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 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.

[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 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).

[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] 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.

[0131] 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.

[0132] 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.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0134] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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."

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0157] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a character creation unit that creates a character based on a user's instruction; a dialogue unit that enables dialogue between the character generated by the character generation unit and a user; a sales department that sells the characters created by the character creation department; a utilization unit that realizes a dialogue between the character sold by the sales unit and the purchaser; A system characterized by:

2. The character creation unit Generate the character's appearance and personality from images and audio data uploaded by the user 2. The system of claim 1.

3. The dialogue unit Analyzing the user's facial expressions and tone of voice during a conversation and adjusting the character's responses accordingly 2. The system of claim 1.

4. The sales department An algorithm will be introduced to automatically adjust the price based on the popularity and ratings of the characters being sold.

2. The system of claim 1.

5. The utilization unit includes: When the purchaser uses the character, the interaction history is analyzed to further personalize the character's responses.

2. The system of claim 1.

6. The character creation unit Using an emotion estimation function, the character is suggested according to the emotional state of the user.

2. The system of claim 1.

7. The dialogue unit Using an emotion estimation function, a response is generated according to the user's emotion, thereby improving the quality of the dialogue.

2. The system of claim 1.

8. The sales department Using an emotion estimation function, the emotions that the purchaser has toward the character are analyzed and reflected on the sales page.

2. The system of claim 1.

Images