system

Abstract

The system according to this embodiment aims to allow users to create their own preferred character and to teach that character through interaction. [Solution] The system according to the embodiment comprises a customization unit, a generation unit, a learning unit, and a sales unit. The customization unit allows the user to customize the character's appearance, personality, way of speaking, hobbies, preferences, etc. The generation unit generates a character based on the information customized by the customization unit. The learning unit allows the character generated by the generation unit to learn through interaction with the user. The sales unit sells the character based on the information learned by the learning unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for a user to create a character according to their preference and learn through interaction with the character.

[0005] The system according to the embodiment aims to enable a user to create a character according to their preference and learn through interaction with the character.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a customization unit, a generation unit, a learning unit, and a sales unit. The customization unit allows the user to customize the character's appearance, personality, way of speaking, hobbies, preferences, etc. The generation unit generates a character based on the information customized by the customization unit. The learning unit allows the character generated by the generation unit to learn through interaction with the user. The sales unit sells the character based on the information learned by the learning unit. [Effects of the Invention]

[0007] The system according to this embodiment allows a user to create a character of their choice and to teach that character through interaction. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The chatbot platform according to an embodiment of the present invention is a unique system that utilizes generative AI technology to allow users to interact with their own ideal character. In this system, users can customize their character down to the smallest detail, including appearance, personality, speaking style, hobbies, and preferences. The created character possesses advanced natural language processing capabilities, learns through interaction with the user, and builds a deeper relationship. For example, users can customize the character's appearance, personality, speaking style, hobbies, and preferences on the platform. For instance, users can select the character's hairstyle, clothing, personality traits, speaking style, hobbies, and interests. This customization information is input into the generative AI, and a character is generated. Next, the generated character possesses advanced natural language processing capabilities and learns through interaction with the user. For example, by engaging in everyday conversations with the character, the character learns the user's preferences and interests, enabling more personalized interactions. This allows users to build a deeper relationship with the character. Furthermore, this platform provides new revenue opportunities for creatively talented users. Users can sell their self-created characters on the marketplace, earning supplementary income by creating appealing characters. Purchasers can use their favorite characters as their personal chat partners and even customize them to their liking. This platform goes beyond a mere communication tool, stimulating creativity and providing a space for new forms of interaction with artificial intelligence. For example, it can address diverse needs such as alleviating loneliness, supporting language learning, and promoting creative activities. This aims to build new relationships in a symbiotic society with AI. As a result, the chatbot platform can provide users with new revenue opportunities as they customize their favorite characters, the generated characters learn, and they can be sold.

[0029] The chatbot platform according to the embodiment comprises a customization unit, a generation unit, a learning unit, and a sales unit. The customization unit allows the user to customize the character's appearance, personality, speaking style, hobbies, preferences, etc. To customize the character's appearance, personality, speaking style, hobbies, preferences, etc., the user can, for example, select from options provided on the platform. For example, the user can select the character's hairstyle and clothing, personality traits, speaking style, hobbies, and interests. The customization unit inputs the information selected by the user into the generation AI and uses it as data for generating the character. The generation unit uses the generation AI to generate a character based on the information customized by the customization unit. For example, the generation AI generates the character's appearance and personality based on the user's customization information. For example, the generation AI generates the character based on the hairstyle and clothing, personality traits, speaking style, hobbies, and interests selected by the user. For example, the generation AI generates the character's appearance and personality based on the user's customization information. The learning unit allows the character generated by the generation unit to learn through interaction with the user. The learning unit, for example, allows the character to learn the user's preferences and interests through interaction with the user. The learning unit, for example, allows the character to learn the user's preferences and interests through everyday conversations with the user. The learning unit, for example, allows the character to learn the user's preferences and interests through interaction with the user, enabling more personalized conversations. The sales unit sells the character based on the information learned by the learning unit. The sales unit, for example, sells the character created by the user on the marketplace. The sales unit, for example, sells the character created by the user on the marketplace, making it available for other users to purchase. The sales unit, for example, sells the character created by the user on the marketplace, making it available for other users to purchase as their own personal chat partner. In this way, the chatbot platform according to the embodiment can provide users with new revenue opportunities by allowing them to customize a character to their liking, and by having the generated character learn and be sold.

[0030] The customization section allows users to customize their character's appearance, personality, speaking style, hobbies, and preferences. To customize these aspects, users can, for example, select from options provided on the platform. Specifically, users can meticulously set details such as the character's hairstyle, clothing, eye color, skin color, and body type. Regarding personality, users can select personality types such as cheerful, calm, kind, or energetic, and then further adjust specific personality traits. For speaking style, users can choose from polite, friendly, or humorous speech patterns according to their preferences. Hobbies and interests can also be set, such as sports, music, reading, or travel. The customization section provides these options to the user, inputs the user's selections into the character generation AI, and uses this data to create the character. This allows users to easily create their own original characters. Furthermore, the customization section includes a function to save the user's selections, allowing for later re-editing and modification. This enables users to change character settings or add new elements. The customization section provides a user-friendly interface to make user operation intuitive and easy, allowing even beginners to easily customize their characters.

[0031] The generation unit uses a generation AI to generate characters based on information customized by the customization unit. For example, the generation unit uses the generation AI to generate the character's appearance and personality based on the user's customization information. Specifically, the generation AI generates a 3D model and animation of the character based on the hairstyle, clothing, personality traits, speaking style, hobbies, and interests selected by the user. The generation AI uses deep learning technology to generate realistic and natural characters that respond to the user's choices. For example, it can describe the character's appearance in detail based on the hairstyle and clothing selected by the user, and express the character's facial expressions and movements naturally based on its personality traits. It can also synthesize the character's voice based on its speaking style, generating voice that matches the speaking style set by the user. The generation unit not only generates the character's appearance and personality based on the user's customization information, but can also generate the character's movements, facial expressions, and voice in real time. This allows users to see their customized character actually move and speak. Furthermore, the generation unit has a function to save the generated character on the platform, allowing users to access it at any time. This allows users to share the generated character with other users or re-edit it later. The generation unit aims to maximize the performance of the generation AI to create high-quality characters that will satisfy users.

[0032] The learning unit allows the character generated by the generation unit to learn through interaction with the user. For example, the learning unit allows the character to learn the user's preferences and interests through interaction with the user. Specifically, the character learns the user's hobbies, interests, preferred topics, and favorite expressions through everyday conversations with the user. The learning unit uses natural language processing technology to analyze the content of the conversation with the user and extract the user's preferences and interests. For example, if the character learns that the user has shown interest in a particular topic during a conversation, the learning unit records that information and brings up that topic in the next conversation. Also, if the user prefers certain expressions or phrases, the learning unit learns that information and reflects it in the character's way of speaking. The learning unit accumulates the information obtained through interaction with the user and improves the character's conversation skills. This allows the character to provide more personalized conversations to the user. Furthermore, the learning unit collects user feedback and uses it as data to improve the character's conversation content and behavior. For example, it provides a function for users to evaluate the content of the character's conversations, and uses that evaluation to improve the character's conversation skills. Furthermore, the learning unit also has a function that allows characters to share information they have learned with other characters. This enables multiple characters to share the same user's preferences and interests, providing a more consistent dialogue. The learning unit aims to provide a better experience for users by having characters continuously learn and evolve through their interactions with the user.

[0033] The sales department sells characters based on information learned by the learning department. For example, the sales department sells user-created characters on the marketplace. Specifically, users list their created characters on the platform's marketplace, making them available for other users to purchase. The sales department provides a function to set the selling price of characters, allowing users to freely set the price. The sales department also provides a function to display detailed information and previews of characters, allowing buyers to check the character's appearance, personality, and speaking style. Furthermore, the sales department provides a function that allows buyers to use the purchased character as their personal chat partner. This allows buyers to enjoy interacting with characters that suit their preferences. The sales department also has a function to manage the sales history and revenue information of characters, allowing users to check the sales status of their characters. For example, users can check how many times their characters have been sold and how much revenue has been earned. Through the operation of the marketplace, the sales department aims to provide users with the opportunity to sell their characters and earn revenue. In addition, the sales department provides promotion and advertising functions for the marketplace to help users make their characters known to more people. This allows users to effectively sell their characters and maximize their revenue. The sales department aims to provide new revenue opportunities by allowing users to create characters using their creativity and sell those characters.

[0034] The customization unit can analyze the user's past customization history and present the optimal customization options. For example, the customization unit can analyze the user's past choices in appearance and personality and present similar options. For example, the customization unit can suggest new characters based on the user's past preferred speaking style and hobbies. For example, the customization unit can suggest characters appropriate for specific times of day or situations based on the user's past customization history. This allows the system to present the optimal customization options based on the user's past customization history. Some or all of the above processes in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's past customization history data into a generating AI and have the generating AI perform the task of presenting the optimal customization options.

[0035] The customization unit can filter customization options based on the user's current interests and trends during the customization process. For example, the customization unit can suggest character preferences based on topics and hobbies the user is currently interested in. For example, the customization unit can suggest character appearances based on the latest fashion and trends. For example, the customization unit can suggest character personalities and speaking styles based on movies the user has recently watched or books they have read. This allows the unit to provide customization options based on the user's current interests and trends. Some or all of the above-described processes in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's current interests and trend data into a generating AI and have the generating AI perform the filtering of customization options.

[0036] The customization unit can prioritize and present highly relevant customization options by considering the user's geographical location during the customization process. For example, the customization unit can suggest character appearances and hobbies based on the culture and trends of the area where the user lives. For example, if the user is traveling, the customization unit can suggest character customizations related to local specialties and tourist attractions in that area. For example, if the user is participating in a specific event, the customization unit can suggest character customizations related to that event. This allows the customization unit to provide customization options based on the user's geographical location. Some or all of the above processing in the customization unit may be performed using AI, for example, or not. For example, the customization unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of presenting highly relevant customization options.

[0037] The customization unit can analyze the user's social media activity during customization and present relevant customization options. For example, the customization unit can suggest character hobbies and preferences based on the accounts the user follows and topics of interest on social media. For example, the customization unit can suggest character appearance and personality based on content the user shares on social media. For example, the customization unit can suggest character speech patterns and interests based on communities the user participates in on social media. This allows for the provision of customization options based on the user's social media activity. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's social media activity data into a generating AI and have the generating AI present relevant customization options.

[0038] The generation unit can adjust the level of detail of the generated characters based on their importance. For example, for major characters, the generation unit generates characters with detailed appearances and personalities. For example, for sub-characters, the generation unit generates characters with simplified appearances and personalities. For example, for temporary characters, the generation unit generates characters with minimal appearances and personalities. This allows the level of detail of the generated characters to be adjusted according to their importance. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input character importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the generated characters.

[0039] The generation unit can apply different generation algorithms depending on the character category during generation. For example, in the case of a fantasy character, the generation unit applies a generation algorithm that incorporates magic and special abilities. For example, in the case of a realistic character, the generation unit applies a generation algorithm based on real-world data. For example, in the case of a comical character, the generation unit applies a generation algorithm that emphasizes humor and fun. This allows the generation unit to apply a generation algorithm appropriate to the character category. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input character category data into a generation AI and have the generation AI execute the application of different generation algorithms.

[0040] The generation unit can determine the priority of character creation based on the character's submission date. For example, the generation unit will prioritize the creation of characters with approaching deadlines. For example, it will postpone the creation of characters for long-term projects. For example, it will give the highest priority to characters with urgent requests. This allows for the determination of a character creation priority according to the character's submission date. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input character submission date data into a generation AI and have the generation AI determine the generation priority.

[0041] The generation unit can adjust the generation order based on the relationships between characters during generation. For example, the generation unit may prioritize generating characters related to the main storyline. For example, it may postpone generating characters related to sub-stories. For example, it may give the highest priority to generating characters related to temporary events. This allows the generation order to be adjusted according to the relationships between characters. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input character relationship data into a generation AI and have the generation AI perform the adjustment of the generation order.

[0042] The learning unit can analyze the user's past conversation history during learning to select the optimal learning method. For example, the learning unit can select learning content based on topics the user has liked in the past. For example, the learning unit can select an effective learning method from the user's past conversation history. For example, the learning unit can select learning content while avoiding topics the user has avoided in the past. This allows the learning unit to select the optimal learning method based on the user's past conversation history. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's past conversation history data into a generating AI and have the generating AI select the optimal learning method.

[0043] The learning unit can customize learning content based on the user's current interests and trends during the learning process. For example, the learning unit can customize learning content based on topics the user is currently interested in. For example, the learning unit can customize learning content based on the latest trends and topics. For example, the learning unit can customize learning content based on movies the user has recently watched or books the user has recently read. This allows the learning unit to provide learning content tailored to the user's current interests and trends. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's current interests and trend data into a generating AI and have the generating AI perform the customization of the learning content.

[0044] During learning, the learning department can select an optimal learning method by considering the user's geographical location information. For example, the learning department selects learning content based on the culture and trends of the region where the user lives. For example, when the user is traveling, the learning department selects learning content related to local specialties and tourist attractions in that region. For example, when the user is participating in a specific event, the learning department selects learning content related to that event. In this way, an optimal learning method based on the user's geographical location information can be selected. Some or all of the above-mentioned processes in the learning department may be performed using, for example, AI, or may be performed without using AI. For example, the learning department can input the user's geographical location information data into a generative AI and have the generative AI execute the selection of the optimal learning method.

[0045] During learning, the learning department can analyze the user's social media activities and propose learning content. For example, the learning department proposes learning content based on the accounts the user follows on social media and the topics the user is interested in. For example, the learning department proposes learning content based on the content the user has shared on social media. For example, the learning department proposes learning content based on the communities the user participates in on social media. In this way, learning content based on the user's social media activities can be provided. Some or all of the above-mentioned processes in the learning department may be performed using, for example, AI, or may be performed without using AI. For example, the learning department can input the user's social media activity data into a generative AI and have the generative AI execute the proposal of learning content.

[0046] The sales department can analyze a user's past purchase history to select the optimal sales method at the time of sale. For example, the sales department can analyze the trends of characters a user has purchased in the past and suggest similar characters. For example, the sales department can select a sales method that is appropriate for a specific time of day or situation based on the user's past purchase history. For example, the sales department can prioritize providing sales methods that the user has preferred in the past (e.g., sales or campaigns). This allows for the selection of the optimal sales method based on the user's past purchase history. Some or all of the above processes in the sales department may be performed using AI, for example, or not using AI. For example, the sales department can input the user's past purchase history data into a generating AI and have the generating AI select the optimal sales method.

[0047] The sales department can customize the product offerings at the time of sale based on the user's current interests and trends. For example, the sales department can suggest characters based on topics or hobbies the user is currently interested in. For example, the sales department can suggest character appearances and personalities based on the latest fashion and trends. For example, the sales department can suggest characters based on movies the user has recently watched or books the user has recently read. This allows the sales department to provide products tailored to the user's current interests and trends. Some or all of the above processes in the sales department may be performed using AI, for example, or not. For example, the sales department can input data on the user's current interests and trends into a generating AI and have the generating AI perform the customization of the product offerings.

[0048] The sales department can select the optimal sales method at the time of sale, taking into account the user's geographical location. For example, the sales department may suggest characters based on the culture and trends of the area where the user lives. For example, if the user is traveling, the sales department may suggest characters related to local specialties or tourist attractions in that area. For example, if the user is participating in a particular event, the sales department may suggest characters related to that event. This allows the sales department to select the optimal sales method based on the user's geographical location. Some or all of the above processes in the sales department may be performed using AI, for example, or not using AI. For example, the sales department can input the user's geographical location data into a generating AI and have the generating AI select the optimal sales method.

[0049] The sales department can analyze a user's social media activity and propose sales content at the time of sale. For example, the sales department can propose characters based on the accounts the user follows on social media and the topics the user is interested in. For example, the sales department can propose characters based on the content the user shares on social media. For example, the sales department can propose characters based on the communities the user participates in on social media. This allows the sales department to provide sales content tailored to the user's social media activity. Some or all of the above processes in the sales department may be performed using AI, for example, or not. For example, the sales department can input user social media activity data into a generating AI and have the generating AI generate sales content suggestions.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The generation unit can analyze the user's past conversation history and adjust the character's conversation style based on the user's preferred conversation style. For example, if the user likes humor, the character can be set to engage in humorous conversations. Similarly, if the user prefers polite language, the character can be customized to use polite language. Furthermore, if the user is interested in a particular topic, the character can be set to prioritize conversations related to that topic. This allows for the provision of a more personalized character based on the user's conversation style.

[0052] The sales department can analyze users' purchase history and suggest new characters based on the trends of characters the user has previously purchased. For example, it can analyze the appearance and personality traits of characters the user has previously purchased and suggest similar characters. It can also suggest new characters based on the speaking style and hobbies / preferences of characters the user has previously purchased. Furthermore, it can suggest characters appropriate for specific times of day or situations based on the user's purchase history. This allows for the suggestion of the most suitable character based on the user's purchase history.

[0053] The customization function analyzes the user's social media activity and can suggest character preferences based on the accounts the user follows and the topics they are interested in. For example, if a user follows a specific artist or sports team, it can suggest a character with interests related to that artist or sports team. It can also suggest a character's appearance and personality based on the content the user shares on social media. Furthermore, it can suggest a character's speaking style and interests based on the communities the user participates in on social media. This allows for the provision of customized options based on the user's social media activity.

[0054] The learning unit can analyze a user's past conversation history and tailor learning content based on the user's preferred conversation style. For example, if a user enjoys humor, it can provide learning content that incorporates humor. Similarly, if a user prefers polite language, it can provide learning content using polite language. Furthermore, if a user is interested in a specific topic, it can prioritize providing learning content related to that topic. This allows for the provision of optimal learning content based on the user's conversation style.

[0055] The customization section can prioritize and present highly relevant customization options by considering the user's geographical location. For example, it can suggest character appearances and hobbies based on the culture and trends of the area where the user lives. If the user is traveling, it can suggest character customizations related to local specialties and tourist attractions. Furthermore, if the user is participating in a specific event, it can suggest character customizations related to that event. This allows for the provision of customization options based on the user's geographical location.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The customization section allows users to customize their character's appearance, personality, speech patterns, hobbies, and preferences. Users can choose from the options provided on the platform to select their character's hairstyle, clothing, personality traits, speaking style, hobbies, and interests. Step 2: The generation unit uses a generation AI to generate a character based on the information customized by the customization unit. The generation AI generates the character's appearance and personality based on the user's selected hairstyle, clothing, personality traits, speaking style, hobbies, and interests. Step 3: The learning unit learns through interaction with the user by the character generated by the generation unit. The character learns the user's preferences and interests through everyday conversations with the user, enabling more personalized dialogue. Step 4: The sales department sells characters based on the information learned by the learning department. Characters created by users are sold on the marketplace and can be purchased by other users. Purchased characters can be used as personal chat partners.

[0058] (Example of form 2) The chatbot platform according to an embodiment of the present invention is a unique system that utilizes generative AI technology to allow users to interact with their own ideal character. In this system, users can customize their character down to the smallest detail, including appearance, personality, speaking style, hobbies, and preferences. The created character possesses advanced natural language processing capabilities, learns through interaction with the user, and builds a deeper relationship. For example, users can customize the character's appearance, personality, speaking style, hobbies, and preferences on the platform. For instance, users can select the character's hairstyle, clothing, personality traits, speaking style, hobbies, and interests. This customization information is input into the generative AI, and a character is generated. Next, the generated character possesses advanced natural language processing capabilities and learns through interaction with the user. For example, by engaging in everyday conversations with the character, the character learns the user's preferences and interests, enabling more personalized interactions. This allows users to build a deeper relationship with the character. Furthermore, this platform provides new revenue opportunities for creatively talented users. Users can sell their self-created characters on the marketplace, earning supplementary income by creating appealing characters. Purchasers can use their favorite characters as their personal chat partners and even customize them to their liking. This platform goes beyond a mere communication tool, stimulating creativity and providing a space for new forms of interaction with artificial intelligence. For example, it can address diverse needs such as alleviating loneliness, supporting language learning, and promoting creative activities. This aims to build new relationships in a symbiotic society with AI. As a result, the chatbot platform can provide users with new revenue opportunities as they customize their favorite characters, the generated characters learn, and they can be sold.

[0059] The chatbot platform according to the embodiment comprises a customization unit, a generation unit, a learning unit, and a sales unit. The customization unit allows the user to customize the character's appearance, personality, speaking style, hobbies, preferences, etc. To customize the character's appearance, personality, speaking style, hobbies, preferences, etc., the user can, for example, select from options provided on the platform. For example, the user can select the character's hairstyle and clothing, personality traits, speaking style, hobbies, and interests. The customization unit inputs the information selected by the user into the generation AI and uses it as data for generating the character. The generation unit uses the generation AI to generate a character based on the information customized by the customization unit. For example, the generation AI generates the character's appearance and personality based on the user's customization information. For example, the generation AI generates the character based on the hairstyle and clothing, personality traits, speaking style, hobbies, and interests selected by the user. For example, the generation AI generates the character's appearance and personality based on the user's customization information. The learning unit allows the character generated by the generation unit to learn through interaction with the user. The learning unit, for example, allows the character to learn the user's preferences and interests through interaction with the user. The learning unit, for example, allows the character to learn the user's preferences and interests through everyday conversations with the user. The learning unit, for example, allows the character to learn the user's preferences and interests through interaction with the user, enabling more personalized conversations. The sales unit sells the character based on the information learned by the learning unit. The sales unit, for example, sells the character created by the user on the marketplace. The sales unit, for example, sells the character created by the user on the marketplace, making it available for other users to purchase. The sales unit, for example, sells the character created by the user on the marketplace, making it available for other users to purchase as their own personal chat partner. In this way, the chatbot platform according to the embodiment can provide users with new revenue opportunities by allowing them to customize a character to their liking, and by having the generated character learn and be sold.

[0060] The customization section allows users to customize their character's appearance, personality, speaking style, hobbies, and preferences. To customize these aspects, users can, for example, select from options provided on the platform. Specifically, users can meticulously set details such as the character's hairstyle, clothing, eye color, skin color, and body type. Regarding personality, users can select personality types such as cheerful, calm, kind, or energetic, and then further adjust specific personality traits. For speaking style, users can choose from polite, friendly, or humorous speech patterns according to their preferences. Hobbies and interests can also be set, such as sports, music, reading, or travel. The customization section provides these options to the user, inputs the user's selections into the character generation AI, and uses this data to create the character. This allows users to easily create their own original characters. Furthermore, the customization section includes a function to save the user's selections, allowing for later re-editing and modification. This enables users to change character settings or add new elements. The customization section provides a user-friendly interface to make user operation intuitive and easy, allowing even beginners to easily customize their characters.

[0061] The generation unit uses a generation AI to generate characters based on information customized by the customization unit. For example, the generation unit uses the generation AI to generate the character's appearance and personality based on the user's customization information. Specifically, the generation AI generates a 3D model and animation of the character based on the hairstyle, clothing, personality traits, speaking style, hobbies, and interests selected by the user. The generation AI uses deep learning technology to generate realistic and natural characters that respond to the user's choices. For example, it can describe the character's appearance in detail based on the hairstyle and clothing selected by the user, and express the character's facial expressions and movements naturally based on its personality traits. It can also synthesize the character's voice based on its speaking style, generating voice that matches the speaking style set by the user. The generation unit not only generates the character's appearance and personality based on the user's customization information, but can also generate the character's movements, facial expressions, and voice in real time. This allows users to see their customized character actually move and speak. Furthermore, the generation unit has a function to save the generated character on the platform, allowing users to access it at any time. This allows users to share the generated character with other users or re-edit it later. The generation unit aims to maximize the performance of the generation AI to create high-quality characters that will satisfy users.

[0062] The learning unit allows the character generated by the generation unit to learn through interaction with the user. For example, the learning unit allows the character to learn the user's preferences and interests through interaction with the user. Specifically, the character learns the user's hobbies, interests, preferred topics, and favorite expressions through everyday conversations with the user. The learning unit uses natural language processing technology to analyze the content of the conversation with the user and extract the user's preferences and interests. For example, if the character learns that the user has shown interest in a particular topic during a conversation, the learning unit records that information and brings up that topic in the next conversation. Also, if the user prefers certain expressions or phrases, the learning unit learns that information and reflects it in the character's way of speaking. The learning unit accumulates the information obtained through interaction with the user and improves the character's conversation skills. This allows the character to provide more personalized conversations to the user. Furthermore, the learning unit collects user feedback and uses it as data to improve the character's conversation content and behavior. For example, it provides a function for users to evaluate the content of the character's conversations, and uses that evaluation to improve the character's conversation skills. Furthermore, the learning unit also has a function that allows characters to share information they have learned with other characters. This enables multiple characters to share the same user's preferences and interests, providing a more consistent dialogue. The learning unit aims to provide a better experience for users by having characters continuously learn and evolve through their interactions with the user.

[0063] The sales department sells characters based on information learned by the learning department. For example, the sales department sells user-created characters on the marketplace. Specifically, users list their created characters on the platform's marketplace, making them available for other users to purchase. The sales department provides a function to set the selling price of characters, allowing users to freely set the price. The sales department also provides a function to display detailed information and previews of characters, allowing buyers to check the character's appearance, personality, and speaking style. Furthermore, the sales department provides a function that allows buyers to use the purchased character as their personal chat partner. This allows buyers to enjoy interacting with characters that suit their preferences. The sales department also has a function to manage the sales history and revenue information of characters, allowing users to check the sales status of their characters. For example, users can check how many times their characters have been sold and how much revenue has been earned. Through the operation of the marketplace, the sales department aims to provide users with the opportunity to sell their characters and earn revenue. In addition, the sales department provides promotion and advertising functions for the marketplace to help users make their characters known to more people. This allows users to effectively sell their characters and maximize their revenue. The sales department aims to provide new revenue opportunities by allowing users to create characters using their creativity and sell those characters.

[0064] The customization unit can estimate the user's emotions and make customization suggestions based on those emotions. For example, if the user is stressed, the customization unit can suggest a character with a relaxing personality and speaking style. For example, if the user is excited, the customization unit can suggest a character with an energetic appearance and hobbies / preferences. For example, if the user is depressed, the customization unit can suggest a character with a personality that provides encouragement and comfort. This enables customization suggestions that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the customization unit may be performed using AI, for example, or not using AI. For example, the customization unit can input user emotion data into a generative AI and have the generative AI execute emotion-based customization suggestions.

[0065] The customization unit can analyze the user's past customization history and present the optimal customization options. For example, the customization unit can analyze the user's past choices in appearance and personality and present similar options. For example, the customization unit can suggest new characters based on the user's past preferred speaking style and hobbies. For example, the customization unit can suggest characters appropriate for specific times of day or situations based on the user's past customization history. This allows the system to present the optimal customization options based on the user's past customization history. Some or all of the above processes in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's past customization history data into a generating AI and have the generating AI perform the task of presenting the optimal customization options.

[0066] The customization unit can filter customization options based on the user's current interests and trends during the customization process. For example, the customization unit can suggest character preferences based on topics and hobbies the user is currently interested in. For example, the customization unit can suggest character appearances based on the latest fashion and trends. For example, the customization unit can suggest character personalities and speaking styles based on movies the user has recently watched or books they have read. This allows the unit to provide customization options based on the user's current interests and trends. Some or all of the above-described processes in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's current interests and trend data into a generating AI and have the generating AI perform the filtering of customization options.

[0067] The customization unit can estimate the user's emotions and determine customization priorities based on those emotions. For example, if the user is tired, the customization unit will prioritize customizing characters that promote relaxation. If the user is energetic, the customization unit will prioritize customizing characters that promote energy. If the user is sad, the customization unit will prioritize customizing characters that offer comfort and encouragement. This allows for the determination of customization priorities that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the customization unit may be performed using AI or not. For example, the customization unit can input user emotion data into a generative AI and have the generative AI determine the priority of customizations based on those emotions.

[0068] The customization unit can prioritize and present highly relevant customization options by considering the user's geographical location during the customization process. For example, the customization unit can suggest character appearances and hobbies based on the culture and trends of the area where the user lives. For example, if the user is traveling, the customization unit can suggest character customizations related to local specialties and tourist attractions in that area. For example, if the user is participating in a specific event, the customization unit can suggest character customizations related to that event. This allows the customization unit to provide customization options based on the user's geographical location. Some or all of the above processing in the customization unit may be performed using AI, for example, or not. For example, the customization unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of presenting highly relevant customization options.

[0069] The customization unit can analyze the user's social media activity during customization and present relevant customization options. For example, the customization unit can suggest character hobbies and preferences based on the accounts the user follows and topics of interest on social media. For example, the customization unit can suggest character appearance and personality based on content the user shares on social media. For example, the customization unit can suggest character speech patterns and interests based on communities the user participates in on social media. This allows for the provision of customization options based on the user's social media activity. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's social media activity data into a generating AI and have the generating AI present relevant customization options.

[0070] The generation unit can estimate the user's emotions and adjust the character generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit will generate a character with a calm personality and speaking style. For example, if the user is excited, the generation unit will generate a lively and energetic character. For example, if the user is sad, the generation unit will generate a character that offers comfort and encouragement. This allows the character generation method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform adjustments to the character generation method based on emotions.

[0071] The generation unit can adjust the level of detail of the generated characters based on their importance. For example, for major characters, the generation unit generates characters with detailed appearances and personalities. For example, for sub-characters, the generation unit generates characters with simplified appearances and personalities. For example, for temporary characters, the generation unit generates characters with minimal appearances and personalities. This allows the level of detail of the generated characters to be adjusted according to their importance. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input character importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the generated characters.

[0072] The generation unit can apply different generation algorithms depending on the character category during generation. For example, in the case of a fantasy character, the generation unit applies a generation algorithm that incorporates magic and special abilities. For example, in the case of a realistic character, the generation unit applies a generation algorithm based on real-world data. For example, in the case of a comical character, the generation unit applies a generation algorithm that emphasizes humor and fun. This allows the generation unit to apply a generation algorithm appropriate to the character category. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input character category data into a generation AI and have the generation AI execute the application of different generation algorithms.

[0073] The generation unit can estimate the user's emotions and determine the priority of characters to generate based on the estimated user emotions. For example, if the user is in a hurry, the generation unit will prioritize characters that can be generated quickly. If the user is relaxed, the generation unit will prioritize characters that allow for detailed customization. If the user is excited, the generation unit will prioritize energetic characters. This allows the generation unit to determine the priority of characters to generate according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI determine the priority of character generation based on emotions.

[0074] The generation unit can determine the priority of character creation based on the character's submission date. For example, the generation unit will prioritize the creation of characters with approaching deadlines. For example, it will postpone the creation of characters for long-term projects. For example, it will give the highest priority to characters with urgent requests. This allows for the determination of a character creation priority according to the character's submission date. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input character submission date data into a generation AI and have the generation AI determine the generation priority.

[0075] The generation unit can adjust the generation order based on the relationships between characters during generation. For example, the generation unit may prioritize generating characters related to the main storyline. For example, it may postpone generating characters related to sub-stories. For example, it may give the highest priority to generating characters related to temporary events. This allows the generation order to be adjusted according to the relationships between characters. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input character relationship data into a generation AI and have the generation AI perform the adjustment of the generation order.

[0076] The learning unit can estimate the user's emotions and adjust the learning method based on the estimated emotions. For example, if the user is relaxed, the learning unit will proceed at a relaxed pace. If the user is in a hurry, the learning unit will proceed efficiently. If the user is excited, the learning unit will actively take in new information. This allows the learning method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input user emotion data into the generative AI and have the generative AI perform emotion-based adjustments to the learning method.

[0077] The learning unit can analyze the user's past conversation history during learning to select the optimal learning method. For example, the learning unit can select learning content based on topics the user has liked in the past. For example, the learning unit can select an effective learning method from the user's past conversation history. For example, the learning unit can select learning content while avoiding topics the user has avoided in the past. This allows the learning unit to select the optimal learning method based on the user's past conversation history. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's past conversation history data into a generating AI and have the generating AI select the optimal learning method.

[0078] The learning unit can customize learning content based on the user's current interests and trends during the learning process. For example, the learning unit can customize learning content based on topics the user is currently interested in. For example, the learning unit can customize learning content based on the latest trends and topics. For example, the learning unit can customize learning content based on movies the user has recently watched or books the user has recently read. This allows the learning unit to provide learning content tailored to the user's current interests and trends. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's current interests and trend data into a generating AI and have the generating AI perform the customization of the learning content.

[0079] The learning unit can estimate the user's emotions and determine learning priorities based on the estimated emotions. For example, if the user is in a hurry, the learning unit will prioritize content that can be learned quickly. For example, if the user is relaxed, the learning unit will prioritize detailed learning content. For example, if the user is excited, the learning unit will prioritize energetic learning content. This allows for the determination of learning priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform the determination of learning priorities based on emotions.

[0080] The learning unit can select the optimal learning method while considering the user's geographical location information. For example, the learning unit can select learning content based on the culture and trends of the area where the user lives. For example, if the user is traveling, the learning unit can select learning content related to local specialties and tourist attractions in that area. For example, if the user is participating in a specific event, the learning unit can select learning content related to that event. This allows the learning unit to select the optimal learning method based on the user's geographical location information. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal learning method.

[0081] The learning unit can analyze the user's social media activity during learning and suggest learning content. For example, the learning unit can suggest learning content based on the accounts the user follows on social media and the topics the user is interested in. For example, the learning unit can suggest learning content based on the content the user shares on social media. For example, the learning unit can suggest learning content based on the communities the user participates in on social media. This allows the learning unit to provide learning content based on the user's social media activity. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's social media activity data into a generating AI and have the generating AI suggest learning content.

[0082] The sales department can estimate the user's emotions and adjust its sales approach based on those emotions. For example, if the user is relaxed, the sales department might offer a sales approach that includes detailed explanations and demonstrations. If the user is in a hurry, the sales department might offer a concise and quick sales approach. If the user is excited, the sales department might offer a sales approach that includes perks and bonuses. This allows the sales department to adjust its approach according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the sales department may be performed using AI or not. For example, the sales department can input user emotion data into a generative AI and have the generative AI perform emotion-based adjustments to its sales approach.

[0083] The sales department can analyze a user's past purchase history to select the optimal sales method at the time of sale. For example, the sales department can analyze the trends of characters a user has purchased in the past and suggest similar characters. For example, the sales department can select a sales method that is appropriate for a specific time of day or situation based on the user's past purchase history. For example, the sales department can prioritize providing sales methods that the user has preferred in the past (e.g., sales or campaigns). This allows for the selection of the optimal sales method based on the user's past purchase history. Some or all of the above processes in the sales department may be performed using AI, for example, or not using AI. For example, the sales department can input the user's past purchase history data into a generating AI and have the generating AI select the optimal sales method.

[0084] The sales department can customize the product offerings at the time of sale based on the user's current interests and trends. For example, the sales department can suggest characters based on topics or hobbies the user is currently interested in. For example, the sales department can suggest character appearances and personalities based on the latest fashion and trends. For example, the sales department can suggest characters based on movies the user has recently watched or books the user has recently read. This allows the sales department to provide products tailored to the user's current interests and trends. Some or all of the above processes in the sales department may be performed using AI, for example, or not. For example, the sales department can input data on the user's current interests and trends into a generating AI and have the generating AI perform the customization of the product offerings.

[0085] The sales department can estimate the user's emotions and determine sales priorities based on those estimated emotions. For example, if the user is in a hurry, the sales department will prioritize suggesting characters that can be purchased quickly. If the user is relaxed, the sales department will prioritize suggesting characters that include detailed explanations and demonstrations. If the user is excited, the sales department will prioritize suggesting characters that include perks and bonuses. This allows for sales priorities to be determined according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sales department may be performed using AI or not. For example, the sales department can input user emotion data into a generative AI and have the generative AI perform emotion-based sales prioritization.

[0086] The sales department can select the optimal sales method at the time of sale, taking into account the user's geographical location. For example, the sales department may suggest characters based on the culture and trends of the area where the user lives. For example, if the user is traveling, the sales department may suggest characters related to local specialties or tourist attractions in that area. For example, if the user is participating in a particular event, the sales department may suggest characters related to that event. This allows the sales department to select the optimal sales method based on the user's geographical location. Some or all of the above processes in the sales department may be performed using AI, for example, or not using AI. For example, the sales department can input the user's geographical location data into a generating AI and have the generating AI select the optimal sales method.

[0087] The sales department can analyze a user's social media activity and propose sales content at the time of sale. For example, the sales department can propose characters based on the accounts the user follows on social media and the topics the user is interested in. For example, the sales department can propose characters based on the content the user shares on social media. For example, the sales department can propose characters based on the communities the user participates in on social media. This allows the sales department to provide sales content tailored to the user's social media activity. Some or all of the above processes in the sales department may be performed using AI, for example, or not. For example, the sales department can input user social media activity data into a generating AI and have the generating AI generate sales content suggestions.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The customization section analyzes the user's voice data and can customize the character's speech based on the user's voice tone and speaking style. For example, if the user has a gentle voice tone, the character can be set to speak gently as well. Similarly, if the user speaks quickly, the character can be customized to speak quickly as well. Furthermore, it is possible to analyze the emotional nuances of the user's voice and reflect them in the character's speech. This allows for the provision of a more personalized character based on the user's voice characteristics.

[0090] The generation unit can analyze the user's past conversation history and adjust the character's conversation style based on the user's preferred conversation style. For example, if the user likes humor, the character can be set to engage in humorous conversations. Similarly, if the user prefers polite language, the character can be customized to use polite language. Furthermore, if the user is interested in a particular topic, the character can be set to prioritize conversations related to that topic. This allows for the provision of a more personalized character based on the user's conversation style.

[0091] The learning unit can estimate the user's emotions and adjust the learning pace based on those emotions. For example, if the user is relaxed, the learning can proceed at a slower pace. If the user is in a hurry, the learning can proceed more efficiently. Furthermore, if the user is excited, they can actively absorb new information. This allows the learning pace to be adjusted according to the user's emotions.

[0092] The sales department can analyze users' purchase history and suggest new characters based on the trends of characters the user has previously purchased. For example, it can analyze the appearance and personality traits of characters the user has previously purchased and suggest similar characters. It can also suggest new characters based on the speaking style and hobbies / preferences of characters the user has previously purchased. Furthermore, it can suggest characters appropriate for specific times of day or situations based on the user's purchase history. This allows for the suggestion of the most suitable character based on the user's purchase history.

[0093] The customization function analyzes the user's social media activity and can suggest character preferences based on the accounts the user follows and the topics they are interested in. For example, if a user follows a specific artist or sports team, it can suggest a character with interests related to that artist or sports team. It can also suggest a character's appearance and personality based on the content the user shares on social media. Furthermore, it can suggest a character's speaking style and interests based on the communities the user participates in on social media. This allows for the provision of customized options based on the user's social media activity.

[0094] The generation unit can estimate the user's emotions and adjust the character generation method based on those emotions. For example, if the user is relaxed, it can generate a character with a calm personality and speaking style. If the user is excited, it can generate a lively and energetic character. Furthermore, if the user is sad, it can generate a character that offers comfort and encouragement. This allows the character generation method to be adjusted according to the user's emotions.

[0095] The learning unit can analyze a user's past conversation history and tailor learning content based on the user's preferred conversation style. For example, if a user enjoys humor, it can provide learning content that incorporates humor. Similarly, if a user prefers polite language, it can provide learning content using polite language. Furthermore, if a user is interested in a specific topic, it can prioritize providing learning content related to that topic. This allows for the provision of optimal learning content based on the user's conversation style.

[0096] The sales department can estimate the user's emotions and prioritize sales based on those emotions. For example, if the user is in a hurry, they can prioritize suggesting characters that can be purchased quickly. If the user is relaxed, they can prioritize suggesting characters that include detailed explanations and demonstrations. Furthermore, if the user is excited, they can prioritize suggesting characters that include perks and bonuses. This allows for sales prioritization that aligns with the user's emotions.

[0097] The customization section can prioritize and present highly relevant customization options by considering the user's geographical location. For example, it can suggest character appearances and hobbies based on the culture and trends of the area where the user lives. If the user is traveling, it can suggest character customizations related to local specialties and tourist attractions. Furthermore, if the user is participating in a specific event, it can suggest character customizations related to that event. This allows for the provision of customization options based on the user's geographical location.

[0098] The learning unit can estimate the user's emotions and determine learning priorities based on those emotions. For example, if the user is in a hurry, it can prioritize content that can be learned quickly. If the user is relaxed, it can prioritize detailed learning content. Furthermore, if the user is excited, it can prioritize energetic learning content. This allows for learning priorities to be determined in accordance with the user's emotions.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The customization section allows users to customize their character's appearance, personality, speech patterns, hobbies, and preferences. Users can choose from the options provided on the platform to select their character's hairstyle, clothing, personality traits, speaking style, hobbies, and interests. Step 2: The generation unit uses a generation AI to generate a character based on the information customized by the customization unit. The generation AI generates the character's appearance and personality based on the user's selected hairstyle, clothing, personality traits, speaking style, hobbies, and interests. Step 3: The learning unit learns through interaction with the user by the character generated by the generation unit. The character learns the user's preferences and interests through everyday conversations with the user, enabling more personalized dialogue. Step 4: The sales department sells characters based on the information learned by the learning department. Characters created by users are sold on the marketplace and can be purchased by other users. Purchased characters can be used as personal chat partners.

[0101] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0102] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0103] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0104] Each of the multiple elements described above, including the customization unit, generation unit, learning unit, and sales unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the customization unit is implemented by the control unit 46A of the smart device 14, allowing the user to customize the character's appearance, personality, way of speaking, hobbies, preferences, etc. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, using a generation AI to generate a character based on the customized information. The learning unit is implemented by the control unit 46A of the smart device 14, allowing the generated character to learn through interaction with the user. The sales unit is implemented by the specific processing unit 290 of the data processing unit 12, selling the character on a marketplace based on the learned information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] As shown in Figure 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.

[0107] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0111] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0112] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0113] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0114] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0115] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0117] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0119] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the customization unit, generation unit, learning unit, and sales unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the customization unit is implemented by the control unit 46A of the smart glasses 214, allowing the user to customize the character's appearance, personality, way of speaking, hobbies, preferences, etc. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, using a generation AI to generate a character based on the customized information. The learning unit is implemented by the control unit 46A of the smart glasses 214, allowing the generated character to learn through interaction with the user. The sales unit is implemented by the specific processing unit 290 of the data processing unit 12, selling the character on a marketplace based on the learned information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the customization unit, generation unit, learning unit, and sales unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the customization unit is implemented by the control unit 46A of the headset terminal 314, allowing the user to customize the character's appearance, personality, way of speaking, hobbies, preferences, etc. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, using a generation AI to generate a character based on the customized information. The learning unit is implemented by the control unit 46A of the headset terminal 314, allowing the generated character to learn through interaction with the user. The sales unit is implemented by the specific processing unit 290 of the data processing unit 12, selling the character on a marketplace based on the learned information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] As shown in Figure 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.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0145] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0146] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0147] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0148] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0150] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] Each of the multiple elements described above, including the customization unit, generation unit, learning unit, and sales unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the customization unit is implemented by the control unit 46A of the robot 414, allowing the user to customize the character's appearance, personality, speech patterns, hobbies, and preferences. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, using a generation AI to generate a character based on the customized information. The learning unit is implemented by the control unit 46A of the robot 414, allowing the generated character to learn through interaction with the user. The sales unit is implemented by the specific processing unit 290 of the data processing unit 12, selling the character on a marketplace based on the learned information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0154] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0156] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0157] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0158] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0162] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0163] 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.

[0164] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0165] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0166] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0167] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0169] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0170] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0171] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0172] (Note 1) The customization section allows users to customize the character's appearance, personality, way of speaking, hobbies, preferences, etc. A generation unit that generates a character based on the information customized by the customization unit, A learning unit in which the character generated by the generation unit learns through interaction with the user, The system comprises a sales unit that sells characters based on information learned by the learning unit. A system characterized by the following features. (Note 2) The aforementioned customization unit is It estimates the user's emotions and makes customized suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned customization unit is Analyze the user's past customization history and suggest the optimal customization options. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned customization unit is When customizing, filter customization options based on the user's current interests and trends. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned customization unit is It estimates the user's emotions and determines the priority of customization based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned customization unit is During customization, the system prioritizes presenting highly relevant customization options by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned customization unit is During customization, the system analyzes the user's social media activity and presents relevant customization options. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is It estimates the user's emotions and adjusts the character generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is During generation, adjust the level of detail based on the importance of the character. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is During generation, different generation algorithms are applied depending on the character category. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is It estimates the user's emotions and determines the priority of characters to generate based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is During generation, the generation priority is determined based on when the character was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is During generation, the generation order is adjusted based on the relationships between characters. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned learning unit, During the learning process, the system analyzes the user's past conversation history to select the optimal learning method. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned learning unit, During learning, the learning content is customized based on the user's current interests and trends. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned learning unit, It estimates the user's emotions and determines learning priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned learning unit, During the learning process, the optimal learning method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned learning unit, During the learning process, the system analyzes the user's social media activity to suggest learning content. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned sales department, We estimate user sentiment and adjust sales methods based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned sales department, At the time of sale, the system analyzes the user's past purchase history to select the optimal sales method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned sales department, At the time of sale, customize the product offering based on the user's current interests and trends. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned sales department, It estimates user sentiment and determines sales priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned sales department, When selling, the optimal sales method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned sales department, When making a sale, we analyze the user's social media activity and propose sales strategies. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The customization section allows users to customize the character's appearance, personality, way of speaking, hobbies, preferences, etc. A generation unit that generates a character based on the information customized by the customization unit, A learning unit in which the character generated by the generation unit learns through interaction with the user, The system comprises a sales unit that sells characters based on information learned by the learning unit. A system characterized by the following features.

2. The aforementioned customization unit is It estimates the user's emotions and makes customized suggestions based on those estimated emotions. The system according to feature 1.

3. The aforementioned customization unit is Analyze the user's past customization history and suggest the optimal customization options. The system according to feature 1.

4. The aforementioned customization unit is When customizing, filter customization options based on the user's current interests and trends. The system according to feature 1.

5. The aforementioned customization unit is It estimates the user's emotions and determines the priority of customization based on those estimated emotions. The system according to feature 1.

6. The aforementioned customization unit is During customization, the system prioritizes presenting highly relevant customization options by considering the user's geographical location. The system according to feature 1.

7. The aforementioned customization unit is During customization, the system analyzes the user's social media activity and presents relevant customization options. The system according to feature 1.

8. The generating unit is It estimates the user's emotions and adjusts the character generation method based on the estimated user emotions. The system according to feature 1.

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