System

Abstract

An object of the system according to the embodiment is to provide a reproducible production AI and to obtain a product that satisfies specific conditions.SOLUTION: A system includes a reception unit, an analysis unit, and a generation unit. The receiving unit receives an input from a user. The analysis unit analyzes the information received by the reception unit and issues an instruction to the generation AI. The generation unit generates a product based on the instruction issued by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Previous technology had the problem that it was difficult for generation AI to generate the same character multiple times or to obtain a product that met specific conditions.

[0005] The system according to the embodiment aims to provide reproducibility to the generation AI and to obtain a product that satisfies specific conditions. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a generation unit. The reception unit receives input from a user. The analysis unit analyzes the information received by the reception unit and issues instructions to the generation AI. The generation unit generates a product based on the instructions issued by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment provides reproducibility to the generation AI, and can obtain a product that satisfies specific conditions. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A generative AI system according to an embodiment of the present invention accepts input from a user, and the generative AI generates a reproducible product. The generative AI system inputs the character and conditions the user wants to generate, and an AI system with advanced reasoning and learning capabilities analyzes the input information and issues instructions to the generative AI to generate a reproducible product. The generative AI generates a reproducible product according to the AI ​​system's instructions. For example, if a user wants to generate an image of a character wearing specific clothing or a specific scene, the user inputs the information into the generative AI system. The generative AI system then analyzes the input information and issues instructions to the generative AI to generate a reproducible product. The generative AI generates an image of a character wearing specific clothing or a specific scene according to the AI ​​system's instructions. This allows the generative AI system to generate the same character multiple times or to generate a product that meets specific conditions. This allows the generative AI system to generate the same character multiple times or to generate a product that meets specific conditions. For example, in game development or animation production, when the same character needs to be generated multiple times, this tool can be used to efficiently generate the character. Furthermore, this tool can be used to generate highly accurate products when generating images that meet specific scenes or conditions.

[0029] A generative AI system according to an embodiment includes a reception unit, an analysis unit, and a generation unit. The reception unit receives input from a user. Examples of user input include, but are not limited to, text input, voice input, and image input. The reception unit receives, for example, a character and conditions the user wants to generate. The analysis unit analyzes the information received by the reception unit and issues instructions to the generation AI. The analysis may be performed using, for example, but is not limited to, natural language processing, image analysis, or voice analysis. The analysis unit infers an optimal method for the generation AI to reproduce the same character and conditions based on past generation results and learning data. The generation unit generates a product based on the instructions issued by the analysis unit. Examples of products include, but are not limited to, text generation, image generation, and voice generation. The generation unit can generate the same character multiple times using the generation AI. The generation unit can also generate a product that satisfies specific conditions using the generation AI. This allows the generation AI to generate a reproducible product based on the user's input.

[0030] The generation unit can generate the same character multiple times using the generation AI. The generation unit, for example, instructs the generation AI in detail about the character's characteristics and attributes. For example, the generation unit specifies the character's appearance, name, attributes, etc., and causes the generation AI to reproduce the same character. The generation unit can also instruct the generation AI about the character's movements and facial expressions. For example, the generation unit can specify the character's movement patterns and facial expression changes, and cause the generation AI to reproduce the same character. This makes it possible to generate the same character multiple times. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the character's characteristics and attributes into the generation AI and cause the generation AI to generate the same character.

[0031] The generation unit can use the generation AI to generate a product that satisfies specific conditions. The generation unit, for example, instructs the generation AI in detail about specific conditions. For example, the generation unit can specify specific attributes, settings, scenarios, etc., and have the generation AI generate a product that satisfies the specific conditions. The generation unit can also instruct the generation AI about specific scenes or situations. For example, the generation unit can specify the background of a specific scene and the placement of characters, etc., and have the generation AI generate a product that satisfies the specific conditions. In this way, a product that satisfies the specific conditions can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input specific conditions into the generation AI and have the generation AI generate a product that satisfies the conditions.

[0032] The analysis unit can infer the optimal method for the generation AI to reproduce the same character or conditions based on past generation results and learning data. The analysis unit, for example, analyzes past generation results and infers the optimal method for reproducing the same character or conditions. For example, the analysis unit extracts character characteristics and attributes from past generation results and issues instructions to the generation AI to obtain a reproducible product. The analysis unit can also infer the optimal method for the generation AI to reproduce the same character or conditions based on learning data. For example, the analysis unit infers the optimal method for obtaining a product that meets specific conditions from the learning data and issues instructions to the generation AI. In this way, a reproducible product can be obtained by inferring the optimal method based on past generation results and learning data. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input past generation results and learning data into the generation AI and cause the generation AI to infer the optimal method.

[0033] The reception unit allows the user to input the character and conditions that the user wants to generate. The reception unit provides, for example, an interface for the user to input the character and conditions that the user wants to generate. For example, the reception unit accepts input from the user by methods such as text input, voice input, and image input. The reception unit also has a function for transmitting the information input by the user to the analysis unit. For example, the reception unit transmits the character's appearance, attributes, and details of conditions input by the user to the analysis unit, and the analysis unit performs analysis based on the information. This allows the user to input the character and conditions that the user wants to generate. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may transmit the user's input to a generation AI, which may analyze the input content.

[0034] The generation unit can generate a character wearing specific clothing using the generation AI. The generation unit, for example, instructs the generation AI on the details, color, design, etc. of the specific clothing. For example, the generation unit specifies the type, color, and design of the clothing to be worn by the character, and causes the generation AI to generate a character wearing the specific clothing. The generation unit can also instruct the generation AI on the material and decoration of the clothing. For example, the generation unit can specify details of the material and decoration of the clothing and cause the generation AI to generate a character wearing the specific clothing. In this way, a character wearing the specific clothing can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input details of the specific clothing into the generation AI and cause the generation AI to generate a character wearing the clothing.

[0035] The reception unit can analyze the user's past input history and suggest the optimal input means. The reception unit, for example, analyzes the user's past input history and suggests the optimal input means. For example, the reception unit automatically displays characters and conditions that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit predicts and suggests characters and conditions to be used in a specific time period based on the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history to a generation AI and have the generation AI suggest the optimal input means.

[0036] The reception unit can filter the input content based on the user's current project or area of ​​interest. The reception unit filters the input content based on the user's current project or area of ​​interest, for example. For example, if the user is working on a game development project, the reception unit can preferentially display related characters and conditions. Also, if the user is interested in animation production, the reception unit can suggest related scenes and characters. For example, if the user is interested in a particular genre, the reception unit can filter and display characters and conditions related to that genre. This allows the input content to be filtered based on the user's current project or area of ​​interest. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input information about the user's project or area of ​​interest to a generation AI and have the generation AI filter the input content.

[0037] The reception unit can select an appropriate input means depending on the user's input method. For example, when a user inputs characters and conditions by voice, the reception unit supports the input using voice recognition technology. For example, the reception unit automatically analyzes the voice using voice recognition software and saves it as text. Furthermore, when a user inputs detailed conditions in text, the reception unit can also understand the input content using text analysis technology. For example, the reception unit can analyze the details of the conditions input by the user using text analysis technology and select an appropriate input means. Furthermore, when a user specifies character characteristics using an image, the reception unit can also analyze the input content using image analysis technology. For example, the reception unit can analyze the characteristics of the character specified by the user using image analysis technology and select an appropriate input means. This allows the optimal input means to be selected depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's input method to a generation AI and have the generation AI select the optimal input means.

[0038] The analysis unit can improve the analysis algorithm by referring to past generation results. For example, the analysis unit selects the optimal algorithm for reproducing the same character based on past generation results. For example, the analysis unit optimizes the algorithm for obtaining a product that meets specific conditions from past generation results. The analysis unit can also analyze past generation results and adjust the algorithm for improving the quality of the product. For example, the analysis unit adjusts the algorithm for improving the quality of the product based on past generation results. This makes it possible to optimize the analysis algorithm based on past generation results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past generation results into the generation AI and cause the generation AI to improve the analysis algorithm.

[0039] The analysis unit can adjust the level of detail of the analysis based on the user's input. For example, if the user inputs detailed conditions, the analysis unit increases the level of detail of the analysis. For example, the analysis unit increases the depth of the analysis or the number of analysis items based on the detailed conditions input by the user. The analysis unit can also decrease the level of detail of the analysis if the user inputs concise conditions. For example, the analysis unit decreases the depth of the analysis or the number of analysis items based on the concise conditions input by the user. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the user's input. For example, the analysis unit adjusts the level of detail of the analysis in real time based on the user's input. This allows the level of detail of the analysis to be adjusted according to the user's input. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's input to a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0040] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit improves the analysis accuracy for reproducing the same character or conditions based on the user's past analysis results. For example, the analysis unit improves the analysis accuracy for obtaining a product that meets specific conditions from the user's past analysis results. The analysis unit can also analyze the user's past analysis results and adjust the analysis accuracy to improve the quality of the product. For example, the analysis unit adjusts the analysis accuracy to improve the quality of the product based on the user's past analysis results. This makes it possible to improve the analysis accuracy based on the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into the generation AI and cause the generation AI to improve the analysis accuracy.

[0041] The generation unit can adjust the level of detail of the generation based on the importance of the character during generation. For example, in the case of a main character, the generation unit generates a product including detailed characteristics and attributes. For example, the generation unit specifies the main character's appearance, personality, background, etc. in detail and causes the generation AI to generate a detailed product. In addition, in the case of a sub-character, the generation unit can generate a product including concise characteristics and attributes. For example, the generation unit specifies the sub-character's basic appearance and role and causes the generation AI to generate a concise product. Furthermore, the generation unit can dynamically adjust the level of detail of the generation based on the character's importance. For example, the generation unit adjusts the level of detail of the product in real time based on the character's importance. This makes it possible to adjust the level of detail of the generation based on the character's importance. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the character's importance to the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0042] The generation unit can apply an appropriate generation algorithm depending on the character category during generation. For example, in the case of a hero character, the generation unit generates the character using a specific algorithm. For example, the generation unit selects a specific generation algorithm based on the characteristics and attributes of the hero character and applies it to the generation AI. The generation unit can also generate a villain character using a different algorithm. For example, the generation unit selects a different generation algorithm based on the characteristics and attributes of the villain character and applies it to the generation AI. The generation unit can also select an optimal generation algorithm depending on the character category. For example, the generation unit selects an optimal generation algorithm in real time based on the character category. This makes it possible to apply an optimal generation algorithm depending on the character category. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the character category to the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0043] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit, for example, improves the generation accuracy for reproducing the same character based on the user's past generation results. For example, the generation unit improves the generation accuracy for obtaining a product that meets specific conditions from the user's past generation results. The generation unit can also analyze the user's past generation results and adjust the generation accuracy to improve the quality of the product. For example, the generation unit adjusts the generation accuracy to improve the quality of the product based on the user's past generation results. This makes it possible to improve the generation accuracy based on the user's past generation results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past generation results into the generation AI and cause the generation AI to improve the generation accuracy.

[0044] The generation unit can determine the generation priority based on the time of character submission at the time of generation. For example, the generation unit sets a high generation priority for a character whose deadline is approaching. For example, the generation unit sets a high generation priority based on the time of character submission and causes the generation AI to generate the character first. The generation unit can also set a low generation priority for a character whose submission date is further away. For example, the generation unit sets a low generation priority based on the time of character submission and causes the generation AI to generate the character later. Furthermore, the generation unit can dynamically adjust the generation priority depending on the time of character submission. For example, the generation unit adjusts the generation priority in real time based on the time of character submission. This makes it possible to adjust the generation priority depending on the time of character submission. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the time of character submission to the generation AI and cause the generation AI to determine the generation priority.

[0045] The generation unit can adjust the order of generation based on the relevance of characters during generation. For example, in the case of a main character, the generation unit prioritizes the order of generation. For example, the generation unit prioritizes the generation of the main character and causes the generation AI to generate the main character first. The generation unit can also postpone the order of generation for a sub-character. For example, the generation unit postpones the generation of a sub-character and causes the generation AI to generate the sub-character later. Furthermore, the generation unit can dynamically adjust the order of generation based on the relevance of characters. For example, the generation unit adjusts the order of generation in real time based on the relevance of characters. This makes it possible to adjust the order of generation based on the relevance of characters. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the relevance of characters to the generation AI and cause the generation AI to adjust the order of generation.

[0046] The generation unit can adjust the use of technical terms in the product during generation according to the user's level of expertise. For example, if the user has technical expertise, the generation unit generates the product using detailed technical terms. For example, the generation unit uses detailed technical terms based on the user's level of expertise and causes the generation AI to generate a technical product. Furthermore, if the user does not have technical expertise, the generation unit can generate the product using concise and easy-to-understand terms. For example, the generation unit uses concise and easy-to-understand terms based on the user's level of expertise and causes the generation AI to generate the product. Furthermore, the generation unit can dynamically adjust the use of technical terms in the product according to the user's level of expertise. For example, the generation unit adjusts the use of technical terms in the product in real time based on the user's level of expertise. This makes it possible to adjust the use of technical terms in the product according to the user's level of expertise. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without AI. For example, the generation unit can input the user's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terms.

[0047] The analysis unit can customize the analysis content based on the user's geographical location information. For example, when the user is in a specific area, the analysis unit prioritizes analysis of characters and conditions related to that area. For example, when the user is in a specific area, the analysis unit prioritizes analysis of characters and conditions related to that area. Furthermore, when the user is traveling, the analysis unit can analyze characters and conditions related to the travel destination. For example, when the user is traveling, the analysis unit analyzes characters and conditions related to the travel destination. Furthermore, the analysis unit can also suggest optimal analysis content based on the user's geographical location information. For example, the analysis unit suggests optimal analysis content based on the user's geographical location information. This allows the analysis content to be customized based on the user's geographical location information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical location information to the generation AI and cause the generation AI to customize the analysis content.

[0048] The analysis unit can analyze the user's social media activities and suggest related analysis content. The analysis unit can suggest analysis content based on, for example, characters and conditions shared by the user on social media. For example, the analysis unit can suggest analysis content based on characters and conditions shared by the user on social media. The analysis unit can also analyze the user's social media posts and suggest related analysis content. For example, the analysis unit can analyze the user's social media posts and suggest related analysis content. Furthermore, the analysis unit can also suggest related analysis content by referring to the activities of the user's friends on social media. For example, the analysis unit can suggest related analysis content by referring to the activities of the user's friends on social media. This makes it possible to suggest related analysis content based on the user's social media activities. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's social media activities into a generation AI and cause the generation AI to suggest related analysis content.

[0049] The analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit displays the analysis results using detailed technical terminology. For example, the analysis unit uses detailed technical terminology based on the user's level of expertise and causes the generation AI to generate technical analysis results. Furthermore, if the user does not have technical expertise, the analysis unit can display the analysis results using concise and easy-to-understand terms. For example, the analysis unit uses concise and easy-to-understand terms based on the user's level of expertise and causes the generation AI to generate analysis results. Furthermore, the analysis unit can dynamically adjust the use of technical terminology in the analysis results according to the user's level of expertise. For example, the analysis unit adjusts the use of technical terminology in the analysis results in real time based on the user's level of expertise. This allows the use of technical terminology in the analysis to be adjusted according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without AI. For example, the analysis unit can input the user's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terminology.

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

[0051] The reception unit can propose variations of the product based on the user's input. For example, if the user wants to generate a specific character, the reception unit can propose variations of the character, such as different clothing, facial expressions, and poses. Also, if the user wants to generate a specific scene, the reception unit can propose variations of the scene, such as different time of day, weather, and viewpoints. Furthermore, the reception unit can customize the variations of the product based on the user's past input history and preferences. This allows the user to have a wider variety of product options and obtain a product that is more satisfying to the user.

[0052] The analysis unit can evaluate the quality of the generated product based on the user's input. For example, the analysis unit can evaluate quality indicators such as the resolution, color, and detail of the generated product and provide feedback to the user. The analysis unit can also evaluate how well the content of the generated product matches the user's requirements. For example, the analysis unit can evaluate how accurately the generated product reproduces the characteristics of a specified character or scene. Furthermore, the analysis unit can issue improvement instructions to the generation AI based on the results of the product quality evaluation. This allows the quality of the generated product to be continuously improved.

[0053] The generation unit can visualize the generation process of a product. For example, the generation unit can display the character generation process step by step, allowing the user to check the generation process in real time. The generation unit can also display the scene generation process in animation format. For example, the generation unit can show, through animation, how the background of a scene or the placement of characters changes. Furthermore, the generation unit can provide an interactive function that allows the user to intervene at each step of the generation process. This allows the user to understand the generation process and make adjustments as necessary.

[0054] The reception unit can suggest usage scenarios for the product based on the user's input. For example, if the user creates a character, the reception unit can suggest ideas for stories and scenes in which the character appears. Also, if the user creates a scene, the reception unit can suggest projects and contexts in which the scene might be used. Furthermore, the reception unit can customize usage scenarios for the product based on the user's past projects and areas of interest. This makes it easier for the user to imagine specific ways in which the product can be used, facilitating the progress of the project.

[0055] After generating a product, the generation unit can collect user feedback and reflect it in the next generation. For example, the generation unit can collect user satisfaction with the product and areas for improvement in the form of a questionnaire. The generation unit can also record corrections and adjustments made by the user to the product and use this information as a reference when generating the product next time. Furthermore, the generation unit can adjust the generation algorithm based on user feedback to improve the quality of the product. In this way, the quality of the product can be continuously improved by utilizing user feedback.

[0056] The reception unit can provide related information about the product based on the user's input. For example, if a user creates a specific character, background information and setting materials related to the character can be provided. Also, if a user creates a specific scene, reference images and videos related to the scene can be provided. Furthermore, the reception unit can provide related tutorials and guides based on the user's input. This allows the user to obtain additional information about the product, enabling a deeper understanding and utilization.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The reception unit receives input from the user. The input from the user includes, for example, text input, voice input, image input, etc. The reception unit inputs the character the user wants to generate and the conditions. Step 2: The analysis unit analyzes the information received by the reception unit and issues instructions to the generation AI. Analysis is performed using methods such as natural language processing, image analysis, and voice analysis. Based on past generation results and learning data, the analysis unit infers the optimal method for the generation AI to reproduce the same character and conditions. Step 3: The generation unit generates a product based on the instructions issued by the analysis unit. The product may be, for example, text generation, image generation, or voice generation. The generation unit can generate the same character multiple times using the generation AI. The generation unit can also generate a product that meets specific conditions using the generation AI.

[0059] (Example 2) A generative AI system according to an embodiment of the present invention accepts input from a user, and the generative AI generates a reproducible product. The generative AI system inputs the character and conditions the user wants to generate, and an AI system with advanced reasoning and learning capabilities analyzes the input information and issues instructions to the generative AI to generate a reproducible product. The generative AI generates a reproducible product according to the AI ​​system's instructions. For example, if a user wants to generate an image of a character wearing specific clothing or a specific scene, the user inputs the information into the generative AI system. The generative AI system then analyzes the input information and issues instructions to the generative AI to generate a reproducible product. The generative AI generates an image of a character wearing specific clothing or a specific scene according to the AI ​​system's instructions. This allows the generative AI system to generate the same character multiple times or to generate a product that meets specific conditions. This allows the generative AI system to generate the same character multiple times or to generate a product that meets specific conditions. For example, in game development or animation production, when the same character needs to be generated multiple times, this tool can be used to efficiently generate the character. Furthermore, this tool can be used to generate highly accurate products when generating images that meet specific scenes or conditions.

[0060] A generative AI system according to an embodiment includes a reception unit, an analysis unit, and a generation unit. The reception unit receives input from a user. Examples of user input include, but are not limited to, text input, voice input, and image input. The reception unit receives, for example, a character and conditions the user wants to generate. The analysis unit analyzes the information received by the reception unit and issues instructions to the generation AI. The analysis may be performed using, for example, but is not limited to, natural language processing, image analysis, or voice analysis. The analysis unit infers an optimal method for the generation AI to reproduce the same character and conditions based on past generation results and learning data. The generation unit generates a product based on the instructions issued by the analysis unit. Examples of products include, but are not limited to, text generation, image generation, and voice generation. The generation unit can generate the same character multiple times using the generation AI. The generation unit can also generate a product that satisfies specific conditions using the generation AI. This allows the generation AI to generate a reproducible product based on the user's input.

[0061] The generation unit can generate the same character multiple times using the generation AI. The generation unit, for example, instructs the generation AI in detail about the character's characteristics and attributes. For example, the generation unit specifies the character's appearance, name, attributes, etc., and causes the generation AI to reproduce the same character. The generation unit can also instruct the generation AI about the character's movements and facial expressions. For example, the generation unit can specify the character's movement patterns and facial expression changes, and cause the generation AI to reproduce the same character. This makes it possible to generate the same character multiple times. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the character's characteristics and attributes into the generation AI and cause the generation AI to generate the same character.

[0062] The generation unit can use the generation AI to generate a product that satisfies specific conditions. The generation unit, for example, instructs the generation AI in detail about specific conditions. For example, the generation unit can specify specific attributes, settings, scenarios, etc., and have the generation AI generate a product that satisfies the specific conditions. The generation unit can also instruct the generation AI about specific scenes or situations. For example, the generation unit can specify the background of a specific scene and the placement of characters, etc., and have the generation AI generate a product that satisfies the specific conditions. In this way, a product that satisfies the specific conditions can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input specific conditions into the generation AI and have the generation AI generate a product that satisfies the conditions.

[0063] The analysis unit can infer the optimal method for the generation AI to reproduce the same character or conditions based on past generation results and learning data. The analysis unit, for example, analyzes past generation results and infers the optimal method for reproducing the same character or conditions. For example, the analysis unit extracts character characteristics and attributes from past generation results and issues instructions to the generation AI to obtain a reproducible product. The analysis unit can also infer the optimal method for the generation AI to reproduce the same character or conditions based on learning data. For example, the analysis unit infers the optimal method for obtaining a product that meets specific conditions from the learning data and issues instructions to the generation AI. In this way, a reproducible product can be obtained by inferring the optimal method based on past generation results and learning data. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input past generation results and learning data into the generation AI and cause the generation AI to infer the optimal method.

[0064] The reception unit allows the user to input the character and conditions that the user wants to generate. The reception unit provides, for example, an interface for the user to input the character and conditions that the user wants to generate. For example, the reception unit accepts input from the user by methods such as text input, voice input, and image input. The reception unit also has a function for transmitting the information input by the user to the analysis unit. For example, the reception unit transmits the character's appearance, attributes, and details of conditions input by the user to the analysis unit, and the analysis unit performs analysis based on the information. This allows the user to input the character and conditions that the user wants to generate. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may transmit the user's input to a generation AI, which may analyze the input content.

[0065] The generation unit can generate a character wearing specific clothing using the generation AI. The generation unit, for example, instructs the generation AI on the details, color, design, etc. of the specific clothing. For example, the generation unit specifies the type, color, and design of the clothing to be worn by the character, and causes the generation AI to generate a character wearing the specific clothing. The generation unit can also instruct the generation AI on the material and decoration of the clothing. For example, the generation unit can specify details of the material and decoration of the clothing and cause the generation AI to generate a character wearing the specific clothing. In this way, a character wearing the specific clothing can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input details of the specific clothing into the generation AI and cause the generation AI to generate a character wearing the clothing.

[0066] The reception unit can estimate the user's emotion and adjust the display method of the input interface based on the estimated user emotion. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expression and adjusts the display method of the input interface. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the display method of the input interface. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on heart rate fluctuations and adjusts the display method of the input interface. This makes it possible to adjust the display method of the input interface according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input user emotion data to the generation AI and cause the generation AI to adjust the interface based on the emotion.

[0067] The reception unit can analyze the user's past input history and suggest the optimal input means. The reception unit, for example, analyzes the user's past input history and suggests the optimal input means. For example, the reception unit automatically displays characters and conditions that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit predicts and suggests characters and conditions to be used in a specific time period based on the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history to a generation AI and have the generation AI suggest the optimal input means.

[0068] The reception unit can filter the input content based on the user's current project or area of ​​interest. The reception unit filters the input content based on the user's current project or area of ​​interest, for example. For example, if the user is working on a game development project, the reception unit can preferentially display related characters and conditions. Also, if the user is interested in animation production, the reception unit can suggest related scenes and characters. For example, if the user is interested in a particular genre, the reception unit can filter and display characters and conditions related to that genre. This allows the input content to be filtered based on the user's current project or area of ​​interest. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input information about the user's project or area of ​​interest to a generation AI and have the generation AI filter the input content.

[0069] The reception unit can select an appropriate input means depending on the user's input method. For example, when a user inputs characters and conditions by voice, the reception unit supports the input using voice recognition technology. For example, the reception unit automatically analyzes the voice using voice recognition software and saves it as text. Furthermore, when a user inputs detailed conditions in text, the reception unit can also understand the input content using text analysis technology. For example, the reception unit can analyze the details of the conditions input by the user using text analysis technology and select an appropriate input means. Furthermore, when a user specifies character characteristics using an image, the reception unit can also analyze the input content using image analysis technology. For example, the reception unit can analyze the characteristics of the character specified by the user using image analysis technology and select an appropriate input means. This allows the optimal input means to be selected depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's input method to a generation AI and have the generation AI select the optimal input means.

[0070] The reception unit can estimate the user's emotion and adjust the design of the input interface based on the estimated user emotion. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expression and adjusts the design of the input interface. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the design of the input interface. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on heart rate fluctuations and adjusts the design of the input interface. This makes it possible to adjust the design of the input interface according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input user emotion data to the generation AI and cause the generation AI to adjust the design of the interface based on the emotion.

[0071] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expressions and adjusts the display method of the analysis results. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the display method of the analysis results. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations and adjusts the display method of the analysis results. This makes it possible to adjust the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data to the generation AI and cause the generation AI to adjust the display method of the analysis results based on the emotion.

[0072] The analysis unit can improve the analysis algorithm by referring to past generation results. For example, the analysis unit selects the optimal algorithm for reproducing the same character based on past generation results. For example, the analysis unit optimizes the algorithm for obtaining a product that meets specific conditions from past generation results. The analysis unit can also analyze past generation results and adjust the algorithm for improving the quality of the product. For example, the analysis unit adjusts the algorithm for improving the quality of the product based on past generation results. This makes it possible to optimize the analysis algorithm based on past generation results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past generation results into the generation AI and cause the generation AI to improve the analysis algorithm.

[0073] The analysis unit can adjust the level of detail of the analysis based on the user's input. For example, if the user inputs detailed conditions, the analysis unit increases the level of detail of the analysis. For example, the analysis unit increases the depth of the analysis or the number of analysis items based on the detailed conditions input by the user. The analysis unit can also decrease the level of detail of the analysis if the user inputs concise conditions. For example, the analysis unit decreases the depth of the analysis or the number of analysis items based on the concise conditions input by the user. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the user's input. For example, the analysis unit adjusts the level of detail of the analysis in real time based on the user's input. This allows the level of detail of the analysis to be adjusted according to the user's input. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's input to a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0074] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit improves the analysis accuracy for reproducing the same character or conditions based on the user's past analysis results. For example, the analysis unit improves the analysis accuracy for obtaining a product that meets specific conditions from the user's past analysis results. The analysis unit can also analyze the user's past analysis results and adjust the analysis accuracy to improve the quality of the product. For example, the analysis unit adjusts the analysis accuracy to improve the quality of the product based on the user's past analysis results. This makes it possible to improve the analysis accuracy based on the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into the generation AI and cause the generation AI to improve the analysis accuracy.

[0075] The generation unit can estimate the user's emotion and adjust the expression method of the product based on the estimated user emotion. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on changes in facial expression and adjusts the expression method of the product. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the expression method of the product. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on heart rate fluctuations and adjusts the expression method of the product. This makes it possible to adjust the expression method of the product according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input user emotion data to the generation AI and cause the generation AI to adjust the expression method of the product based on the emotion.

[0076] The generation unit can adjust the level of detail of the generation based on the importance of the character during generation. For example, in the case of a main character, the generation unit generates a product including detailed characteristics and attributes. For example, the generation unit specifies the main character's appearance, personality, background, etc. in detail and causes the generation AI to generate a detailed product. In addition, in the case of a sub-character, the generation unit can generate a product including concise characteristics and attributes. For example, the generation unit specifies the sub-character's basic appearance and role and causes the generation AI to generate a concise product. Furthermore, the generation unit can dynamically adjust the level of detail of the generation based on the character's importance. For example, the generation unit adjusts the level of detail of the product in real time based on the character's importance. This makes it possible to adjust the level of detail of the generation based on the character's importance. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the character's importance to the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0077] The generation unit can apply an appropriate generation algorithm depending on the character category during generation. For example, in the case of a hero character, the generation unit generates the character using a specific algorithm. For example, the generation unit selects a specific generation algorithm based on the characteristics and attributes of the hero character and applies it to the generation AI. The generation unit can also generate a villain character using a different algorithm. For example, the generation unit selects a different generation algorithm based on the characteristics and attributes of the villain character and applies it to the generation AI. The generation unit can also select an optimal generation algorithm depending on the character category. For example, the generation unit selects an optimal generation algorithm in real time based on the character category. This makes it possible to apply an optimal generation algorithm depending on the character category. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the character category to the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0078] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit, for example, improves the generation accuracy for reproducing the same character based on the user's past generation results. For example, the generation unit improves the generation accuracy for obtaining a product that meets specific conditions from the user's past generation results. The generation unit can also analyze the user's past generation results and adjust the generation accuracy to improve the quality of the product. For example, the generation unit adjusts the generation accuracy to improve the quality of the product based on the user's past generation results. This makes it possible to improve the generation accuracy based on the user's past generation results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past generation results into the generation AI and cause the generation AI to improve the generation accuracy.

[0079] The generation unit can estimate the user's emotion and adjust the length of the product based on the estimated user emotion. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on changes in facial expression and adjusts the length of the product. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the length of the product. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on heart rate fluctuations and adjusts the length of the product. This allows the length of the product to be adjusted according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input user emotion data to the generation AI and cause the generation AI to adjust the length of the product based on the emotion.

[0080] The generation unit can determine the generation priority based on the time of character submission at the time of generation. For example, the generation unit sets a high generation priority for a character whose deadline is approaching. For example, the generation unit sets a high generation priority based on the time of character submission and causes the generation AI to generate the character first. The generation unit can also set a low generation priority for a character whose submission date is further away. For example, the generation unit sets a low generation priority based on the time of character submission and causes the generation AI to generate the character later. Furthermore, the generation unit can dynamically adjust the generation priority depending on the time of character submission. For example, the generation unit adjusts the generation priority in real time based on the time of character submission. This makes it possible to adjust the generation priority depending on the time of character submission. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the time of character submission to the generation AI and cause the generation AI to determine the generation priority.

[0081] The generation unit can adjust the order of generation based on the relevance of characters during generation. For example, in the case of a main character, the generation unit prioritizes the order of generation. For example, the generation unit prioritizes the generation of the main character and causes the generation AI to generate the main character first. The generation unit can also postpone the order of generation for a sub-character. For example, the generation unit postpones the generation of a sub-character and causes the generation AI to generate the sub-character later. Furthermore, the generation unit can dynamically adjust the order of generation based on the relevance of characters. For example, the generation unit adjusts the order of generation in real time based on the relevance of characters. This makes it possible to adjust the order of generation based on the relevance of characters. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the relevance of characters to the generation AI and cause the generation AI to adjust the order of generation.

[0082] The generation unit can adjust the use of technical terms in the product during generation according to the user's level of expertise. For example, if the user has technical expertise, the generation unit generates the product using detailed technical terms. For example, the generation unit uses detailed technical terms based on the user's level of expertise and causes the generation AI to generate a technical product. Furthermore, if the user does not have technical expertise, the generation unit can generate the product using concise and easy-to-understand terms. For example, the generation unit uses concise and easy-to-understand terms based on the user's level of expertise and causes the generation AI to generate the product. Furthermore, the generation unit can dynamically adjust the use of technical terms in the product according to the user's level of expertise. For example, the generation unit adjusts the use of technical terms in the product in real time based on the user's level of expertise. This makes it possible to adjust the use of technical terms in the product according to the user's level of expertise. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without AI. For example, the generation unit can input the user's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terms.

[0083] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expressions and determines the analysis priority. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice, calculates an emotion score, and determines the analysis priority. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations and determines the analysis priority. This allows the analysis priority 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 can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data to the generation AI and have the generation AI determine the priority of analysis based on emotion.

[0084] The analysis unit can customize the analysis content based on the user's geographical location information. For example, when the user is in a specific area, the analysis unit prioritizes analysis of characters and conditions related to that area. For example, when the user is in a specific area, the analysis unit prioritizes analysis of characters and conditions related to that area. Furthermore, when the user is traveling, the analysis unit can analyze characters and conditions related to the travel destination. For example, when the user is traveling, the analysis unit analyzes characters and conditions related to the travel destination. Furthermore, the analysis unit can also suggest optimal analysis content based on the user's geographical location information. For example, the analysis unit suggests optimal analysis content based on the user's geographical location information. This allows the analysis content to be customized based on the user's geographical location information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical location information to the generation AI and cause the generation AI to customize the analysis content.

[0085] The analysis unit can analyze the user's social media activities and suggest related analysis content. The analysis unit can suggest analysis content based on, for example, characters and conditions shared by the user on social media. For example, the analysis unit can suggest analysis content based on characters and conditions shared by the user on social media. The analysis unit can also analyze the user's social media posts and suggest related analysis content. For example, the analysis unit can analyze the user's social media posts and suggest related analysis content. Furthermore, the analysis unit can also suggest related analysis content by referring to the activities of the user's friends on social media. For example, the analysis unit can suggest related analysis content by referring to the activities of the user's friends on social media. This makes it possible to suggest related analysis content based on the user's social media activities. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's social media activities into a generation AI and cause the generation AI to suggest related analysis content.

[0086] The analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit displays the analysis results using detailed technical terminology. For example, the analysis unit uses detailed technical terminology based on the user's level of expertise and causes the generation AI to generate technical analysis results. Furthermore, if the user does not have technical expertise, the analysis unit can display the analysis results using concise and easy-to-understand terms. For example, the analysis unit uses concise and easy-to-understand terms based on the user's level of expertise and causes the generation AI to generate analysis results. Furthermore, the analysis unit can dynamically adjust the use of technical terminology in the analysis results according to the user's level of expertise. For example, the analysis unit adjusts the use of technical terminology in the analysis results in real time based on the user's level of expertise. This allows the use of technical terminology in the analysis to be adjusted according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without AI. For example, the analysis unit can input the user's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and generation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives text input or voice input from a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes information received from the reception unit and issues instructions to the generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a product based on the instructions of the analysis unit. For example, the generation unit can display the generated image or text using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and generation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives voice input from the user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes information received from the reception unit and issues instructions to the generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a product based on instructions from the analysis unit. For example, the generation unit can output the generated voice using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and generation unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives voice input from the user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes information received from the reception unit and issues instructions to the generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a product based on instructions from the analysis unit. For example, the generation unit can display the generated image using the display 343 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and generation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives voice input from the user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the information received from the reception unit and issues instructions to the generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a product based on the instructions of the analysis unit. For example, the generation unit can output the generated voice using the speaker 240 of the robot 414.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The reception unit can propose variations of the product based on the user's input. For example, if the user wants to generate a specific character, the reception unit can propose variations of the character, such as different clothing, facial expressions, and poses. Also, if the user wants to generate a specific scene, the reception unit can propose variations of the scene, such as different time of day, weather, and viewpoints. Furthermore, the reception unit can customize the variations of the product based on the user's past input history and preferences. This allows the user to have a wider variety of product options and obtain a product that is more satisfying to the user.

[0089] The analysis unit can evaluate the quality of the generated product based on the user's input. For example, the analysis unit can evaluate quality indicators such as the resolution, color, and detail of the generated product and provide feedback to the user. The analysis unit can also evaluate how well the content of the generated product matches the user's requirements. For example, the analysis unit can evaluate how accurately the generated product reproduces the characteristics of a specified character or scene. Furthermore, the analysis unit can issue improvement instructions to the generation AI based on the results of the product quality evaluation. This allows the quality of the generated product to be continuously improved.

[0090] The generation unit can visualize the generation process of a product. For example, the generation unit can display the character generation process step by step, allowing the user to check the generation process in real time. The generation unit can also display the scene generation process in animation format. For example, the generation unit can show, through animation, how the background of a scene or the placement of characters changes. Furthermore, the generation unit can provide an interactive function that allows the user to intervene at each step of the generation process. This allows the user to understand the generation process and make adjustments as necessary.

[0091] The analysis unit can estimate the user's emotions and adjust the style of the product based on the estimated emotions. For example, if the user is happy, the analysis unit can suggest a bright and colorful product. If the user is depressed, the analysis unit can suggest a product with calm colors. Furthermore, the analysis unit can adjust the details and complexity of the product according to the user's emotions. For example, if the user is relaxed, the analysis unit can suggest a simple and intuitive product, and if the user is focused, the analysis unit can suggest a detailed and complex product. In this way, it is possible to provide an optimal product according to the user's emotions.

[0092] The reception unit can suggest usage scenarios for the product based on the user's input. For example, if the user creates a character, the reception unit can suggest ideas for stories and scenes in which the character appears. Also, if the user creates a scene, the reception unit can suggest projects and contexts in which the scene might be used. Furthermore, the reception unit can customize usage scenarios for the product based on the user's past projects and areas of interest. This makes it easier for the user to imagine specific ways in which the product can be used, facilitating the progress of the project.

[0093] After generating a product, the generation unit can collect user feedback and reflect it in the next generation. For example, the generation unit can collect user satisfaction with the product and areas for improvement in the form of a questionnaire. The generation unit can also record corrections and adjustments made by the user to the product and use this information as a reference when generating the product next time. Furthermore, the generation unit can adjust the generation algorithm based on user feedback to improve the quality of the product. In this way, the quality of the product can be continuously improved by utilizing user feedback.

[0094] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated emotions. For example, if the user is excited, the analysis unit can present the analysis results in visually appealing graphics. Also, if the user is tired, the analysis unit can present the analysis results in a concise and easy-to-understand format. Furthermore, the analysis unit can adjust the level of detail of the analysis results according to the user's emotions. For example, if the user is concentrating, the analysis unit can provide detailed analysis results, and if the user is relaxed, the analysis unit can provide only the main points. This makes it possible to provide an optimal presentation method of the analysis results according to the user's emotions.

[0095] The reception unit can provide related information about the product based on the user's input. For example, if a user creates a specific character, background information and setting materials related to the character can be provided. Also, if a user creates a specific scene, reference images and videos related to the scene can be provided. Furthermore, the reception unit can provide related tutorials and guides based on the user's input. This allows the user to obtain additional information about the product, enabling a deeper understanding and utilization.

[0096] After generating a product, the generation unit can estimate the user's emotion and evaluate the product based on the estimated emotion. For example, the generation unit can capture the user's facial expression when looking at the product with a camera and estimate the emotion using an emotion estimation algorithm. It can also record the user's voice when looking at the product and estimate the emotion using voice analysis technology. Furthermore, the generation unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion. This allows the generation unit to evaluate the emotional impact the product has on the user and reflect this in the next generation.

[0097] The analysis unit can estimate the user's emotions and adjust the speed of the analysis based on the estimated emotions. For example, the analysis unit can speed up the analysis if the user is impatient. Also, the analysis unit can slow down the analysis if the user is relaxed. Furthermore, the analysis unit can display the analysis progress in real time according to the user's emotions. For example, if the user is concentrating, a detailed progress status is displayed, and if the user is tired, a concise progress status is displayed. This makes it possible to provide an optimal analysis progress speed according to the user's emotions.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The reception unit receives input from the user. The input from the user includes, for example, text input, voice input, image input, etc. The reception unit inputs the character the user wants to generate and the conditions. Step 2: The analysis unit analyzes the information received by the reception unit and issues instructions to the generation AI. Analysis is performed using methods such as natural language processing, image analysis, and voice analysis. Based on past generation results and learning data, the analysis unit infers the optimal method for the generation AI to reproduce the same character and conditions. Step 3: The generation unit generates a product based on the instructions issued by the analysis unit. The product may be, for example, text generation, image generation, or voice generation. The generation unit can generate the same character multiple times using the generation AI. The generation unit can also generate a product that meets specific conditions using the generation AI.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0103] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0110] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0111] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0112] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0114] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0119] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0128] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0135] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0153] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0161] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0162] 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.

[0163] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0165] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0166] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0171] [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input from a user; an analysis unit that analyzes the information received by the reception unit and issues instructions to the generation AI; a generating unit that generates a product based on an instruction issued by the analyzing unit; Equipped with A system characterized by:

2. The generation unit Generate the same character multiple times using the generation AI 2. The system of claim 1.

3. The generation unit Generate products that meet specific conditions using generation AI 2. The system of claim 1.

4. The analysis unit Based on past generation results and learning data, the generative AI infers the optimal method for reproducing the same character and conditions.

2. The system of claim 1.

5. The reception unit The user inputs the character and conditions they want to generate.

2. The system of claim 1.

6. The generation unit Generate characters wearing specific clothing using generation AI 2. The system of claim 1.

7. The reception unit The system estimates the user's emotions and adjusts the display method of the input interface based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyzes the user's past input history and suggests the optimal input method 2. The system of claim 1.

9. The reception unit Filtering input based on the user's current projects or interests 2. The system of claim 1.

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